Abstracts

Heritability of the Resting-State MEG Brain Network Topology

Abstract number : 2.029
Submission category : 3. Neurophysiology / 3D. MEG
Year : 2021
Submission ID : 1826624
Source : www.aesnet.org
Presentation date : 12/5/2021 12:00:00 PM
Published date : Nov 22, 2021, 06:55 AM

Authors :
Haatef Pourmotabbed, MS - The University of Texas at Austin; Dave Clarke - The University of Texas at Austin; Amy de Jongh Curry - University of Memphis; Elizabeth Tyler-Kabara - The University of Texas at Austin; Abbas Babajani-Feremi - The University of Texas at Austin

Rationale: Graph measures based on functional connectivity (FC) analysis of resting-state magnetoencephalography (rs-MEG) can characterize the abnormal brain network topology of epilepsy patients (Pourmotabbed et al., 2020). To be clinically relevant as biomarkers, these measures should ideally depend on naturally occurring genetic variations in the patient population. Previous studies have examined heritability of rs-MEG FC in the source space (Colclough et al., 2017) and rs-MEG global graph measures in the sensor space (Babajani-Feremi et al., 2018). Heritability of rs-MEG global graph measures in the source space has not been examined. The purpose of this study was to investigate heritability of rs-MEG global graph measures in the source space for FC metrics of amplitude and phase synchrony.

Methods: This study included the preprocessed rs-MEG data of the 89 healthy subjects (28.6 ± 3.9 years, 41 females) of the HCP1200 data release (Larson-Prior et al., 2013). These subjects consist of 19 monozygotic twin pairs, 13 dizygotic twin pairs, and 25 non-twin individuals. Beamforming was used to derive sources for the brain regions of the Brainnetome atlas. FC between the sources in five frequency bands was estimated with three metrics (debiased weighted phase lag index [dwPLI], amplitude envelope correlation [AEC], leakage-corrected AEC [lcAEC]). The AEC is sensitive to source leakage while the lcAEC and dwPLI are insensitive. Four graph measures (global efficiency [GE], characteristic path length [CPL], transitivity [T], synchronizability [S]) were calculated for the functional networks. The SOLAR-Eclipse toolbox was used to evaluate the heritability (h2) of each graph measure after applying an inverse Gaussian transformation to ensure normality and adjusting for age, age2, sex, age × sex, and age2 × sex covariates. p-values of the h2 estimates were false discovery rate (FDR)-adjusted for four graph measures, three FC metrics, and five frequency bands.

Results: Heritability of the graph measures is shown in Table 1. For the dwPLI, all the graph measures in the alpha band and the GE, CPL, and T in the theta and low beta bands were significantly heritable (p < 0.05). For the AEC and lcAEC, all the graph measures were significantly heritable except for the S of the lcAEC in the delta band. The greatest heritability was obtained using the dwPLI in the alpha band. Heritability was greater for the AEC and lcAEC than for the dwPLI in the delta, theta, and high beta bands while heritability for the AEC and lcAEC was largely similar in all the frequency bands. For all the FC metrics, the S was less heritable than the other graph measures in the theta, alpha, and low beta bands.
Neurophysiology