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The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach

2024Journal article Open access
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dc.contributor.authorGuimarães, Pedro
dc.contributor.authorSerranho, Pedro
dc.contributor.authorDuarte, João V.
dc.contributor.authorCrisóstomo, Joana
dc.contributor.authorMoreno, Carolina
dc.contributor.authorGomes, Leonor
dc.contributor.authorBernardes, Rui
dc.contributor.authorCastelo-Branco, Miguel
dc.date.accessioned2024-09-12T16:16:31Z
dc.date.available2024-09-12T16:16:31Z
dc.date.issued2024
dc.description.abstractIntroduction: There is a need to better understand the neurophysiological changes associated with early brain dysfunction in Type 2 diabetes mellitus (T2DM) before vascular or structural lesions. Our aim was to use a novel unbiased data-driven approach to detect and characterize hemodynamic response function (HRF) alterations in T2DM patients, focusing on their potential as biomarkers. Methods: We meshed task-based event-related (visual speed discrimination) functional magnetic resonance imaging with DL to show, from an unbiased perspective, that T2DM patients’ blood-oxygen-level dependent response is altered. Relevance analysis determined which brain regions were more important for discrimination. We combined explainability with deconvolution generalized linear model to provide a more accurate picture of the nature of the neural changes. Results: The proposed approach to discriminate T2DM patients achieved up to 95% accuracy. Higher performance was achieved at higher stimulus (speed) contrast, showing a direct relationship with stimulus properties, and in the hemispherically dominant left visual hemifield, demonstrating biological interpretability. Differences are explained by physiological asymmetries in cortical spatial processing (right hemisphere dominance) and larger neural signal-to-noise ratios related to stimulus contrast. Relevance analysis revealed the most important regions for discrimination, such as extrastriate visual cortex, parietal cortex, and insula. These are disease/task related, providing additional evidence for pathophysiological significance. Our data-driven design allowed us to compute the unbiased HRF without assumptions. Conclusion: We can accurately differentiate T2DM patients using a datadriven classification of the HRF. HRF differences hold promise as biomarkers and could contribute to a deeper understanding of neurophysiological changes associated with T2DM.pt_PT
dc.description.sponsorshipThe author(s) declare financial support was received for the research, authorship, and/or publication of this article. FCT DSAIPA/DS/0041/2020, FCT/UIDB/4950/2020 and FCT/UIDP/4950/2020 and EASD Innovative Outcomes 2019.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3389/fninf.2023.1321178pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.2/16553
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectType 2 diabetespt_PT
dc.subjectFunctional magnetic resonance imagingpt_PT
dc.subjectNeuroimagingpt_PT
dc.subjectDeep learningpt_PT
dc.subjectHemodynamic responsept_PT
dc.titleThe hemodynamic response function as a type 2 diabetes biomarker: a data-driven approachpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0041%2F2020/PT
oaire.citation.titleFrontiers in Neuroinformaticspt_PT
oaire.citation.volume17pt_PT
oaire.fundingStream3599-PPCDT
person.familyNameGuimarães
person.familyNameSerranho
person.familyNameDias Cortesão dos Santos Bernardes
person.givenNamePedro
person.givenNamePedro
person.givenNameRui Manuel
person.identifier1587486
person.identifierM-4231-2013
person.identifier.ciencia-idC012-547B-1F6A
person.identifier.ciencia-id031F-5D62-E6EC
person.identifier.ciencia-idDB19-B18E-690C
person.identifier.orcid0000-0002-9465-4413
person.identifier.orcid0000-0003-2176-3923
person.identifier.orcid0000-0002-6677-2754
person.identifier.scopus-author-id56647722300
person.identifier.scopus-author-id7004634386
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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