[ENH] use BIDS stats model to select input task, space and override options

CITATION.cff

@@ -62,7 +62,7 @@ authors:
   orcid: "https://orcid.org/0000-0002-0174-9326"
   affiliation: "Université de Montréal"
 
-- family-names: "Cerpelloni"  
+- family-names: "Cerpelloni"
   given-names: "Filippo"
   orcid: "https://orcid.org/0000-0001-8070-5753"
   affiliation: "Université catholique de Louvain"

demos/MoAE/moae_01_preproc.m

@@ -6,8 +6,8 @@
 clear;
 clc;
 
-download_data = true;
-clean = true;
+download_data = false;
+clean = false;
 
 try
   run ../../initCppSpm.m;
@@ -20,12 +20,8 @@
 
 reportBIDS(opt);
 
-opt.pipeline.type = 'preproc';
-
 bidsCopyInputFolder(opt);
 
-bidsSTC(opt);
-
 bidsSpatialPrepro(opt);
 
 anatomicalQA(opt);

demos/MoAE/moae_03_create_roi_extract_data.m

@@ -16,6 +16,8 @@
 subLabel = '01';
 
 opt = moae_get_option_stats();
+opt.pipeline.type = 'stats';
+opt = checkOptions(opt);
 
 %% Get the con image to extract data
 % we can do this by using the "label-XXXX"

demos/MoAE/moae_04_slice_display.m

@@ -18,6 +18,8 @@
 subLabel = '01';
 
 opt = moae_get_option_stats();
+opt.pipeline.type = 'stats';
+opt = checkOptions(opt);
 
 use_schema = false;
 BIDS_ROI = bids.layout(opt.dir.roi, use_schema);
@@ -78,8 +80,8 @@
 
 % we reuse the details for the SPM montage
 settings.slice.orientation = opt.result.Nodes(1).Output.montage.orientation;
-settings.slice.disp_slices = -15:3:30;
-settings.fig_specs.n.slice_column = 5;
+settings.slice.disp_slices = -15:3:18;
+settings.fig_specs.n.slice_column = 4;
 settings.fig_specs.title = opt.result.Nodes(1).Contrasts(1).Name;
 
 %% Display the layers

demos/MoAE/moae_get_option_fmriprep_preproc.m

@@ -20,7 +20,6 @@
   opt.space = {'MNI152NLin2009cAsym'};
 
   opt.query.desc = {'preproc', 'confounds'};
-  %   opt.query.space = opt.space;
 
   %% DO NOT TOUCH
   opt = checkOptions(opt);

demos/MoAE/moae_get_option_stats.m

@@ -19,10 +19,6 @@
   opt.dir.roi = fullfile(opt.dir.derivatives, 'cpp_spm-roi');
   opt.dir.stats = fullfile(opt.dir.derivatives, 'cpp_spm-stats');
 
-  opt.pipeline.type = 'stats';
-
-  opt.space = {'IXI549Space'};
-
   opt.model.file = fullfile(fileparts(mfilename('fullpath')), ...
                             'models', 'model-MoAE_smdl.json');
   % to add the hrf temporal derivative = [1 0]

demos/MoAE/models/model-MoAE_smdl.json

@@ -3,7 +3,8 @@
   "BIDSModelVersion": "1.0.0",
   "Description": "contrasts to compute for the FIL MoAE dataset",
   "Input": {
-    "task": "auditory"
+    "task": "auditory",
+    "space": "IXI549Space"
   },
   "Nodes": [
     {

demos/face_repetition/face_rep_03_roi_analysis.m

@@ -16,6 +16,7 @@
 end
 
 opt = face_rep_get_option_results();
+opt.taskName = {'facerepetition'};
 
 opt.roi.atlas = 'wang';
 opt.roi.name = {'V1v', 'V1d'};

demos/face_repetition/face_rep_get_option.m

@@ -13,8 +13,6 @@
   opt.dir.raw = fullfile(fileparts(mfilename('fullpath')), 'outputs', 'raw');
   opt.dir.derivatives = fullfile(opt.dir.raw, '..', 'derivatives');
 
-  opt.pipeline.type = 'preproc';
-
   %% DO NOT TOUCH
   opt = checkOptions(opt);
   saveOptions(opt);

demos/face_repetition/face_rep_get_option_results.m

@@ -4,18 +4,13 @@
 
   opt = [];
 
-  opt.taskName = 'facerepetition';
   opt.verbosity = 1;
 
   opt.dir.raw = fullfile(fileparts(mfilename('fullpath')), 'outputs', 'raw');
   opt.dir.derivatives = fullfile(opt.dir.raw, '..', 'derivatives');
   opt.dir.preproc = fullfile(opt.dir.derivatives, 'cpp_spm-preproc');
   opt.dir.roi = fullfile(opt.dir.derivatives, 'cpp_spm-roi');
 
-  opt.pipeline.type = 'stats';
-
-  opt.space = {'IXI549Space'};
-
   opt.model.file = fullfile( ...
                             fileparts(mfilename('fullpath')), ...
                             'models', ...

demos/face_repetition/face_rep_resolution.m

@@ -31,7 +31,6 @@
 
 for iResolution = 2:1:3
 
-  opt.pipeline.type = 'preproc';
   opt.pipeline.name = ['cpp_spm-res' num2str(iResolution)];
 
   % set the final output resolution
@@ -70,7 +69,6 @@
 
   bidsSmoothing(FWHM, opt);
 
-  opt.pipeline.type = 'stats';
   opt.pipeline.name = ['cpp_spm-stats-res' num2str(iResolution)];
   opt = checkOptions(opt);
 

demos/face_repetition/models/model-faceRepetition_smdl.json

@@ -3,7 +3,8 @@
    "BIDSModelVersion": "1.0.0",
    "Description": "model for face repetition to check resampling effects",
    "Input": {
-      "task": "facerepetition"
+      "task": "facerepetition",
+      "space": "IXI549Space"
    },
    "Nodes": [
       {
@@ -82,4 +83,4 @@
          ]
       }
    ]
-}
+}

demos/openneuro/models/model-defaultBalloonanalogrisktask_smdl.json

@@ -119,4 +119,4 @@
          ]
       }
    ]
-}
+}

demos/openneuro/models/model-ds000114-linebisection_smdl.json

@@ -59,4 +59,4 @@
       ]
     }
   ]
-}
+}

docs/source/_static/README.md

@@ -0,0 +1,8 @@
+# Making figures for the doc
+
+Making DAGs (directed acyclic graphs) with makefile and
+[makefile2graph](https://github.com/lindenb/makefile2graph).
+
+```
+make -Bnd | make2graph | dot -Tpng -o out.png
+```

docs/source/_static/bidsSegmentSkullstrip/Makefile

@@ -0,0 +1,36 @@
+.PHONY: all sub-01_T1w.nii setBatchSegmentation.m setBatSkullstripping.m bidsRename.m
+
+all: sub-01_space-individual_label-GM_probseg.nii
+
+# Rename
+sub-01_space-individual_label-GM_probseg.nii: bidsRename.m c1sub-01_T1w.nii
+
+sub-01_space-individual_label-WM_probseg.nii: bidsRename.m c2sub-01_T1w.nii
+
+sub-01_space-individual_label-CSF_probseg.nii: bidsRename.m c3sub-01_T1w.nii
+
+sub-01_from-IXI549Space_to-T1w_mode-image_xfm.nii: bidsRename.m iysub-01_T1w.nii
+
+sub-01_from-T1w_to-IXI549Space_mode-image_xfm.nii: bidsRename.m ysub-01_T1w.nii
+
+
+# Skullstripping
+sub-01_space-individual_desc-skullstripped_T1w.nii: setBatSkullstripping.m msub-01_T1w.nii c1sub-01_T1w.nii c2sub-01_T1w.nii c3sub-01_T1w.nii
+
+sub-01_space-individual_label-brain_mask.nii: setBatSkullstripping.m msub-01_T1w.nii c1sub-01_T1w.nii c2sub-01_T1w.nii c3sub-01_T1w.nii
+
+
+# Segmentation
+msub-01_T1w.nii: setBatchSegmentation.m sub-01_T1w.nii
+
+c1sub-01_T1w.nii: setBatchSegmentation.m sub-01_T1w.nii
+
+c2sub-01_T1w.nii: setBatchSegmentation.m sub-01_T1w.nii
+
+c3sub-01_T1w.nii: setBatchSegmentation.m sub-01_T1w.nii
+
+sub-01_label-T1w_segparam: setBatchSegmentation.m sub-01_T1w.nii
+
+ysub-01_T1w.nii: setBatchSegmentation.m sub-01_T1w.nii
+
+iysub-01_T1w.nii: setBatchSegmentation.m sub-01_T1w.nii

lib/utils/iqr_method.m

@@ -0,0 +1,59 @@
+function [I] = iqr_method(a, out)
+    %
+    % Returns a logical vector that flags outliers as 1s based
+    % on the IQR (interquantile range) methods described in Wilcox 2012 p 96-97.
+    %
+    % Uses Carling's modification of the boxplot rule.
+    %
+    % An observation Xi is declared an outlier if:
+    %
+    %   Xi<M-k(q2-q1) or Xi>M+k(q2-q1)
+    %
+    % where:
+    %  - M is the sample median
+    %  - k = (17.63n - 23.64) / (7.74n - 3.71),
+    %   - where n is the sample size.
+    %
+    % INPUT a is a vector of data
+    %       out determines the thresholding 1 bilateral 2 unilateral
+    %
+    % OUTPUTS: I =     logical vector with 1s for outliers
+    %          value = IQR, the inter-quartile range
+    %
+    % Cyril Pernet - spmup - adapted from Corr_toolbox
+    % https://github.com/CPernet/spmup
+    % ----------------------------------------
+    % (C) Copyright 2016 Cyril Pernet
+
+    if nargin == 1
+        out = 1;
+    end
+
+    a = a(:);
+    n = length(a);
+
+    % inter-quartile range
+    j = floor(n / 4 + 5 / 12);
+
+    y = sort(a);
+
+    g = (n / 4) - j + (5 / 12);
+
+    q1 = (1 - g) .* y(j) + g .* y(j + 1);
+
+    k = n - j + 1;
+    q2 = (1 - g) .* y(k) + g .* y(k - 1);
+
+    value = q2 - q1;
+
+    % outliers
+    M = median(a);
+    k = (17.63 * n - 23.64) / (7.74 * n - 3.71);
+    if out == 1
+        I = a < (M - k * value) | a > (M + k * value);
+    else
+        I = a > (M + k * value); % only reject data with a too high value
+    end
+    I = I + isnan(a);
+
+end