diff --git a/docs/index.md b/docs/index.md index e15af40..f58db2d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,9 +6,9 @@ The ABCC houses a community-shared and continually updated ABCD neuroimaging dat In addition to the data, the ABCC read-the-docs provides helpful links to how to use, process, and analyze ABCD data. The documentation guide below will take users to different sections for more information. -## 2. Background +## Background -As a community share, the ABCC enables researchers to access **available derivatives** and share their **own derivatives.**. The ABCD-BIDS datasets are continually updated as new ABCD releases become available. A list of currently available datasets are provided below. +As a community share, the ABCC enables researchers to access **available derivatives** and share their **own derivatives**. The ABCD-BIDS datasets are continually updated as new ABCD releases become available. A list of currently available datasets are provided below. 1. `BIDS inputs` The input DICOM data to this [BIDS version 1.2.0](https://www.nature.com/articles/sdata201644) data collection were retrieved from the [NIMH Data Archive (NDA) share of ABCD fast-track data](https://nda.nih.gov/edit_collection.html?id=2573) and were last accessed on May 1, 2019. BIDS input data were converted from DICOMs using [Dcm2Bids](https://github.com/cbedetti/Dcm2Bids). 2. `abcd-hcp-pipeline` BIDS derivatives data were derived from the [DCAN Labs ABCD-BIDS MRI (version 0.0.3) processing pipeline](https://doi.org/10.5281/zenodo.2587210) which outputs [Human Connectome Project (HCP) Minimal Preprocessing Pipelines-style data](https://doi.org/10.1016/j.neuroimage.2013.04.127) in both volume and surface spaces. This collection is independent from ABCD Data Collection 2573. Users may access ABCD DICOM files via the ABCD fast-track imaging data release in Collection 2573. @@ -30,12 +30,12 @@ Latest updates are detailed below. - Additional year 1 BIDS input and abcd-hcp-pipeline derivatives -- The timeseries data will be reprocessed with an updated version of the abcd-hcp-pipeline (v1.0.3) with improved bandpass filtering to the BOLD data. The new implementation zero pads the BOLD data prior to filtering to minimize distortions at the beginning and ending timepoints. It's important to note that this is not a bug, but rather an improvement. This release does not invalidate previous results, it reduces variance towards the beginning and end of the time-series data. In the previous release, those frames are labeled as "outliers" and discarded according to the provided mask. Using these updated timeseries users should be able to include more data in their analyses. +- The timeseries data will be reprocessed with an updated version of the abcd-hcp-pipeline (v0.1.3) with improved bandpass filtering to the BOLD data. The new implementation zero pads the BOLD data prior to filtering to minimize distortions at the beginning and ending timepoints. It's important to note that this is not a bug, but rather an improvement. This release does not invalidate previous results, it reduces variance towards the beginning and end of the time-series data. In the previous release, those frames are labeled as "outliers" and discarded according to the provided mask. Using these updated timeseries users should be able to include more data in their analyses. - New version of [QSIPrep](https://qsiprep.readthedocs.io/en/stable/) v0.14.2 year 1 derivatives. - There was in issue for some subjects in distortion correction that resulted in very inaccurate distortion correction results. This was due to TOPUP being given a denoised b=0 image from the DWI series and a raw b=0 image in the opposite phase encoding direction (taken from the image in the fmap/ directory). We updated QSIPrep to use the unprocessed b=0 images in both phase encoding directions, which resulted in TOPUP performing as expected. The bug affected a subset of subjects, but it is worth suggesting that anyone using the initial data re-calculate their analysis using the updated version. -- New version of [fMRIPrep](https://fmriprep.org/) 23.0.0rc0 year 1 derivatives. For specifics on what has changed since fMRIprep v20.2.0 and fMRIprep 23.0.0rc0, see the change log for the software [here](https://fmriprep.org/en/stable/changes.html). +- New version of [fMRIPrep](https://fmriprep.org/) 23.0.0rc1 year 1 derivatives. For specifics on what has changed since fMRIprep v20.2.0 and fMRIprep 23.0.0rc1, see the change log for the software [here](https://fmriprep.org/en/stable/changes.html). - Improved distortion correction - Improved bold projection to surface - New CIFTI outputs diff --git a/docs/inputs.md b/docs/inputs.md index d25d1a5..2ebb168 100644 --- a/docs/inputs.md +++ b/docs/inputs.md @@ -1,7 +1,5 @@ # Inputs -Note: Clicking any link within the readthedocs site will not open a new web browser tab. If you want to keep your docs open, either middle-click or right-click and choose open in new tab for the links you would like to follow. - --- ## 1. About this Document @@ -46,7 +44,7 @@ QC is performed by scan operators at the time of the scan. Subjects may fail for ## 4. DICOM to BIDS Conversion -DICOMs were first converted into NIfTIs using [Christophe Bedetti's Dcm2Bids](https://github.com/cbedetti/Dcm2Bids), which is a wrapper for [the Chris Rorden's Lab dcm2niix](https://github.com/rordenlab/dcm2niix) that restructures NIfTIs into BIDS format. +DICOMS were converted using the [abcd-dcm2bids wrapper](https://github.com/ABCD-STUDY/abcd-dicom2bids). The wrapper includes multiple steps involving pulling the data from ABCD fast-track, as described in recommendations [here](https://collection3165.readthedocs.io/en/stable/recommendations/#4-downloading-and-unpacking-data). ABCD-dcm2bids wrapper pulls DICOMS based on the ABCD fast track QC. DICOMs were first converted into NIfTIs using [Christophe Bedetti's Dcm2Bids](https://github.com/cbedetti/Dcm2Bids), which is a wrapper for [the Chris Rorden's Lab dcm2niix](https://github.com/rordenlab/dcm2niix) that restructures NIfTIs into BIDS format. ## 5. MRI Acquisition Parameters diff --git a/docs/pipelines.md b/docs/pipelines.md index 3a36934..2b7a7df 100644 --- a/docs/pipelines.md +++ b/docs/pipelines.md @@ -5,13 +5,13 @@ This document lightly describes the ABCD-BIDS pipeline, fMRIPrep, and QSIPrep used to process the BIDS input data and output the BIDS derivative data. Further documentation for these pipelines can be found by clicking on their respective links.: -1. [abcd-hcp-pipeline](https://hub.docker.com/r/dcanlabs/abcd-hcp-pipeline) -1. [fMRIPrep](https://fmriprep.org/) -1. [QSIPrep](https://qsiprep.readthedocs.io/en/stable/) +[abcd-hcp-pipeline](https://hub.docker.com/r/dcanlabs/abcd-hcp-pipeline) +[fMRIPrep](https://fmriprep.org/) +[QSIPrep](https://qsiprep.readthedocs.io/en/stable/) ## 2. ABCD-BIDS Pipeline -The ABCD-BIDS pipeline is available on [GitHub](https://github.com/ABCD-STUDY/abcd-hcp-pipeline), [Zenodo](https://zenodo.org/record/2587210#.Xc59yldKg7Y), and [DockerHub](https://hub.docker.com/r/dcanlabs/abcd-hcp-pipeline) at the time of this release as the `abcd-hcp-pipeline`. It is a [BIDS App](https://bids-apps.neuroimaging.io/about/) which takes BIDS input data and uses the methods from both the [Human Connectome Project's minimal preprocessing pipeline](https://doi.org/10.1016/j.neuroimage.2013.04.127) and the [DCAN Labs resting state fMRI analysis tools](https://github.com/DCAN-Labs/dcan_bold_processing) to output preprocessed MRI data in both volume and surface spaces. +The ABCD-BIDS pipeline is available on [GitHub](https://github.com/ABCD-STUDY/abcd-hcp-pipeline), [OSF](https://doi.org/10.17605/OSF.IO/89PYD), and [DockerHub](https://hub.docker.com/r/dcanlabs/abcd-hcp-pipeline) at the time of this release as the `abcd-hcp-pipeline`. It is a [BIDS App](https://bids-apps.neuroimaging.io/about/) which takes BIDS input data and uses the methods from both the [Human Connectome Project's minimal preprocessing pipeline](https://doi.org/10.1016/j.neuroimage.2013.04.127) and the [DCAN Labs resting state fMRI analysis tools](https://github.com/DCAN-Labs/dcan_bold_processing) to output preprocessed MRI data in both volume and surface spaces. It has been designed to be as BIDS compliant and user friendly as possible. While it has been used here specifically to process the ABCD data, it can be run by any investigator to process a wide variety of BIDS input MRI data as long as the data set contains a T1w image. @@ -20,7 +20,8 @@ Each stage of the larger pipeline has a distinct beginning and ending which is w For full details read the following references: 1. [The minimal preprocessing pipelines for the Human Connectome Project. Glasser, et al. NeuroImage. 2013.](https://doi.org/10.1016/j.neuroimage.2013.04.127) -1. [Correction of respiratory artifacts in MRI head motion estimates. Fair, et al. NeuroImage. 2019.](https://doi.org/10.1016/j.neuroimage.2019.116400) +2. [Correction of respiratory artifacts in MRI head motion estimates. Fair, et al. NeuroImage. 2019.](https://doi.org/10.1016/j.neuroimage.2019.116400) +3. [Adolescent Brain Cognitive Development (ABCD) Community MRI Collection and Utilities. Feczko, et al. Biorxiv, 2021](https://www.biorxiv.org/content/10.1101/2021.07.09.451638v1) ### Stage 1: PreFreeSurfer diff --git a/docs/recommendations.md b/docs/recommendations.md index 7b3045d..3b67f28 100644 --- a/docs/recommendations.md +++ b/docs/recommendations.md @@ -81,7 +81,7 @@ The brain coverage score is an estimate of how much overlap exists between the f There are two ways to download ABCD Study data and get BIDS inputs or derivatives: 1. (***PREFERRED***) Downloading from NDA Collection 3165 will provide a "data structure manifest" spreadsheet with AWS S3 links and other key information. DCAN Labs has designed [a GitHub repository for selectively downloading only parts of the BIDS input and derivative data, the "nda-abcd-s3-downloader"](https://github.com/ABCD-STUDY/nda-abcd-s3-downloader). -1. [ABCD Fast Track Data on the NDA](https://nda.nih.gov/abcd/query/abcd-fast-track-data.html) can alternatively be downloaded and unpacked into BIDS with the [ABCD-STUDY abcd-dicom2bids GitHub repository](https://github.com/ABCD-STUDY/abcd-dicom2bids). This is if you need DICOM files specifically. +2. [ABCD Fast Track Data on the NDA](https://nda.nih.gov/abcd/query/abcd-fast-track-data.html) can alternatively be downloaded and unpacked into BIDS with the [ABCD-STUDY abcd-dicom2bids GitHub repository](https://github.com/ABCD-STUDY/abcd-dicom2bids). This is if you need DICOM files specifically. ### [`nda-abcd-s3-downloader`](https://github.com/ABCD-STUDY/nda-abcd-s3-downloader) diff --git a/docs/release_notes.md b/docs/release_notes.md index 5bdf98d..95e825c 100644 --- a/docs/release_notes.md +++ b/docs/release_notes.md @@ -1,6 +1,6 @@ # Release Notes -## 3. Ongoing Releases +## Ongoing Releases A guiding principle for this collection is to release essential data for analysis. This collection will be updated with waves of data preparation and processing. As waves complete preparation or processing they will be uploaded and version-stamped with updated and versioned release notes. @@ -9,7 +9,7 @@ User feedback will guide our course for future releases. Provide feedback on wh - [NDA Collection 3165 documentation repository](https://github.com/ABCD-STUDY/nda-abcd-collection-3165) - [Direct link to repository's GitHub issues for requests and feedback](https://github.com/ABCD-STUDY/nda-abcd-collection-3165/issues) -## 4. Release History +## Release History ### Release 2.0.0 (6/22/2022) @@ -39,7 +39,7 @@ These subjects have had their connectivity matrices regenerated and replaced (se #### Connectivity matrices -The 144 participants with replaced fast track QC information mentioned above produced new Gordon 10 and 5 minute connectivity matrices. The old matrices remain valid, but may use different frames from the new matrices. The labels for these connectivity matrices were defined in Gordon, et al, 2017. These connectivity matrices were created using the DCAN Labs cifti connectivity wrapper (https://github.com/DCAN-Labs/cifti-connectivity/). Timepoints used for connectivity calculations were thresholded based on data quality. Data quality was measured by the total frame displacement (FD) calculated from the frame-by-frame realignment parameters; Frames above an FD of 0.2 mm were excluded. An outlier detection procedure was used to exclude remaining frames that were 2 standard deviations away from the mean. These procedures match the original procedures used to generate the connectivity matrices in the November release. +The 144 participants with replaced fast track QC information mentioned above produced new Gordon 10 and 5 minute connectivity matrices. The old matrices remain valid, but may use different frames from the new matrices. The labels for these connectivity matrices were defined in Gordon, et al, 2017. These connectivity matrices were created using the [DCAN Labs cifti connectivity wrapper](https://github.com/DCAN-Labs/cifti-connectivity/). Timepoints used for connectivity calculations were thresholded based on data quality. Data quality was measured by the total frame displacement (FD) calculated from the frame-by-frame realignment parameters; Frames above an FD of 0.2 mm were excluded. An outlier detection procedure was used to exclude remaining frames that were 2 standard deviations away from the mean. These procedures match the original procedures used to generate the connectivity matrices in the November release. *Submission IDs: 36449 - 36452* @@ -57,13 +57,13 @@ Maps are generated with all available minutes below an FD threshold of 0.2mm (an #### Template Matching -(Template Matching)[https://github.com/DCAN-Labs/compare_matrices_to_assign_networks] +[Template Matching] Template matching is a supervised algorithm for identifying neural networks using resting state connectivity data, based on the spatial topography. Click [here](https://github.com/DCAN-Labs/compare_matrices_to_assign_networks) for documentation of source code as well as a written tutorial. Multiple versions of the time series are provided, to allow investigator flexibility in their desired analysis: either exactly 10 minutes of randomly sampled frames, all available frames below the 0.2mm FD threshold, or concatenated rest and task time series data in the following order: rest, MID, n-back, and SST (provided that the participant had an available scan for the task). For full details of inter- and intra- participant reliability, and motion correction, see Hermosillo et al. 2021 (in prep). *Submission IDs: 36458 - 36630* #### Task outputs -(abcd-bids-tfmripipeline)[https://github.com/DCAN-Labs/abcd-bids-tfmri-pipeline] a modified version of the TaskfMRIAnalysis stage of the HCP-pipeline (Glasser et al., 2013) developed at University of Vermont by Anthony Juliano, was used to process task-fmri data from the minimally processed ABCD-BIDS (Feczko et al., 2020b) processing pipeline (v.1.0) data, as well as derived ABCC data (Feczko, 2020; ABCD-3165). An example fsf file template for ABCD's MID task is made available for users to review on ABCC (https://osf.io/psv5m/). MID, Nback, and SST level-2 task outputs are available for the baseline sessions for all data that passed task QC. These outputs include the fully-processed dtseries data that are subsequently ready for the user to perform their desired third-level or group-wise analyses. +[abcd-bids-tfmripipeline](https://github.com/DCAN-Labs/abcd-bids-tfmri-pipeline) a modified version of the TaskfMRIAnalysis stage of the HCP-pipeline (Glasser et al., 2013) developed at University of Vermont by Anthony Juliano, was used to process task-fmri data from the minimally processed ABCD-BIDS (Feczko et al., 2020b) processing pipeline (v.1.0) data, as well as derived ABCC data (Feczko, 2020; ABCD-3165). An example fsf file template for ABCD's MID task is made available for users to review on ABCC (https://osf.io/psv5m/). MID, Nback, and SST level-2 task outputs are available for the baseline sessions for all data that passed task QC. These outputs include the fully-processed dtseries data that are subsequently ready for the user to perform their desired third-level or group-wise analyses. ### Release 1.1.1 (10/7/2020) @@ -92,7 +92,7 @@ This was the initial release of DCAN Labs ABCD-BIDS inputs and derivatives conta ##### `task-rest_bold.json` -Discovered in the middle of June 2020, the modality-specific BIDS inherited `task-rest_bold.json` file at the top of the directory tree which is nested in almost every `task-rest` associated record in the NDA database has a typo in it. The `"TaskDescription"` key has a value of `"See http://www.cognitiveatlas.org/task/id/tsk_4a57abb949e1a/"`. However, this link goes to the stop signal task page on the Cognitive Atlas website. Instead you should refer to [the Cognitive Atlas website for "rest eyes open"](http://www.cognitiveatlas.org/task/id/trm_4c8a834779883/). This site describes the task as: +Discovered in the middle of June 2020, the modality-specific BIDS inherited `task-rest_bold.json` file at the top of the directory tree which is nested in almost every `task-rest` associated record in the NDA database has a typo in it. The `"TaskDescription"` key has a value of `"See http://www.cognitiveatlas.org/task/id/tsk_4a57abb949e1a/"`. However, this link goes to the stop signal task page on the Cognitive Atlas website. Instead you should refer to [the Cognitive Atlas website for "rest eyes open"](http://www.cognitiveatlas.org/task/id/trm_4c8a834779883/). This website describes the task as: "Subjects rest passively with their eyes open. Often used as a baseline for comparison for other tasks."