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dc.contributor.author
Clark, Ruaridh A.
dc.contributor.author
Smith, Keith
dc.contributor.author
Escudero, Javier
dc.contributor.author
Ibañez, Agustin Mariano
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dc.contributor.author
Parra, Mario A.
dc.date.available
2023-12-13T14:55:25Z
dc.date.issued
2022-07
dc.identifier.citation
Clark, Ruaridh A.; Smith, Keith; Escudero, Javier; Ibañez, Agustin Mariano; Parra, Mario A.; Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease; Frontiers Media; Frontiers in Neuroimaging; 1; 7-2022; 1-14
dc.identifier.issn
2813-1193
dc.identifier.uri
http://hdl.handle.net/11336/220146
dc.description.abstract
The prevalence of dementia, including Alzheimer’s disease (AD), is on the rise globallywith screening and intervention of particular importance and benefit to those with limitedaccess to healthcare. Electroencephalogram (EEG) is an inexpensive, scalable, andportable brain imaging technology that could deliver AD screening to those without localtertiary healthcare infrastructure. We study EEG recordings of subjects with sporadicmild cognitive impairment (MCI) and prodromal familial, early-onset, AD for the sameworking memory tasks using high- and low-density EEG, respectively. A challenge indetecting electrophysiological changes from EEG recordings is that noise and volumeconduction effects are common and disruptive. It is known that the imaginary part ofcoherency (iCOH) can generate functional connectivity networks that mitigate againstvolume conduction, while also erasing true instantaneous activity (zero or π-phase).We aim to expose topological differences in these iCOH connectivity networks using aglobal network measure, eigenvector alignment (EA), shown to be robust to networkalterations that emulate the erasure of connectivities by iCOH. Alignments assessedby EA capture the relationship between a pair of EEG channels from the similarity oftheir connectivity patterns. Significant alignments—from comparison with random nullmodels—are seen to be consistent across frequency ranges (delta, theta, alpha, andbeta) for the working memory tasks, where consistency of iCOH connectivities is alsonoted. For high-density EEG recordings, stark differences in the control and sporadicMCI results are observed with the control group demonstrating far more consistentalignments. Differences between the control and pre-dementia groupings are detectedfor significant correlation and iCOH connectivities, but only EA suggests a notabledifference in network topology when comparing between subjects with sporadicMCI and prodromal familial AD. The consistency of alignments, across frequency ranges, providesa measure of confidence in EA’s detection of topological structure, an important aspectthat marks this approach as a promising direction for developing a reliable test for earlyonset AD.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Frontiers Media
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
EEG
dc.subject
COHERENCY
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NETWORK TOPOLOGICAL ANALYSIS
dc.subject
EIGENVECTOR,
dc.subject.classification
Neurociencias
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dc.subject.classification
Medicina Básica
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dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
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dc.title
Robust Assessment of EEG Connectivity Patterns in Mild Cognitive Impairment and Alzheimer's Disease
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-12-12T13:20:56Z
dc.journal.volume
1
dc.journal.pagination
1-14
dc.journal.pais
Suiza
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dc.description.fil
Fil: Clark, Ruaridh A.. University of Strathclyde; Reino Unido
dc.description.fil
Fil: Smith, Keith. University of Nottingham; Estados Unidos
dc.description.fil
Fil: Escudero, Javier. University of Edinburgh; Reino Unido
dc.description.fil
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés; Argentina. Universidad Adolfo Ibañez; Chile
dc.description.fil
Fil: Parra, Mario A.. University of Strathclyde; Reino Unido
dc.journal.title
Frontiers in Neuroimaging
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fnimg.2022.924811/full
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3389/fnimg.2022.924811
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