Select data and code for «Breaking (Musical) Boundaries By Investigating Brain Dynamics Of Event Segmentation During Real-Life Music-Listening».
Creators: name: Burunat, Iballa
Publisher: name: Zenodo
Year: 2024

description: This repository contains select data and code for the study Breaking (Musical) Boundaries By Investigating Brain Dynamics Of Event Segmentation During Real-Life Music-Listening.

Contents: 

DATA:



KDEind.mat: individual participants' KDEs (both musicians and nonmusicians). The provided data comprises a two structures named 'mus' and 'nonmus' which contains for each stimulus a field named 'participant' (a 1x18 cell array). This cell array contains the KDE (Kernel Density Estimation) data for 18 participants associated with the respective stimulus:

KDE_groups.mat: group KDEs (musicians and nonmusicians). The provided file 'KDE_groups.mat' contains three structures named 'dreamtheater,' 'piazzolla,' and 'stravinsky.' Each structure contains 'mus' and 'nonmus', which are the group KDE of musicians or nonmusicians for the specific stimulus.

bregressor.mat: HRF-convolved group KDEs. The provided file 'bregressor.mat' contains the structure 'boundary', which contains one field per participatn group. This field is a 706x1 double array and contains the HRF-convolved boundary KDE of musicians or nonmusicians for the concatenated stimulus (706 scans).

rms2.mat: root mean square (RMS) time series of the concatenated musical stimulus.

GC.mat: IC temporal courses for all participants associated with the Early Auditory Network and Boundary Transition Network (names IC41 and IC3, respectively). These data are needed to perform causality analyses.

boundarymask.mat: the ROI mask used for the GLM and ICA analyses.


CODE:



bRegressor.m: loads behavioral data related to fMRI stimuli segmentation, preprocesses Perceptual Time Series, computes the correlation between musical and non-musical data, concatenates and Z-scores the concatenated data for further analysis.

GLM_residual.m: computes residuals after regressing out the RMS-related variance, performs GLM, calculates effective Degrees of Freedom (DoF), conducts Group-Level Analysis using Fischer Pooling, and saves pooled Z-scores.

nulldistSpearman.m: performs nonparametric significance estimation for the Spearman correlation maps via phase-randomizing the boundary regressor.

DualPCA.m: performs both individual and group PCA REDUCTION, prior to ICA analyses.

ICA_icasso.m: performs ICA analyses based on icasso algorithm.

GICA3_temporal.m: performs GICA3 temporal reconstruction of subject-level IC temporal courses.

GICA3_spatial.m: performs GICA3 spatial reconstruction of subject-level spatial maps.

signrank1sampleCS.m: performs nonparametric cluster size estimation for the 1-sample signrank test of the ICA spatial maps.

signrank2sampleCS.m: performs nonparametric cluster size estimation for the 2-sample signrank test of the ICA spatial maps.

hClustering.m: performs agglomerative hierarchical clustering on vectorized averaged data

GC.m: performs GC using the MVGC template script.

GC_DoITerm.m: generates null distribution by permuting the time series data and re-calculating the difference of influence term.

fmri_doublegamma.m: computes the double-gamma HRF.

fmri_cleanclusters.m: removes small clusters from a thresholded map based on specified criteria for minimum cluster size and minimum number of nearest neighbors

fmri_detrend.m: performs detrending using spline interpolation.


Third-party toolboxes needed:



Icasso Toolbox: https://research.ics.aalto.fi/ica/icasso/

SPM12: https://www.fil.ion.ucl.ac.uk/spm/software/spm12/

The MVGC Matlab® Toolbox: https://users.sussex.ac.uk/~lionelb/MVGC/html/mvgchelp.html