PT  - JOURNAL ARTICLE
AU  - Yun Zhao
AU  - Mario Boley
AU  - Andria Pelentritou
AU  - Philippa J. Karoly
AU  - Dean R. Freestone
AU  - Yueyang Liu
AU  - Suresh Muthukumaraswamy
AU  - William Woods
AU  - David Liley
AU  - Levin Kuhlmann
TI  - Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm
AID  - 10.1101/2022.05.03.490402
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.05.03.490402
4099  - http://biorxiv.org/content/early/2022/05/04/2022.05.03.490402.short
4100  - http://biorxiv.org/content/early/2022/05/04/2022.05.03.490402.full
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.