[94GU] Pipeline reproduction (SPM - raw)

[bclenet]

Softwares

• SPM: SPM12 RRID:SCR_007037
• Artifact Detection Tools (ART) RRID:SCR_005994
• aCompCor in PhysIO TOOLBOX (TAPAS software collection)
• MATLAB RRID:SCR_001622

Input data

raw data

Additional context

see description below

List of tasks

Please tick the boxes below once the corresponding task is finished. 👍

• 👌 A maintainer of the project approved the issue, by assigning a 🏁status: ready for dev label to it.
• 🌳 Create a branch on your fork to start the reproduction.
• 🌅 Create a file team_{team_id}.py inside the narps_open/pipelines/ directory. You can use a file inside narps_open/pipelines/templates as a template if needed.
• 📥 Create a pull request as soon as you completed the previous task.
• 🧠 Write the code for the pipeline, using Nipype and the file architecture described in docs/pipelines.md.
• 📘 Make sure your code is documented enough.
• 🐍 Make sure your code is explicit and conforms with PEP8.
• 🔬 Create tests for your pipeline. You can use files in tests/pipelines/test_team_* as examples.
• 🔬 Make sure your code passes all the tests you created (see docs/testing.md).

---

NARPS team description : 94GU

General

• teamID : 94GU
• NV_collection_link : https://neurovault.org/collections/5626/
• results_comments : Thresholded maps ( pFDR < 0.05; 5 vx)
• preregistered : No
• link_preregistration_form : NA
• regions_definition : Anatomical identification was performed via Xjview 9.6 http://www.alivelearn.net/xjview9.6
  (Automated Anatomical Labeling human brain atlas and Brodmann Areas). For regions not
  explicitly defined in these atlas (such as the ventral striatum or the VMPFC) the
  neuroanatomy atlas by Haines (2011) was consulted. Two experienced neuroscientists (both
  part of the team) supervised the results.
• softwares : SPM: SPM12 https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ RRID:SCR_007037 ,
  Artifact Detection Tools (ART)
  https://www.nitrc.org/projects/artifact_detect/ RRID:SCR_005994
  and aCompCor in PhysIO TOOLBOX (TAPAS software collection)
  https://www.tnu.ethz.ch/en/software/tapas/documentations/physio-toolbox.html
  MATLAB https://www.mathworks.com/products/matlab.html RRID:SCR_001622
• general_comments : NA

Exclusions

• n_participants : 108
• exclusions_details : No participants were excluded

Preprocessing

• used_fmriprep_data : No
• preprocessing_order : 1) Fieldmap distorsion calculation; 2) functional Realignment & unwarp & phase
  correction (subject motion estimation and correction); 3) functional Indirect Segmentation
  & Normalization (coregister functional/structural; structural segmentation &
  normalization; apply same deformation field to functional) 6) functional Smoothing
  (spatial convolution with Gaussian kernel); 7) Artifact and structured noise identification;

8. Statistical anaysis (1st level and 2nd level).

• brain_extraction : Brain extraction was not performed
• segmentation : Method used = the unified segmentation algorithm implemented in SPM12
• slice_time_correction : Slice time correction was not performed.
• motion_correction : Motion correction was performed in SPM12 (realign and unwarp and the fielmap toolbox).
  Default parameters were used: least squares approach and a 6 parameter (rigid body)
  spatial transformation. Fieldmap-based unwarping was performed at this step (SPM12).
  Reference scan = 1st scan. Image similarity metric = normalised mutual information.
  Interpolation type: B-spline.
• motion :
• gradient_distortion_correction : Already described in the motion correction tab
• intra_subject_coreg : Coregistration was performed in SPM12 (coregister: estimate and reslice). Type of
  transformation: rigid body model. Cost function = mutual infrormation. Interpolation
  method = B-spline.
• distortion_correction : Already described
• inter_subject_reg : Normalization was performed in SPM12 (normalise). Volume-based registration applied on
  T1, the resulting transformation map applied then to T2*. MNI/ICBM space template -
  European brains: T1, 2 mm. Choice of warp = nonlinear. Type of transformation = DCT.
  Warping regularisation parameters = 0 0.001 0.5 0.05 0.2
• intensity_correction : Bias regularisation = very light regularisation (0.0001). Bias FWHM 60mm cutoff
• intensity_normalization : SPM12's mean centering
• noise_removal : aCompCor was used to calculate the variance of the signal arising from specific tissue types
  (white matter, CSF, defined in the segmentation step). These data were introduced as
  regressors in the model (1st level analysis).
• volume_censoring : ART was used to calculate the artifacted volumes. Criteria = frame-by-frame
  displacement and percentage BOLD change. These data (artifacted volumes) were also
  introduced as regressors in the model (1st level analysis).
• spatial_smoothing : Smoothing was performed in SPM12 (smooth: 8 mm FWHM). Fixed kernel. Performed in
  normalised volumes.
• preprocessing_comments : NA

Analysis

• data_submitted_to_model : Number of time points = 453; Number of subjects = 108
• spatial_region_modeled : Full brain analysis (FDR correction for multiple comparisons)
• independent_vars_first_level : First-level analysis was performed in SPM12 using an event-related design (modeled
  duration = 4s; condition = all trials; parametric modulation = gain and loss; HRF basis =
  canonical plus temporal and dispersion derivatives; drift regressors = DCT basis;
  orthogonalise modulations; regressors = aCompCor and ART data; HPF = optimized for
  specific SOA using MATLAB). Four contrasts (positive gain, positive loss, negative gain,
  negative loss) were defined for each session in each subject. Finally, before second level
  analysis, contrasts were averaged across sessions obtaining one contrast for each condition
  and subject.
• RT_modeling : none
• movement_modeling : 1
• independent_vars_higher_level : Four one-sample t-tests were used to explore the possitive and negative effects of gain
  and loss (covariates = age and sex). A two independent samples t-test was used to explore
  intergroup differences (covariates = age and sex)
• model_type : Mass univariate
• model_settings : Autocorrelation model = AR(1). Random or mixed-effects model implemented with OLS.
  Specific variance structure (2 sample t-test) = unequal variance between groups (globally
  pooled)
• inference_contrast_effect : The effects tested (linear contrasts) included = the parametric effect of gain (both
  positive and negative) in the Equal Range group; the parametric effect of gain (both
  positive and negative) in the Equal Indifference group; the parametric effect of loss (both
  positive and negative) in the Equal Range group; the parametric effect of loss (both
  positive and negative) in the Equal Indifference group.
• search_region : Whole brain
• statistic_type : Voxel-wise and cluster-wise
• pval_computation : Standard parametric inference was used.
• multiple_testing_correction : FDR (Benjamini & Hochberg)
• comments_analysis : Decisions were made on the basis of FDR correction (p < 0.05) at the peak level.
  Several significant clusters (including the ones located in the cingulate gyrus,
  extraestriate cortex and posterior/superior parietal cortex) survive FWE correction
  both at the peak and at the cluster level. The reported result in the ventromedial
  prefrontal cortex survived FDR correction at the peak level, and FWE correction only
  at the cluster level.

Categorized for analysis

• region_definition_vmpfc : Other
• region_definition_striatum : Xjview 9.6
• region_definition_amygdala : Xjview 9.6
• analysis_SW : SPM
• analysis_SW_with_version : SPM12
• smoothing_coef : 8
• testing : parametric
• testing_thresh : z>3.32
• correction_method : FDR
• correction_thresh_ : p<0.05

Derived

• n_participants : 108
• excluded_participants : n/a
• func_fwhm : 8
• con_fwhm :

Comments

• excluded_from_narps_analysis : No
• exclusion_comment : N/A
• reproducibility : 1
• reproducibility_comment : Multiple software dependencies : SPM + ART + TAPAS + Matlab.

[youennmerel]

Submitted preprocessing, see #230