Distinct physiological mechanisms drive grey matter plasticity in complex vs. simple sequence learning

This repository contains all preprocessing and analysis scripts (MATLAB batch scripts, python scripts and terminal command runs) executed in our experimental design to examine GMV changes during motor sequence learning stages and the physiological alterations that drive them. We have used MATLAB (specifically SPM and CAT12 toolboxes)and Python for our entire analyses.
For transparency and reproducibility of the code, the order outlined below is intended to facilitate navigation and support reproduction of the analysis.

Repository Framework:

1. Longitudinal Segmentation of all subject data : This file contains MATLAB script for the first step into preprocessing of raw data. We used the longitudinal segmentation pipeline from CAT12 Toolbox to get normalized, modulated GM segmented maps.
2. Smoothening with 4mm kernel : We then smoothed all the segmented GM maps (after quality check) for further processing to improve SNR.
3. Flexible factorial analysis for group by time interaction design : This MATLAB script was used to create the flexible factorial analysis model based on the average binary mask, custom-made with respect to our dataset.
4. Model Estimation of our factorial design : This MATLAB script was to estimate our model to set contrasts for group by time interaction analysis.
5. Threshold-Free Cluster Enhancement (TFCE) estimate : This MATLAB script loads the SPM.mat file from the factorial design and runs TFCE based on the provided number of iterations.
6. Region of Interest Extraction : This script contains all the commands executed on terminal window using softwares like FSL and AFNI.
7. Behavioural and ROI Correlational analyses : This python script was used to extract ROI and perform all behavioural correlational analyses.
8. Environtment packages list : This file contains the python environment specifics like package dependencies that we used during execution of our python script. To reproduce our analysis, please import this package list.

Data availabilty and ethics:

The repository contains no personally identifiable information. The shared (files and average binary mask) data have been anonymized and distributed following institutional ethical approvals and data-sharing agreements. According to the Ethics Committee of University of Leipzig, all experiment procedures were in accordance with the general ethical standards and raw neuroimaging data could not be shared.

Contact:

For questions regarding the data, analyses or reproducabilty of this framework, please contact: Jhelum Paul