DOI: 10.5061/dryad.7m0cfxq2g
title: Data from: Neural dynamics of predictive timing and motor engagement in music listening

description: 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: <strong>Participants. </strong>66, 30 and 15
 participants (age range: 19-71 years; 77 % females) were recruited for the
 online, Magnetoencephalography (MEG) and control tapping experiments. The
 online experiment was accessible for two weeks, with no stopping rule. The
 MEG experiment followed guidelines of our MEG center (Epileptology and
 Cerebral Rhythmology Unit from the La Timone hospital, APHM, Marseille
 (France)), balancing data collection costs with statistical power. All
 experiments followed the local ethics guidelines from Aix-Marseille
 University. Informed consent was obtained from all participants before the
 experiments. All had normal audition and vision and reported no history of
 neurological or psychiatric disorders. We did not select participants
 based on musical or dance training and a short survey made at the end of
 the experiment informed us that none of them were professional musicians.
 Participants were financially compensated for their time during the MEG
 experiment. The behavioural and neurophysiological data
 were acquired and processed as described in the manuscript.
description: # Neural dynamics of predictive timing and motor engagement in music
 listening 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) : \ ``` ├─
 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. │ │ └─ │ ├─ onlinexp :
 Contains behavioral responses of the participants included in the online
 sessions. │ │ ├─ │ │ └─ │ └─ 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 :
 Comments and requests should be
 addressed to Arnaud Zalta or Benjamin Morillon:
 ; . 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!