Data from: Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations

  • Felix Siebenhühner (University of Helsinki) (Creator)
  • Jaana Simola (Creator)
  • Vladislav Myrov (Contributor)
  • Katri Kantojärvi (University of Helsinki) (Creator)
  • Tiina Paunio (University of Helsinki) (Creator)
  • Matias Palva (Creator)
  • Elvira Brattico (University of Helsinki) (Creator)
  • Satu Palva (University of Helsinki) (Creator)

Dataset

Description

Neuronal oscillations, their inter-areal synchronization and scale-free dynamics constitute fundamental mechanisms for cognition by regulating neuronal communication. These oscillatory dynamics have large inter-individual variability that is partly heritable. However, the genetic underpinnings of oscillatory dynamics have remained poorly understood. We investigated whether local and global oscillation dynamics were influenced by polymorphisms in Catechol-O-methyltransferase (COMT) Val158Met and brain-derived neurotrophic factor (BDNF) Val66Met genes that regulate brain catecholaminergic and serotonergic levels. Resting-state magnetoencephalography (MEG) was recorded from 82 participants and local oscillation amplitudes, their scale-free long-range temporal correlations (LRTCs), and global synchronization were estimated from source-reconstructed MEG data. Both COMT and BDNF polymorphisms influenced local oscillation dynamics, while only BDNF polymorphism influenced global synchronization. Computational modelling of near-critical synchronization dynamics suggested that COMT and BDNF polymorphisms influenced local oscillations via variances in brain net excitation levels. We demonstrate that genetic polymorphisms COMT and BDNF contribute to inter-individual variability in local and global oscillation dynamics.,Resting-state brain activity was recorded from healthy volunteers (N=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 β 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.,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.
Date made available10 Nov 2021
PublisherDryad Digital Repository

Dataset Licences

  • CC0-1.0

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