DOI: 10.5281/zenodo.10458884
title: Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills - UPHUMMEL - EPFL

description: Dataset and code to reproduce the main results of the paper "Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills" 

see Vassiliadis et al., Nature Human Behaviour (2024)

 

Different Readme are provided to facilitate the analyses in the corresponding subfolders.

 

1) Behavioural data:

Data

Raw data (in the "Data/Behav/Raw") for each subject are provided and the corresponding Matlab code (in the "Code" folder) to reproduce the main results

The summary table which can be used to run statistics is also provided ("Data/Behav/myTab1.mat") or can be generated with the code.

Each subject folder contains the different files which correspond to one block of the task:

rml_FTT_MRI_B0_XXX: quick re-familiarization in MRI

rml_FTT_MRI_B*_XXX: *=1 to 6 : 6 main experimental blocks --> The ones analysed by the code

rml_FTT_MRI_fam: different familiarisation blocks (1: regular sequence, 2: irregular sequence, 0: short familiarisation with the visual uncertainty and with or without reinforcement)

 

Code

"analysis_block_FTT_MRI_Zenodo.m" 

--> opens every raw subject file (one per block) during the experiment (6 blocks)--> computes the Error for each trial --> the main figures--> creates Tab1, a table in which each trial is stored with the corresponding features and Error value --> generates frame-by-frame matrices for possible analyses of kinematics, error profiles, color changes etc.

More specific explanations are in the comments of the Matlab code.

 

2) MRI data

!!! Structural images were defaced in order to anonymize the dataset, but analysis were originally performed on raw images.

 

## Structural data analysis:

- Run ./Code/1_struct/3_freesurfer.py --subject_idx 83Y02 83Y04 83Y05 83Y06 83Y07 83Y08 83Y09 83Y10 83Y11 83Y12 83Y13 83Y14 83Y15 83Y16 83Y17 83Y18 83Y19 83Y20 83Y21 83Y22 83Y23 83Y24 83Y26 83Y27 --session reward --data_folder ./Data/MRI --project_locations source_data --T2_fs 1

 

- python ./Code/1_struct/4_creat_BNA_atlas_masks.py --subject_idx 83Y02 83Y04 83Y05 83Y06 83Y07 83Y08 83Y09 83Y10 83Y11 83Y12 83Y13 83Y14 83Y15 83Y16 83Y17 83Y18 83Y19 83Y20 83Y21 83Y22 83Y23 83Y24 83Y26 83Y27 --session reward

 

It uses files in ./Data/MRI/derivatives/1_freesurfer/

 

 

## Functional data analysis:

Whole brain contrasts

- Run ./Code/2_funct_MRI/SPM_PlasMA83_1_common_preproc_script.m

 

It computes from preprocessing to group level whole branalysis

- ./Code/2_funct_MRI/spm_functions/SPMCommonPreprocess4Sess.m

- ./Code/2_funct_MRI/tools/extractTimingVar.m

- ./Code/2_funct_MRI/spm_functions/SPMFirstLevelAnalysis.m

- ./Code/2_funct_MRI/spm_functions/SPMSecondLevelAnalysis.m

 

Behavioral regressors

- Run ./Code/2_funct_MRI/SPM_PlasMA83_3_common_preproc_behav_reg_script.m

 

It perform whole brain behavioral correlations for:

- main behavioral outputs: ./Code/2_funct_MRI/spm_functions/SPMSecondLevel_task_behav_reg.m

- MCQ results: ./Code/2_funct_MRI/spm_functions/SPMSecondLevel_task_MCQ_reg.m

 

Individual masks BOLD extraction

- Run python ./Code/1_struct/5_extract_individual_freesurfer_ROI.py --subject_idx 83Yxx --session reward --data_folder ./Data/MRI --project_locations source_data --parc BNA --ROIs xxx --ROIs_name xxx

 

- Run ./Code/2_funct_MRI/SPM_PlasMA83_3_common_preproc_individual_masks_script.m

 

It extract contrast values and average them in predefined masks

- coregistration of individual masks ./Code/2_funct_MRI/individual_masks/coregister_individual_masks_to_MNI.m

- isolate betas within mask ./Code/2_funct_MRI/individual_masks/extract_betas_from_individual_mask.m

- extract average beta values within the mask ./Code/2_funct_MRI/individual_masks/extract_avg_signal_stim_comp.m

 

Connectivity

- Run ./Code/2_funct_MRI/SPM_PlasMA83_4_conn_batch_script.m

 

It computes gPPI connectivity

- set up conn structure ./Code/2_funct_MRI/conn_functions/create_batch_task.m

- manually run first-level analysis (see also matrix ./Data/MRI/derivatives/2_fMRI/04_task_conn/task_83_roi_to_roi.m)

- Extract and plot functional connectivity ./Code/2_funct_MRI/conn_functions/ROI2ROI_connectivity.m

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Contact: pierre.vassiliadis@epfl.ch