Journal of NeuroscienceNeurosciencePeer-reviewed journal article
Human sensorimotor beta event characteristics and aperiodic signal are highly heritable
K. Amande M. PaulsiD, Elina SalmelaiD, Olesia KorsuniD, Jan KujalaiD, Riitta H. SalmeliniD, Hanna RenvalliD
Abstract
Individuals phenotypes, including the brains structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns which can be characterized non-invasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor ( rolandic) beta rhythm, shows intermittent high amplitude events which predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic ( 1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm and aperiodic activity in 210 healthy adult siblings spontaneous MEG activity. Both the overall beta spectral power as well as time-resolved beta event amplitude parameters were highly heritable, whereas the heritabilities for peak frequency and measures of event duration remained nonsignificant. Interestingly, the most heritable trait was the aperiodic 1/f signal, with a heritability of 0.94 in the right hemisphere. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that differences in heritability in part reflect different underlying signal generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.
- organism: human
- assay: electronic health records
- research focus: genome assembly
- approach: statistical model
- Citations
- 9
- FWCI
- 1.34
- Field percentile
- top 21%
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