Space-time resolved inference-based neurophysiological process imaging: application to resting-state alpha rhythm

Yun Zhao, Mario Boley, Andria Pelentritou, Philippa J. Karoly, Dean R. Freestone, Yueyang Liu, Suresh Muthukumaraswamy, William Woods, David Liley, Levin Kuhlmann

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Neural processes are complex and difficult to image. This paper presents a new space-time resolved brain imaging framework, called Neurophysiological Process Imaging (NPI), that identifies neurophysiological processes within cerebral cortex at the macroscopic scale. By fitting uncoupled neural mass models to each electromagnetic source time-series using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately inferred and imaged across the whole cerebral cortex 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 processes of different brain states.

Original languageEnglish
Article number119592
Number of pages16
JournalNeuroImage
Volume263
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Alpha rhythm
  • Brain imaging
  • Kalman filtering
  • Neural mass model
  • Parameter estimation
  • Resting state

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