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preprocessing steps that were applied to the data. +Note: this is not a replacement for running quality control of your images before running fMRIPrep (FAQ link: https://fmriprep.readthedocs.io/en/stable/faq.html) +Below is a guide to interpreting the information contained in the visual reports, organized by subsection of the report. + +If you have examples of “bad” preprocessing results that you’re willing to share, please [submit them somewhere?] with a short description of the problem (for example, “bad skull stripping”). + +When you open a report, it may be helpful to first jump to the [Errors subsection](LINK) to see whether fMRIPrep encountered any errors during preprocessing. Note that “errors” refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results. + +## **Summary** + +The first section of the report is a text summary, containing the subject ID, number of structural and functional images, task name, output spaces in which fMRIPrep generated your preprocessed data, and whether surface reconstruction with Freesurfer was run. +It is good to check that all of this information is as expected. + +## **Anatomical** + +The reports first present results of anatomical preprocessing. + +### Anatomical conformation + +* This section provides information about the structural images. +* Check for obvious failures, such as missing images or implausible values for voxel size, orientation, or dimensions. + * For example: you’re expecting two input T1w images but it says “Input T1w images: 1” + +### Brain mask and brain tissue segmentation of the T1w + +The brain mask report shows the quality of intensity non-uniformity (INU) correction, skull stripping, and tissue segmentation. +* Good: + * The brain mask (red) covers the whole brain and only the brain - not the dura or anything else outside the brain. It should closely follow the contour of the brain. + * The outlines of the gray matter (magenta) and white matter (blue) segmentation are correctly drawn. The blue line should follow the boundary between gray and white matter, while the magenta line should outline ventricles. + +* Bad: + * Intensity non-uniformity artifacts (looks like the “original image” panel in the figure below): + + ![inu](../assets/fmriprep_visual_report/inu.png) + *from Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging, 26(3), 405-421.* + + + * Skull-stripping failed leading to an inaccurate brain mask + * The brain mask cuts off part of the brain and/or contains holes surrounding signal drop-out regions. + * The brain mask includes parts that are clearly NOT brain. + + * For example, fMRIPrep might have misclassified part of the dura, epidural space, or skull as belonging inside the brain mask. + + * The gray matter (magenta)/white matter (blue) outlines don’t match where those tissue classes are distributed in the underlying image. + +* Common pitfalls in interpretation: + * At the inter-hemispheric space, masks (and in particular the brain mask, despite its smooth edges) may intersect the visualization plane several times, giving the impression that the mask is cutting off brain regions. However, this is more of a visual effect on the cutting plane. + + +### Spatial normalization of the anatomical T1w reference + +The normalization report shows how successfully your T1w image(s) were resampled into standard space, for each of the template spaces used. +* Good: + * Your T1w image and the template image line up well when you toggle between the images (hover mouse over the panel): + * The standard templates provided with fMRIPrep (such as `MNI152NLin2009cAsym`) are averaged across multiple subjects, so the template image will look blurrier than the T1w image. Because of this averaging, it is normal for sulci to be less pronounced and gyri to be wider on the template than the T1w image. + +* Bad: + * Stretching or distortion in the participant’s T1w image, indicating failed normalization. + * If skull-stripping was not successful, you might see some places where there are non-brain voxels outside of the contours of the brain. + + +### Surface reconstruction + +If you used the `--fs-no-reconall` flag to skip surface-based preprocessing, this section of the report will not exist. +The FreeSurfer [fischl2012] [1] subject reconstruction report shows the white matter (blue outline) and pial (red outline) surfaces overlaid on the T1w image. +* Good: + * The white-gray boundary outlined (blue link) matches the underlying image. + * White matter and pial surface boundary outlines do not cross or overlap each other. + +* Bad: + * The white-gray boundary outline (blue line) does not correspond well to the boundary observed in the underlying image. + * White matter and pial surface boundaries cross or overlap each other. + * Pial surface (red outline) extends past the actual pial boundary (see images [here](https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/PialEdits_tktools) for an example ; this can be a result of bad skull-stripping). + +* Common pitfalls in interpretation: + * Note that cerebellum and brainstem are excluded from the surface reconstruction in fMRIPrep; it is thus normal that the outlines do not include these areas. + +## **Functional** + +### Textual summary + +* The BOLD summary describes the interpretation of the inputs by fMRIPrep, and the applied heuristics. For instance, if the dataset is multi-echo EPI and comprehends three or more echos, then fMRIPrep should indicate this in the BOLD summary. + +* Other potential issues are missing images or implausible values. + * Functional data can vary based on available data, metadata, user selections. + * What does “Heuristics - BBR may fall back to volume-based coregistration” mean? + In fMRIPrep’s workflow, if boundary-based registration fails (meaning that `bbr` distorts the affine registration more than 15mm), fMRIPrep reverts to using the initial affine transform from Freesurfer’s `mri_coreg` tool instead of using the bbr refinement of the initial transform ([relevant code](https://github.com/poldracklab/fmriprep/pull/694/files#diff-939042e0a10e509ce5708c3e112376ffR29)) + “Note on orientation: qform matrix overwritten/ this data has been copied from sform/sform matrix set” warnings + `qform` and `sform` refer to metadata fields in the NIfTI file header, prescribing a coordinate system for the image. + This message warns you that information in the header was altered during the preprocessing. + This is an advisory message and does not necessarily indicate issues in data quality or registration. + A common scenario is that the sform code was 0, so fMRIPrep copied the code from qform in order to keep the affine matrices aligned, ensuring that the images will be treated as having the same orientation later down the pipeline. + See Chris Markewicz’s Neurostars posts [here](https://neurostars.org/t/note-on-orientation-qform-and-sform-warning/4379/4) and [here](https://neurostars.org/t/note-on-orientation-sform-matrix-set/5939) for a longer explanation of when/why fMRIPrep produces these warnings. + You can read more about how `qform` and `sform` work at the following links: [Recommended usage of qform and sform](https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/qsform_brief_usage) and [Nifti Qform and Sform](http://gru.stanford.edu/doku.php/mrTools/coordinateTransforms). + +### Susceptibility distortion correction + +* `--use-syn-sdc` +* `--force-syn` +* `--ignore fieldmaps` + +The functional images can have some warping or distortion due to inhomogeneities in the magnetic field. (fMRIPrep has several options to try to estimate the displacement map)[https://fmriprep.readthedocs.io/en/stable/sdc.html], based either on command-line arguments or automatic selection based on the available data). +This section of the report shows the participant’s functional images before and after distortion correction, with the GM/WM boundary from the anatomical images overlaid as reference. +* Good: + * The distortion-corrected (or “after”) image shows improved alignment between the brain and the GM/WM boundary, compared to the “before” image. + * The shape of the brain in the distortion-corrected image is not . + +* Bad: + * Alignment between the brain and the GM/WM boundary is worse in the “after” compared to the “before” image. + * The brain in the “after” image looks more distorted, stretched, smeared or otherwise misshapen, indicating that distortion correction was not successful. + +### Alignment of functional and anatomical data + +The text tells you which method fMRIPrep used to align the functional and anatomical data - for example, by running `bbregis +ter`. The images show the registered BOLD reference with the white and pial surfaces overlaid (red and blue lines) +* Good: + * The BOLD and T1w images are aligned; image boundaries and anatomical landmarks (for example, ventricles; corpus callosum) appear to be in the same place when toggling between the images. + * White and pial surface outlines (red and blue lines) appear to correspond well to the tissue boundaries in the functional images. + +* Bad: + * Functional and anatomical images are not aligned and clearly differ in their spatial location or orientation. + * White and pial surface boundaries (red and blue lines) overlays correspond poorly to the tissue boundaries in the underlying images. + * Some drop out in inferior brain regions (such as the OFC or medial temporal lobe) in functional images is somewhat inevitable. Depending on the type of analysis you’re doing and what you’re interested in, this may be more/less of an issue for you. If you see a lot of signal drop out, you will probably want to check the mask generated by fMRIPrep to see how that drop-out impacts the location and quantity of missing voxels in your mask. + +* Note that the BOLD images displayed in the report may have what appears to be an “artifact” in the data, as shown below. This is because the reports use a faster but less precise type of interpolation (Nearest Neighbor interpolation) for display. In actuality, fMRIPrep uses Lanczos interpolation for resampling, so these apparent “artifacts” are not present in the actual data (you can confirm this by checking the preprocessed BOLD NIFTI file and its registration to the T1w image in your preferred software). + + ![segmentation](../assets/fmriprep_visual_report/segmentation.png) + +### Brain mask and temporal/anatomical CompCor ROIs + +BOLD brain mask with the aCompCor and tCompCor masks +The aCompCor mask (magenta outline) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. +The tCompCor mask (blue outline) contains the top 5% most variable voxels within a heavily-eroded brain-mask. +* Good: + +* Bad: + +### Variance explained by t/a CompCor components + +Explained in more depth by but may be useful to duplicate some of this information here? + +### BOLD summary + +The BOLD summary report shows several characteristic statistics along with a carpetplot (Power, 2016), giving a view of the temporal characteristics of the preprocessed BOLD series. +* Good: +* Bad: +Note on how variance and scaling affects this display? (in other words, spikes are relative to other datapoints in the timeseries) +* Definitions: + * GS + * GSCSF + * GSWM + * DVARS + * *standardized DVARS!* + * FD + +### Correlations among nuisance regressors + +This may also be addressed in more depth by the confounds and regressors documentation (link to page) +Can be used to diagnose partial volume effects +PVE indicated by high correlations of a confound time series (or several) with the global signal + +### ICA-AROMA +What you’re looking at: +* Glass brain +* Map with time on the top and frequency on the bottom +* 1) the spatial map, 2) the time course, and 3) the power spectrum of the time c +* Artifact components (red) should have more high-frequency noise whereas signal components (green) should have their peak in the lower-frequency range +* Artifacts you might see: + * Multi-band artifacts in ICA-AROMA: + * Note on ICA-AROMA and multiband data +* Slice-timing artifacts in ICA-AROMA: +* Link to documentation on confounds: + +## About + +## Methods + +## Errors + +This section tells you whether fMRIPrep encountered any problems during the preprocessing. Note that “errors” refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results, or in which the input data was of lower-than-desirable quality. + +## Things that aren’t an issue: +* “Missing” [not visualized] slices + * In the report visualizations that display a panel of slices, sometimes it will appear as though there is a slice missing - for example, nothing is shown for x = -12. This is a problem with the visualization and does NOT indicate the slice is missing from the actual data. You can try reloading the report, or opening it with Chrome if you’re using another browser, as well as using your preferred NIFTI viewer to check the data. From 8908a2ade3229dd870c30b6271048be023eea2e7 Mon Sep 17 00:00:00 2001 From: cprovins Date: Fri, 16 Dec 2022 11:32:11 +0100 Subject: [PATCH 02/24] doc : complete how to read the normalization visualisation with the info from my paper --- docs/users/howto_read_fmriprep_visual_report.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 6e6ab9a..288e8bc 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -54,10 +54,13 @@ The brain mask report shows the quality of intensity non-uniformity (INU) correc The normalization report shows how successfully your T1w image(s) were resampled into standard space, for each of the template spaces used. * Good: * Your T1w image and the template image line up well when you toggle between the images (hover mouse over the panel): + * In order of importance, the following structures should be correctly aligned : 1. ventricles, 2. subcortical regions, 3. corpus callosum, 4. cerebellum, 5. cortical gray matter. * The standard templates provided with fMRIPrep (such as `MNI152NLin2009cAsym`) are averaged across multiple subjects, so the template image will look blurrier than the T1w image. Because of this averaging, it is normal for sulci to be less pronounced and gyri to be wider on the template than the T1w image. * Bad: * Stretching or distortion in the participant’s T1w image, indicating failed normalization. + * A misalignment of the ventricles, the subcortical regions, or the corpus callosum should lead to immediate exclusion. You can however be more lenient with the misalignment of cortical GM because volumetric (image) registration may not resolve substantial inter-individual differences (e.g., a sulcus missing in an individual’s brain but typically present in the population of the template). + * If skull-stripping was not successful, you might see some places where there are non-brain voxels outside of the contours of the brain. From 35903e2a47657bbdf96d79dae327e8328cdd9a79 Mon Sep 17 00:00:00 2001 From: cprovins Date: Fri, 16 Dec 2022 11:33:07 +0100 Subject: [PATCH 03/24] doc : complete how to read the brain mask and segmentation visualisation with the info from my paper --- .../noise-in-segmentation.svg | 255 ++++++++++++++++++ .../howto_read_fmriprep_visual_report.md | 13 +- 2 files changed, 264 insertions(+), 4 deletions(-) create mode 100644 docs/assets/fmriprep_visual_report/noise-in-segmentation.svg diff --git a/docs/assets/fmriprep_visual_report/noise-in-segmentation.svg b/docs/assets/fmriprep_visual_report/noise-in-segmentation.svg new file mode 100644 index 0000000..2911596 --- /dev/null +++ b/docs/assets/fmriprep_visual_report/noise-in-segmentation.svg @@ -0,0 +1,255 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 288e8bc..f2d4039 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -40,15 +40,20 @@ The brain mask report shows the quality of intensity non-uniformity (INU) correc * Skull-stripping failed leading to an inaccurate brain mask * The brain mask cuts off part of the brain and/or contains holes surrounding signal drop-out regions. * The brain mask includes parts that are clearly NOT brain. + * For example, fMRIPrep might have misclassified part of the dura, epidural space, or skull as belonging inside the brain mask; as a result the brain mask presents “bumps” surrounding high-intensity areas of signal outside of the cortex. - * For example, fMRIPrep might have misclassified part of the dura, epidural space, or skull as belonging inside the brain mask. + * Having an accurate brain mask makes the downstream preprocessing of an fMRI scan faster (excluding voxels of non-interest) and more accurate (less bias from voxels of non-interest). Consequently, it is important to discard subjects for which the brain mask is not well defined. - * The gray matter (magenta)/white matter (blue) outlines don’t match where those tissue classes are distributed in the underlying image. + * The gray matter (magenta)/white matter (blue) outlines don’t match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between gray and white matter or the magenta line does not outline ventricles. + * Inclusion of tissues other than the tissue of interest in the contour delineations should lead to exclusion of the scan. + * T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). + * These scans should be excluded except for images where these voxels are only present at subcortical structures (e.g., thalamus) or nearby tissue boundaries. In the latter case, the misclassification results from partial volume effects (i.e., indeed, such voxels contain varying fractions of two or more tissues). The figure below illustrates the difference between individual dots caused by noise versus partial volume effects. + ![noise-in-segmentation](../assets/fmriprep_visual_report/noise-in-segmentation.svg) + *Fig. 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between WM and GM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* * Common pitfalls in interpretation: * At the inter-hemispheric space, masks (and in particular the brain mask, despite its smooth edges) may intersect the visualization plane several times, giving the impression that the mask is cutting off brain regions. However, this is more of a visual effect on the cutting plane. - ### Spatial normalization of the anatomical T1w reference The normalization report shows how successfully your T1w image(s) were resampled into standard space, for each of the template spaces used. @@ -60,7 +65,7 @@ The normalization report shows how successfully your T1w image(s) were resampled * Bad: * Stretching or distortion in the participant’s T1w image, indicating failed normalization. * A misalignment of the ventricles, the subcortical regions, or the corpus callosum should lead to immediate exclusion. You can however be more lenient with the misalignment of cortical GM because volumetric (image) registration may not resolve substantial inter-individual differences (e.g., a sulcus missing in an individual’s brain but typically present in the population of the template). - + * If skull-stripping was not successful, you might see some places where there are non-brain voxels outside of the contours of the brain. From 3a07ad1dbf7b194b5308c6209becf37784059ab6 Mon Sep 17 00:00:00 2001 From: cprovins Date: Fri, 16 Dec 2022 12:04:03 +0100 Subject: [PATCH 04/24] doc : complete how to read surface reconstruction visualisation with info from my paper --- docs/users/howto_read_fmriprep_visual_report.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index f2d4039..147def5 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -72,7 +72,7 @@ The normalization report shows how successfully your T1w image(s) were resampled ### Surface reconstruction If you used the `--fs-no-reconall` flag to skip surface-based preprocessing, this section of the report will not exist. -The FreeSurfer [fischl2012] [1] subject reconstruction report shows the white matter (blue outline) and pial (red outline) surfaces overlaid on the T1w image. +The FreeSurfer [fischl2012][1] subject reconstruction report shows the white matter (blue outline) and pial (red outline) surfaces overlaid on the T1w image. * Good: * The white-gray boundary outlined (blue link) matches the underlying image. * White matter and pial surface boundary outlines do not cross or overlap each other. @@ -81,6 +81,8 @@ The FreeSurfer [fischl2012] [1] subject reconstruction report shows the white ma * The white-gray boundary outline (blue line) does not correspond well to the boundary observed in the underlying image. * White matter and pial surface boundaries cross or overlap each other. * Pial surface (red outline) extends past the actual pial boundary (see images [here](https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/PialEdits_tktools) for an example ; this can be a result of bad skull-stripping). + * QC assessment of FreeSurfer outcomes is comprehensively covered elsewhere (e.g., White et al. 2018 [white2018][2]; Klapwijk et al. 2019 [klapwijk2019][3]), and fMRI studies using vertex-wise (surface) analyses should rigorously assess these surfaces. + * In a voxel-wise analysis, data should be excluded only when the reconstructed surfaces are extremely inaccurate, which typically only happens in the presence of artifacts easily captured previously by [MRIQC](https://mriqc.readthedocs.io/en/latest/). * Common pitfalls in interpretation: * Note that cerebellum and brainstem are excluded from the surface reconstruction in fMRIPrep; it is thus normal that the outlines do not include these areas. @@ -189,6 +191,11 @@ What you’re looking at: This section tells you whether fMRIPrep encountered any problems during the preprocessing. Note that “errors” refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results, or in which the input data was of lower-than-desirable quality. -## Things that aren’t an issue: +## Things that aren’t an issue * “Missing” [not visualized] slices * In the report visualizations that display a panel of slices, sometimes it will appear as though there is a slice missing - for example, nothing is shown for x = -12. This is a problem with the visualization and does NOT indicate the slice is missing from the actual data. You can try reloading the report, or opening it with Chrome if you’re using another browser, as well as using your preferred NIFTI viewer to check the data. + +## References +[1] : Fischl, Bruce. 2012. “FreeSurfer.” NeuroImage 62 (2): 774–81. . +[2] : White, Tonya, Philip R. Jansen, Ryan L. Muetzel, Gustavo Sudre, Hanan El Marroun, Henning Tiemeier, Anqi Qiu, Philip Shaw, Andrew M. Michael, and Frank C. Verhulst. 2018. “Automated Quality Assessment of Structural Magnetic Resonance Images in Children: Comparison with Visual Inspection and Surface-Based Reconstruction.” Human Brain Mapping 39 (3): 1218–31. . +[3] : Klapwijk, Eduard T., Ferdi van de Kamp, Mara van der Meulen, Sabine Peters, and Lara M. Wierenga. 2019. “Qoala-T: A Supervised-Learning Tool for Quality Control of FreeSurfer Segmented MRI Data.” NeuroImage 189 (April): 116–29. . From 9a6466cd92ea2ced949a81681ef74a75c04e5580 Mon Sep 17 00:00:00 2001 From: cprovins Date: Fri, 16 Dec 2022 12:11:46 +0100 Subject: [PATCH 05/24] doc : modify layout of functional textual summary section --- docs/users/howto_read_fmriprep_visual_report.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 147def5..95b05b7 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -96,14 +96,16 @@ The FreeSurfer [fischl2012][1] subject reconstruction report shows the white mat * Other potential issues are missing images or implausible values. * Functional data can vary based on available data, metadata, user selections. * What does “Heuristics - BBR may fall back to volume-based coregistration” mean? - In fMRIPrep’s workflow, if boundary-based registration fails (meaning that `bbr` distorts the affine registration more than 15mm), fMRIPrep reverts to using the initial affine transform from Freesurfer’s `mri_coreg` tool instead of using the bbr refinement of the initial transform ([relevant code](https://github.com/poldracklab/fmriprep/pull/694/files#diff-939042e0a10e509ce5708c3e112376ffR29)) - “Note on orientation: qform matrix overwritten/ this data has been copied from sform/sform matrix set” warnings + + In fMRIPrep’s workflow, if boundary-based registration fails (meaning that `bbr` distorts the affine registration more than 15mm), fMRIPrep reverts to using the initial affine transform from Freesurfer’s `mri_coreg` tool instead of using the bbr refinement of the initial transform ([relevant code](https://github.com/nipreps/fmriprep/pull/694/files#diff-939042e0a10e509ce5708c3e112376ffR29)) + + * “Note on orientation: qform matrix overwritten/ this data has been copied from sform/sform matrix set” warnings + `qform` and `sform` refer to metadata fields in the NIfTI file header, prescribing a coordinate system for the image. This message warns you that information in the header was altered during the preprocessing. This is an advisory message and does not necessarily indicate issues in data quality or registration. A common scenario is that the sform code was 0, so fMRIPrep copied the code from qform in order to keep the affine matrices aligned, ensuring that the images will be treated as having the same orientation later down the pipeline. - See Chris Markewicz’s Neurostars posts [here](https://neurostars.org/t/note-on-orientation-qform-and-sform-warning/4379/4) and [here](https://neurostars.org/t/note-on-orientation-sform-matrix-set/5939) for a longer explanation of when/why fMRIPrep produces these warnings. - You can read more about how `qform` and `sform` work at the following links: [Recommended usage of qform and sform](https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/qsform_brief_usage) and [Nifti Qform and Sform](http://gru.stanford.edu/doku.php/mrTools/coordinateTransforms). + See Chris Markewicz’s Neurostars posts [here](https://neurostars.org/t/note-on-orientation-qform-and-sform-warning/4379/4) and [here](https://neurostars.org/t/note-on-orientation-sform-matrix-set/5939) for a longer explanation of when/why fMRIPrep produces these warnings. You can read more about how `qform` and `sform` work at the following links: [Recommended usage of qform and sform](https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/qsform_brief_usage) and [Nifti Qform and Sform](http://gru.stanford.edu/doku.php/mrTools/coordinateTransforms). ### Susceptibility distortion correction From e684ce21dce6b83d0ed5dfcb3288ceec2f878ec0 Mon Sep 17 00:00:00 2001 From: cprovins Date: Fri, 16 Dec 2022 12:38:43 +0100 Subject: [PATCH 06/24] fix : remove subject labels from the figure extracted from the supplementary material of my paper --- .../noise-in-segmentation.svg | 115 +++++++++--------- 1 file changed, 60 insertions(+), 55 deletions(-) diff --git a/docs/assets/fmriprep_visual_report/noise-in-segmentation.svg b/docs/assets/fmriprep_visual_report/noise-in-segmentation.svg index 2911596..4579f87 100644 --- a/docs/assets/fmriprep_visual_report/noise-in-segmentation.svg +++ 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z>+9=-=1o^vB>I>!(W37=O_x8azoVcc7{sZgqf;4tdG@k(t|bt6x;t&bUkMB&D*3ZN zi--hVoRaI0hNYf{UGjOp})76Omp{Qqju79q1j((Zy<6cDWGV>TS@6x*liLGj`2qC-Q2+> zB00ZgvHFSH;^|#7)+kzDE-2LA(XqI!3|Qh!>t1tmaY=^agM55^TwVDYw~zd^GT{D4 zE5CmJ{ONzPQD#!hg+R2{)frYyGsFE3y$|kZ>7Y|n``yzf9S3`QNH^h5Q>9@>_)6<; zc|=7~Q5Kp>Cg_}~m6xCYAy6FlqqDQqt(+jsEiXU+?DX^q-Wqgq4AVBy)xBkG)%)d1 zRwlpMk2qaN(Nv48lRFRjo~zZrp1r?e%YwDyFBacE{R_~pa&vP(F^C}>y|$BGOG-+N zN?!1c4T?F#ugR`S4?wp zadBf~<0j>Y*w{<~>#h%EDhgm=a4jq>6crVbk&*HAtC2i@4CwyN_@<+Vg*-Pj{;f@P z0!pZ^&d==6pn18X_z7t13`OG|Un8Is;RM2LaDK45yF$1YAbW2b(x zV99;y)S{=s2mj!{a>cB crsEBXs&Vhvv6f)*f5bCYB`w8rdCTzs2kK7*F8}}l From 82cc3954cadc58d4ec9718011948648042bba41b Mon Sep 17 00:00:00 2001 From: cprovins Date: Fri, 16 Dec 2022 13:26:43 +0100 Subject: [PATCH 08/24] doc : separate how to read brain mask and segmentation visualisation into quality check the three preprocessing step evaluated here --- .../howto_read_fmriprep_visual_report.md | 99 ++++++++++--------- 1 file changed, 52 insertions(+), 47 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 95b05b7..25f40e0 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -26,30 +26,39 @@ The reports first present results of anatomical preprocessing. ### Brain mask and brain tissue segmentation of the T1w The brain mask report shows the quality of intensity non-uniformity (INU) correction, skull stripping, and tissue segmentation. -* Good: - * The brain mask (red) covers the whole brain and only the brain - not the dura or anything else outside the brain. It should closely follow the contour of the brain. - * The outlines of the gray matter (magenta) and white matter (blue) segmentation are correctly drawn. The blue line should follow the boundary between gray and white matter, while the magenta line should outline ventricles. -* Bad: - * Intensity non-uniformity artifacts (looks like the “original image” panel in the figure below): +* INU correction + * Good: + * The intensity of the image is uniform throughout the brain + * Bad: + * Intensity non-uniformity artifacts (looks like the “original image” panel in the figure below): ![inu](../assets/fmriprep_visual_report/inu.png) *from Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging, 26(3), 405-421.* - - - * Skull-stripping failed leading to an inaccurate brain mask - * The brain mask cuts off part of the brain and/or contains holes surrounding signal drop-out regions. - * The brain mask includes parts that are clearly NOT brain. - * For example, fMRIPrep might have misclassified part of the dura, epidural space, or skull as belonging inside the brain mask; as a result the brain mask presents “bumps” surrounding high-intensity areas of signal outside of the cortex. - - * Having an accurate brain mask makes the downstream preprocessing of an fMRI scan faster (excluding voxels of non-interest) and more accurate (less bias from voxels of non-interest). Consequently, it is important to discard subjects for which the brain mask is not well defined. - - * The gray matter (magenta)/white matter (blue) outlines don’t match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between gray and white matter or the magenta line does not outline ventricles. - * Inclusion of tissues other than the tissue of interest in the contour delineations should lead to exclusion of the scan. - * T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). - * These scans should be excluded except for images where these voxels are only present at subcortical structures (e.g., thalamus) or nearby tissue boundaries. In the latter case, the misclassification results from partial volume effects (i.e., indeed, such voxels contain varying fractions of two or more tissues). The figure below illustrates the difference between individual dots caused by noise versus partial volume effects. - ![noise-in-segmentation](../assets/fmriprep_visual_report/noise-in-segmentation.svg) - *Fig. 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between WM and GM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* + +* Skull stripping + * Good: + * The brain mask (red) covers the whole brain and only the brain - not the dura or anything else outside the brain. It should closely follow the contour of the brain. + + * Bad: + * Skull-stripping failed leading to an inaccurate brain mask + * The brain mask cuts off part of the brain and/or contains holes surrounding signal drop-out regions. + * The brain mask includes parts that are clearly NOT brain. + * For example, fMRIPrep might have misclassified part of the dura, epidural space, or skull as belonging inside the brain mask; as a result the brain mask presents “bumps” surrounding high-intensity areas of signal outside of the cortex. + + * Having an accurate brain mask makes the downstream preprocessing of an fMRI scan faster (excluding voxels of non-interest) and more accurate (less bias from voxels of non-interest). Consequently, it is important to discard subjects for which the brain mask is not well defined. + +* Tissue segmentation + * Good: + * The outlines of the gray matter (magenta) and white matter (blue) segmentation are correctly drawn. The blue line should follow the boundary between gray and white matter, while the magenta line should outline ventricles. + + * Bad: + * The gray matter (magenta)/white matter (blue) outlines don’t match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between gray and white matter or the magenta line does not outline ventricles. + * Inclusion of tissues other than the tissue of interest in the contour delineations should lead to exclusion of the scan. + * T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). + * These scans should be excluded except for images where these voxels are only present at subcortical structures (e.g., thalamus) or nearby tissue boundaries. In the latter case, the misclassification results from partial volume effects (i.e., indeed, such voxels contain varying fractions of two or more tissues). The figure below illustrates the difference between individual dots caused by noise versus partial volume effects. + ![noise-in-segmentation](../assets/fmriprep_visual_report/noise-in-segmentation.svg) + *Fig. 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between WM and GM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* * Common pitfalls in interpretation: * At the inter-hemispheric space, masks (and in particular the brain mask, despite its smooth edges) may intersect the visualization plane several times, giving the impression that the mask is cutting off brain regions. However, this is more of a visual effect on the cutting plane. @@ -106,39 +115,35 @@ The FreeSurfer [fischl2012][1] subject reconstruction report shows the white mat This is an advisory message and does not necessarily indicate issues in data quality or registration. A common scenario is that the sform code was 0, so fMRIPrep copied the code from qform in order to keep the affine matrices aligned, ensuring that the images will be treated as having the same orientation later down the pipeline. See Chris Markewicz’s Neurostars posts [here](https://neurostars.org/t/note-on-orientation-qform-and-sform-warning/4379/4) and [here](https://neurostars.org/t/note-on-orientation-sform-matrix-set/5939) for a longer explanation of when/why fMRIPrep produces these warnings. You can read more about how `qform` and `sform` work at the following links: [Recommended usage of qform and sform](https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/qsform_brief_usage) and [Nifti Qform and Sform](http://gru.stanford.edu/doku.php/mrTools/coordinateTransforms). - -### Susceptibility distortion correction - -* `--use-syn-sdc` -* `--force-syn` -* `--ignore fieldmaps` - -The functional images can have some warping or distortion due to inhomogeneities in the magnetic field. (fMRIPrep has several options to try to estimate the displacement map)[https://fmriprep.readthedocs.io/en/stable/sdc.html], based either on command-line arguments or automatic selection based on the available data). -This section of the report shows the participant’s functional images before and after distortion correction, with the GM/WM boundary from the anatomical images overlaid as reference. -* Good: - * The distortion-corrected (or “after”) image shows improved alignment between the brain and the GM/WM boundary, compared to the “before” image. - * The shape of the brain in the distortion-corrected image is not . - -* Bad: - * Alignment between the brain and the GM/WM boundary is worse in the “after” compared to the “before” image. - * The brain in the “after” image looks more distorted, stretched, smeared or otherwise misshapen, indicating that distortion correction was not successful. ### Alignment of functional and anatomical data -The text tells you which method fMRIPrep used to align the functional and anatomical data - for example, by running `bbregis -ter`. The images show the registered BOLD reference with the white and pial surfaces overlaid (red and blue lines) -* Good: - * The BOLD and T1w images are aligned; image boundaries and anatomical landmarks (for example, ventricles; corpus callosum) appear to be in the same place when toggling between the images. - * White and pial surface outlines (red and blue lines) appear to correspond well to the tissue boundaries in the functional images. +The alignment report shows the quality of co-registration and susceptibility distortion correction. -* Bad: - * Functional and anatomical images are not aligned and clearly differ in their spatial location or orientation. - * White and pial surface boundaries (red and blue lines) overlays correspond poorly to the tissue boundaries in the underlying images. - * Some drop out in inferior brain regions (such as the OFC or medial temporal lobe) in functional images is somewhat inevitable. Depending on the type of analysis you’re doing and what you’re interested in, this may be more/less of an issue for you. If you see a lot of signal drop out, you will probably want to check the mask generated by fMRIPrep to see how that drop-out impacts the location and quantity of missing voxels in your mask. +* Co-registration + * The text tells you which method fMRIPrep used to align the functional and anatomical data - for example, by running `bbregister`. The images show the registered BOLD reference with the white and pial surfaces overlaid (red and blue lines) + * Good: + * The BOLD and T1w images are aligned; image boundaries and anatomical landmarks (for example, ventricles; corpus callosum) appear to be in the same place when toggling between the images. + * White and pial surface outlines (red and blue lines) appear to correspond well to the tissue boundaries in the functional images. + + * Bad: + * Functional and anatomical images are not aligned and clearly differ in their spatial location or orientation. + * White and pial surface boundaries (red and blue lines) overlays correspond poorly to the tissue boundaries in the underlying images. -* Note that the BOLD images displayed in the report may have what appears to be an “artifact” in the data, as shown below. This is because the reports use a faster but less precise type of interpolation (Nearest Neighbor interpolation) for display. In actuality, fMRIPrep uses Lanczos interpolation for resampling, so these apparent “artifacts” are not present in the actual data (you can confirm this by checking the preprocessed BOLD NIFTI file and its registration to the T1w image in your preferred software). +* Susceptibility distortion correction + * The functional images can have some warping or distortion due to inhomogeneities in the $B_0$ magnetic field. For more details on what causes susceptibility distortion, refer to [the educational notebook of SDCFlows](https://github.com/nipreps/sdcflows/blob/master/docs/notebooks/SDC%20-%20Theory%20and%20physics.ipynb). Susceptibility distortion artifacts manifest in two different ways on the functional and structural images: as signal drop-out, that is, a region where the signal vanishes, or as brain distortions. Signal drop-outs often appear close to brain-air interfaces; these include ventromedial prefrontal cortex, the anterior part of the prefrontal cortex, and the region next to the ear cavities. + * Good: + * No signal drop out or brain distortion affects your region of interest. + * The pial surface outlines (blue lines) appear to correspond well to the tissue boundaries in the functional images. + * Bad: + * If the susceptibility distortion correction is unsuccessfull, residual susceptibility distortion artifacts can be observed. If the latter overlaps with regions of interest, the scan should be excluded. + * Note however that some drop out in inferior brain regions (such as the OFC or medial temporal lobe) in functional images is somewhat inevitable. Depending on the type of analysis you’re doing and what you’re interested in, this may be more/less of an issue for you. If you see a lot of signal drop out, you will probably want to check the brain mask generated by fMRIPrep to see how that drop-out impacts the location and quantity of missing voxels in your brain mask. + + +* Common pitfalls in interpretation: + * Note that the BOLD images displayed in the report may have what appears to be an “artifact” in the data, as shown below. This is because the reports use a faster but less precise type of interpolation (Nearest Neighbor interpolation) for display. In actuality, fMRIPrep uses Lanczos interpolation for resampling, so these apparent “artifacts” are not present in the actual data (you can confirm this by checking the preprocessed BOLD NIFTI file and its registration to the T1w image in your preferred software). - ![segmentation](../assets/fmriprep_visual_report/segmentation.png) + ![apparent-artifact-interpolation](../assets/fmriprep_visual_report/apparent-artifact-interpolation.png) ### Brain mask and temporal/anatomical CompCor ROIs From fc839d77f114b65e192d82379cca6d4683b192f3 Mon Sep 17 00:00:00 2001 From: cprovins Date: Mon, 23 Jan 2023 13:26:49 +0100 Subject: [PATCH 09/24] enh : complete the section about brain mask and confound estimation fix : replace gray matter and white matter by their abbreviations fix : Add precision about the fact that the brain mask extracted from T1w and BOLD are different and they obey different exclusion criteria --- .../hole-bold-brainmask.png | Bin 0 -> 39104 bytes .../suspicious-compcor.png | 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index 25f40e0..514aeff 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -25,7 +25,7 @@ The reports first present results of anatomical preprocessing. ### Brain mask and brain tissue segmentation of the T1w -The brain mask report shows the quality of intensity non-uniformity (INU) correction, skull stripping, and tissue segmentation. +This report displays the brain mask and the brain tissue segmentation computed from the T1w image. It shows the quality of intensity non-uniformity (INU) correction, skull stripping, and tissue segmentation. * INU correction * Good: @@ -50,18 +50,19 @@ The brain mask report shows the quality of intensity non-uniformity (INU) correc * Tissue segmentation * Good: - * The outlines of the gray matter (magenta) and white matter (blue) segmentation are correctly drawn. The blue line should follow the boundary between gray and white matter, while the magenta line should outline ventricles. + * The outlines of the gray matter (GM; magenta) and white matter (WM; blue) segmentation are correctly drawn. The blue line should follow the boundary between GM and WM, while the magenta line should outline ventricles. * Bad: - * The gray matter (magenta)/white matter (blue) outlines don’t match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between gray and white matter or the magenta line does not outline ventricles. + * The GM (magenta)/WM (blue) outlines don’t match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between GM and WM or the magenta line does not outline ventricles. * Inclusion of tissues other than the tissue of interest in the contour delineations should lead to exclusion of the scan. * T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). * These scans should be excluded except for images where these voxels are only present at subcortical structures (e.g., thalamus) or nearby tissue boundaries. In the latter case, the misclassification results from partial volume effects (i.e., indeed, such voxels contain varying fractions of two or more tissues). The figure below illustrates the difference between individual dots caused by noise versus partial volume effects. ![noise-in-segmentation](../assets/fmriprep_visual_report/noise-in-segmentation.svg) - *Fig. 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between WM and GM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* + *Fig. 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between GM and WM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* * Common pitfalls in interpretation: * At the inter-hemispheric space, masks (and in particular the brain mask, despite its smooth edges) may intersect the visualization plane several times, giving the impression that the mask is cutting off brain regions. However, this is more of a visual effect on the cutting plane. + * Note that the brain mask plotted in the “brain mask and (anatomical/temporal) CompCor ROIs” panel under the functional section is computed from the BOLD image and thus is not identical to the brain mask mentioned in this paragraph. Hence, they follow different exclusion criteria. ### Spatial normalization of the anatomical T1w reference @@ -77,25 +78,24 @@ The normalization report shows how successfully your T1w image(s) were resampled * If skull-stripping was not successful, you might see some places where there are non-brain voxels outside of the contours of the brain. - ### Surface reconstruction If you used the `--fs-no-reconall` flag to skip surface-based preprocessing, this section of the report will not exist. -The FreeSurfer [fischl2012][1] subject reconstruction report shows the white matter (blue outline) and pial (red outline) surfaces overlaid on the T1w image. +The FreeSurfer [fischl2012][1] subject reconstruction report shows the WM (blue outline) and pial (red outline) surfaces overlaid on the T1w image. * Good: * The white-gray boundary outlined (blue link) matches the underlying image. - * White matter and pial surface boundary outlines do not cross or overlap each other. + * WM and pial surface boundary outlines do not cross or overlap each other. * Bad: * The white-gray boundary outline (blue line) does not correspond well to the boundary observed in the underlying image. - * White matter and pial surface boundaries cross or overlap each other. + * WM and pial surface boundaries cross or overlap each other. * Pial surface (red outline) extends past the actual pial boundary (see images [here](https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/PialEdits_tktools) for an example ; this can be a result of bad skull-stripping). * QC assessment of FreeSurfer outcomes is comprehensively covered elsewhere (e.g., White et al. 2018 [white2018][2]; Klapwijk et al. 2019 [klapwijk2019][3]), and fMRI studies using vertex-wise (surface) analyses should rigorously assess these surfaces. * In a voxel-wise analysis, data should be excluded only when the reconstructed surfaces are extremely inaccurate, which typically only happens in the presence of artifacts easily captured previously by [MRIQC](https://mriqc.readthedocs.io/en/latest/). * Common pitfalls in interpretation: * Note that cerebellum and brainstem are excluded from the surface reconstruction in fMRIPrep; it is thus normal that the outlines do not include these areas. - + ## **Functional** ### Textual summary @@ -147,12 +147,24 @@ The alignment report shows the quality of co-registration and susceptibility dis ### Brain mask and temporal/anatomical CompCor ROIs -BOLD brain mask with the aCompCor and tCompCor masks -The aCompCor mask (magenta outline) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. -The tCompCor mask (blue outline) contains the top 5% most variable voxels within a heavily-eroded brain-mask. +This report shows the brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used by CompCor [behzadi2017][4] for the estimation of physiological and movement confounding components. The user can then choose those components as nuisance regressors in analysis. + +The anatomical CompCor ROI (magenta contour) is a mask combining cerebrospinal fluid (CSF) and WM, where voxels containing a minimal partial volume of GM have been removed. It represents a mask of region of no interest. + +The temporal CompCor ROI (blue contour) contains the top 2% most variable voxels within the brain mask. + +The brain edge (or crown) ROI (green contour) picks signals outside but close to the brain, which are decomposed into 24 principal components. + * Good: + * The brain mask correctly surrounds the brain boundary, not leaving out brain area. Note that holes in the brain mask such as on the figure below, is not problematic as it should not disrupt co-registration. + ![hole-bold-brainmask](../assets/fmriprep_visual_report/hold-bold-brainmask.png) * Bad: + * The brain mask computed from the BOLD image mainly influences confounds estimation, but also co-registration, although the latter is primarily driven by the WM mask. As such the brain mask must not leave out any brain area, but it can be a bit loose around the brain. If the mask intersects with brain-originating signal, the nuisance regressors should not be used. + * If the study plan prescribes using CompCor or brain-edge regressors, it is critical to exclude BOLD runs where any of these masks substantially overlap regions of interest. + * The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (see figure below). In this case, the scan should be excluded. + ![suspicious-compcor](../assets/fmriprep_visual_report/suspicious-compcor.png) + ### Variance explained by t/a CompCor components @@ -206,3 +218,4 @@ This section tells you whether fMRIPrep encountered any problems during the prep [1] : Fischl, Bruce. 2012. “FreeSurfer.” NeuroImage 62 (2): 774–81. . [2] : White, Tonya, Philip R. Jansen, Ryan L. Muetzel, Gustavo Sudre, Hanan El Marroun, Henning Tiemeier, Anqi Qiu, Philip Shaw, Andrew M. Michael, and Frank C. Verhulst. 2018. “Automated Quality Assessment of Structural Magnetic Resonance Images in Children: Comparison with Visual Inspection and Surface-Based Reconstruction.” Human Brain Mapping 39 (3): 1218–31. . [3] : Klapwijk, Eduard T., Ferdi van de Kamp, Mara van der Meulen, Sabine Peters, and Lara M. Wierenga. 2019. “Qoala-T: A Supervised-Learning Tool for Quality Control of FreeSurfer Segmented MRI Data.” NeuroImage 189 (April): 116–29. . +[4] : Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based FMRI.” NeuroImage 37 (1): 90–101. . From 55ddf7bc8be55acc4166d1567f5aaefb35c1e7b8 Mon Sep 17 00:00:00 2001 From: cprovins Date: Thu, 26 Jan 2023 14:08:37 +0100 Subject: [PATCH 10/24] enh : add link to our frontiers QC paper --- docs/users/howto_read_fmriprep_visual_report.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 514aeff..8ba7b01 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -214,6 +214,9 @@ This section tells you whether fMRIPrep encountered any problems during the prep * “Missing” [not visualized] slices * In the report visualizations that display a panel of slices, sometimes it will appear as though there is a slice missing - for example, nothing is shown for x = -12. This is a problem with the visualization and does NOT indicate the slice is missing from the actual data. You can try reloading the report, or opening it with Chrome if you’re using another browser, as well as using your preferred NIFTI viewer to check the data. +## Further material +If you are interested in knowing more about quality control, we wrote [a paper entitled "Quality control in functional MRI studies with MRIQC and fMRIPrep"](https://doi.org/10.3389/fnimg.2022.1073734) that describes how the visual inspection of fMRIPrep's report lies within the broader scope of quality control. In that paper, we demonstrate in details a protocol to perform quality control of unprocessed and minimally preprocessed BOLD fMRI images based on the visual assessment of MRIQC and fMRIPrep reports respectively. We additionally apply the protocol on a composite dataset drawn from open fMRI studies and illustrate exclusion criteria with examples. + ## References [1] : Fischl, Bruce. 2012. “FreeSurfer.” NeuroImage 62 (2): 774–81. . [2] : White, Tonya, Philip R. Jansen, Ryan L. Muetzel, Gustavo Sudre, Hanan El Marroun, Henning Tiemeier, Anqi Qiu, Philip Shaw, Andrew M. Michael, and Frank C. Verhulst. 2018. “Automated Quality Assessment of Structural Magnetic Resonance Images in Children: Comparison with Visual Inspection and Surface-Based Reconstruction.” Human Brain Mapping 39 (3): 1218–31. . From f2981feb3599e6e505cd739831585c2de45093c8 Mon Sep 17 00:00:00 2001 From: cprovins Date: Thu, 26 Jan 2023 14:19:18 +0100 Subject: [PATCH 11/24] enh: add info about cumulative variance explained by CompCor --- docs/users/howto_read_fmriprep_visual_report.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 8ba7b01..331b2c7 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -168,7 +168,10 @@ The brain edge (or crown) ROI (green contour) picks signals outside but close to ### Variance explained by t/a CompCor components -Explained in more depth by but may be useful to duplicate some of this information here? +The figure displays the cumulative variance explained by components for each of four CompCor decompositions (left to right: anatomical CSF mask, anatomical white matter mask, anatomical combined mask, temporal). Dotted lines indicate the minimum number of components necessary to explain 50%, 70%, and 90% of the variance in the nuisance mask. By default, only the components that explain the top 50% of the variance are saved. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression. + +* Good: +* Bad: ### BOLD summary From 72152dbf720a91347709d4696c1288b27c0d7c7f Mon Sep 17 00:00:00 2001 From: cprovins Date: Thu, 26 Jan 2023 14:30:40 +0100 Subject: [PATCH 12/24] enh : complete information about the sections about and methods --- docs/users/howto_read_fmriprep_visual_report.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 331b2c7..07d06b0 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -169,9 +169,6 @@ The brain edge (or crown) ROI (green contour) picks signals outside but close to ### Variance explained by t/a CompCor components The figure displays the cumulative variance explained by components for each of four CompCor decompositions (left to right: anatomical CSF mask, anatomical white matter mask, anatomical combined mask, temporal). Dotted lines indicate the minimum number of components necessary to explain 50%, 70%, and 90% of the variance in the nuisance mask. By default, only the components that explain the top 50% of the variance are saved. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression. - -* Good: -* Bad: ### BOLD summary @@ -206,11 +203,12 @@ What you’re looking at: * Link to documentation on confounds: ## About +This section is a textual summary, containing the version of fMRIPrep, which command was run and the dates when the data were preprocessed. It is good to check that all of this information is as expected. ## Methods - -## Errors +This section provides a boilerplate describing in detail the preprocessing of the images. We kindly ask to report results preprocessed with fMRIPrep using that boilerplate. The latter is available in three languages (HTML, Markdown and Latex) to faciliate integration in manuscripts. +## Errors This section tells you whether fMRIPrep encountered any problems during the preprocessing. Note that “errors” refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results, or in which the input data was of lower-than-desirable quality. ## Things that aren’t an issue From 30196dd1160ed625b506166471621c24faa75cf6 Mon Sep 17 00:00:00 2001 From: cprovins Date: Thu, 26 Jan 2023 15:27:05 +0100 Subject: [PATCH 13/24] enh: reorganize section about regressors correlation --- .../howto_read_fmriprep_visual_report.md | 20 +++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 07d06b0..695c221 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -162,7 +162,7 @@ The brain edge (or crown) ROI (green contour) picks signals outside but close to * Bad: * The brain mask computed from the BOLD image mainly influences confounds estimation, but also co-registration, although the latter is primarily driven by the WM mask. As such the brain mask must not leave out any brain area, but it can be a bit loose around the brain. If the mask intersects with brain-originating signal, the nuisance regressors should not be used. * If the study plan prescribes using CompCor or brain-edge regressors, it is critical to exclude BOLD runs where any of these masks substantially overlap regions of interest. - * The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (see figure below). In this case, the scan should be excluded. + * The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (e.g see figure below). In this case, the scan should be excluded. ![suspicious-compcor](../assets/fmriprep_visual_report/suspicious-compcor.png) @@ -186,9 +186,21 @@ Note on how variance and scaling affects this display? (in other words, spikes a ### Correlations among nuisance regressors -This may also be addressed in more depth by the confounds and regressors documentation (link to page) -Can be used to diagnose partial volume effects -PVE indicated by high correlations of a confound time series (or several) with the global signal +This report presents a plot of correlations among confound regressors. The left-hand panel shows the matrix of correlations among selected confound time series as a heat-map. The right-hand panel displays the correlation of selected confound time series with the mean global signal computed across the whole brain; the regressors shown are those with greatest correlation with the global signal. These plots can be used to guide selection of a confound model or to assess the extent to which tissue-specific regressors correlate with global signal.; e.g if two regressors are highly correlated, it is recommended to include in the confound model only one of the two. + +* Selection of a confound model + * Good: + * Carefully choose a serie of regressors that are not highly correlated to include in the confound model. + * Or proceed with feature orthogonalization before confound regression. + * Bad: + * Include in the confound model two regressors that are highly correlated. + * Common pitfall in interpreptation: + * The CompCor components extracted from the same mask and the cosine bases are inherently orthogonal, implying a zero correlation by construction. + +* Assessement of partial volume effect [NEEDS COMPLETION] + * Good: + * Bad: + * High correlation of confound time series with the global signal is an indicator of high partial volume effect. ### ICA-AROMA What you’re looking 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b/docs/users/howto_read_fmriprep_visual_report.md index 695c221..784936e 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -165,24 +165,36 @@ The brain edge (or crown) ROI (green contour) picks signals outside but close to * The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (e.g see figure below). In this case, the scan should be excluded. ![suspicious-compcor](../assets/fmriprep_visual_report/suspicious-compcor.png) - ### Variance explained by t/a CompCor components The figure displays the cumulative variance explained by components for each of four CompCor decompositions (left to right: anatomical CSF mask, anatomical white matter mask, anatomical combined mask, temporal). Dotted lines indicate the minimum number of components necessary to explain 50%, 70%, and 90% of the variance in the nuisance mask. By default, only the components that explain the top 50% of the variance are saved. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression. ### BOLD summary -The BOLD summary report shows several characteristic statistics along with a carpetplot (Power, 2016), giving a view of the temporal characteristics of the preprocessed BOLD series. +The BOLD summary report shows several characteristic statistics along with a carpet plot [power2017][5], giving a view of the temporal characteristics of the preprocessed BOLD series. The carpet plot is a tool to visualize changes in voxel intensities throughout an fMRI scan. It works by plotting voxel time series in close spatial proximity so that the eye notes temporal coincidence. One particular innovation of this carpet plot implementation is that it contains the "crown" area corresponding to voxels located on a closed band around the brain's outer edge [patriat2015][6]. As those voxels are outside the brain, we do not expect any signal there, meaning that the presence of signal can be interpreted as produced by an artifact. +* Definitions: + * GS - global signal calculated in the whole-brain show the mean BOLD signal in the corresponding mask. + * GSCSF - global signal calculated within cerebrospinal fluid (CSF) + * GSWM - global signal calculated within white matter (WM) + * DVARS - [*standardized DVARS!*](https://neurostars.org/t/fmriprep-standardised-dvars/5271) for each time point + * FD - framewise-displacement measures for each time point + * Good: + * The carpet plot is homogeneous, particularly in the edge. + * Bad: -Note on how variance and scaling affects this display? (in other words, spikes are relative to other datapoints in the timeseries) -* Definitions: - * GS - * GSCSF - * GSWM - * DVARS - * *standardized DVARS!* - * FD + * Strongly structured crown region in the carpet plot is a sign that artifacts are compromising the fMRI scan [provins2022][7]. Several types of carpet plot modulations can be differentiated and are illustrated in the figure below. + * Motion outbursts, visible as peaks in the FD trace, are often paired with prolonged dark deflections derived from spin-history effects (see subfigure A). + * Periodic modulations on the carpet plot indicate regular and slow motion, e.g., caused by respiration, which may also compromise the signal of interest (see subfigure B). + * Coil failures may be identifiable as a sudden change in overall signal intensity on the carpet plot not paired that is not paired with motion peaks and generally sustained through the end of the scan (see subfigure C). + * A strong polarized structure revealed by the clustering of carpet plot rows suggests that artifacts mitigate the signal of interest. Indeed, sorting the rows (i.e., the time series) of each segment of the carpet plot such that voxels with similar BOLD dynamics appear close to one another reveals non-global structure in the signal, which is obscured when voxels are ordered randomly [aquino2020][8]. + * Finding temporal patterns similar in gray matter areas and simultaneously in regions of no interest (for instance, CSF or the crown) indicates the presence of artifacts, typically derived from head motion. If the planned analysis specifies noise regression techniques based on information from these regions of no interest (which is standard and recommended [ciric2017][9]), the risk of removing signals with neural origins is high, and affected scans should be excluded. + + ![carpetplot_artifacts](../assets/fmriprep_visual_report/carpetplot_artifacts.png) + + + * Common pitfalls in interpretation + * The variance and the scaling of the trace plots affect a lot their display and interpretation (in other words, spikes are relative to other datapoints in the timeseries). Therefore, always pay attention to the trace metrics like its maximum and mean, before deriving any conclusion. ### Correlations among nuisance regressors @@ -190,7 +202,7 @@ This report presents a plot of correlations among confound regressors. The left- * Selection of a confound model * Good: - * Carefully choose a serie of regressors that are not highly correlated to include in the confound model. + * Carefully choose a serie of regressors that are not highly correlated to include in the confound model. Refer to for more information on how to choose the nuisance regressors. * Or proceed with feature orthogonalization before confound regression. * Bad: * Include in the confound model two regressors that are highly correlated. @@ -235,3 +247,9 @@ If you are interested in knowing more about quality control, we wrote [a paper e [2] : White, Tonya, Philip R. Jansen, Ryan L. Muetzel, Gustavo Sudre, Hanan El Marroun, Henning Tiemeier, Anqi Qiu, Philip Shaw, Andrew M. Michael, and Frank C. Verhulst. 2018. “Automated Quality Assessment of Structural Magnetic Resonance Images in Children: Comparison with Visual Inspection and Surface-Based Reconstruction.” Human Brain Mapping 39 (3): 1218–31. . [3] : Klapwijk, Eduard T., Ferdi van de Kamp, Mara van der Meulen, Sabine Peters, and Lara M. Wierenga. 2019. “Qoala-T: A Supervised-Learning Tool for Quality Control of FreeSurfer Segmented MRI Data.” NeuroImage 189 (April): 116–29. . [4] : Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based FMRI.” NeuroImage 37 (1): 90–101. . +[5] : Power, Jonathan D. 2017. “A Simple but Useful Way to Assess FMRI Scan Qualities.” NeuroImage 154 (July): 150–58. https://doi.org/10.1016/j.neuroimage.2016.08.009. +[6] : Patriat R, Molloy E, Birn R, Guitchev T, Popov A. Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies. Brain Connect. 2015;5(9):582-595. doi:10.1089/brain.2014.0321 +[7] : Provins, Céline, Christopher J. Markiewicz, Rastko Ciric, Mathias Goncalves, César Caballero-Gaudes, Russell Poldrack, Patric Hagmann, and Oscar Esteban. 2022. “Quality Control and Nuisance Regression of FMRI, Looking out Where Signal Should Not Be Found.” Proc. Intl. Soc. Mag. Reson. Med. 31, (ISMRM), pp. 2683. https://doi.org/10.31219/osf.io/hz52v. +[8] : Aquino KM, Fulcher BD, Parkes L, Sabaroedin K, Fornito A. Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. NeuroImage. 2020;212:116614. doi:10.1016/j.neuroimage.2020.116614 +[9] : Ciric, Rastko, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham L. Baum, Kosha Ruparel, Russell T. Shinohara, et al. 2017. “Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity.” NeuroImage, 154: 174–87. https://doi.org/10.1016/j.neuroimage.2017.03.020. + From 0c0a9bd8415842917366bef0c779c86c22849975 Mon Sep 17 00:00:00 2001 From: cprovins Date: Mon, 13 Feb 2023 15:05:20 +0100 Subject: [PATCH 15/24] enh : add details about ICA-AROMA --- .../fmriprep_visual_report/ica_multiband.jpeg | Bin 0 -> 76138 bytes .../ica_slice_timing.png | Bin 0 -> 142089 bytes .../ica_spin_history_effect.png | Bin 0 -> 144299 bytes .../howto_read_fmriprep_visual_report.md | 33 ++++++++++++------ 4 files changed, 22 insertions(+), 11 deletions(-) create mode 100644 docs/assets/fmriprep_visual_report/ica_multiband.jpeg create mode 100644 docs/assets/fmriprep_visual_report/ica_slice_timing.png create mode 100644 docs/assets/fmriprep_visual_report/ica_spin_history_effect.png diff --git 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z3K^sCaDJat1=>{(`enfOu96u@q!`(pPjLl&ACY0Uz`+dTfJF^0PgJ2`Vx_2_CbNKS;VcuKA$-K< zF&Pb^5EjWI-8Jeu3e=EPK-EC25@?LT5I7d=8WwZ~i9muqr~5Pia`Br{Ar<1i+u|R-+ - * Note on ICA-AROMA and multiband data -* Slice-timing artifacts in ICA-AROMA: -* Link to documentation on confounds: + +If fMRIPrep is run with the --use-aroma argument is generates an independent component decomposition using FSL MELODIC [beckmann2004][10]. Such techniques have been thoroughly described elsewhere [griffanti2017][11], but in short AROMA is an ICA based procedure to identify confounding time series related to head-motion [prium2015][12]. Each component is mapped on a glass brain next to an indication of its frequency spectrum and its corresponding weight over time. + +* Good: + * Artifact components (red) should have more high-frequency noise whereas signal components (green) should have their peak in the lower-frequency range. + * Refer to for more details on how to denoise your data using ICA-AROMA confounds. + +* Bad: + * Spin-history effects in ICA-AROMA : One recurring artifactual family of components emerges when motion interacts with interleaved acquisition giving rise to the so-called spin-history effects. The spin-history effects appear as parallel stripes covering the whole brain in one direction (see figure below). They are a consequence of the repetition time not being much larger than the T1 relaxation time in typical fMRI designs. This implies that the spins will not completely relax when the next acquisition starts . In addition, specific movements (e.g., rotation around one imaging axis, such as nodding) will exacerbate spin-history effects as slices will cut through the brain at different locations between consecutive BOLD time points. These two considerations combined mean that motion will produce spins with different excitation histories, and thus, the signal intensity will differ. Components showcasing parallel stripes concurring with slices in extreme poles of the brain or even across the whole brain are likely to capture these effects. + ![ica_spin_history_effect](../assets/fmriprep_visual_report/ica_spin_history_effect.png) + + * Slice-timing artifacts in ICA-AROMA: A stripping pattern is visible and it matches alternative slices . + ![ica_slice_timing](../assets/fmriprep_visual_report/ica_slice_timing.png) + + * Multi-band artifacts in ICA-AROMA: The multi-band artifact also appears as a stripping pattern, however their spacing do not match alternative slices . What you see resembles one band being acquired. + ![ica_multiband](../assets/fmriprep_visual_report/ica_multiband.png) + +* Common pitfall in interpretation: + * Note that AROMA has not been trained on multiband data, so it is possible that some of the components showing multiband artifacts are classified as signal. ## About This section is a textual summary, containing the version of fMRIPrep, which command was run and the dates when the data were preprocessed. It is good to check that all of this information is as expected. @@ -252,4 +261,6 @@ If you are interested in knowing more about quality control, we wrote [a paper e [7] : Provins, Céline, Christopher J. Markiewicz, Rastko Ciric, Mathias Goncalves, César Caballero-Gaudes, Russell Poldrack, Patric Hagmann, and Oscar Esteban. 2022. “Quality Control and Nuisance Regression of FMRI, Looking out Where Signal Should Not Be Found.” Proc. Intl. Soc. Mag. Reson. Med. 31, (ISMRM), pp. 2683. https://doi.org/10.31219/osf.io/hz52v. [8] : Aquino KM, Fulcher BD, Parkes L, Sabaroedin K, Fornito A. Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. NeuroImage. 2020;212:116614. doi:10.1016/j.neuroimage.2020.116614 [9] : Ciric, Rastko, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham L. Baum, Kosha Ruparel, Russell T. Shinohara, et al. 2017. “Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity.” NeuroImage, 154: 174–87. https://doi.org/10.1016/j.neuroimage.2017.03.020. - +[10] : Beckmann, Christian F., and Stephen M. Smith. 2004. “Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging.” IEEE Transactions on Medical Imaging 23 (2): 137–52. https://doi.org/10.1109/TMI.2003.822821. +[11] : Griffanti, Ludovica, Gwenaëlle Douaud, Janine Bijsterbosch, Stefania Evangelisti, Fidel Alfaro-Almagro, Matthew F. Glasser, Eugene P. Duff, et al. 2017. “Hand Classification of FMRI ICA Noise Components.” NeuroImage 154 (July): 188–205. https://doi.org/10.1016/j.neuroimage.2016.12.036. +[12] : Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015 May 15;112:267–77. doi:10.1016/j.neuroimage.2015.02.064. From 119bb986a87b225d223877cebd4bccfcc65cce2b Mon Sep 17 00:00:00 2001 From: cprovins Date: Mon, 13 Feb 2023 15:22:59 +0100 Subject: [PATCH 16/24] sty : add captions to figures --- .../howto_read_fmriprep_visual_report.md | 37 +++++++++++-------- 1 file changed, 21 insertions(+), 16 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index e0481fb..52a72c8 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -1,12 +1,12 @@ # How To Read fMRIPrep visual Report fMRIPrep generates HTML visual reports that allow you to check the results of the preprocessing steps that were applied to the data. -Note: this is not a replacement for running quality control of your images before running fMRIPrep (FAQ link: https://fmriprep.readthedocs.io/en/stable/faq.html) +Note: this is not a replacement for running quality control of your images before running fMRIPrep (FAQ link: https://fmriprep.readthedocs.io/en/stable/faq.html). Below is a guide to interpreting the information contained in the visual reports, organized by subsection of the report. If you have examples of “bad” preprocessing results that you’re willing to share, please [submit them somewhere?] with a short description of the problem (for example, “bad skull stripping”). -When you open a report, it may be helpful to first jump to the [Errors subsection](LINK) to see whether fMRIPrep encountered any errors during preprocessing. Note that “errors” refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results. +When you open a report, it may be helpful to first jump to the Errors subsection to see whether fMRIPrep encountered any errors during preprocessing. Note that “errors” refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results. ## **Summary** @@ -31,10 +31,10 @@ This report displays the brain mask and the brain tissue segmentation computed f * Good: * The intensity of the image is uniform throughout the brain * Bad: - * Intensity non-uniformity artifacts (looks like the “original image” panel in the figure below): + * Intensity non-uniformity artifacts (looks like the “original image” panel in Figure 1): ![inu](../assets/fmriprep_visual_report/inu.png) - *from Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging, 26(3), 405-421.* + *Figure 1. from Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging, 26(3), 405-421.* * Skull stripping * Good: @@ -58,7 +58,7 @@ This report displays the brain mask and the brain tissue segmentation computed f * T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). * These scans should be excluded except for images where these voxels are only present at subcortical structures (e.g., thalamus) or nearby tissue boundaries. In the latter case, the misclassification results from partial volume effects (i.e., indeed, such voxels contain varying fractions of two or more tissues). The figure below illustrates the difference between individual dots caused by noise versus partial volume effects. ![noise-in-segmentation](../assets/fmriprep_visual_report/noise-in-segmentation.svg) - *Fig. 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between GM and WM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* + *Figure 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between GM and WM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* * Common pitfalls in interpretation: * At the inter-hemispheric space, masks (and in particular the brain mask, despite its smooth edges) may intersect the visualization plane several times, giving the impression that the mask is cutting off brain regions. However, this is more of a visual effect on the cutting plane. @@ -139,11 +139,11 @@ The alignment report shows the quality of co-registration and susceptibility dis * If the susceptibility distortion correction is unsuccessfull, residual susceptibility distortion artifacts can be observed. If the latter overlaps with regions of interest, the scan should be excluded. * Note however that some drop out in inferior brain regions (such as the OFC or medial temporal lobe) in functional images is somewhat inevitable. Depending on the type of analysis you’re doing and what you’re interested in, this may be more/less of an issue for you. If you see a lot of signal drop out, you will probably want to check the brain mask generated by fMRIPrep to see how that drop-out impacts the location and quantity of missing voxels in your brain mask. - * Common pitfalls in interpretation: * Note that the BOLD images displayed in the report may have what appears to be an “artifact” in the data, as shown below. This is because the reports use a faster but less precise type of interpolation (Nearest Neighbor interpolation) for display. In actuality, fMRIPrep uses Lanczos interpolation for resampling, so these apparent “artifacts” are not present in the actual data (you can confirm this by checking the preprocessed BOLD NIFTI file and its registration to the T1w image in your preferred software). ![apparent-artifact-interpolation](../assets/fmriprep_visual_report/apparent-artifact-interpolation.png) + *Figure 3. The use of a fast interpolation for display can lead to artifact-like structure that are not present in the actual data.* ### Brain mask and temporal/anatomical CompCor ROIs @@ -156,14 +156,16 @@ The temporal CompCor ROI (blue contour) contains the top 2% most variable voxels The brain edge (or crown) ROI (green contour) picks signals outside but close to the brain, which are decomposed into 24 principal components. * Good: - * The brain mask correctly surrounds the brain boundary, not leaving out brain area. Note that holes in the brain mask such as on the figure below, is not problematic as it should not disrupt co-registration. + * The brain mask correctly surrounds the brain boundary, not leaving out brain area. Note that holes in the brain mask such as on Figure 4, is not problematic as it should not disrupt co-registration. ![hole-bold-brainmask](../assets/fmriprep_visual_report/hold-bold-brainmask.png) + *Figure 4. Such holes in the brain mask is not problematic as it should not disrupt co-registration.* * Bad: * The brain mask computed from the BOLD image mainly influences confounds estimation, but also co-registration, although the latter is primarily driven by the WM mask. As such the brain mask must not leave out any brain area, but it can be a bit loose around the brain. If the mask intersects with brain-originating signal, the nuisance regressors should not be used. * If the study plan prescribes using CompCor or brain-edge regressors, it is critical to exclude BOLD runs where any of these masks substantially overlap regions of interest. - * The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (e.g see figure below). In this case, the scan should be excluded. - ![suspicious-compcor](../assets/fmriprep_visual_report/suspicious-compcor.png) + * The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (e.g see Figure 5). In this case, the scan should be excluded. + ![suspicious-compcor](../assets/fmriprep_visual_report/suspicious-compcor.png) + *Figure 5. The shape and the large overlap of the tCompCor with region of interest indicates the presence of an artifact that was missed in the other visualizations.* ### Variance explained by t/a CompCor components @@ -184,14 +186,14 @@ The BOLD summary report shows several characteristic statistics along with a car * Bad: * Strongly structured crown region in the carpet plot is a sign that artifacts are compromising the fMRI scan [provins2022][7]. Several types of carpet plot modulations can be differentiated and are illustrated in the figure below. - * Motion outbursts, visible as peaks in the FD trace, are often paired with prolonged dark deflections derived from spin-history effects (see subfigure A). - * Periodic modulations on the carpet plot indicate regular and slow motion, e.g., caused by respiration, which may also compromise the signal of interest (see subfigure B). - * Coil failures may be identifiable as a sudden change in overall signal intensity on the carpet plot not paired that is not paired with motion peaks and generally sustained through the end of the scan (see subfigure C). - * A strong polarized structure revealed by the clustering of carpet plot rows suggests that artifacts mitigate the signal of interest. Indeed, sorting the rows (i.e., the time series) of each segment of the carpet plot such that voxels with similar BOLD dynamics appear close to one another reveals non-global structure in the signal, which is obscured when voxels are ordered randomly [aquino2020][8]. + * Motion outbursts, visible as peaks in the FD trace, are often paired with prolonged dark deflections derived from spin-history effects (see Figure 6A). + * Periodic modulations on the carpet plot indicate regular and slow motion, e.g., caused by respiration, which may also compromise the signal of interest (see Figure 6B). + * Coil failures may be identifiable as a sudden change in overall signal intensity on the carpet plot not paired that is not paired with motion peaks and generally sustained through the end of the scan (see Figure 6C). + * A strong polarized structure revealed by the clustering of carpet plot rows suggests that artifacts mitigate the signal of interest (see Figure 6D). Indeed, sorting the rows (i.e., the time series) of each segment of the carpet plot such that voxels with similar BOLD dynamics appear close to one another reveals non-global structure in the signal, which is obscured when voxels are ordered randomly [aquino2020][8]. * Finding temporal patterns similar in gray matter areas and simultaneously in regions of no interest (for instance, CSF or the crown) indicates the presence of artifacts, typically derived from head motion. If the planned analysis specifies noise regression techniques based on information from these regions of no interest (which is standard and recommended [ciric2017][9]), the risk of removing signals with neural origins is high, and affected scans should be excluded. ![carpetplot_artifacts](../assets/fmriprep_visual_report/carpetplot_artifacts.png) - + *Figure 6. Assessment of time series with the carpet plot. The crown region of the carpet plot comprises voxels outside the brain. As such, structure in the crown can be interpreted as artifactual, and thus corresponds to an exclusion criteria. (A) Motion outbursts, visible as peaks in the framewise displacement (FD) trace, are often paired with prolonged dark deflections. (B) Periodic modulations are indicative of regular, slow motion, e.g., caused by respiration. (C) An abrupt change in overall signal intensity that is not paired with motion peaks can be attributed to coil failure. (D) A strong polarized structure revealed by the clustering of carpet plot rows also suggests that artifacts mitigate the signal of interest.* * Common pitfalls in interpretation * The variance and the scaling of the trace plots affect a lot their display and interpretation (in other words, spikes are relative to other datapoints in the timeseries). Therefore, always pay attention to the trace metrics like its maximum and mean, before deriving any conclusion. @@ -223,14 +225,17 @@ If fMRIPrep is run with the --use-aroma argument is generates an independent com * Refer to for more details on how to denoise your data using ICA-AROMA confounds. * Bad: - * Spin-history effects in ICA-AROMA : One recurring artifactual family of components emerges when motion interacts with interleaved acquisition giving rise to the so-called spin-history effects. The spin-history effects appear as parallel stripes covering the whole brain in one direction (see figure below). They are a consequence of the repetition time not being much larger than the T1 relaxation time in typical fMRI designs. This implies that the spins will not completely relax when the next acquisition starts . In addition, specific movements (e.g., rotation around one imaging axis, such as nodding) will exacerbate spin-history effects as slices will cut through the brain at different locations between consecutive BOLD time points. These two considerations combined mean that motion will produce spins with different excitation histories, and thus, the signal intensity will differ. Components showcasing parallel stripes concurring with slices in extreme poles of the brain or even across the whole brain are likely to capture these effects. + * Spin-history effects in ICA-AROMA : One recurring artifactual family of components emerges when motion interacts with interleaved acquisition giving rise to the so-called spin-history effects. The spin-history effects appear as parallel stripes covering the whole brain in one direction (see Figure 7). They are a consequence of the repetition time not being much larger than the T1 relaxation time in typical fMRI designs. This implies that the spins will not completely relax when the next acquisition starts . In addition, specific movements (e.g., rotation around one imaging axis, such as nodding) will exacerbate spin-history effects as slices will cut through the brain at different locations between consecutive BOLD time points. These two considerations combined mean that motion will produce spins with different excitation histories, and thus, the signal intensity will differ. Components showcasing parallel stripes concurring with slices in extreme poles of the brain or even across the whole brain are likely to capture these effects. ![ica_spin_history_effect](../assets/fmriprep_visual_report/ica_spin_history_effect.png) + *Figure 7. Spin-history effect in ICA-AROMA* * Slice-timing artifacts in ICA-AROMA: A stripping pattern is visible and it matches alternative slices . ![ica_slice_timing](../assets/fmriprep_visual_report/ica_slice_timing.png) + *Figure 8. Slice-timing artifact in ICA-AROMA* * Multi-band artifacts in ICA-AROMA: The multi-band artifact also appears as a stripping pattern, however their spacing do not match alternative slices . What you see resembles one band being acquired. - ![ica_multiband](../assets/fmriprep_visual_report/ica_multiband.png) + ![ica_multiband](../assets/fmriprep_visual_report/ica_multiband.png) + *Figure 9. Multi-band artifact in ICA-AROMA* * Common pitfall in interpretation: * Note that AROMA has not been trained on multiband data, so it is possible that some of the components showing multiband artifacts are classified as signal. From 6d69d2af3607b84890fd473cd2fd0ee52ef2829e Mon Sep 17 00:00:00 2001 From: "Saren H. Seeley" <31291534+sarenseeley@users.noreply.github.com> Date: Sun, 5 Mar 2023 11:21:05 -0500 Subject: [PATCH 17/24] update visual reports document Responded to questions in issue #21 --- .../howto_read_fmriprep_visual_report.md | 43 ++++++++++++++----- 1 file changed, 32 insertions(+), 11 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 52a72c8..30aa382 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -169,16 +169,22 @@ The brain edge (or crown) ROI (green contour) picks signals outside but close to ### Variance explained by t/a CompCor components -The figure displays the cumulative variance explained by components for each of four CompCor decompositions (left to right: anatomical CSF mask, anatomical white matter mask, anatomical combined mask, temporal). Dotted lines indicate the minimum number of components necessary to explain 50%, 70%, and 90% of the variance in the nuisance mask. By default, only the components that explain the top 50% of the variance are saved. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression. +The figure displays the cumulative variance explained by components for each of four CompCor decompositions (left to right: anatomical CSF mask, anatomical white matter mask, anatomical combined mask, temporal). Dotted lines indicate the minimum number of components necessary to explain 50%, 70%, and 90% of the variance in the nuisance mask.The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression. + + * High variance explained: A high variance explained by t/aCompCor components indicates that the selected regions of interest are capturing a significant portion of the motion and physiological noise in the data, which can improve the quality of subsequent analyses. In the fMRIPrep reports, by default only the components that explain the top 50% of the variance are saved. + + * Low variance explained: A low variance explained by t/aCompCor components may indicate that the selected regions of interest are not capturing enough of the motion and physiological noise in the data, which can lead to reduced sensitivity and specificity in subsequent analyses. + +The benchmark for % variance explained and/or optimal number of CompCor components to include in the analysis can vary, depending on the specific dataset and analysis goals. In general, the goal is to select enough components to capture the majority of the motion and physiological noise in the data, while avoiding overfitting the model to noise or task-related signal. ### BOLD summary The BOLD summary report shows several characteristic statistics along with a carpet plot [power2017][5], giving a view of the temporal characteristics of the preprocessed BOLD series. The carpet plot is a tool to visualize changes in voxel intensities throughout an fMRI scan. It works by plotting voxel time series in close spatial proximity so that the eye notes temporal coincidence. One particular innovation of this carpet plot implementation is that it contains the "crown" area corresponding to voxels located on a closed band around the brain's outer edge [patriat2015][6]. As those voxels are outside the brain, we do not expect any signal there, meaning that the presence of signal can be interpreted as produced by an artifact. * Definitions: - * GS - global signal calculated in the whole-brain show the mean BOLD signal in the corresponding mask. + * GS - global signal, or the mean of the voxel time series within the whole-brain mask. In additional to the BOLD signal of interest, GS fluctuations can reflect non-neuronal contributions to the voxel time series, e.g., by motion, the MRI system (e.g., low frequency drift), physiology (respiration, cardiac rate), and/or participant state-related factors (e.g., vigilance or fatigue) [Liu2017][13]. * GSCSF - global signal calculated within cerebrospinal fluid (CSF) * GSWM - global signal calculated within white matter (WM) - * DVARS - [*standardized DVARS!*](https://neurostars.org/t/fmriprep-standardised-dvars/5271) for each time point + * DVARS - [*standardized DVARS!*](https://neurostars.org/t/fmriprep-standardised-dvars/5271) for each time point. DVARS is the average change in mean intensity between each pair of fMRI volumes in a series. Higher values indicate more dramatic changes (e.g., due to motion or spiking). * FD - framewise-displacement measures for each time point * Good: @@ -190,13 +196,14 @@ The BOLD summary report shows several characteristic statistics along with a car * Periodic modulations on the carpet plot indicate regular and slow motion, e.g., caused by respiration, which may also compromise the signal of interest (see Figure 6B). * Coil failures may be identifiable as a sudden change in overall signal intensity on the carpet plot not paired that is not paired with motion peaks and generally sustained through the end of the scan (see Figure 6C). * A strong polarized structure revealed by the clustering of carpet plot rows suggests that artifacts mitigate the signal of interest (see Figure 6D). Indeed, sorting the rows (i.e., the time series) of each segment of the carpet plot such that voxels with similar BOLD dynamics appear close to one another reveals non-global structure in the signal, which is obscured when voxels are ordered randomly [aquino2020][8]. - * Finding temporal patterns similar in gray matter areas and simultaneously in regions of no interest (for instance, CSF or the crown) indicates the presence of artifacts, typically derived from head motion. If the planned analysis specifies noise regression techniques based on information from these regions of no interest (which is standard and recommended [ciric2017][9]), the risk of removing signals with neural origins is high, and affected scans should be excluded. + * Finding temporal patterns similar in gray matter areas and simultaneously in regions of no interest (for instance, CSF or the crown) indicates the presence of artifacts, typically derived from head motion. If the planned analysis specifies noise regression techniques based on information from these regions of no interest (which is standard and recommended [ciric2017][9]), the risk of removing signals with neural origins is high, and affected scans should be excluded. For example, in standard task-based fMRI, stimulus presentation might evoke both participant movement and neural responses (_stimulus-correlated motion_). + ![carpetplot_artifacts](../assets/fmriprep_visual_report/carpetplot_artifacts.png) *Figure 6. Assessment of time series with the carpet plot. The crown region of the carpet plot comprises voxels outside the brain. As such, structure in the crown can be interpreted as artifactual, and thus corresponds to an exclusion criteria. (A) Motion outbursts, visible as peaks in the framewise displacement (FD) trace, are often paired with prolonged dark deflections. (B) Periodic modulations are indicative of regular, slow motion, e.g., caused by respiration. (C) An abrupt change in overall signal intensity that is not paired with motion peaks can be attributed to coil failure. (D) A strong polarized structure revealed by the clustering of carpet plot rows also suggests that artifacts mitigate the signal of interest.* * Common pitfalls in interpretation - * The variance and the scaling of the trace plots affect a lot their display and interpretation (in other words, spikes are relative to other datapoints in the timeseries). Therefore, always pay attention to the trace metrics like its maximum and mean, before deriving any conclusion. + * The variance and the scaling of the trace plots affect a lot their display and interpretation (in other words, spikes are relative to other datapoints in the timeseries). Therefore, always pay attention to the trace metrics like its maximum and mean, before deriving any conclusion or visually comparing the trace plots between two participants. ### Correlations among nuisance regressors @@ -211,10 +218,15 @@ This report presents a plot of correlations among confound regressors. The left- * Common pitfall in interpreptation: * The CompCor components extracted from the same mask and the cosine bases are inherently orthogonal, implying a zero correlation by construction. -* Assessement of partial volume effect [NEEDS COMPLETION] - * Good: +* Assessment of partial volume effect [NEEDS COMPLETION] + * Partial volume effects refers to the mixing of signals from different tissue types within a single voxel in fMRI data, so that the MR signal reflects fractional contributions from multiple tissue types. This can lead to inaccurate estimates of neural activity. (However, note that other sources, such as motion or physiological noise, can also contribute to high correlations among the regressors). + * High partial volume effects in one group vs. another may reflect underlying anatomical differences in the groups. For example, older adults with Alzheimers have greater grey matter volume loss and ventricle enlargement, so may be more affected by PVE than their healthy controls [dukart][14]. + + * Good: + * There is no specific threshold for the correlation coefficient that indicates the absence (or presence) of partial volume effects (PVE) in fMRI data. The strength of the correlation between nuisance regressors may depend on several factors, including the tissue properties of the brain region being examined, the voxel size, the signal-to-noise ratio of the data, and the specific nuisance regressors used in the analysis. + * Bad: - * High correlation of confound time series with the global signal is an indicator of high partial volume effect. + * Generally, a high correlation coefficient among nuisance regressors -- especially spatial/anatomical regressors -- may indicate the presence of PVE if it is accompanied by other evidence, such as spatial co-localization of the voxels with high correlation coefficients with regions known to be susceptible to PVE such as the gray/white matter boundary. ### ICA-AROMA @@ -233,12 +245,19 @@ If fMRIPrep is run with the --use-aroma argument is generates an independent com ![ica_slice_timing](../assets/fmriprep_visual_report/ica_slice_timing.png) *Figure 8. Slice-timing artifact in ICA-AROMA* +### Note on multiband/multi-echo fMRI data + +Multiband/multiecho fMRI acquisitions can be used to improve the temporal and spatial resolution of fMRI data, but they can also introduce several types of artifacts, such as slice crosstalk, aliasing, and signal dropout. ICA-AROMA was not been trained on multiband/multi-echo data, so it is possible that it may not perform as accurately as it does for single-band data. For an ICA-based denoising approach specifically for multi-echo fMRI, see [TEDANA](https://tedana.readthedocs.io/en/stable/). + * Multi-band artifacts in ICA-AROMA: The multi-band artifact also appears as a stripping pattern, however their spacing do not match alternative slices . What you see resembles one band being acquired. ![ica_multiband](../assets/fmriprep_visual_report/ica_multiband.png) *Figure 9. Multi-band artifact in ICA-AROMA* - -* Common pitfall in interpretation: - * Note that AROMA has not been trained on multiband data, so it is possible that some of the components showing multiband artifacts are classified as signal. + + * Aliasing artifacts may be visible as duplicate components in the ICA-AROMA plots. Aliasing artifacts can occur in multiband fMRI data when the phase-encoding direction is not sampled densely enough, resulting in aliasing of signal from adjacent slices (these artifacts may also appear elsewhere, as ghosting or blurring of the brain image). + + * Signal dropout (due to susceptibility effects) may be visible in the ICA-AROMA plots as components with very low or absent time courses. + + * Slice crosstalk artifacts may be visible as components with time courses that resemble signal from adjacent slices. Slice crosstalk can occur in multiband fMRI data due to the inter-slice signal leakage, and can appear as spurious signals in the neighboring slices. ## About This section is a textual summary, containing the version of fMRIPrep, which command was run and the dates when the data were preprocessed. It is good to check that all of this information is as expected. @@ -269,3 +288,5 @@ If you are interested in knowing more about quality control, we wrote [a paper e [10] : Beckmann, Christian F., and Stephen M. Smith. 2004. “Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging.” IEEE Transactions on Medical Imaging 23 (2): 137–52. https://doi.org/10.1109/TMI.2003.822821. [11] : Griffanti, Ludovica, Gwenaëlle Douaud, Janine Bijsterbosch, Stefania Evangelisti, Fidel Alfaro-Almagro, Matthew F. Glasser, Eugene P. Duff, et al. 2017. “Hand Classification of FMRI ICA Noise Components.” NeuroImage 154 (July): 188–205. https://doi.org/10.1016/j.neuroimage.2016.12.036. [12] : Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015 May 15;112:267–77. doi:10.1016/j.neuroimage.2015.02.064. +[13] : Liu TT, Nalci A, Falahpour M. 2017. The global signal in fMRI: Nuisance or Information? Neuroimage: 150:213-29. doi:10.1016/j.neuroimage.2017.02.036. +[14] : Dukart J, Bertolino A. When structure affects function–the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies. PloS one:9(12);e114227. doi:10.1371/journal.pone.0114227 From cc19b9086139119db2368cdb429e6a0758a47f10 Mon Sep 17 00:00:00 2001 From: celprov <77437752+celprov@users.noreply.github.com> Date: Wed, 29 Mar 2023 10:34:23 +0200 Subject: [PATCH 18/24] fix : Correct typos --- docs/users/howto_read_fmriprep_visual_report.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 30aa382..d786424 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -181,10 +181,10 @@ The benchmark for % variance explained and/or optimal number of CompCor componen The BOLD summary report shows several characteristic statistics along with a carpet plot [power2017][5], giving a view of the temporal characteristics of the preprocessed BOLD series. The carpet plot is a tool to visualize changes in voxel intensities throughout an fMRI scan. It works by plotting voxel time series in close spatial proximity so that the eye notes temporal coincidence. One particular innovation of this carpet plot implementation is that it contains the "crown" area corresponding to voxels located on a closed band around the brain's outer edge [patriat2015][6]. As those voxels are outside the brain, we do not expect any signal there, meaning that the presence of signal can be interpreted as produced by an artifact. * Definitions: - * GS - global signal, or the mean of the voxel time series within the whole-brain mask. In additional to the BOLD signal of interest, GS fluctuations can reflect non-neuronal contributions to the voxel time series, e.g., by motion, the MRI system (e.g., low frequency drift), physiology (respiration, cardiac rate), and/or participant state-related factors (e.g., vigilance or fatigue) [Liu2017][13]. + * GS - global signal, or the mean of the voxel time series within the whole-brain mask. In addition to the BOLD signal of interest, GS fluctuations can reflect non-neuronal contributions to the voxel time series, e.g., by motion, the MRI system (e.g., low frequency drift), physiology (respiration, cardiac rate), and/or participant state-related factors (e.g., vigilance or fatigue) [Liu2017][13]. * GSCSF - global signal calculated within cerebrospinal fluid (CSF) * GSWM - global signal calculated within white matter (WM) - * DVARS - [*standardized DVARS!*](https://neurostars.org/t/fmriprep-standardised-dvars/5271) for each time point. DVARS is the average change in mean intensity between each pair of fMRI volumes in a series. Higher values indicate more dramatic changes (e.g., due to motion or spiking). + * DVARS - [*standardized DVARS!*](https://neurostars.org/t/fmriprep-standardised-dvars/5271) for each time point. DVARS is the average change in mean intensity between each pair of fMRI volumes in a series and can also be interpreted as the first derivative of the mean intensity. Higher values indicate more dramatic changes (e.g., due to motion or spiking). * FD - framewise-displacement measures for each time point * Good: @@ -247,7 +247,7 @@ If fMRIPrep is run with the --use-aroma argument is generates an independent com ### Note on multiband/multi-echo fMRI data -Multiband/multiecho fMRI acquisitions can be used to improve the temporal and spatial resolution of fMRI data, but they can also introduce several types of artifacts, such as slice crosstalk, aliasing, and signal dropout. ICA-AROMA was not been trained on multiband/multi-echo data, so it is possible that it may not perform as accurately as it does for single-band data. For an ICA-based denoising approach specifically for multi-echo fMRI, see [TEDANA](https://tedana.readthedocs.io/en/stable/). +Multiband/multiecho fMRI acquisitions can be used to improve the temporal and spatial resolution of fMRI data, but they can also introduce several types of artifacts, such as slice crosstalk, aliasing, and signal dropout. ICA-AROMA was not trained on multiband/multi-echo data, so it is possible that it may not perform as accurately as it does for single-band data. For an ICA-based denoising approach specifically for multi-echo fMRI, see [TEDANA](https://tedana.readthedocs.io/en/stable/). * Multi-band artifacts in ICA-AROMA: The multi-band artifact also appears as a stripping pattern, however their spacing do not match alternative slices . What you see resembles one band being acquired. ![ica_multiband](../assets/fmriprep_visual_report/ica_multiband.png) From fcea9c41ab114cf9600b238f234e91571e6f14db Mon Sep 17 00:00:00 2001 From: cprovins Date: Wed, 29 Mar 2023 10:15:08 +0200 Subject: [PATCH 19/24] fix : make the document less prescriptive by replacing good/bad by better/worse --- .../howto_read_fmriprep_visual_report.md | 48 +++++++++---------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index d786424..0454d46 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -28,19 +28,19 @@ The reports first present results of anatomical preprocessing. This report displays the brain mask and the brain tissue segmentation computed from the T1w image. It shows the quality of intensity non-uniformity (INU) correction, skull stripping, and tissue segmentation. * INU correction - * Good: + * Better: * The intensity of the image is uniform throughout the brain - * Bad: + * Worse: * Intensity non-uniformity artifacts (looks like the “original image” panel in Figure 1): ![inu](../assets/fmriprep_visual_report/inu.png) *Figure 1. from Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging, 26(3), 405-421.* * Skull stripping - * Good: + * Better: * The brain mask (red) covers the whole brain and only the brain - not the dura or anything else outside the brain. It should closely follow the contour of the brain. - * Bad: + * Worse: * Skull-stripping failed leading to an inaccurate brain mask * The brain mask cuts off part of the brain and/or contains holes surrounding signal drop-out regions. * The brain mask includes parts that are clearly NOT brain. @@ -49,10 +49,10 @@ This report displays the brain mask and the brain tissue segmentation computed f * Having an accurate brain mask makes the downstream preprocessing of an fMRI scan faster (excluding voxels of non-interest) and more accurate (less bias from voxels of non-interest). Consequently, it is important to discard subjects for which the brain mask is not well defined. * Tissue segmentation - * Good: + * Better: * The outlines of the gray matter (GM; magenta) and white matter (WM; blue) segmentation are correctly drawn. The blue line should follow the boundary between GM and WM, while the magenta line should outline ventricles. - * Bad: + * Worse: * The GM (magenta)/WM (blue) outlines don’t match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between GM and WM or the magenta line does not outline ventricles. * Inclusion of tissues other than the tissue of interest in the contour delineations should lead to exclusion of the scan. * T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). @@ -67,12 +67,12 @@ This report displays the brain mask and the brain tissue segmentation computed f ### Spatial normalization of the anatomical T1w reference The normalization report shows how successfully your T1w image(s) were resampled into standard space, for each of the template spaces used. -* Good: +* Better: * Your T1w image and the template image line up well when you toggle between the images (hover mouse over the panel): * In order of importance, the following structures should be correctly aligned : 1. ventricles, 2. subcortical regions, 3. corpus callosum, 4. cerebellum, 5. cortical gray matter. * The standard templates provided with fMRIPrep (such as `MNI152NLin2009cAsym`) are averaged across multiple subjects, so the template image will look blurrier than the T1w image. Because of this averaging, it is normal for sulci to be less pronounced and gyri to be wider on the template than the T1w image. -* Bad: +* Worse: * Stretching or distortion in the participant’s T1w image, indicating failed normalization. * A misalignment of the ventricles, the subcortical regions, or the corpus callosum should lead to immediate exclusion. You can however be more lenient with the misalignment of cortical GM because volumetric (image) registration may not resolve substantial inter-individual differences (e.g., a sulcus missing in an individual’s brain but typically present in the population of the template). @@ -82,11 +82,11 @@ The normalization report shows how successfully your T1w image(s) were resampled If you used the `--fs-no-reconall` flag to skip surface-based preprocessing, this section of the report will not exist. The FreeSurfer [fischl2012][1] subject reconstruction report shows the WM (blue outline) and pial (red outline) surfaces overlaid on the T1w image. -* Good: +* Better: * The white-gray boundary outlined (blue link) matches the underlying image. * WM and pial surface boundary outlines do not cross or overlap each other. -* Bad: +* Worse: * The white-gray boundary outline (blue line) does not correspond well to the boundary observed in the underlying image. * WM and pial surface boundaries cross or overlap each other. * Pial surface (red outline) extends past the actual pial boundary (see images [here](https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/PialEdits_tktools) for an example ; this can be a result of bad skull-stripping). @@ -122,20 +122,20 @@ The alignment report shows the quality of co-registration and susceptibility dis * Co-registration * The text tells you which method fMRIPrep used to align the functional and anatomical data - for example, by running `bbregister`. The images show the registered BOLD reference with the white and pial surfaces overlaid (red and blue lines) - * Good: + * Better: * The BOLD and T1w images are aligned; image boundaries and anatomical landmarks (for example, ventricles; corpus callosum) appear to be in the same place when toggling between the images. * White and pial surface outlines (red and blue lines) appear to correspond well to the tissue boundaries in the functional images. - * Bad: + * Worse: * Functional and anatomical images are not aligned and clearly differ in their spatial location or orientation. * White and pial surface boundaries (red and blue lines) overlays correspond poorly to the tissue boundaries in the underlying images. * Susceptibility distortion correction * The functional images can have some warping or distortion due to inhomogeneities in the $B_0$ magnetic field. For more details on what causes susceptibility distortion, refer to [the educational notebook of SDCFlows](https://github.com/nipreps/sdcflows/blob/master/docs/notebooks/SDC%20-%20Theory%20and%20physics.ipynb). Susceptibility distortion artifacts manifest in two different ways on the functional and structural images: as signal drop-out, that is, a region where the signal vanishes, or as brain distortions. Signal drop-outs often appear close to brain-air interfaces; these include ventromedial prefrontal cortex, the anterior part of the prefrontal cortex, and the region next to the ear cavities. - * Good: + * Better: * No signal drop out or brain distortion affects your region of interest. * The pial surface outlines (blue lines) appear to correspond well to the tissue boundaries in the functional images. - * Bad: + * Worse: * If the susceptibility distortion correction is unsuccessfull, residual susceptibility distortion artifacts can be observed. If the latter overlaps with regions of interest, the scan should be excluded. * Note however that some drop out in inferior brain regions (such as the OFC or medial temporal lobe) in functional images is somewhat inevitable. Depending on the type of analysis you’re doing and what you’re interested in, this may be more/less of an issue for you. If you see a lot of signal drop out, you will probably want to check the brain mask generated by fMRIPrep to see how that drop-out impacts the location and quantity of missing voxels in your brain mask. @@ -155,12 +155,12 @@ The temporal CompCor ROI (blue contour) contains the top 2% most variable voxels The brain edge (or crown) ROI (green contour) picks signals outside but close to the brain, which are decomposed into 24 principal components. -* Good: +* Better: * The brain mask correctly surrounds the brain boundary, not leaving out brain area. Note that holes in the brain mask such as on Figure 4, is not problematic as it should not disrupt co-registration. ![hole-bold-brainmask](../assets/fmriprep_visual_report/hold-bold-brainmask.png) *Figure 4. Such holes in the brain mask is not problematic as it should not disrupt co-registration.* -* Bad: +* Worse: * The brain mask computed from the BOLD image mainly influences confounds estimation, but also co-registration, although the latter is primarily driven by the WM mask. As such the brain mask must not leave out any brain area, but it can be a bit loose around the brain. If the mask intersects with brain-originating signal, the nuisance regressors should not be used. * If the study plan prescribes using CompCor or brain-edge regressors, it is critical to exclude BOLD runs where any of these masks substantially overlap regions of interest. * The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (e.g see Figure 5). In this case, the scan should be excluded. @@ -187,10 +187,10 @@ The BOLD summary report shows several characteristic statistics along with a car * DVARS - [*standardized DVARS!*](https://neurostars.org/t/fmriprep-standardised-dvars/5271) for each time point. DVARS is the average change in mean intensity between each pair of fMRI volumes in a series and can also be interpreted as the first derivative of the mean intensity. Higher values indicate more dramatic changes (e.g., due to motion or spiking). * FD - framewise-displacement measures for each time point -* Good: +* Better: * The carpet plot is homogeneous, particularly in the edge. -* Bad: +* Worse: * Strongly structured crown region in the carpet plot is a sign that artifacts are compromising the fMRI scan [provins2022][7]. Several types of carpet plot modulations can be differentiated and are illustrated in the figure below. * Motion outbursts, visible as peaks in the FD trace, are often paired with prolonged dark deflections derived from spin-history effects (see Figure 6A). * Periodic modulations on the carpet plot indicate regular and slow motion, e.g., caused by respiration, which may also compromise the signal of interest (see Figure 6B). @@ -210,10 +210,10 @@ The BOLD summary report shows several characteristic statistics along with a car This report presents a plot of correlations among confound regressors. The left-hand panel shows the matrix of correlations among selected confound time series as a heat-map. The right-hand panel displays the correlation of selected confound time series with the mean global signal computed across the whole brain; the regressors shown are those with greatest correlation with the global signal. These plots can be used to guide selection of a confound model or to assess the extent to which tissue-specific regressors correlate with global signal.; e.g if two regressors are highly correlated, it is recommended to include in the confound model only one of the two. * Selection of a confound model - * Good: + * Better: * Carefully choose a serie of regressors that are not highly correlated to include in the confound model. Refer to for more information on how to choose the nuisance regressors. * Or proceed with feature orthogonalization before confound regression. - * Bad: + * Worse: * Include in the confound model two regressors that are highly correlated. * Common pitfall in interpreptation: * The CompCor components extracted from the same mask and the cosine bases are inherently orthogonal, implying a zero correlation by construction. @@ -222,21 +222,21 @@ This report presents a plot of correlations among confound regressors. The left- * Partial volume effects refers to the mixing of signals from different tissue types within a single voxel in fMRI data, so that the MR signal reflects fractional contributions from multiple tissue types. This can lead to inaccurate estimates of neural activity. (However, note that other sources, such as motion or physiological noise, can also contribute to high correlations among the regressors). * High partial volume effects in one group vs. another may reflect underlying anatomical differences in the groups. For example, older adults with Alzheimers have greater grey matter volume loss and ventricle enlargement, so may be more affected by PVE than their healthy controls [dukart][14]. - * Good: + * Better: * There is no specific threshold for the correlation coefficient that indicates the absence (or presence) of partial volume effects (PVE) in fMRI data. The strength of the correlation between nuisance regressors may depend on several factors, including the tissue properties of the brain region being examined, the voxel size, the signal-to-noise ratio of the data, and the specific nuisance regressors used in the analysis. - * Bad: + * Worse: * Generally, a high correlation coefficient among nuisance regressors -- especially spatial/anatomical regressors -- may indicate the presence of PVE if it is accompanied by other evidence, such as spatial co-localization of the voxels with high correlation coefficients with regions known to be susceptible to PVE such as the gray/white matter boundary. ### ICA-AROMA If fMRIPrep is run with the --use-aroma argument is generates an independent component decomposition using FSL MELODIC [beckmann2004][10]. Such techniques have been thoroughly described elsewhere [griffanti2017][11], but in short AROMA is an ICA based procedure to identify confounding time series related to head-motion [prium2015][12]. Each component is mapped on a glass brain next to an indication of its frequency spectrum and its corresponding weight over time. -* Good: +* Better: * Artifact components (red) should have more high-frequency noise whereas signal components (green) should have their peak in the lower-frequency range. * Refer to for more details on how to denoise your data using ICA-AROMA confounds. -* Bad: +* Worse: * Spin-history effects in ICA-AROMA : One recurring artifactual family of components emerges when motion interacts with interleaved acquisition giving rise to the so-called spin-history effects. The spin-history effects appear as parallel stripes covering the whole brain in one direction (see Figure 7). They are a consequence of the repetition time not being much larger than the T1 relaxation time in typical fMRI designs. This implies that the spins will not completely relax when the next acquisition starts . In addition, specific movements (e.g., rotation around one imaging axis, such as nodding) will exacerbate spin-history effects as slices will cut through the brain at different locations between consecutive BOLD time points. These two considerations combined mean that motion will produce spins with different excitation histories, and thus, the signal intensity will differ. Components showcasing parallel stripes concurring with slices in extreme poles of the brain or even across the whole brain are likely to capture these effects. ![ica_spin_history_effect](../assets/fmriprep_visual_report/ica_spin_history_effect.png) *Figure 7. Spin-history effect in ICA-AROMA* From b1db375847fe5bd2693590fd8ded68be33fef8cb Mon Sep 17 00:00:00 2001 From: cprovins Date: Wed, 29 Mar 2023 11:00:16 +0200 Subject: [PATCH 20/24] sty : replace crown with brain edge to be more consistent fix : fix markdown layout problem --- .../howto_read_fmriprep_visual_report.md | 62 +++++++++++-------- 1 file changed, 37 insertions(+), 25 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 0454d46..9f76b84 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -171,8 +171,10 @@ The brain edge (or crown) ROI (green contour) picks signals outside but close to The figure displays the cumulative variance explained by components for each of four CompCor decompositions (left to right: anatomical CSF mask, anatomical white matter mask, anatomical combined mask, temporal). Dotted lines indicate the minimum number of components necessary to explain 50%, 70%, and 90% of the variance in the nuisance mask.The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression. + * Better: * High variance explained: A high variance explained by t/aCompCor components indicates that the selected regions of interest are capturing a significant portion of the motion and physiological noise in the data, which can improve the quality of subsequent analyses. In the fMRIPrep reports, by default only the components that explain the top 50% of the variance are saved. +* Worse: * Low variance explained: A low variance explained by t/aCompCor components may indicate that the selected regions of interest are not capturing enough of the motion and physiological noise in the data, which can lead to reduced sensitivity and specificity in subsequent analyses. The benchmark for % variance explained and/or optimal number of CompCor components to include in the analysis can vary, depending on the specific dataset and analysis goals. In general, the goal is to select enough components to capture the majority of the motion and physiological noise in the data, while avoiding overfitting the model to noise or task-related signal. @@ -188,19 +190,19 @@ The BOLD summary report shows several characteristic statistics along with a car * FD - framewise-displacement measures for each time point * Better: - * The carpet plot is homogeneous, particularly in the edge. + * The carpet plot is homogeneous, particularly in the brain edge. * Worse: - * Strongly structured crown region in the carpet plot is a sign that artifacts are compromising the fMRI scan [provins2022][7]. Several types of carpet plot modulations can be differentiated and are illustrated in the figure below. + * Strongly structured brain edge region in the carpet plot is a sign that artifacts are compromising the fMRI scan [provins2022][7]. Several types of carpet plot modulations can be differentiated and are illustrated in the figure below. * Motion outbursts, visible as peaks in the FD trace, are often paired with prolonged dark deflections derived from spin-history effects (see Figure 6A). * Periodic modulations on the carpet plot indicate regular and slow motion, e.g., caused by respiration, which may also compromise the signal of interest (see Figure 6B). * Coil failures may be identifiable as a sudden change in overall signal intensity on the carpet plot not paired that is not paired with motion peaks and generally sustained through the end of the scan (see Figure 6C). * A strong polarized structure revealed by the clustering of carpet plot rows suggests that artifacts mitigate the signal of interest (see Figure 6D). Indeed, sorting the rows (i.e., the time series) of each segment of the carpet plot such that voxels with similar BOLD dynamics appear close to one another reveals non-global structure in the signal, which is obscured when voxels are ordered randomly [aquino2020][8]. - * Finding temporal patterns similar in gray matter areas and simultaneously in regions of no interest (for instance, CSF or the crown) indicates the presence of artifacts, typically derived from head motion. If the planned analysis specifies noise regression techniques based on information from these regions of no interest (which is standard and recommended [ciric2017][9]), the risk of removing signals with neural origins is high, and affected scans should be excluded. For example, in standard task-based fMRI, stimulus presentation might evoke both participant movement and neural responses (_stimulus-correlated motion_). + * Finding temporal patterns similar in gray matter areas and simultaneously in regions of no interest (for instance, CSF or the brain edge) indicates the presence of artifacts, typically derived from head motion. If the planned analysis specifies noise regression techniques based on information from these regions of no interest (which is standard and recommended [ciric2017][9]), the risk of removing signals with neural origins is high, and affected scans should be excluded. For example, in standard task-based fMRI, stimulus presentation might evoke both participant movement and neural responses (_stimulus-correlated motion_). ![carpetplot_artifacts](../assets/fmriprep_visual_report/carpetplot_artifacts.png) - *Figure 6. Assessment of time series with the carpet plot. The crown region of the carpet plot comprises voxels outside the brain. As such, structure in the crown can be interpreted as artifactual, and thus corresponds to an exclusion criteria. (A) Motion outbursts, visible as peaks in the framewise displacement (FD) trace, are often paired with prolonged dark deflections. (B) Periodic modulations are indicative of regular, slow motion, e.g., caused by respiration. (C) An abrupt change in overall signal intensity that is not paired with motion peaks can be attributed to coil failure. (D) A strong polarized structure revealed by the clustering of carpet plot rows also suggests that artifacts mitigate the signal of interest.* + *Figure 6. Assessment of time series with the carpet plot. The brain edge region of the carpet plot comprises voxels outside the brain. As such, structure in the brain edge can be interpreted as artifactual, and thus corresponds to an exclusion criteria. (A) Motion outbursts, visible as peaks in the framewise displacement (FD) trace, are often paired with prolonged dark deflections. (B) Periodic modulations are indicative of regular, slow motion, e.g., caused by respiration. (C) An abrupt change in overall signal intensity that is not paired with motion peaks can be attributed to coil failure. (D) A strong polarized structure revealed by the clustering of carpet plot rows also suggests that artifacts mitigate the signal of interest.* * Common pitfalls in interpretation * The variance and the scaling of the trace plots affect a lot their display and interpretation (in other words, spikes are relative to other datapoints in the timeseries). Therefore, always pay attention to the trace metrics like its maximum and mean, before deriving any conclusion or visually comparing the trace plots between two participants. @@ -245,19 +247,19 @@ If fMRIPrep is run with the --use-aroma argument is generates an independent com ![ica_slice_timing](../assets/fmriprep_visual_report/ica_slice_timing.png) *Figure 8. Slice-timing artifact in ICA-AROMA* -### Note on multiband/multi-echo fMRI data +#### Note on multiband/multi-echo fMRI data Multiband/multiecho fMRI acquisitions can be used to improve the temporal and spatial resolution of fMRI data, but they can also introduce several types of artifacts, such as slice crosstalk, aliasing, and signal dropout. ICA-AROMA was not trained on multiband/multi-echo data, so it is possible that it may not perform as accurately as it does for single-band data. For an ICA-based denoising approach specifically for multi-echo fMRI, see [TEDANA](https://tedana.readthedocs.io/en/stable/). - * Multi-band artifacts in ICA-AROMA: The multi-band artifact also appears as a stripping pattern, however their spacing do not match alternative slices . What you see resembles one band being acquired. - ![ica_multiband](../assets/fmriprep_visual_report/ica_multiband.png) - *Figure 9. Multi-band artifact in ICA-AROMA* - - * Aliasing artifacts may be visible as duplicate components in the ICA-AROMA plots. Aliasing artifacts can occur in multiband fMRI data when the phase-encoding direction is not sampled densely enough, resulting in aliasing of signal from adjacent slices (these artifacts may also appear elsewhere, as ghosting or blurring of the brain image). - - * Signal dropout (due to susceptibility effects) may be visible in the ICA-AROMA plots as components with very low or absent time courses. - - * Slice crosstalk artifacts may be visible as components with time courses that resemble signal from adjacent slices. Slice crosstalk can occur in multiband fMRI data due to the inter-slice signal leakage, and can appear as spurious signals in the neighboring slices. +* Multi-band artifacts in ICA-AROMA: The multi-band artifact also appears as a stripping pattern, however their spacing do not match alternative slices . What you see resembles one band being acquired. +![ica_multiband](../assets/fmriprep_visual_report/ica_multiband.png) +*Figure 9. Multi-band artifact in ICA-AROMA* + +* Aliasing artifacts may be visible as duplicate components in the ICA-AROMA plots. Aliasing artifacts can occur in multiband fMRI data when the phase-encoding direction is not sampled densely enough, resulting in aliasing of signal from adjacent slices (these artifacts may also appear elsewhere, as ghosting or blurring of the brain image). + +* Signal dropout (due to susceptibility effects) may be visible in the ICA-AROMA plots as components with very low or absent time courses. + +* Slice crosstalk artifacts may be visible as components with time courses that resemble signal from adjacent slices. Slice crosstalk can occur in multiband fMRI data due to the inter-slice signal leakage, and can appear as spurious signals in the neighboring slices. ## About This section is a textual summary, containing the version of fMRIPrep, which command was run and the dates when the data were preprocessed. It is good to check that all of this information is as expected. @@ -273,20 +275,30 @@ This section tells you whether fMRIPrep encountered any problems during the prep * In the report visualizations that display a panel of slices, sometimes it will appear as though there is a slice missing - for example, nothing is shown for x = -12. This is a problem with the visualization and does NOT indicate the slice is missing from the actual data. You can try reloading the report, or opening it with Chrome if you’re using another browser, as well as using your preferred NIFTI viewer to check the data. ## Further material -If you are interested in knowing more about quality control, we wrote [a paper entitled "Quality control in functional MRI studies with MRIQC and fMRIPrep"](https://doi.org/10.3389/fnimg.2022.1073734) that describes how the visual inspection of fMRIPrep's report lies within the broader scope of quality control. In that paper, we demonstrate in details a protocol to perform quality control of unprocessed and minimally preprocessed BOLD fMRI images based on the visual assessment of MRIQC and fMRIPrep reports respectively. We additionally apply the protocol on a composite dataset drawn from open fMRI studies and illustrate exclusion criteria with examples. +If you are interested in knowing more about quality control, we wrote [a paper entitled "Quality control in functional MRI studies with MRIQC and fMRIPrep"](https://doi.org/10.3389/fnimg.2022.1073734) that describes how the visual inspection of fMRIPrep's report lies within the broader scope of quality control of fMRI data. In that paper, we demonstrate in details a protocol to perform quality control of unprocessed and minimally preprocessed BOLD fMRI images based on the visual assessment of MRIQC and fMRIPrep reports respectively. We additionally apply the protocol on a composite dataset drawn from open fMRI studies and illustrate exclusion criteria with examples. ## References [1] : Fischl, Bruce. 2012. “FreeSurfer.” NeuroImage 62 (2): 774–81. . + [2] : White, Tonya, Philip R. Jansen, Ryan L. Muetzel, Gustavo Sudre, Hanan El Marroun, Henning Tiemeier, Anqi Qiu, Philip Shaw, Andrew M. Michael, and Frank C. Verhulst. 2018. “Automated Quality Assessment of Structural Magnetic Resonance Images in Children: Comparison with Visual Inspection and Surface-Based Reconstruction.” Human Brain Mapping 39 (3): 1218–31. . + [3] : Klapwijk, Eduard T., Ferdi van de Kamp, Mara van der Meulen, Sabine Peters, and Lara M. Wierenga. 2019. “Qoala-T: A Supervised-Learning Tool for Quality Control of FreeSurfer Segmented MRI Data.” NeuroImage 189 (April): 116–29. . + [4] : Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based FMRI.” NeuroImage 37 (1): 90–101. . -[5] : Power, Jonathan D. 2017. “A Simple but Useful Way to Assess FMRI Scan Qualities.” NeuroImage 154 (July): 150–58. https://doi.org/10.1016/j.neuroimage.2016.08.009. -[6] : Patriat R, Molloy E, Birn R, Guitchev T, Popov A. Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies. Brain Connect. 2015;5(9):582-595. doi:10.1089/brain.2014.0321 -[7] : Provins, Céline, Christopher J. Markiewicz, Rastko Ciric, Mathias Goncalves, César Caballero-Gaudes, Russell Poldrack, Patric Hagmann, and Oscar Esteban. 2022. “Quality Control and Nuisance Regression of FMRI, Looking out Where Signal Should Not Be Found.” Proc. Intl. Soc. Mag. Reson. Med. 31, (ISMRM), pp. 2683. https://doi.org/10.31219/osf.io/hz52v. -[8] : Aquino KM, Fulcher BD, Parkes L, Sabaroedin K, Fornito A. Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. NeuroImage. 2020;212:116614. doi:10.1016/j.neuroimage.2020.116614 -[9] : Ciric, Rastko, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham L. Baum, Kosha Ruparel, Russell T. Shinohara, et al. 2017. “Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity.” NeuroImage, 154: 174–87. https://doi.org/10.1016/j.neuroimage.2017.03.020. -[10] : Beckmann, Christian F., and Stephen M. Smith. 2004. “Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging.” IEEE Transactions on Medical Imaging 23 (2): 137–52. https://doi.org/10.1109/TMI.2003.822821. -[11] : Griffanti, Ludovica, Gwenaëlle Douaud, Janine Bijsterbosch, Stefania Evangelisti, Fidel Alfaro-Almagro, Matthew F. Glasser, Eugene P. Duff, et al. 2017. “Hand Classification of FMRI ICA Noise Components.” NeuroImage 154 (July): 188–205. https://doi.org/10.1016/j.neuroimage.2016.12.036. -[12] : Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015 May 15;112:267–77. doi:10.1016/j.neuroimage.2015.02.064. -[13] : Liu TT, Nalci A, Falahpour M. 2017. The global signal in fMRI: Nuisance or Information? Neuroimage: 150:213-29. doi:10.1016/j.neuroimage.2017.02.036. -[14] : Dukart J, Bertolino A. When structure affects function–the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies. PloS one:9(12);e114227. doi:10.1371/journal.pone.0114227 + +[5] : Power, Jonathan D. 2017. “A Simple but Useful Way to Assess FMRI Scan Qualities.” NeuroImage 154 (July): 150–58. . +[7] : Provins, Céline, Christopher J. Markiewicz, Rastko Ciric, Mathias Goncalves, César Caballero-Gaudes, Russell Poldrack, Patric Hagmann, and Oscar Esteban. 2022. “Quality Control and Nuisance Regression of FMRI, Looking out Where Signal Should Not Be Found.” Proc. Intl. Soc. Mag. Reson. Med. 31, (ISMRM), pp. 2683. . + +[8] : Aquino KM, Fulcher BD, Parkes L, Sabaroedin K, Fornito A. Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. NeuroImage. 2020;212:116614. . + +[9] : Ciric, Rastko, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham L. Baum, Kosha Ruparel, Russell T. Shinohara, et al. 2017. “Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity.” NeuroImage, 154: 174–87. . + +[10] : Beckmann, Christian F., and Stephen M. Smith. 2004. “Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging.” IEEE Transactions on Medical Imaging 23 (2): 137–52. . + +[11] : Griffanti, Ludovica, Gwenaëlle Douaud, Janine Bijsterbosch, Stefania Evangelisti, Fidel Alfaro-Almagro, Matthew F. Glasser, Eugene P. Duff, et al. 2017. “Hand Classification of FMRI ICA Noise Components.” NeuroImage 154 (July): 188–205. . + +[12] : Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015 May 15;112:267–77. . + +[13] : Liu TT, Nalci A, Falahpour M. 2017. The global signal in fMRI: Nuisance or Information? Neuroimage: 150:213-29. . + +[14] : Dukart J, Bertolino A. When structure affects function–the need for partial volume effect correction in functional and resting state magnetic resonance imaging studies. PloS one:9(12);e114227. From 393296a87b67706b5473d73354fc99d4ac129a31 Mon Sep 17 00:00:00 2001 From: Chris Markiewicz Date: Mon, 12 Jun 2023 16:46:55 -0400 Subject: [PATCH 21/24] DOC: Add guide to TOC --- mkdocs.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mkdocs.yml b/mkdocs.yml index aca5ebf..7b7d33e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -30,6 +30,8 @@ nav: - Executing with Singularity: apps/singularity.md - Presentations: users/talks.md - Educational: users/educational.md + - Guides: + - How to read an fMRIPrep visual report: users/howto_read_fmriprep_visual_report.md - Documentation (devs): - Developer Environment: devs/devenv.md - Versions Matrix: devs/versions.md From 3e519cd277a1f8c5e502ae060c1a76ccb578fd82 Mon Sep 17 00:00:00 2001 From: celprov <77437752+celprov@users.noreply.github.com> Date: Tue, 21 Nov 2023 17:58:34 -0800 Subject: [PATCH 22/24] Apply suggestions from code review Co-authored-by: Oscar Esteban --- .../howto_read_fmriprep_visual_report.md | 42 +++++++++++++------ 1 file changed, 30 insertions(+), 12 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 9f76b84..a47a5ae 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -1,27 +1,45 @@ -# How To Read fMRIPrep visual Report +# How to read *fMRIPrep*'s visual reports -fMRIPrep generates HTML visual reports that allow you to check the results of the preprocessing steps that were applied to the data. -Note: this is not a replacement for running quality control of your images before running fMRIPrep (FAQ link: https://fmriprep.readthedocs.io/en/stable/faq.html). -Below is a guide to interpreting the information contained in the visual reports, organized by subsection of the report. +*fMRIPrep* generates an HTML visual report for each anatomical or functional image at the input. +The visual report assists in checking the results of the preprocessing steps applied to the data. +*fMRIPrep* is designed so relevant steps across the preprocessing chain generate targeted visualizations for assessing that particular step. +We generally refer to these *processing breadcrumbs* that help *reconstruct* a particular derivative's provenance as ***reportlets***. +For example, to assess the spatial standardization (by means of nonlinear image registration), we generate a reportlet that flickers between the individual's image and the standard template selected for reference (Figure 1). +[[[[[ -If you have examples of “bad” preprocessing results that you’re willing to share, please [submit them somewhere?] with a short description of the problem (for example, “bad skull stripping”). +ADD A FIGURE 1 HERE WITH ONE OF THESE REPORTLETS -When you open a report, it may be helpful to first jump to the Errors subsection to see whether fMRIPrep encountered any errors during preprocessing. Note that “errors” refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results. +Caption should say something along the lines that by flickering between the two images we can get a sense of the accuracy of the mapping between the two images, and therefore the overall performance of this particular image registration step. +]]]]] +Therefore, during the execution of the *fMRIPrep*'s workflow, a number of reportlets are generated and stored in a particular folder. +When the workflow concludes, the reportlets found in the intermediate location are assembled together into the final visual report corresponding to a particular subject and stored in the output folder. +Both reportlets and assembler are implemented in a separate package called [*NiReports*](https://github.com/nipreps/nireports), which we share and maintain to allow researchers build their own reporting summaries with ease. +Note: This is not a replacement for [QC'ing the *unprocessed* data before *fMRIPrep*](https://fmriprep.org/en/stable/faq.html#should-i-run-quality-control-of-my-images-before-running-fmriprep). +Reports are structured in subsections focusing on different facets of the input data, and each section will display a number of specific reportlets. +We suggest using such an organization to interpret the information in the visual reports, as it permits a sequential understanding of the overall processing workflow. +Not only should individual reports facilitate the assessment of the performance of processing steps, our intent is that they also serve as a *learning scaffold* to understand *fMRIPrep*'s design decisions in particular and preprocessing more generally. -## **Summary** +If you have examples of "bad" preprocessing results that you’re willing to share, please drop us an email at nipreps@gmail.com, with a short description of the problem (for example, "bad skull stripping"). -The first section of the report is a text summary, containing the subject ID, number of structural and functional images, task name, output spaces in which fMRIPrep generated your preprocessed data, and whether surface reconstruction with Freesurfer was run. -It is good to check that all of this information is as expected. +Reports can be visualized with web browsers, and we recommend Google Chrome or Firefox for a better experience. +When you open a report, it may be helpful to first jump to the Errors subsection to see whether *fMRIPrep* encountered any errors during preprocessing. +Note that "errors" refers only to problems in running *fMRIPrep*, and NOT problems related to the performance of preprocessing steps. +This section won't flag participants in which *fMRIPrep* was able to successfully run to completion but yielded poor results. -## **Anatomical** +## Summary section + +At the head, the report includes a textual summary of relevant aspects of the data. +These are the participant identifier, the number of structural and functional images, task(s) name(s), output spaces in which *fMRIPrep* generated your preprocessed data, and whether surface reconstruction with *FreeSurfer* was run. +Deviations from the expectations (for instance, confused participant identifier, incorrect number of tasks or runs, etc.) should be carefully examined. + +## Anatomical section The reports first present results of anatomical preprocessing. ### Anatomical conformation -* This section provides information about the structural images. * Check for obvious failures, such as missing images or implausible values for voxel size, orientation, or dimensions. - * For example: you’re expecting two input T1w images but it says “Input T1w images: 1” + * For example: you're expecting two input T1w images but it says "Input T1w images: 1" ### Brain mask and brain tissue segmentation of the T1w From 4617047c96be884bc89025ced50928a536149369 Mon Sep 17 00:00:00 2001 From: celprov Date: Tue, 21 Nov 2023 18:11:04 -0800 Subject: [PATCH 23/24] fix: switch to ASCII quotes and apostroph fix: remove ICA-AROMA section as it does not exist anymore --- .../howto_read_fmriprep_visual_report.md | 53 +++++++------------ 1 file changed, 18 insertions(+), 35 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index a47a5ae..46e94a4 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -19,7 +19,7 @@ Reports are structured in subsections focusing on different facets of the input We suggest using such an organization to interpret the information in the visual reports, as it permits a sequential understanding of the overall processing workflow. Not only should individual reports facilitate the assessment of the performance of processing steps, our intent is that they also serve as a *learning scaffold* to understand *fMRIPrep*'s design decisions in particular and preprocessing more generally. -If you have examples of "bad" preprocessing results that you’re willing to share, please drop us an email at nipreps@gmail.com, with a short description of the problem (for example, "bad skull stripping"). +If you have examples of "bad" preprocessing results that you're willing to share, please drop us an email at nipreps@gmail.com, with a short description of the problem (for example, "bad skull stripping"). Reports can be visualized with web browsers, and we recommend Google Chrome or Firefox for a better experience. When you open a report, it may be helpful to first jump to the Errors subsection to see whether *fMRIPrep* encountered any errors during preprocessing. @@ -49,7 +49,7 @@ This report displays the brain mask and the brain tissue segmentation computed f * Better: * The intensity of the image is uniform throughout the brain * Worse: - * Intensity non-uniformity artifacts (looks like the “original image” panel in Figure 1): + * Intensity non-uniformity artifacts (looks like the "original image" panel in Figure 1): ![inu](../assets/fmriprep_visual_report/inu.png) *Figure 1. from Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging, 26(3), 405-421.* @@ -71,7 +71,7 @@ This report displays the brain mask and the brain tissue segmentation computed f * The outlines of the gray matter (GM; magenta) and white matter (WM; blue) segmentation are correctly drawn. The blue line should follow the boundary between GM and WM, while the magenta line should outline ventricles. * Worse: - * The GM (magenta)/WM (blue) outlines don’t match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between GM and WM or the magenta line does not outline ventricles. + * The GM (magenta)/WM (blue) outlines don't match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between GM and WM or the magenta line does not outline ventricles. * Inclusion of tissues other than the tissue of interest in the contour delineations should lead to exclusion of the scan. * T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). * These scans should be excluded except for images where these voxels are only present at subcortical structures (e.g., thalamus) or nearby tissue boundaries. In the latter case, the misclassification results from partial volume effects (i.e., indeed, such voxels contain varying fractions of two or more tissues). The figure below illustrates the difference between individual dots caused by noise versus partial volume effects. @@ -80,7 +80,7 @@ This report displays the brain mask and the brain tissue segmentation computed f * Common pitfalls in interpretation: * At the inter-hemispheric space, masks (and in particular the brain mask, despite its smooth edges) may intersect the visualization plane several times, giving the impression that the mask is cutting off brain regions. However, this is more of a visual effect on the cutting plane. - * Note that the brain mask plotted in the “brain mask and (anatomical/temporal) CompCor ROIs” panel under the functional section is computed from the BOLD image and thus is not identical to the brain mask mentioned in this paragraph. Hence, they follow different exclusion criteria. + * Note that the brain mask plotted in the "brain mask and (anatomical/temporal) CompCor ROIs" panel under the functional section is computed from the BOLD image and thus is not identical to the brain mask mentioned in this paragraph. Hence, they follow different exclusion criteria. ### Spatial normalization of the anatomical T1w reference @@ -124,7 +124,7 @@ The FreeSurfer [fischl2012][1] subject reconstruction report shows the WM (blue * Functional data can vary based on available data, metadata, user selections. * What does “Heuristics - BBR may fall back to volume-based coregistration” mean? - In fMRIPrep’s workflow, if boundary-based registration fails (meaning that `bbr` distorts the affine registration more than 15mm), fMRIPrep reverts to using the initial affine transform from Freesurfer’s `mri_coreg` tool instead of using the bbr refinement of the initial transform ([relevant code](https://github.com/nipreps/fmriprep/pull/694/files#diff-939042e0a10e509ce5708c3e112376ffR29)) + In fMRIPrep's workflow, if boundary-based registration fails (meaning that `bbr` distorts the affine registration more than 15mm), fMRIPrep reverts to using the initial affine transform from Freesurfer’s `mri_coreg` tool instead of using the bbr refinement of the initial transform ([relevant code](https://github.com/nipreps/fmriprep/pull/694/files#diff-939042e0a10e509ce5708c3e112376ffR29)) * “Note on orientation: qform matrix overwritten/ this data has been copied from sform/sform matrix set” warnings @@ -132,7 +132,7 @@ The FreeSurfer [fischl2012][1] subject reconstruction report shows the WM (blue This message warns you that information in the header was altered during the preprocessing. This is an advisory message and does not necessarily indicate issues in data quality or registration. A common scenario is that the sform code was 0, so fMRIPrep copied the code from qform in order to keep the affine matrices aligned, ensuring that the images will be treated as having the same orientation later down the pipeline. - See Chris Markewicz’s Neurostars posts [here](https://neurostars.org/t/note-on-orientation-qform-and-sform-warning/4379/4) and [here](https://neurostars.org/t/note-on-orientation-sform-matrix-set/5939) for a longer explanation of when/why fMRIPrep produces these warnings. You can read more about how `qform` and `sform` work at the following links: [Recommended usage of qform and sform](https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/qsform_brief_usage) and [Nifti Qform and Sform](http://gru.stanford.edu/doku.php/mrTools/coordinateTransforms). + See Chris Markewicz's Neurostars posts [here](https://neurostars.org/t/note-on-orientation-qform-and-sform-warning/4379/4) and [here](https://neurostars.org/t/note-on-orientation-sform-matrix-set/5939) for a longer explanation of when/why fMRIPrep produces these warnings. You can read more about how `qform` and `sform` work at the following links: [Recommended usage of qform and sform](https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/qsform_brief_usage) and [Nifti Qform and Sform](http://gru.stanford.edu/doku.php/mrTools/coordinateTransforms). ### Alignment of functional and anatomical data @@ -246,24 +246,7 @@ This report presents a plot of correlations among confound regressors. The left- * There is no specific threshold for the correlation coefficient that indicates the absence (or presence) of partial volume effects (PVE) in fMRI data. The strength of the correlation between nuisance regressors may depend on several factors, including the tissue properties of the brain region being examined, the voxel size, the signal-to-noise ratio of the data, and the specific nuisance regressors used in the analysis. * Worse: - * Generally, a high correlation coefficient among nuisance regressors -- especially spatial/anatomical regressors -- may indicate the presence of PVE if it is accompanied by other evidence, such as spatial co-localization of the voxels with high correlation coefficients with regions known to be susceptible to PVE such as the gray/white matter boundary. - -### ICA-AROMA - -If fMRIPrep is run with the --use-aroma argument is generates an independent component decomposition using FSL MELODIC [beckmann2004][10]. Such techniques have been thoroughly described elsewhere [griffanti2017][11], but in short AROMA is an ICA based procedure to identify confounding time series related to head-motion [prium2015][12]. Each component is mapped on a glass brain next to an indication of its frequency spectrum and its corresponding weight over time. - -* Better: - * Artifact components (red) should have more high-frequency noise whereas signal components (green) should have their peak in the lower-frequency range. - * Refer to for more details on how to denoise your data using ICA-AROMA confounds. - -* Worse: - * Spin-history effects in ICA-AROMA : One recurring artifactual family of components emerges when motion interacts with interleaved acquisition giving rise to the so-called spin-history effects. The spin-history effects appear as parallel stripes covering the whole brain in one direction (see Figure 7). They are a consequence of the repetition time not being much larger than the T1 relaxation time in typical fMRI designs. This implies that the spins will not completely relax when the next acquisition starts . In addition, specific movements (e.g., rotation around one imaging axis, such as nodding) will exacerbate spin-history effects as slices will cut through the brain at different locations between consecutive BOLD time points. These two considerations combined mean that motion will produce spins with different excitation histories, and thus, the signal intensity will differ. Components showcasing parallel stripes concurring with slices in extreme poles of the brain or even across the whole brain are likely to capture these effects. - ![ica_spin_history_effect](../assets/fmriprep_visual_report/ica_spin_history_effect.png) - *Figure 7. Spin-history effect in ICA-AROMA* - - * Slice-timing artifacts in ICA-AROMA: A stripping pattern is visible and it matches alternative slices . - ![ica_slice_timing](../assets/fmriprep_visual_report/ica_slice_timing.png) - *Figure 8. Slice-timing artifact in ICA-AROMA* + * Generally, a high correlation coefficient among nuisance regressors -- especially spatial/anatomical regressors -- may indicate the presence of PVE if it is accompanied by other evidence, such as spatial co-localization of the voxels with high correlation coefficients with regions known to be susceptible to PVE such as the gray/white matter boundary. #### Note on multiband/multi-echo fMRI data @@ -286,34 +269,34 @@ This section is a textual summary, containing the version of fMRIPrep, which com This section provides a boilerplate describing in detail the preprocessing of the images. We kindly ask to report results preprocessed with fMRIPrep using that boilerplate. The latter is available in three languages (HTML, Markdown and Latex) to faciliate integration in manuscripts. ## Errors -This section tells you whether fMRIPrep encountered any problems during the preprocessing. Note that “errors” refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results, or in which the input data was of lower-than-desirable quality. +This section tells you whether fMRIPrep encountered any problems during the preprocessing. Note that "errors" refers only to problems in running fMRIPrep, and NOT problems with the quality of the resulting images. This section won’t flag participants in which fMRIPrep was able to successfully run to completion but yielded poor results, or in which the input data was of lower-than-desirable quality. -## Things that aren’t an issue -* “Missing” [not visualized] slices +## Things that are not an issue +* "Missing" [not visualized] slices * In the report visualizations that display a panel of slices, sometimes it will appear as though there is a slice missing - for example, nothing is shown for x = -12. This is a problem with the visualization and does NOT indicate the slice is missing from the actual data. You can try reloading the report, or opening it with Chrome if you’re using another browser, as well as using your preferred NIFTI viewer to check the data. ## Further material If you are interested in knowing more about quality control, we wrote [a paper entitled "Quality control in functional MRI studies with MRIQC and fMRIPrep"](https://doi.org/10.3389/fnimg.2022.1073734) that describes how the visual inspection of fMRIPrep's report lies within the broader scope of quality control of fMRI data. In that paper, we demonstrate in details a protocol to perform quality control of unprocessed and minimally preprocessed BOLD fMRI images based on the visual assessment of MRIQC and fMRIPrep reports respectively. We additionally apply the protocol on a composite dataset drawn from open fMRI studies and illustrate exclusion criteria with examples. ## References -[1] : Fischl, Bruce. 2012. “FreeSurfer.” NeuroImage 62 (2): 774–81. . +[1] : Fischl, Bruce. 2012. "FreeSurfer." NeuroImage 62 (2): 774–81. . -[2] : White, Tonya, Philip R. Jansen, Ryan L. Muetzel, Gustavo Sudre, Hanan El Marroun, Henning Tiemeier, Anqi Qiu, Philip Shaw, Andrew M. Michael, and Frank C. Verhulst. 2018. “Automated Quality Assessment of Structural Magnetic Resonance Images in Children: Comparison with Visual Inspection and Surface-Based Reconstruction.” Human Brain Mapping 39 (3): 1218–31. . +[2] : White, Tonya, Philip R. Jansen, Ryan L. Muetzel, Gustavo Sudre, Hanan El Marroun, Henning Tiemeier, Anqi Qiu, Philip Shaw, Andrew M. Michael, and Frank C. Verhulst. 2018. "Automated Quality Assessment of Structural Magnetic Resonance Images in Children: Comparison with Visual Inspection and Surface-Based Reconstruction." Human Brain Mapping 39 (3): 1218–31. . -[3] : Klapwijk, Eduard T., Ferdi van de Kamp, Mara van der Meulen, Sabine Peters, and Lara M. Wierenga. 2019. “Qoala-T: A Supervised-Learning Tool for Quality Control of FreeSurfer Segmented MRI Data.” NeuroImage 189 (April): 116–29. . +[3] : Klapwijk, Eduard T., Ferdi van de Kamp, Mara van der Meulen, Sabine Peters, and Lara M. Wierenga. 2019. "Qoala-T: A Supervised-Learning Tool for Quality Control of FreeSurfer Segmented MRI Data." NeuroImage 189 (April): 116–29. . -[4] : Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based FMRI.” NeuroImage 37 (1): 90–101. . +[4] : Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. "A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based FMRI." NeuroImage 37 (1): 90–101. . [5] : Power, Jonathan D. 2017. “A Simple but Useful Way to Assess FMRI Scan Qualities.” NeuroImage 154 (July): 150–58. . -[7] : Provins, Céline, Christopher J. Markiewicz, Rastko Ciric, Mathias Goncalves, César Caballero-Gaudes, Russell Poldrack, Patric Hagmann, and Oscar Esteban. 2022. “Quality Control and Nuisance Regression of FMRI, Looking out Where Signal Should Not Be Found.” Proc. Intl. Soc. Mag. Reson. Med. 31, (ISMRM), pp. 2683. . +[7] : Provins, Céline, Christopher J. Markiewicz, Rastko Ciric, Mathias Goncalves, César Caballero-Gaudes, Russell Poldrack, Patric Hagmann, and Oscar Esteban. 2022. "Quality Control and Nuisance Regression of FMRI, Looking out Where Signal Should Not Be Found." Proc. Intl. Soc. Mag. Reson. Med. 31, (ISMRM), pp. 2683. . [8] : Aquino KM, Fulcher BD, Parkes L, Sabaroedin K, Fornito A. Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem. NeuroImage. 2020;212:116614. . -[9] : Ciric, Rastko, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham L. Baum, Kosha Ruparel, Russell T. Shinohara, et al. 2017. “Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity.” NeuroImage, 154: 174–87. . +[9] : Ciric, Rastko, Daniel H. Wolf, Jonathan D. Power, David R. Roalf, Graham L. Baum, Kosha Ruparel, Russell T. Shinohara, et al. 2017. "Benchmarking of Participant-Level Confound Regression Strategies for the Control of Motion Artifact in Studies of Functional Connectivity." NeuroImage, 154: 174–87. . -[10] : Beckmann, Christian F., and Stephen M. Smith. 2004. “Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging.” IEEE Transactions on Medical Imaging 23 (2): 137–52. . +[10] : Beckmann, Christian F., and Stephen M. Smith. 2004. "Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging." IEEE Transactions on Medical Imaging 23 (2): 137–52. . -[11] : Griffanti, Ludovica, Gwenaëlle Douaud, Janine Bijsterbosch, Stefania Evangelisti, Fidel Alfaro-Almagro, Matthew F. Glasser, Eugene P. Duff, et al. 2017. “Hand Classification of FMRI ICA Noise Components.” NeuroImage 154 (July): 188–205. . +[11] : Griffanti, Ludovica, Gwenaëlle Douaud, Janine Bijsterbosch, Stefania Evangelisti, Fidel Alfaro-Almagro, Matthew F. Glasser, Eugene P. Duff, et al. 2017. "Hand Classification of FMRI ICA Noise Components." NeuroImage 154 (July): 188–205. . [12] : Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015 May 15;112:267–77. . From 680118c143401d00d9e66e77985a59006d66f23c Mon Sep 17 00:00:00 2001 From: celprov Date: Tue, 21 Nov 2023 18:40:40 -0800 Subject: [PATCH 24/24] sty: change layout, eliminate bulletpoints which were taking too much space --- .../howto_read_fmriprep_visual_report.md | 260 +++++++++--------- 1 file changed, 126 insertions(+), 134 deletions(-) diff --git a/docs/users/howto_read_fmriprep_visual_report.md b/docs/users/howto_read_fmriprep_visual_report.md index 46e94a4..dd4cda0 100644 --- a/docs/users/howto_read_fmriprep_visual_report.md +++ b/docs/users/howto_read_fmriprep_visual_report.md @@ -38,78 +38,72 @@ The reports first present results of anatomical preprocessing. ### Anatomical conformation -* Check for obvious failures, such as missing images or implausible values for voxel size, orientation, or dimensions. - * For example: you're expecting two input T1w images but it says "Input T1w images: 1" +Check for obvious failures, such as missing images or implausible values for voxel size, orientation, or dimensions.For example: you're expecting two input T1w images but it says "Input T1w images: 1" ### Brain mask and brain tissue segmentation of the T1w This report displays the brain mask and the brain tissue segmentation computed from the T1w image. It shows the quality of intensity non-uniformity (INU) correction, skull stripping, and tissue segmentation. -* INU correction - * Better: - * The intensity of the image is uniform throughout the brain - * Worse: - * Intensity non-uniformity artifacts (looks like the "original image" panel in Figure 1): - - ![inu](../assets/fmriprep_visual_report/inu.png) - *Figure 1. from Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging, 26(3), 405-421.* +#### INU correction +**Better**: The intensity of the image is uniform throughout the brain -* Skull stripping - * Better: - * The brain mask (red) covers the whole brain and only the brain - not the dura or anything else outside the brain. It should closely follow the contour of the brain. +**Worse**: Intensity non-uniformity artifacts (looks like the "original image" panel in Figure 1): - * Worse: - * Skull-stripping failed leading to an inaccurate brain mask - * The brain mask cuts off part of the brain and/or contains holes surrounding signal drop-out regions. - * The brain mask includes parts that are clearly NOT brain. - * For example, fMRIPrep might have misclassified part of the dura, epidural space, or skull as belonging inside the brain mask; as a result the brain mask presents “bumps” surrounding high-intensity areas of signal outside of the cortex. +![inu](../assets/fmriprep_visual_report/inu.png) - * Having an accurate brain mask makes the downstream preprocessing of an fMRI scan faster (excluding voxels of non-interest) and more accurate (less bias from voxels of non-interest). Consequently, it is important to discard subjects for which the brain mask is not well defined. +*Figure 1. from Vovk, U., Pernus, F., & Likar, B. (2007). A review of methods for correction of intensity inhomogeneity in MRI. IEEE transactions on medical imaging, 26(3), 405-421.* -* Tissue segmentation - * Better: - * The outlines of the gray matter (GM; magenta) and white matter (WM; blue) segmentation are correctly drawn. The blue line should follow the boundary between GM and WM, while the magenta line should outline ventricles. +#### Skull stripping +**Better**: The brain mask (red) covers the whole brain and only the brain - not the dura or anything else outside the brain. It should closely follow the contour of the brain. - * Worse: - * The GM (magenta)/WM (blue) outlines don't match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between GM and WM or the magenta line does not outline ventricles. - * Inclusion of tissues other than the tissue of interest in the contour delineations should lead to exclusion of the scan. - * T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). - * These scans should be excluded except for images where these voxels are only present at subcortical structures (e.g., thalamus) or nearby tissue boundaries. In the latter case, the misclassification results from partial volume effects (i.e., indeed, such voxels contain varying fractions of two or more tissues). The figure below illustrates the difference between individual dots caused by noise versus partial volume effects. - ![noise-in-segmentation](../assets/fmriprep_visual_report/noise-in-segmentation.svg) - *Figure 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between GM and WM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* +**Worse**: Skull-stripping failed leading to an inaccurate brain mask +* The brain mask cuts off part of the brain and/or contains holes surrounding signal drop-out regions. +* The brain mask includes parts that are clearly NOT brain. For example, fMRIPrep might have misclassified part of the dura, epidural space, or skull as belonging inside the brain mask; as a result the brain mask presents “bumps” surrounding high-intensity areas of signal outside of the cortex. -* Common pitfalls in interpretation: - * At the inter-hemispheric space, masks (and in particular the brain mask, despite its smooth edges) may intersect the visualization plane several times, giving the impression that the mask is cutting off brain regions. However, this is more of a visual effect on the cutting plane. - * Note that the brain mask plotted in the "brain mask and (anatomical/temporal) CompCor ROIs" panel under the functional section is computed from the BOLD image and thus is not identical to the brain mask mentioned in this paragraph. Hence, they follow different exclusion criteria. +**Note**: Having an accurate brain mask makes the downstream preprocessing of an fMRI scan faster (excluding voxels of non-interest) and more accurate (less bias from voxels of non-interest). Consequently, it is important to discard subjects for which the brain mask is not well defined. + +#### Tissue segmentation +**Better**: The outlines of the gray matter (GM; magenta) and white matter (WM; blue) segmentation are correctly drawn. The blue line should follow the boundary between GM and WM, while the magenta line should outline ventricles. + +**Worse**: +* The GM (magenta)/WM (blue) outlines don't match where those tissue classes are distributed in the underlying image, so either the blue line does not follow the boundary between GM and WM or the magenta line does not outline ventricles. +* Inclusion of tissues other than the tissue of interest in the contour delineations should lead to exclusion of the scan. +* T1w scans showcasing a low signal-to-noise ratio because of thermal noise will present scattered misclassified voxels within piecewise-smooth regions (generally more identifiable in the WM and inside the ventricles). + * These scans should be excluded except for images where these voxels are only present at subcortical structures (e.g., thalamus) or nearby tissue boundaries. In the latter case, the misclassification results from partial volume effects (i.e., indeed, such voxels contain varying fractions of two or more tissues). The figure below illustrates the difference between individual dots caused by noise versus partial volume effects. + ![noise-in-segmentation](../assets/fmriprep_visual_report/noise-in-segmentation.svg) + *Figure 2. Error in brain tissue segmentation of T1w images. (A) The presence of noise compromises the segmentation leading to single voxels being excluded from the ventricle mask. The subject has thus been excluded from further analysis. (B) A series of spots are visible at the boundary between GM and WM. Those spots are due to partial volume effect and thus is a flaw of the fMRIPrep segmentation implementation not of the image quality.* + +**Common pitfalls in interpretation**: +* At the inter-hemispheric space, masks (and in particular the brain mask, despite its smooth edges) may intersect the visualization plane several times, giving the impression that the mask is cutting off brain regions. However, this is more of a visual effect on the cutting plane. +* Note that the brain mask plotted in the "brain mask and (anatomical/temporal) CompCor ROIs" panel under the functional section is computed from the BOLD image and thus is not identical to the brain mask mentioned in this paragraph. Hence, they follow different exclusion criteria. ### Spatial normalization of the anatomical T1w reference The normalization report shows how successfully your T1w image(s) were resampled into standard space, for each of the template spaces used. -* Better: - * Your T1w image and the template image line up well when you toggle between the images (hover mouse over the panel): - * In order of importance, the following structures should be correctly aligned : 1. ventricles, 2. subcortical regions, 3. corpus callosum, 4. cerebellum, 5. cortical gray matter. - * The standard templates provided with fMRIPrep (such as `MNI152NLin2009cAsym`) are averaged across multiple subjects, so the template image will look blurrier than the T1w image. Because of this averaging, it is normal for sulci to be less pronounced and gyri to be wider on the template than the T1w image. +**Better**: Your T1w image and the template image line up well when you toggle between the images (hover mouse over the panel): +* In order of importance, the following structures should be correctly aligned : 1. ventricles, 2. subcortical regions, 3. corpus callosum, 4. cerebellum, 5. cortical gray matter. +* The standard templates provided with fMRIPrep (such as `MNI152NLin2009cAsym`) are averaged across multiple subjects, so the template image will look blurrier than the T1w image. Because of this averaging, it is normal for sulci to be less pronounced and gyri to be wider on the template than the T1w image. -* Worse: - * Stretching or distortion in the participant’s T1w image, indicating failed normalization. - * A misalignment of the ventricles, the subcortical regions, or the corpus callosum should lead to immediate exclusion. You can however be more lenient with the misalignment of cortical GM because volumetric (image) registration may not resolve substantial inter-individual differences (e.g., a sulcus missing in an individual’s brain but typically present in the population of the template). +**Worse**: +* Stretching or distortion in the participant’s T1w image, indicating failed normalization. + * A misalignment of the ventricles, the subcortical regions, or the corpus callosum should lead to immediate exclusion. You can however be more lenient with the misalignment of cortical GM because volumetric (image) registration may not resolve substantial inter-individual differences (e.g., a sulcus missing in an individual’s brain but typically present in the population of the template). - * If skull-stripping was not successful, you might see some places where there are non-brain voxels outside of the contours of the brain. +* If skull-stripping was not successful, you might see some places where there are non-brain voxels outside of the contours of the brain. ### Surface reconstruction If you used the `--fs-no-reconall` flag to skip surface-based preprocessing, this section of the report will not exist. The FreeSurfer [fischl2012][1] subject reconstruction report shows the WM (blue outline) and pial (red outline) surfaces overlaid on the T1w image. -* Better: - * The white-gray boundary outlined (blue link) matches the underlying image. - * WM and pial surface boundary outlines do not cross or overlap each other. +**Better**: +* The white-gray boundary outlined (blue link) matches the underlying image. +* WM and pial surface boundary outlines do not cross or overlap each other. -* Worse: - * The white-gray boundary outline (blue line) does not correspond well to the boundary observed in the underlying image. - * WM and pial surface boundaries cross or overlap each other. - * Pial surface (red outline) extends past the actual pial boundary (see images [here](https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/PialEdits_tktools) for an example ; this can be a result of bad skull-stripping). - * QC assessment of FreeSurfer outcomes is comprehensively covered elsewhere (e.g., White et al. 2018 [white2018][2]; Klapwijk et al. 2019 [klapwijk2019][3]), and fMRI studies using vertex-wise (surface) analyses should rigorously assess these surfaces. - * In a voxel-wise analysis, data should be excluded only when the reconstructed surfaces are extremely inaccurate, which typically only happens in the presence of artifacts easily captured previously by [MRIQC](https://mriqc.readthedocs.io/en/latest/). +**Worse**: +* The white-gray boundary outline (blue line) does not correspond well to the boundary observed in the underlying image. +* WM and pial surface boundaries cross or overlap each other. +* Pial surface (red outline) extends past the actual pial boundary (see images [here](https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/PialEdits_tktools) for an example ; this can be a result of bad skull-stripping). +* QC assessment of FreeSurfer outcomes is comprehensively covered elsewhere (e.g., White et al. 2018 [white2018][2]; Klapwijk et al. 2019 [klapwijk2019][3]), and fMRI studies using vertex-wise (surface) analyses should rigorously assess these surfaces. +* In a voxel-wise analysis, data should be excluded only when the reconstructed surfaces are extremely inaccurate, which typically only happens in the presence of artifacts easily captured previously by [MRIQC](https://mriqc.readthedocs.io/en/latest/). * Common pitfalls in interpretation: * Note that cerebellum and brainstem are excluded from the surface reconstruction in fMRIPrep; it is thus normal that the outlines do not include these areas. @@ -118,50 +112,52 @@ The FreeSurfer [fischl2012][1] subject reconstruction report shows the WM (blue ### Textual summary -* The BOLD summary describes the interpretation of the inputs by fMRIPrep, and the applied heuristics. For instance, if the dataset is multi-echo EPI and comprehends three or more echos, then fMRIPrep should indicate this in the BOLD summary. +The BOLD summary describes the interpretation of the inputs by fMRIPrep, and the applied heuristics. For instance, if the dataset is multi-echo EPI and comprehends three or more echos, then fMRIPrep should indicate this in the BOLD summary. -* Other potential issues are missing images or implausible values. - * Functional data can vary based on available data, metadata, user selections. - * What does “Heuristics - BBR may fall back to volume-based coregistration” mean? +Other potential issues are missing images or implausible values. +* Functional data can vary based on available data, metadata, user selections. + * What does “Heuristics - BBR may fall back to volume-based coregistration” mean? - In fMRIPrep's workflow, if boundary-based registration fails (meaning that `bbr` distorts the affine registration more than 15mm), fMRIPrep reverts to using the initial affine transform from Freesurfer’s `mri_coreg` tool instead of using the bbr refinement of the initial transform ([relevant code](https://github.com/nipreps/fmriprep/pull/694/files#diff-939042e0a10e509ce5708c3e112376ffR29)) + In fMRIPrep's workflow, if boundary-based registration fails (meaning that `bbr` distorts the affine registration more than 15mm), fMRIPrep reverts to using the initial affine transform from Freesurfer’s `mri_coreg` tool instead of using the bbr refinement of the initial transform ([relevant code](https://github.com/nipreps/fmriprep/pull/694/files#diff-939042e0a10e509ce5708c3e112376ffR29)) - * “Note on orientation: qform matrix overwritten/ this data has been copied from sform/sform matrix set” warnings +* “Note on orientation: qform matrix overwritten/ this data has been copied from sform/sform matrix set” warnings - `qform` and `sform` refer to metadata fields in the NIfTI file header, prescribing a coordinate system for the image. - This message warns you that information in the header was altered during the preprocessing. - This is an advisory message and does not necessarily indicate issues in data quality or registration. - A common scenario is that the sform code was 0, so fMRIPrep copied the code from qform in order to keep the affine matrices aligned, ensuring that the images will be treated as having the same orientation later down the pipeline. - See Chris Markewicz's Neurostars posts [here](https://neurostars.org/t/note-on-orientation-qform-and-sform-warning/4379/4) and [here](https://neurostars.org/t/note-on-orientation-sform-matrix-set/5939) for a longer explanation of when/why fMRIPrep produces these warnings. You can read more about how `qform` and `sform` work at the following links: [Recommended usage of qform and sform](https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/qsform_brief_usage) and [Nifti Qform and Sform](http://gru.stanford.edu/doku.php/mrTools/coordinateTransforms). + `qform` and `sform` refer to metadata fields in the NIfTI file header, prescribing a coordinate system for the image. + This message warns you that information in the header was altered during the preprocessing. + This is an advisory message and does not necessarily indicate issues in data quality or registration. + A common scenario is that the sform code was 0, so fMRIPrep copied the code from qform in order to keep the affine matrices aligned, ensuring that the images will be treated as having the same orientation later down the pipeline. + See Chris Markewicz's Neurostars posts [here](https://neurostars.org/t/note-on-orientation-qform-and-sform-warning/4379/4) and [here](https://neurostars.org/t/note-on-orientation-sform-matrix-set/5939) for a longer explanation of when/why fMRIPrep produces these warnings. You can read more about how `qform` and `sform` work at the following links: [Recommended usage of qform and sform](https://nifti.nimh.nih.gov/nifti-1/documentation/nifti1fields/nifti1fields_pages/qsform_brief_usage) and [Nifti Qform and Sform](http://gru.stanford.edu/doku.php/mrTools/coordinateTransforms). ### Alignment of functional and anatomical data The alignment report shows the quality of co-registration and susceptibility distortion correction. -* Co-registration - * The text tells you which method fMRIPrep used to align the functional and anatomical data - for example, by running `bbregister`. The images show the registered BOLD reference with the white and pial surfaces overlaid (red and blue lines) - * Better: - * The BOLD and T1w images are aligned; image boundaries and anatomical landmarks (for example, ventricles; corpus callosum) appear to be in the same place when toggling between the images. - * White and pial surface outlines (red and blue lines) appear to correspond well to the tissue boundaries in the functional images. - - * Worse: - * Functional and anatomical images are not aligned and clearly differ in their spatial location or orientation. - * White and pial surface boundaries (red and blue lines) overlays correspond poorly to the tissue boundaries in the underlying images. - -* Susceptibility distortion correction - * The functional images can have some warping or distortion due to inhomogeneities in the $B_0$ magnetic field. For more details on what causes susceptibility distortion, refer to [the educational notebook of SDCFlows](https://github.com/nipreps/sdcflows/blob/master/docs/notebooks/SDC%20-%20Theory%20and%20physics.ipynb). Susceptibility distortion artifacts manifest in two different ways on the functional and structural images: as signal drop-out, that is, a region where the signal vanishes, or as brain distortions. Signal drop-outs often appear close to brain-air interfaces; these include ventromedial prefrontal cortex, the anterior part of the prefrontal cortex, and the region next to the ear cavities. - * Better: - * No signal drop out or brain distortion affects your region of interest. - * The pial surface outlines (blue lines) appear to correspond well to the tissue boundaries in the functional images. - * Worse: - * If the susceptibility distortion correction is unsuccessfull, residual susceptibility distortion artifacts can be observed. If the latter overlaps with regions of interest, the scan should be excluded. - * Note however that some drop out in inferior brain regions (such as the OFC or medial temporal lobe) in functional images is somewhat inevitable. Depending on the type of analysis you’re doing and what you’re interested in, this may be more/less of an issue for you. If you see a lot of signal drop out, you will probably want to check the brain mask generated by fMRIPrep to see how that drop-out impacts the location and quantity of missing voxels in your brain mask. + #### Co-registration +The text tells you which method fMRIPrep used to align the functional and anatomical data - for example, by running `bbregister`. The images show the registered BOLD reference with the white and pial surfaces overlaid (red and blue lines) + +**Better**: +* The BOLD and T1w images are aligned; image boundaries and anatomical landmarks (for example, ventricles; corpus callosum) appear to be in the same place when toggling between the images. +* White and pial surface outlines (red and blue lines) appear to correspond well to the tissue boundaries in the functional images. + +**Worse**: +* Functional and anatomical images are not aligned and clearly differ in their spatial location or orientation. +* White and pial surface boundaries (red and blue lines) overlays correspond poorly to the tissue boundaries in the underlying images. + +#### Susceptibility distortion correction +The functional images can have some warping or distortion due to inhomogeneities in the $B_0$ magnetic field. For more details on what causes susceptibility distortion, refer to [the educational notebook of SDCFlows](https://github.com/nipreps/sdcflows/blob/master/docs/notebooks/SDC%20-%20Theory%20and%20physics.ipynb). Susceptibility distortion artifacts manifest in two different ways on the functional and structural images: as signal drop-out, that is, a region where the signal vanishes, or as brain distortions. Signal drop-outs often appear close to brain-air interfaces; these include ventromedial prefrontal cortex, the anterior part of the prefrontal cortex, and the region next to the ear cavities. + +**Better**: +* No signal drop out or brain distortion affects your region of interest. +* The pial surface outlines (blue lines) appear to correspond well to the tissue boundaries in the functional images. + +**Worse**: +* If the susceptibility distortion correction is unsuccessfull, residual susceptibility distortion artifacts can be observed. If the latter overlaps with regions of interest, the scan should be excluded. +* Note however that some drop out in inferior brain regions (such as the OFC or medial temporal lobe) in functional images is somewhat inevitable. Depending on the type of analysis you’re doing and what you’re interested in, this may be more/less of an issue for you. If you see a lot of signal drop out, you will probably want to check the brain mask generated by fMRIPrep to see how that drop-out impacts the location and quantity of missing voxels in your brain mask. -* Common pitfalls in interpretation: - * Note that the BOLD images displayed in the report may have what appears to be an “artifact” in the data, as shown below. This is because the reports use a faster but less precise type of interpolation (Nearest Neighbor interpolation) for display. In actuality, fMRIPrep uses Lanczos interpolation for resampling, so these apparent “artifacts” are not present in the actual data (you can confirm this by checking the preprocessed BOLD NIFTI file and its registration to the T1w image in your preferred software). +**Common pitfalls in interpretation**: Note that the BOLD images displayed in the report may have what appears to be an “artifact” in the data, as shown below. This is because the reports use a faster but less precise type of interpolation (Nearest Neighbor interpolation) for display. In actuality, fMRIPrep uses Lanczos interpolation for resampling, so these apparent “artifacts” are not present in the actual data (you can confirm this by checking the preprocessed BOLD NIFTI file and its registration to the T1w image in your preferred software). - ![apparent-artifact-interpolation](../assets/fmriprep_visual_report/apparent-artifact-interpolation.png) - *Figure 3. The use of a fast interpolation for display can lead to artifact-like structure that are not present in the actual data.* +![apparent-artifact-interpolation](../assets/fmriprep_visual_report/apparent-artifact-interpolation.png) +*Figure 3. The use of a fast interpolation for display can lead to artifact-like structure that are not present in the actual data.* ### Brain mask and temporal/anatomical CompCor ROIs @@ -173,80 +169,76 @@ The temporal CompCor ROI (blue contour) contains the top 2% most variable voxels The brain edge (or crown) ROI (green contour) picks signals outside but close to the brain, which are decomposed into 24 principal components. -* Better: - * The brain mask correctly surrounds the brain boundary, not leaving out brain area. Note that holes in the brain mask such as on Figure 4, is not problematic as it should not disrupt co-registration. - ![hole-bold-brainmask](../assets/fmriprep_visual_report/hold-bold-brainmask.png) - *Figure 4. Such holes in the brain mask is not problematic as it should not disrupt co-registration.* +**Better**:The brain mask correctly surrounds the brain boundary, not leaving out brain area. Note that holes in the brain mask such as on Figure 4, is not problematic as it should not disrupt co-registration. +![hole-bold-brainmask](../assets/fmriprep_visual_report/hold-bold-brainmask.png) +*Figure 4. Such holes in the brain mask is not problematic as it should not disrupt co-registration.* -* Worse: - * The brain mask computed from the BOLD image mainly influences confounds estimation, but also co-registration, although the latter is primarily driven by the WM mask. As such the brain mask must not leave out any brain area, but it can be a bit loose around the brain. If the mask intersects with brain-originating signal, the nuisance regressors should not be used. - * If the study plan prescribes using CompCor or brain-edge regressors, it is critical to exclude BOLD runs where any of these masks substantially overlap regions of interest. - * The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (e.g see Figure 5). In this case, the scan should be excluded. - ![suspicious-compcor](../assets/fmriprep_visual_report/suspicious-compcor.png) - *Figure 5. The shape and the large overlap of the tCompCor with region of interest indicates the presence of an artifact that was missed in the other visualizations.* +**Worse**: +* The brain mask computed from the BOLD image mainly influences confounds estimation, but also co-registration, although the latter is primarily driven by the WM mask. As such the brain mask must not leave out any brain area, but it can be a bit loose around the brain. If the mask intersects with brain-originating signal, the nuisance regressors should not be used. +* If the study plan prescribes using CompCor or brain-edge regressors, it is critical to exclude BOLD runs where any of these masks substantially overlap regions of interest. +* The shape and the large overlap of the tCompCor with region of interest can also indicate the presence of an artifact that was missed in the other visualizations (e.g see Figure 5). In this case, the scan should be excluded. +![suspicious-compcor](../assets/fmriprep_visual_report/suspicious-compcor.png) +*Figure 5. The shape and the large overlap of the tCompCor with region of interest indicates the presence of an artifact that was missed in the other visualizations.* ### Variance explained by t/a CompCor components The figure displays the cumulative variance explained by components for each of four CompCor decompositions (left to right: anatomical CSF mask, anatomical white matter mask, anatomical combined mask, temporal). Dotted lines indicate the minimum number of components necessary to explain 50%, 70%, and 90% of the variance in the nuisance mask.The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression. - * Better: - * High variance explained: A high variance explained by t/aCompCor components indicates that the selected regions of interest are capturing a significant portion of the motion and physiological noise in the data, which can improve the quality of subsequent analyses. In the fMRIPrep reports, by default only the components that explain the top 50% of the variance are saved. + **Better**: High variance explained: A high variance explained by t/aCompCor components indicates that the selected regions of interest are capturing a significant portion of the motion and physiological noise in the data, which can improve the quality of subsequent analyses. In the fMRIPrep reports, by default only the components that explain the top 50% of the variance are saved. -* Worse: - * Low variance explained: A low variance explained by t/aCompCor components may indicate that the selected regions of interest are not capturing enough of the motion and physiological noise in the data, which can lead to reduced sensitivity and specificity in subsequent analyses. +**Worse**: Low variance explained: A low variance explained by t/aCompCor components may indicate that the selected regions of interest are not capturing enough of the motion and physiological noise in the data, which can lead to reduced sensitivity and specificity in subsequent analyses. The benchmark for % variance explained and/or optimal number of CompCor components to include in the analysis can vary, depending on the specific dataset and analysis goals. In general, the goal is to select enough components to capture the majority of the motion and physiological noise in the data, while avoiding overfitting the model to noise or task-related signal. ### BOLD summary The BOLD summary report shows several characteristic statistics along with a carpet plot [power2017][5], giving a view of the temporal characteristics of the preprocessed BOLD series. The carpet plot is a tool to visualize changes in voxel intensities throughout an fMRI scan. It works by plotting voxel time series in close spatial proximity so that the eye notes temporal coincidence. One particular innovation of this carpet plot implementation is that it contains the "crown" area corresponding to voxels located on a closed band around the brain's outer edge [patriat2015][6]. As those voxels are outside the brain, we do not expect any signal there, meaning that the presence of signal can be interpreted as produced by an artifact. -* Definitions: - * GS - global signal, or the mean of the voxel time series within the whole-brain mask. In addition to the BOLD signal of interest, GS fluctuations can reflect non-neuronal contributions to the voxel time series, e.g., by motion, the MRI system (e.g., low frequency drift), physiology (respiration, cardiac rate), and/or participant state-related factors (e.g., vigilance or fatigue) [Liu2017][13]. - * GSCSF - global signal calculated within cerebrospinal fluid (CSF) - * GSWM - global signal calculated within white matter (WM) - * DVARS - [*standardized DVARS!*](https://neurostars.org/t/fmriprep-standardised-dvars/5271) for each time point. DVARS is the average change in mean intensity between each pair of fMRI volumes in a series and can also be interpreted as the first derivative of the mean intensity. Higher values indicate more dramatic changes (e.g., due to motion or spiking). - * FD - framewise-displacement measures for each time point - -* Better: - * The carpet plot is homogeneous, particularly in the brain edge. - -* Worse: - * Strongly structured brain edge region in the carpet plot is a sign that artifacts are compromising the fMRI scan [provins2022][7]. Several types of carpet plot modulations can be differentiated and are illustrated in the figure below. - * Motion outbursts, visible as peaks in the FD trace, are often paired with prolonged dark deflections derived from spin-history effects (see Figure 6A). - * Periodic modulations on the carpet plot indicate regular and slow motion, e.g., caused by respiration, which may also compromise the signal of interest (see Figure 6B). - * Coil failures may be identifiable as a sudden change in overall signal intensity on the carpet plot not paired that is not paired with motion peaks and generally sustained through the end of the scan (see Figure 6C). - * A strong polarized structure revealed by the clustering of carpet plot rows suggests that artifacts mitigate the signal of interest (see Figure 6D). Indeed, sorting the rows (i.e., the time series) of each segment of the carpet plot such that voxels with similar BOLD dynamics appear close to one another reveals non-global structure in the signal, which is obscured when voxels are ordered randomly [aquino2020][8]. - * Finding temporal patterns similar in gray matter areas and simultaneously in regions of no interest (for instance, CSF or the brain edge) indicates the presence of artifacts, typically derived from head motion. If the planned analysis specifies noise regression techniques based on information from these regions of no interest (which is standard and recommended [ciric2017][9]), the risk of removing signals with neural origins is high, and affected scans should be excluded. For example, in standard task-based fMRI, stimulus presentation might evoke both participant movement and neural responses (_stimulus-correlated motion_). + +| Name | Definitions | +| -------- | ------- | +| GS | global signal, or the mean of the voxel time series within the whole-brain mask. In addition to the BOLD signal of interest, GS fluctuations can reflect non-neuronal contributions to the voxel time series, e.g., by motion, the MRI system (e.g., low frequency drift), physiology (respiration, cardiac rate), and/or participant state-related factors (e.g., vigilance or fatigue) [Liu2017][13]. | +| GSCSF | global signal calculated within cerebrospinal fluid (CSF) | +| GSWM | global signal calculated within white matter (WM) | +| DVARS | [*standardized DVARS!*](https://neurostars.org/t/fmriprep-standardised-dvars/5271) for each time point. DVARS is the average change in mean intensity between each pair of fMRI volumes in a series and can also be interpreted as the first derivative of the mean intensity. Higher values indicate more dramatic changes (e.g., due to motion or spiking). | +| FD | framewise-displacement measures for each time point | + +**Better**: The carpet plot is homogeneous, particularly in the brain edge. + +**Worse**: Strongly structured brain edge region in the carpet plot is a sign that artifacts are compromising the fMRI scan [provins2022][7]. Several types of carpet plot modulations can be differentiated and are illustrated in the figure below. +* Motion outbursts, visible as peaks in the FD trace, are often paired with prolonged dark deflections derived from spin-history effects (see Figure 6A). +* Periodic modulations on the carpet plot indicate regular and slow motion, e.g., caused by respiration, which may also compromise the signal of interest (see Figure 6B). +* Coil failures may be identifiable as a sudden change in overall signal intensity on the carpet plot not paired that is not paired with motion peaks and generally sustained through the end of the scan (see Figure 6C). +* A strong polarized structure revealed by the clustering of carpet plot rows suggests that artifacts mitigate the signal of interest (see Figure 6D). Indeed, sorting the rows (i.e., the time series) of each segment of the carpet plot such that voxels with similar BOLD dynamics appear close to one another reveals non-global structure in the signal, which is obscured when voxels are ordered randomly [aquino2020][8]. +* Finding temporal patterns similar in gray matter areas and simultaneously in regions of no interest (for instance, CSF or the brain edge) indicates the presence of artifacts, typically derived from head motion. If the planned analysis specifies noise regression techniques based on information from these regions of no interest (which is standard and recommended [ciric2017][9]), the risk of removing signals with neural origins is high, and affected scans should be excluded. For example, in standard task-based fMRI, stimulus presentation might evoke both participant movement and neural responses (_stimulus-correlated motion_). - ![carpetplot_artifacts](../assets/fmriprep_visual_report/carpetplot_artifacts.png) - *Figure 6. Assessment of time series with the carpet plot. The brain edge region of the carpet plot comprises voxels outside the brain. As such, structure in the brain edge can be interpreted as artifactual, and thus corresponds to an exclusion criteria. (A) Motion outbursts, visible as peaks in the framewise displacement (FD) trace, are often paired with prolonged dark deflections. (B) Periodic modulations are indicative of regular, slow motion, e.g., caused by respiration. (C) An abrupt change in overall signal intensity that is not paired with motion peaks can be attributed to coil failure. (D) A strong polarized structure revealed by the clustering of carpet plot rows also suggests that artifacts mitigate the signal of interest.* +![carpetplot_artifacts](../assets/fmriprep_visual_report/carpetplot_artifacts.png) +*Figure 6. Assessment of time series with the carpet plot. The brain edge region of the carpet plot comprises voxels outside the brain. As such, structure in the brain edge can be interpreted as artifactual, and thus corresponds to an exclusion criteria. (A) Motion outbursts, visible as peaks in the framewise displacement (FD) trace, are often paired with prolonged dark deflections. (B) Periodic modulations are indicative of regular, slow motion, e.g., caused by respiration. (C) An abrupt change in overall signal intensity that is not paired with motion peaks can be attributed to coil failure. (D) A strong polarized structure revealed by the clustering of carpet plot rows also suggests that artifacts mitigate the signal of interest.* - * Common pitfalls in interpretation - * The variance and the scaling of the trace plots affect a lot their display and interpretation (in other words, spikes are relative to other datapoints in the timeseries). Therefore, always pay attention to the trace metrics like its maximum and mean, before deriving any conclusion or visually comparing the trace plots between two participants. + **Common pitfalls in interpretation**: The variance and the scaling of the trace plots affect a lot their display and interpretation (in other words, spikes are relative to other datapoints in the timeseries). Therefore, always pay attention to the trace metrics like its maximum and mean, before deriving any conclusion or visually comparing the trace plots between two participants. ### Correlations among nuisance regressors This report presents a plot of correlations among confound regressors. The left-hand panel shows the matrix of correlations among selected confound time series as a heat-map. The right-hand panel displays the correlation of selected confound time series with the mean global signal computed across the whole brain; the regressors shown are those with greatest correlation with the global signal. These plots can be used to guide selection of a confound model or to assess the extent to which tissue-specific regressors correlate with global signal.; e.g if two regressors are highly correlated, it is recommended to include in the confound model only one of the two. -* Selection of a confound model - * Better: - * Carefully choose a serie of regressors that are not highly correlated to include in the confound model. Refer to for more information on how to choose the nuisance regressors. - * Or proceed with feature orthogonalization before confound regression. - * Worse: - * Include in the confound model two regressors that are highly correlated. - * Common pitfall in interpreptation: - * The CompCor components extracted from the same mask and the cosine bases are inherently orthogonal, implying a zero correlation by construction. - -* Assessment of partial volume effect [NEEDS COMPLETION] - * Partial volume effects refers to the mixing of signals from different tissue types within a single voxel in fMRI data, so that the MR signal reflects fractional contributions from multiple tissue types. This can lead to inaccurate estimates of neural activity. (However, note that other sources, such as motion or physiological noise, can also contribute to high correlations among the regressors). - * High partial volume effects in one group vs. another may reflect underlying anatomical differences in the groups. For example, older adults with Alzheimers have greater grey matter volume loss and ventricle enlargement, so may be more affected by PVE than their healthy controls [dukart][14]. - - * Better: - * There is no specific threshold for the correlation coefficient that indicates the absence (or presence) of partial volume effects (PVE) in fMRI data. The strength of the correlation between nuisance regressors may depend on several factors, including the tissue properties of the brain region being examined, the voxel size, the signal-to-noise ratio of the data, and the specific nuisance regressors used in the analysis. +#### Selection of a confound model +**Better**: +* Carefully choose a serie of regressors that are not highly correlated to include in the confound model. Refer to for more information on how to choose the nuisance regressors. +* Or proceed with feature orthogonalization before confound regression. + +**Worse**: +* Include in the confound model two regressors that are highly correlated. +* Common pitfall in interpreptation: + * The CompCor components extracted from the same mask and the cosine bases are inherently orthogonal, implying a zero correlation by construction. + +#### Assessment of partial volume effect [NEEDS COMPLETION] +Partial volume effects refers to the mixing of signals from different tissue types within a single voxel in fMRI data, so that the MR signal reflects fractional contributions from multiple tissue types. This can lead to inaccurate estimates of neural activity. (However, note that other sources, such as motion or physiological noise, can also contribute to high correlations among the regressors). + +High partial volume effects in one group vs. another may reflect underlying anatomical differences in the groups. For example, older adults with Alzheimers have greater grey matter volume loss and ventricle enlargement, so may be more affected by PVE than their healthy controls [dukart][14]. + +**Better**: There is no specific threshold for the correlation coefficient that indicates the absence (or presence) of partial volume effects (PVE) in fMRI data. The strength of the correlation between nuisance regressors may depend on several factors, including the tissue properties of the brain region being examined, the voxel size, the signal-to-noise ratio of the data, and the specific nuisance regressors used in the analysis. - * Worse: - * Generally, a high correlation coefficient among nuisance regressors -- especially spatial/anatomical regressors -- may indicate the presence of PVE if it is accompanied by other evidence, such as spatial co-localization of the voxels with high correlation coefficients with regions known to be susceptible to PVE such as the gray/white matter boundary. +**Worse**: Generally, a high correlation coefficient among nuisance regressors -- especially spatial/anatomical regressors -- may indicate the presence of PVE if it is accompanied by other evidence, such as spatial co-localization of the voxels with high correlation coefficients with regions known to be susceptible to PVE such as the gray/white matter boundary. #### Note on multiband/multi-echo fMRI data