https://doi.org/10.5061/dryad.7m0cfxq2g

This folder contains the data files need to reproduce the figures of the article:

Zalta A., Large E., Schon D. and Morillon, B. Neural dynamics of predictive timing and motor engagement in music listening.

Why do humans spontaneously dance to music? To test the hypothesis that motor dynamics reflect predictive timing during music listening, we created melodies with varying degrees of rhythmic predictability (syncopation) and asked participants to rate their wanting-to-move (groove) experience. Degree of syncopation and groove ratings are quadratically correlated. Magnetoencephalography data showed that while auditory regions track the rhythm of melodies, beat-related 2 Hz activity and neural dynamics at delta (1.4 Hz) and beta (20-30 Hz) rates in the dorsal auditory pathway code for the experience of groove. Critically, the left sensorimotor cortex coordinates these groove-related delta and beta activity. These findings align with the predictions of a neurodynamic model, suggesting that oscillatory motor engagement during music listening reflects predictive timing and is effected by interaction of neural dynamics along the dorsal auditory pathway.

Description of the Data folder structure

The Data.zip contains the data files needed to generate the figures in the article.

Data are classified according to the different analyses (behaviour, MEG analyses and modelling) :

<Data>

     ├─ bad_bn_process.mat	: number of irrelevant subjects and trials to remove from the analyses. This matlab matrix is needed in complement to the other files in this folder. 

     ├─ Chan_data_7regr.mat	: Matlab matrix containing the regressors used in the multivariate patter analysis applied on Ps MEG data.

     ├─ Decoding_Scouts_Ps_az_Frites_Atlas_Contrast.mat	: results of the multivariate patter analysis applied on the scouts sources.

     ├─ GoodChannel_ImagingKernel.mat	

     ├─ Pac_bn_1.4Hz_3_computes_brut.mat	: results of the Phase amplitude coupling applied on the sources of Ps.

     ├─ Ps_az_decoding_goodchan_goodtrial_GrooveS_nfold10_alpha2.mat	Matlab matrix containing the results of the multivariate patter analysis applied on Ps MEG data.

     ├─ Ps_brut_spatial_profile_scout_nbin8_p3_zscored_0.mat	Matlab matrix containing the power spectrum results of each scouts MEG data.

     ├─ scout_Frites_Atlas_Contrast.mat	

     ├─ behavior

     │      ├─ MEG          : Contains behavioral responses of the participants included in the MEG session. These responses were recorded into a .log file. 4 blocs, one training session and a resting bloc were recorded.

     │      │      └─ <logfiles>

     │      ├─ onlinexp : Contains behavioral responses of the participants included in the online sessions.

     │      │      ├─ <data_en>

     │      │      └─ <data_fr>

     │      └─ tapping_exp  : Contains the data recorded for the tapping control experiment (one file per participant  : ***number_of_subject_exp_control_***.mat)

     ├─ MEG

     │      └─ Ps

     │             └─ Ps_az_decoding_goodchan_goodtrial_GrooveS_nfold10_alpha2.mat  (matrix data file of the decoding of regressors on Power spectrum (Ps) computed on the magnetoencephalographic (MEG) data).

     ├─ model

     │      ├─ model_3l_v2_n29_fs60_dup0_tmin2_230106_final.mat	: results of the GrFNN model applied on acoustic data.

     │      └─ Mvpa_chanPs_Edall.mat	 : Multivariate patter analysis results using model output as regressors.

     ├─ Ps_rest

     │      ├─ Ps_rest_gradient_subj_3cond.mat	

     │      └─ timefreq_fft_211015_1606.mat	: Time frequency matrix applied to resting state subject data.

     ├─ Ps_task

     │      ├─ Ps_task_gradient_subj_3cond.mat	

     │      └─ timefreq_fft_211015_1605.mat	: Time frequency matrix applied to task subject data.

     └─ stimuli

            ├─ accenv_Ed.mat	 : Accoustic enveloppe of the acoustic stimuli used in the experiment.

            ├─ Index de syncopation.xlsx : Syncope index of the acoustic stimuli used in the experiment.

            └─ Mvpa_chanPs_accEd.mat	: Multivariate patter analysis applied on Ps MEG data using acoustic enveloppe as regressors.

All the analyses were computed using Matlab 2018b. The Magnetoencephalography results (Ps, PAC…) are recorded in a matrix structure with number_of_subject[30]number_of_trials[144]other_dimension.

To note, model analyses require the GrFNN Toolbox [for more information, please see on musicdynamicslab.uconn.edu/home/multimedia/grfnn-toolbox/]. To run the scripts used in the experiment, please download the GrFNN Toolbox into the tools folder.

Moreover, to run the scripts used in the experiment, please download Data.zip into a “Groove_final” folder.

Sharing/Access information

The scripts and experimental materials (music files) used in the experiment could be found here :

https://github.com/DCP-INS/Groove/

Comments and requests should be addressed to Arnaud Zalta or Benjamin Morillon: arnaud.zalta@hotmail.fr; bnmorillon@gmail.com. All material is free of use, but we would appreciate being told, and this dataset and the matching paper cited if appropriate.

Files are shared to promote discussions, exchanges, collaborations. Don’t hesitate to contact us!