## **ReadMe** ## In this repository, you will find the data, scripts and materials necessary to reproduce the work reported in: Quiroga-Martinez, D. R., Hansen, N. C., Højlund, A., Pearce, M., Brattico, E., & Vuust, P. (2020). Decomposing neural responses to melodic surprise in musicians and non-musicians: Evidence for a hierarchy of predictions in the auditory system. NeuroImage, 116816. https://doi.org/10.1016/j.neuroimage.2020.116816 ## **Contents description** ## The scripts, inputs and outputs are all in the MEG folder. In the following, we describe the scripts, folders and files grouped by the type of analysis that they addressed. ### **Data** ### **Demographic information** - Participants' information as well as musical expertise scores can be found in the file "**participants_info_anonymized.csv**". **MEG data** - The data for the main analyses in the paper can be found in the folder "**timelocked_data/data/**". These correspond to timelock averages per condition for each subject, stored in a matlab structure. Timelocked data are given for different IDyOM configurations, as indicated in the name of the file (e.g. "01_timelock_**both_plus_scaled_interval**_IC_10cat"). In addition, the suffixes "IC_MMN" and "int_control" are used for the timelock files used in the MMN/N1 comparison and interval-size control analyses. - Raw data files and outputs of the epoching scripts can be requested by personal communication. **Mean amplitude data** - Data for the mean amplitude analyses can be found in the folder "**MA**". A similar naming pattern as for MEG data is used. ### **Preprocessing** ### **Maxfilter_same_init.py** - Script to submit maxfiltering jobs to Elekta's software. - Input: raw ".fif" file - Output: Maxfiltered and motion corrected ".fif" file. **analysisPipelineFunctions_eog-ecg.py** - Python functions to perform ICA. - Input: Maxfiltered and motion corrected ".fif" file. - Output: ICA decompositions and clean raw files **analysisPipeline_clusterize_offline.py** - Wrapper to loop and submit ICA jobs - Input: Maxfiltered and motion corrected ".fif" file. - Output: ICA decompositions and clean raw files **visRejV5.py** - Python script to visually inspect ICA outputs and reject additional components, if necesary. - Input: Maxfiltered and motion corrected ".fif" file. - Output: Clean raw files **epoching_IC.m** - Matlab script to perform epoching. This script requires a wrapper to be called. - Input: Clean ".fif" files and IDyOM estimates (folder "**IDyOM_estimates**". - Output: epoched data per subject and trigger checks. Note that the output of this step is not provided in the repository, but can be requested by personal communication. ### **IDyOM analyses** ### **melody_models/IDyOM_models.lisp** - LISP script with the commands to perform IDyOM analyses for our stimuli. - Input: melody and training corpus stored in IDyOM's library. - Output: Model estimates stored in "**melody_models/**" **stimuli_and_corpus/** - folder where our stimuli (in midi format) and the training corpus can be found. **add_IC.R** - R script to map the output of the IDyOM models with the log files of the MEG stimulation. - Input: MEG log files (in "**Logs/**") and IDyOM's output in "**melody_models/**". - Output: ".csv" files in "**IDyOM_estimates/**" **calculate_quants.R** - R script to calculate 10 IC quantiles for each IDyOM configuration compared. - Input: IDyOM's output in "**melody_models/**". - Output: "**IDyOM_estimates/quantiles.csv**" ### **Main MEG IC analyses** ### **timelocking_IC.m** - Matlab script to average epochs per condition and participant. - Input: Epoched data and "**IDyOM_estimates/quantiles.csv**" file. - Output: Timelock files and figures in "**timelocked_data/**" **grand_avg_stats_and_plots_IC.m** - Matlab script to do cluster-based statistics, grand averages and some plotting. - Input: Timelock files in "**timelocked_data/data/**". - Output: Grand averages, figures and statistics in their respective subfolders in "**results/**". **grand_avg_stats_and_plots_IC_wrapper.m** - Matlab script to iteratively call **"grand_avg_stats_and_plots_IC.m"** for different IDyOM configurations. **Extract_MA_IC_peak.m** - Matlab function to extract mean amplitudes from timelock averages per participant. - Input: Timelock files in "**timelocked_data/data/**" and subjects information in "**participants_info_anonymized.csv**". - Output: Mean amplitude data stored in "**MA/**". **MA_stats_IC_peaks.R** - R script for the Bayesian analyses of mean amplitudes. - Input: Mean amplitude data stored in "**MA/**". - Output: Results stored in "**results/stats/**". **MA_compare_models.R** - R script for the comparison of different IDyOM configurations with regard to MA amplitudes. - Input: Mean amplitude data stored in "**MA/**". - Output: Comparison results in "**results/stats/**". ### **Interval size control analyses** ### **timelocking_interval_control.m** - Matlab script to average epochs corresponding to orthogonal manipulations of interval size and information content. - Input: Epoched data and "**IDyOM_estimates/quantiles.csv**" file. - Output: Timelock files and figures in "**timelocked_data/**" **grand_avg_stats_and_plots_int_control.m** - Matlab script to do cluster-based statistics, grand averages and some plotting for the interval size control analyses. - Input: Timelock files in "**timelocked_data/data/**". - Output: Grand averages, figures and statistics in their respective subfolders in "**results/**". **grand_avg_stats_and_plots_int_control_wrapper.m** - Matlab script to iteratively call **"grand_avg_stats_and_plots_int_control.m"** for different IDyOM configurations. ### **Comparison between MMNm and N1m** ### **timelocking_MMN_vs_IC.m** - Matlab script to average epochs per condition of interest for the MMNm vs N1m comparison. - Input: Epoched data and "**IDyOM_estimates/quantiles.csv**" file. - Output: Timelock files and figures in "**timelocked_data/**" **grand_avg_stats_and_plots_IC_vs_MMN.m** - Matlab function to do cluster-based statistics, grand averages and some plotting. - Input: Timelock files in "**timelocked_data/data/**". - Output: Grand averages, figures and statistics in their respective subfolders in "**results/**". **grand_avg_stats_and_plots_IC_vs_MMN_wrapper.m** - Matlab script to iteratively call **"grand_avg_stats_and_plots_IC_vs_MMN.m"** for different IDyOM configurations. **Extract_MA_IC_vs_MMN_peak.m** - Matlab function to extract mean amplitudes from timelock averages per participant. - Input: Timelock files in "**timelocked_data/data/**" and subjects information in "**participants_info_anonymized.csv**". - Output: Mean amplitude data stored in "**MA/**". **MA_stats_IC_vs_MMN_peaks.R** - R script for comparison of mean amplitudes and peak latencies of MMNm vs N1m. - Input: Mean amplitude data stored in "**MA/**". - Output: Results stored in "**results/stats/**". ### **Source localization** ### **convert_to_spm.m** - Matlab script to convert fieldtrip timelock files to SPM format. - Input: Timelock data in "**timelocked_data/data/**" and raw MEG files (to extract sensor coordinates and stuff). - Output: SPM timelock files (available upon request). **head_model_and_inversion.m** - Matlab/SPM batch script to perform source localization. - Input: SPM timelock files plus individual anatomical MRIs (not shared due to data protection restrictions). - Output: Inverse solutions stored in the corresponding SPM files and average volumes in a given time window shared in the respective subfolder of "source_localization/". **one_samples_t_source.m** - Matlab/SPM batch script to perform 1 samples t-test for the extracted volumes per conditon. - Input: Average volumes in the respective subfolder of **"source_localization/"**. - Output: SPM statistics file and statistical maps in **"source_reconstruction/XXX/stats/"**. **N1_MMN_source_stats.m** - Matlab/SPM batch script to perform paired samples t-test for the comparison between N1 and MMN. - Input: Average volumes in the respective subfolderS of **"source_localization/"**. - Output: SPM statistics file and statistical maps in **"source_reconstruction/stats/"**. ### **Scripts for plotting** ### - **plot_channsel_IC_vs_MMN.m**: N1/MMN comparison - **plot_r2_values.m**: Comparison of IDyOM configurations - **plot_topo_q10_q1.m**: Topomaps of different effects - **render_source_results.m**: Result of source localization ### **Other files and scripts** ### - **SuppFile1 - instruments_info.pdf**: supplementary file 1 - **SuppFile2 - pars_plot.jpeg**: supplementary file 2 - **SuppFile3 - acoustic_analysis.pdf**: supplementary file 2 - **pool.csv**: tone codes of the melodic sequences used in the experiment. This file is use for melody recognition in **"add_IC.R"**. - **cortex_20484.surf.gii**: high resolution template mesh for rendering source localization results. Taken from SPM. - **cortex_8196.surf.gii**: lower resolution template mesh for rendering source localization results. Taken from SPM. - **acoustic_analysis/calculate_similarities.R**: R script for the acoustic analyses reported in supplementary file 3. It takes the tone pool stored in **/acoustic_analysis/stimuli/** and makes the plots in **/acoustic_analysis/**. ### **List of folders** ### - **acoustic_analysis/**: stores files related to supplementary file 3. - **IDyOM_estimates/**: stores IDyOM's output mapped onto individual participants' MEG log files. - **Logs/**: MEG log files - **MA/**: Stores extracted mean amplitudes. - **melody_models/**: stores IDyOM's output. - **results/**: stores stats, figures, and data resulting from the analyses, in their respective subfolder. - **source_localization/**: stores volumes and statistical outputs for source localization analyses. - **stimuli_and_corpus/**: stores our stimuli (in midi format) and the training corpus used for IDyOM analyses. - **timelocked_data/**: store timelock averages per participant and condition, as well as some figures.