>> conn_subjects_loader
Note: data is expected to be already preprocessed
(realignment/slicetiming/coregistration/segmentation/normalization/smoothing)
Note2: this script expects a very specific directry tree layout
for your images (see script header comment). If not met,
this may produce errors like "Index exceeds matrix dimensions."
when executing conn_batch(CONN_x).
Note3: if in inter-subjects mode you get the error
"Reference to non-existent field t. Error in spm_hrf", you have to
edit spm_hrf.m to replace stats.fmri.t by stats.fmri.fmri_t .
== Conn subjects loader ==
-- Loading subjects files --
Loading conditions (subjects groups)...
Detect images for all subjects...
Detect images for subject 1/4...
Detect images for subject 2/4...
Detect images for subject 3/4...
Detect images for subject 4/4...
ROIs maps detection...
-- Generating CONN project struct --
Struct initialization...
Loading images...
Loading groups...
-- Save into CONN and run batch --
Save and run project via conn_batch (may take a while for the whole analysis to finish)...
CONN: RUNNING SETUP STEP
Checking if data files have been edited or moved. Please wait...

Step 2/7: Segmentation
100.0% (Subject 4)

Step 3/7: Importing conditions/covariates
100.0% (Subject 4 Session 1)

Step 4/7: Importing functional data
100.0% (Subject 4 Session 1)

Step 5/7: Importing ROI data
100.0% (Subject 4 Session 1)

Step 6/7: Checking ROI data consistency across subjects
100.0% (Subject 4)

Step 7/7: Updating Denosing variables

CONN: RUNNING DENOISING STEP

Step 1/7: Expanding conditions
100.0% (Condition rest)

Step 2/7: Importing conditions/covariates
100.0% (Subject 4 Session 1)

Step 3/7: Updating Denosing variables

Step 4/7: Denoising functional data
100.0% (Subject 4)

Step 5/7: Denoising ROI data
100.0% (Subject 4)

Step 6/7: preprocessing voxel-to-voxel covariance
100.0% (Subject 4 Condition 1)

Step 7/7: Updating Analysis variables

CONN: RUNNING ANALYSIS STEP

Step 1/5: Updating Analysis variables

Step 2/5: ROI-to-voxel first-level analyses

Step 3/5: ROI-to-ROI first-level analyses

Step 4/5: Voxel-to-voxel first-level analyses
100.0% (Subject 4 Condition 1)

computing subject x subject covariance
100.0% (Slice 91)

computing MVPA components
100.0% (Slice 91 Subject 4)

computing group-level dimensionality reduction. Please wait...
100.0%

computing group-PCA components
100.0% (Condition 1 Subject 4)

computing ICA decomposition. Please wait...
100.0%
computing subject-level components
100.0% (Condition 1 Subject 4)

computing group-level dimensionality reduction. Please wait...
100.0%

computing group-PCA components
100.0% (Condition 1 Subject 4)

computing subject-level components
100.0% (Condition 1 Subject 4)

Step 5/5: Preparing second-level ROI analyses

Load project into CONN GUI...
Done!