DOI: 10.5061/dryad.3n5tb2rjp
title: Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations

description: Neuronal oscillations, their inter-areal synchronization, and scale-free
 dynamics constitute fundamental mechanisms for cognition by regulating
 communication in neuronal networks. These oscillatory dynamics have large
 inter-individual variability that is partly heritable. We hypothesized
 that this variability could be partially explained by genetic polymorphism
 in neuromodulatory genes. We recorded resting-state magnetoencephalography
 (MEG) from 82 healthy participants and investigated whether oscillation
 dynamics were influenced by genetic polymorphisms in
 Catechol-O-methyltransferase (COMT) Val158Met and brain-derived
 neurotrophic factor (BDNF) Val66Met. Both COMT and BDNF polymorphisms
 influenced local oscillation amplitudes and their long-range temporal
 correlations (LRTCs), while only BDNF polymorphism affected the strength
 of large-scale synchronization. Our findings demonstrate that COMT and
 BDNF genetic polymorphisms contribute to inter-individual variability in
 neuronal oscillation dynamics. Comparison of these results to
 computational modeling of near-critical synchronization dynamics further
 suggested that COMT and BDNF polymorphisms influenced local oscillations
 by modulating the excitation-inhibition balance according to the brain
 criticality framework.
description: Resting-state brain activity was recorded from healthy volunteers
 (<em>N</em>=82, 18–55 years of age; mean age: 29 years; 6
 left-handed; 44 female) with 306-channel MEG (Vectorview, Elekta-Neuromag,
 LtD) at a sampling rate of 600 Hz. The subjects fixated on a central
 fixation cross throughout the ~8 min resting-state MEG-recording (duration
 7.8 ± 2.9 min, mean ± standard deviation). MEG data was preprocessed using
 temporal signal-space separation with Maxfilter software and artifact
 removal was carried out using independent component analysis. MNE inverse
 operators were then computed for all wavelet frequencies and used to
 project the sensor-space data into source-space. Source-vertex time series
 were collapsed into cortical parcel time series with individual collapse
 operators that maximize source-reconstruction accuracy. A cortical
 parcellation in individual anatomy but with labels shared among the
 subject was obtained by iteratively splitting the 148-parcel Destrieux
 atlas into 400 parcels. The parcels were also assigned functional labels
 based on Yeo’s 7-functional-brain-systems atlas. 26 Morlet wavelets
 (log-spaced center frequencies 3-60 Hz) were used for obtaining the
 amplitude and phase time series of cortical parcels. LRTCs in amplitude
 time series were quantified with detrended fluctuation analysis (DFA)
 where the power-law-scaling exponent <em>β</em> is the slope
 of the DFA function and obtained with linear regression. Phase
 synchronization between parcels was computed using the weighted phase-lag
 index (wPLI). Amplitude, DFA, and phase synchonization values were then
 collapsed back to the 148-parcel Destrieux atlas, and phase synchonization
 also to the 7 systems (per hemisphere) of the Yeo atlas.
description: This data includes mean amplitudes, connectivity matrices, and
 detrended fluctuation analysis scaling exponents derived from 82
 subjects' magnetoencephalographic resting-state recordings. The
 results of polymorphism analysis can be found, along with parcellation
 information, in the "settings" subfolder. This data can be used
 for replicating the main results from "Genetic polymorphisms in COMT
 and BDNF influence synchronization dynamics of human neuronal
 oscillations" with the python code in the associated code
 repository https://github.com/palvalab/RS-Gen.