Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922007078 |
| _version_ | 1797991749048598528 |
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| author | Yun Zhao Mario Boley Andria Pelentritou Philippa J. Karoly Dean R. Freestone Yueyang Liu Suresh Muthukumaraswamy William Woods David Liley Levin Kuhlmann |
| author_facet | Yun Zhao Mario Boley Andria Pelentritou Philippa J. Karoly Dean R. Freestone Yueyang Liu Suresh Muthukumaraswamy William Woods David Liley Levin Kuhlmann |
| author_sort | Yun Zhao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T08:57:15Z |
| format | Article |
| id | doaj.art-69436e75ed7d4345a7b664e1538b1c69 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-11T08:57:15Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-69436e75ed7d4345a7b664e1538b1c692022-12-22T04:33:09ZengElsevierNeuroImage1095-95722022-11-01263119592Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythmYun Zhao0Mario Boley1Andria Pelentritou2Philippa J. Karoly3Dean R. Freestone4Yueyang Liu5Suresh Muthukumaraswamy6William Woods7David Liley8Levin Kuhlmann9Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, AustraliaDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, AustraliaSwinburne University of Technology, Hawthorn, Australia; Laboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, SwitzerlandDepartment of Biomedical Engineering, The University of Melbourne, Parkville, Australia; Department of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, AustraliaDepartment of Medicine-St Vincent's Hospital, The University of Melbourne, Parkville, Australia; Seer Medical Pty Ltd, Melbourne, AustraliaDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, AustraliaSchool of Pharmacy, University of Auckland, New ZealandSchool of Health Sciences, Swinburne University of Technology, Hawthorn, AustraliaSwinburne 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, AustraliaDepartment 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.http://www.sciencedirect.com/science/article/pii/S1053811922007078Brain imagingNeural mass modelKalman filteringParameter estimationAlpha rhythmResting state |
| spellingShingle | Yun Zhao Mario Boley Andria Pelentritou Philippa J. Karoly Dean R. Freestone Yueyang Liu Suresh Muthukumaraswamy William Woods David Liley Levin Kuhlmann Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm NeuroImage Brain imaging Neural mass model Kalman filtering Parameter estimation Alpha rhythm Resting state |
| title | Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm |
| title_full | Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm |
| title_fullStr | Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm |
| title_full_unstemmed | Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm |
| title_short | Space-time resolved inference-based neurophysiological process imaging: Application to resting-state alpha rhythm |
| title_sort | space time resolved inference based neurophysiological process imaging application to resting state alpha rhythm |
| topic | Brain imaging Neural mass model Kalman filtering Parameter estimation Alpha rhythm Resting state |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922007078 |
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