High-dimensional brain-wide functional connectivity mapping in magnetoencephalography.

Autor: Sanchez-Bornot JM; Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry, Londonderry, UK. Electronic address: jm.sanchez-bornot@ulster.ac.uk., Lopez ME; Department of Experimental Psychology, Cognitive Processes and Speech Therapy Universidad Complutense de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain., Bruña R; Department of Experimental Psychology, Cognitive Processes and Speech Therapy Universidad Complutense de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain; Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain., Maestu F; Department of Experimental Psychology, Cognitive Processes and Speech Therapy Universidad Complutense de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain; Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain., Youssofzadeh V; Department of Neurology, Medical College of Wisconsin, Milwaukee, USA., Yang S; Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Jiangsu, China., Finn DP; Pharmacology and Therapeutics, School of Medicine, National University of Ireland Galway, Ireland., Todd S; Altnagelvin Area Hospital, Western Health and Social Care Trust, Derry, Londonderry, UK., McLean PL; Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Northern Ireland, UK., Prasad G; Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry, Londonderry, UK., Wong-Lin K; Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee campus, Derry, Londonderry, UK. Electronic address: k.wong-lin@ulster.ac.uk.
Jazyk: angličtina
Zdroj: Journal of neuroscience methods [J Neurosci Methods] 2021 Jan 15; Vol. 348, pp. 108991. Date of Electronic Publication: 2020 Nov 09.
DOI: 10.1016/j.jneumeth.2020.108991
Abstrakt: Background: Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing.
New Method: We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis.
Results: We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer's disease.
Conclusions: Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.
(Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: