Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm

Zhao, Y.; Boley, M.; Pelentritou, A.; Karoly, P.; Freestone, D.; Liu, Y.; Muthukumaraswamy, S. D.; Woods, W.; Liley, D.; Kuhlmann, L.

doi:10.1101/2022.05.03.490402

bioRxiv · 10.1101/2022.05.03.490402

Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm

Zhao, Y.·Boley, M.·Pelentritou, A.·Karoly, P.·Freestone, D.·Liu, Y.·Muthukumaraswamy, S. D.·Woods, W.·Liley, D.·Kuhlmann, L.

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Abstract

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. HighlightsO_LIThe whole-brain imaging framework can disclose the neurophysiological substrates of complicated brain functions in a spatiotemporal manner. C_LIO_LIDeveloped a semi-analytical Kalman filter to estimate neurophysiological variables in the nonlinear neural mass model efficiently and accurately from large-scale electromagnetic time-series. C_LIO_LIThe semi-analytical Kalman filter is 7.5 times faster and 5% more accurate in estimating model parameters than the unscented Kalman filter. C_LIO_LIProvided several group-level statistical observations based on neurophysiological variables and visualised them in a whole-brain manner to show different perspectives of neurophysiological mechanisms. C_LIO_LIApplied the framework to study resting-state alpha oscillation and found novel relationships between local neurophysiological variables in specific brain regions and alpha power. C_LI

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Zhao, Y., Boley, M., Pelentritou, A., Karoly, P., Freestone, D., Liu, Y., Muthukumaraswamy, S. D., Woods, W., Liley, D., Kuhlmann, L.. 2022-05-04. Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm. https://doi.org/10.1101/2022.05.03.490402

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