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
Affiliations
Yun Zhao
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
Mario Boley
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
Andria Pelentritou
Swinburne University of Technology, Hawthorn, Australia; Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
Philippa J. Karoly
Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia
Dean R. Freestone
Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; Seer Medical Pty Ltd, Melbourne, Australia
Yueyang Liu
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
Suresh Muthukumaraswamy
School of Pharmacy, University of Auckland, New Zealand
William Woods
School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
David Liley
Swinburne University of Technology, Hawthorn, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; School of Health Sciences, Swinburne University of Technology, Hawthorn, Australia
Levin Kuhlmann
Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; Corresponding author.
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.
