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High-dimensional brain-wide functional connectivity mapping in magnetoencephalography

Jose M. Sanchez-Bornot, Maria E. Lopez, Ricardo Bruña, Fernando Maestu, Vahab Youssofzadeh, Scott Yang Orcid Logo, David P. Finn, Stephen Todd, Paula L. McLean, Girijesh Prasad, KongFatt Wong-Lin

Journal of Neuroscience Methods, Volume: 348, Start page: 108991

Swansea University Author: Scott Yang Orcid Logo

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DOI (Published version): 10.1016/j.jneumeth.2020.108991

Abstract

BackgroundBrain 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...

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Published in: Journal of Neuroscience Methods
ISSN: 0165-0270
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58943
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Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing.New methodWe 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&#x2019;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.ResultsWe found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1&#x2212;4 Hz) and higher-theta (6&#x2212;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&#x2019;s disease.ConclusionsOur 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.</abstract><type>Journal Article</type><journal>Journal of Neuroscience Methods</journal><volume>348</volume><journalNumber/><paginationStart>108991</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0165-0270</issnPrint><issnElectronic/><keywords>Functional connectivity; Cluster permutation statistics; Nonparametric statistics; Multiple comparison correction; EEG and MEG biomarkers; Alzheimer&#x2019;s disease</keywords><publishedDay>15</publishedDay><publishedMonth>1</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-01-15</publishedDate><doi>10.1016/j.jneumeth.2020.108991</doi><url/><notes/><college>COLLEGE NANME</college><department>Computer Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>SCS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>EU&#x2019;s INTERREG VA Programme; the Northern Ireland Functional Brain Mapping Project (1303/101154803); the Spanish Ministry of Economy and Competitiveness (PSI2009-14415-C03-01) and Madrid Neurocenter; Alzheimer&#x2019;s Research UK (ARUK) Pump Priming Awards; Medical College of Wisconsin</funders><lastEdited>2021-12-30T13:50:48.8478135</lastEdited><Created>2021-12-07T09:53:47.3753273</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Jose M.</firstname><surname>Sanchez-Bornot</surname><order>1</order></author><author><firstname>Maria E.</firstname><surname>Lopez</surname><order>2</order></author><author><firstname>Ricardo</firstname><surname>Bru&#xF1;a</surname><order>3</order></author><author><firstname>Fernando</firstname><surname>Maestu</surname><order>4</order></author><author><firstname>Vahab</firstname><surname>Youssofzadeh</surname><order>5</order></author><author><firstname>Scott</firstname><surname>Yang</surname><orcid>0000-0002-6618-7483</orcid><order>6</order></author><author><firstname>David P.</firstname><surname>Finn</surname><order>7</order></author><author><firstname>Stephen</firstname><surname>Todd</surname><order>8</order></author><author><firstname>Paula L.</firstname><surname>McLean</surname><order>9</order></author><author><firstname>Girijesh</firstname><surname>Prasad</surname><order>10</order></author><author><firstname>KongFatt</firstname><surname>Wong-Lin</surname><order>11</order></author></authors><documents><document><filename>58943__21967__77e1ad4a7bda4584897ae92d5e8d4bec.pdf</filename><originalFilename>58943.pdf</originalFilename><uploaded>2021-12-30T13:41:21.7149142</uploaded><type>Output</type><contentLength>10319699</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2020 The Authors. 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spelling 2021-12-30T13:50:48.8478135 v2 58943 2021-12-07 High-dimensional brain-wide functional connectivity mapping in magnetoencephalography 81dc663ca0e68c60908d35b1d2ec3a9b 0000-0002-6618-7483 Scott Yang Scott Yang true false 2021-12-07 SCS BackgroundBrain 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 methodWe 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.ResultsWe 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.ConclusionsOur 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. Journal Article Journal of Neuroscience Methods 348 108991 Elsevier BV 0165-0270 Functional connectivity; Cluster permutation statistics; Nonparametric statistics; Multiple comparison correction; EEG and MEG biomarkers; Alzheimer’s disease 15 1 2021 2021-01-15 10.1016/j.jneumeth.2020.108991 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University EU’s INTERREG VA Programme; the Northern Ireland Functional Brain Mapping Project (1303/101154803); the Spanish Ministry of Economy and Competitiveness (PSI2009-14415-C03-01) and Madrid Neurocenter; Alzheimer’s Research UK (ARUK) Pump Priming Awards; Medical College of Wisconsin 2021-12-30T13:50:48.8478135 2021-12-07T09:53:47.3753273 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jose M. Sanchez-Bornot 1 Maria E. Lopez 2 Ricardo Bruña 3 Fernando Maestu 4 Vahab Youssofzadeh 5 Scott Yang 0000-0002-6618-7483 6 David P. Finn 7 Stephen Todd 8 Paula L. McLean 9 Girijesh Prasad 10 KongFatt Wong-Lin 11 58943__21967__77e1ad4a7bda4584897ae92d5e8d4bec.pdf 58943.pdf 2021-12-30T13:41:21.7149142 Output 10319699 application/pdf Version of Record true © 2020 The Authors. This is an open access article under the CC BY-NC-ND license true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title High-dimensional brain-wide functional connectivity mapping in magnetoencephalography
spellingShingle High-dimensional brain-wide functional connectivity mapping in magnetoencephalography
Scott Yang
title_short High-dimensional brain-wide functional connectivity mapping in magnetoencephalography
title_full High-dimensional brain-wide functional connectivity mapping in magnetoencephalography
title_fullStr High-dimensional brain-wide functional connectivity mapping in magnetoencephalography
title_full_unstemmed High-dimensional brain-wide functional connectivity mapping in magnetoencephalography
title_sort High-dimensional brain-wide functional connectivity mapping in magnetoencephalography
author_id_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b
author_id_fullname_str_mv 81dc663ca0e68c60908d35b1d2ec3a9b_***_Scott Yang
author Scott Yang
author2 Jose M. Sanchez-Bornot
Maria E. Lopez
Ricardo Bruña
Fernando Maestu
Vahab Youssofzadeh
Scott Yang
David P. Finn
Stephen Todd
Paula L. McLean
Girijesh Prasad
KongFatt Wong-Lin
format Journal article
container_title Journal of Neuroscience Methods
container_volume 348
container_start_page 108991
publishDate 2021
institution Swansea University
issn 0165-0270
doi_str_mv 10.1016/j.jneumeth.2020.108991
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description BackgroundBrain 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 methodWe 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.ResultsWe 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.ConclusionsOur 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.
published_date 2021-01-15T04:15:52Z
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