RT Journal Article
SR Electronic
T1 Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.03.490402
DO 10.1101/2022.05.03.490402
A1 Yun Zhao
A1 Mario Boley
A1 Andria Pelentritou
A1 Philippa J. Karoly
A1 Dean R. Freestone
A1 Yueyang Liu
A1 Suresh Muthukumaraswamy
A1 William Woods
A1 David Liley
A1 Levin Kuhlmann
YR 2022
UL http://biorxiv.org/content/early/2022/05/04/2022.05.03.490402.abstract
AB Neural mechanisms are complex and difficult to image. This paper presents a new space-time resolved whole-brain imaging framework, called Neurophysiological Mechanism Imaging (NMI), that identifies neurophysiological mechanisms within cerebral cortex at the macroscopic scale. By fitting neural mass models to electromagnetic source imaging data using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately imaged across the whole brain at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural mechanisms in different brain states.HighlightsThe whole-brain imaging framework can disclose the neurophysiological substrates of complicated brain functions in a spatiotemporal manner.Developed a semi-analytical Kalman filter to estimate neurophysiological variables in the nonlinear neural mass model efficiently and accurately from large-scale electromagnetic time-series.The semi-analytical Kalman filter is 7.5 times faster and 5% more accurate in estimating model parameters than the unscented Kalman filter.Provided several group-level statistical observations based on neurophysiological variables and visualised them in a whole-brain manner to show different perspectives of neurophysiological mechanisms.Applied the framework to study resting-state alpha oscillation and found novel relationships between local neurophysiological variables in specific brain regions and alpha power.Competing Interest StatementThe authors have declared no competing interest.