DOI: 10.5061/dryad.rbnzs7hhf
title: Data from: Rhythmicity of neuronal oscillations delineates their cortical and spectral architecture

description: Neuronal oscillations are commonly analyzed with power spectral methods
 that quantify signal amplitude, but not rhythmicity or
 'oscillatoriness' per se. Here we introduce a new approach, the
 phase-autocorrelation function (pACF), for direct quantification of
 rhythmicity. We applied pACF to human intracerebral
 stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) data
 and uncovered a spectrally and anatomically fine-grained cortical
 architecture in the rhythmicity of single- and multi-frequency neuronal
 oscillations. Evidencing the functional significance of rhythmicity, we
 found it to be a prerequisite for long-range synchronization in
 resting-state networks and to be dynamically modulated during
 event-related processing. We also extended the pACF approach to measure
 'burstiness' of oscillatory processes and characterized regions
 with stable and bursty oscillations. These findings show that rhythmicity
 is double-dissociable from amplitude and constitutes a functionally
 relevant and dynamic characteristic of neuronal oscillations.
description: We assembled a large database of human intracerebral
 stereoelectroencephalography (SEEG, N=61) and magnetoencephalography (MEG,
 N=52) recordings. We applied the Phase Autocorrelation Function (pACF) for
 such recordings to obtain the rhytmicity map of human cortex for both
 modalities. We also applied the time-resolved pACF to analyze the
 event-related visual Threshold-Stimulus Detection Task (TSDT) and build
 maps of responses for it. To compare other methods to the pACF we also
 computed Power Spectral Density (PSD) and Wavelet Amplitude Spectra for
 the resting-state recordings alongside with evoked and phase-reset
 responses for the TSDT data.
description: ## Usage notes The data consists of Python numpy and pickle files and is
 grouped in the next directories: 1. anatomy \* brain_anatomy.pickle - a
 file with brain surfaces required for visualization of brain maps 2. MEG\
 Common MEG derivatives that are used across multiple figures. In includes
 pACF and PSD brain maps for the MEG data, wPLI for the resting-state
 cohort, surrogate pACF level, parcel names mapping between yeo7 and yeo17
 parcellations. \* 7_to_17_400.csv \ A table with parcel names mapping
 between yeo7 and yeo17 parcellations \* meg_noise_level.npy \ pACF noise
 level for MEG data \* meg_pac_results_full.pickle \ pACF brain map for the
 resting-state MEG cohort \* meg_pac_with_statistics_wpli.pickle \ pACF
 with wPLI and PSD statistics for the MEG cohort 3. SEEG\ Common SEEG
 derivatives that are used across multiple figures. In includes pACF brain
 maps for the SEEG data, surrogate pACF level, number of contacts per
 parcel and the parcellation adjacency matrix. \* adjacency_matrix.npy -
 binary adjacency matrix for the SEEG parcellation \*
 cohort_seeg_pac_significant.npy - significant pACF values for the SEEG
 cohort \* cohort_seeg_pac_values.npy - pACF values for the SEEG cohort \*
 counter_known.npy - number of contacs per parcel \*
 noise_pacf_freqwise.npy - surrogate-level cohort pACF values for the SEEG
 cohort \* Schaefer2018_400Parcels_17Networks_order_LUT - LUT for the SEEG
 parcellation \* seeg_noise_level.npy - pACF noise level for SEEG data 4.
 figure_1\ Data files specific for reproduction of the first figure
 including pACF statistics for different kinds of simulated data. \*
 corr_data.pickle - autocorrelation function values for amplitude-normed
 and original narrow-band signal \* noise_exp_data.pickle - pACF properties
 for the noise with different power-law exponentials \*
 non_stat_data.pickle - pACF properties for the oscillaty signal with
 different stationarity properties \* pac_data.pickle - pACF properties for
 signals with varied rhythmicity and power 5. figure_2\ Data files specific
 for reproduction of the second figure including individual PSD, pACF
 curves and lifetimes. \* example_subject - pACF curve values for an
 example subject \* figure_psd_data - PSD values for an example subject \*
 target_subject - pACF lifetime values for an example subject \*
 noise_level - pACF surrogate level for an example subject \* noise_mean -
 mean surrogate pACF for an example subject 6. figure_3\ Data files
 specific for reproduction of the third figure including surrogate level
 pACF correlations between different frequencies, spectrum of significant
 PSD values. \* cohort_seeg_psd_significant - brain map of significant PSD
 values for the SEEG cohort \* meg_pac_data.pickle - MEG pACF brainmap \*
 surr_freq_corr_values.npy - distribution of surrogate frequency-frequency
 correlations 7. figure_4\ Data files specific for reproduction of the
 fourth figure including brain maps of peak frequencies. \*
 seeg_cortical_peaks_boot.npy - distribution of peak frequencies per parcel
 \* seeg_peaks_map.pickle 8. figure_5\ Data files specific for reproduction
 of the fifth figure including correlations between pACF lifetime, PSD and
 Amplitude Spectrum as a function of frequency for SEEG and MEG data. \*
 psd_correlations_eyes.pickle - correlation of PSD spectra with amplitude
 and pACF lifetime values for the MEG data \* seeg_data.pickle -
 correlation of PSD spectra with amplitude and pACF lifetime values for the
 SEEG data 9. figure_6\ Data files specific for reproduction of the sixth
 figure including Functional Connectivity (FC) data for the simulated data
 (PLV), SEEG (PLV) and MEG (wPLI), correlations between their pACF
 lifetimes and FC and surrogate data. \* kuramoto_pac_plv.pickle -
 single-simulation pACF and PLV values \* kuramoto_pac_plv_cohort.pickle -
 pACF and PLV values across multiple simulations \*
 kuramoto_plv_vs_pac.pickle - binned PLV as a function of pACF for the
 simulation data \* seeg_data.pickle - pACF and PLV correlation for the
 SEEG data \* surr_levels.pickle - surrogate levels for the correlation and
 predictivity 10. figure_7\ Data files specific for reproduction of the
 seventh figure including stability index brain maps for SEEG and MEG data,
 surrogate level and frequency communities for SEEG and MEG data
 separately. \* cohort_seeg_burst_known.npy - stability index for the SEEG
 cohort \* burst_coeff_example.pickle - simulated timeseries with
 burst-like and stable behaviour \* meg_burst_data.pickle - stability index
 for the MEG cohort \* meg_communities_new - frequency communities for the
 MEG cohort \* meg_data_nsamples.pickle - number of samples in MEG
 recordings \* meg_noise_level.npy - noise-level for the MEG data \*
 noise_pacf_by_ncycles.npy - surrogate pACF level as a function of number
 of cycles \* seeg_communities_new - frequency communities for the SEEG
 cohort \* seeg_data_nsamples.pickle - number of samples in SEEG cohort \*
 seeg_new_names_yeo17_400 - parcel names for the SEEG data \*
 yeo17_400_adjacency_matrix.npy - adjacency matrix for the Schaefer yeo17
 parcellation with 400 parcels 11. figure_8\ Data files specific for
 reproduction of the eighth figure including TSDT TFR pACF, amplitude and
 phase-reset responses and their surrogate levels. \* tsdt_data.pickle -
 TSDT brain maps for different kinds of responses \*
 tsdt_surr_level_parcelwise.npy - surrogate level for the TSDT pACF
 response \* tsdt_surr_level_parcelwise_amp.npy - surrogate level for the
 TSDT amplitude response \* tsdt_surr_level_parcelwise_evoked.npy -
 surrogate level for the TSDT phase-reset response ###
 Code/Software All the code is written in python + several CUDA
 functionals and either Jupyter notebooks (to reproduce the paper figures)
 or Python scripts (to process raw data files). The code is open-source and
 can be found on github
 [https://github.com/palvalab/discovering\\_rhythmicity](https://github.com/palvalab/discovering\\_rhythmicity)