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dc.contributor.authorLin, Lin
dc.contributor.authorZhang, Jindi
dc.contributor.authorLiu, Yutong
dc.contributor.authorHao, Xinyu
dc.contributor.authorShen, Jing
dc.contributor.authorYu, Yang
dc.contributor.authorXu, Huashuai
dc.contributor.authorCong, Fengyu
dc.contributor.authorLi, Huanjie
dc.contributor.authorWu, Jianlin
dc.date.accessioned2022-12-02T12:38:59Z
dc.date.available2022-12-02T12:38:59Z
dc.date.issued2022
dc.identifier.citationLin, L., Zhang, J., Liu, Y., Hao, X., Shen, J., Yu, Y., Xu, H., Cong, F., Li, H., & Wu, J. (2022). Aberrant brain functional networks in type 2 diabetes mellitus : A graph theoretical and support-vector machine approach. <i>Frontiers in Human Neuroscience</i>, <i>16</i>, Article 974094. <a href="https://doi.org/10.3389/fnhum.2022.974094" target="_blank">https://doi.org/10.3389/fnhum.2022.974094</a>
dc.identifier.otherCONVID_160487428
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/84210
dc.description.abstractObjective: Type 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities. Methods: A total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were analyzed using a graph-theoretic approach, the FC and topological characteristics of the network were compared between T2DM and HC using a general linear model, and correlations between networks and clinical and cognitive characteristics were identified. The support vector machine (SVM) model was used to identify differences between T2DM and HC. Results: In patients with T2DM, FC was higher in two core regions [precuneus/posterior cingulated cortex (PCC)_1 and later prefrontal cortex_1] in the default mode network and lower in bilateral superior parietal lobes (within dorsal attention network), and decreased between the right medial frontal cortex and left auditory cortex. The FC of the right frontal medial-left auditory cortex was positively correlated with the Montreal Cognitive Assessment scales and negatively correlated with the blood glucose levels. Long-range connectivity between bilateral auditory cortex was missing in the T2DM. The nodal degree centrality and efficiency of PCC were higher in T2DM than in HC (P < 0.005). The nodal degree centrality in the PCC in the SVM model was 97.56% accurate in distinguishing T2DM patients from HC, demonstrating the reliability of the prediction model. Conclusion: Functional abnormalities in the auditory cortex in T2DM may be related to cognitive impairment, such as memory and attention, and nodal degree centrality in the PCC might serve as a potential neuroimaging biomarker to predict and identify T2DM.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherFrontiers Media SA
dc.relation.ispartofseriesFrontiers in Human Neuroscience
dc.rightsCC BY 4.0
dc.subject.othertype 2 diabetes mellitus
dc.subject.othercognitive function
dc.subject.otherauditory cortex
dc.subject.otherresting-state MRI
dc.subject.othersupport vector machine
dc.subject.othertopological properties
dc.titleAberrant brain functional networks in type 2 diabetes mellitus : A graph theoretical and support-vector machine approach
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202212025474
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTekniikkafi
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingfi
dc.contributor.oppiaineEngineeringen
dc.contributor.oppiaineSecure Communications Engineering and Signal Processingen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn1662-5161
dc.relation.volume16
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 Lin, Zhang, Liu, Hao, Shen, Yu, Xu, Cong, Li and Wu.
dc.rights.accesslevelopenAccessfi
dc.subject.ysokoneoppiminen
dc.subject.ysomagneettikuvaus
dc.subject.ysohermoverkot (biologia)
dc.subject.ysoaivokuori
dc.subject.ysobiomarkkerit
dc.subject.ysoaikuistyypin diabetes
dc.subject.ysokognitiiviset taidot
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p12131
jyx.subject.urihttp://www.yso.fi/onto/yso/p38811
jyx.subject.urihttp://www.yso.fi/onto/yso/p7039
jyx.subject.urihttp://www.yso.fi/onto/yso/p12288
jyx.subject.urihttp://www.yso.fi/onto/yso/p8303
jyx.subject.urihttp://www.yso.fi/onto/yso/p24920
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3389/fnhum.2022.974094
jyx.fundinginformationThis work was supported by National Natural Science Foundation of China (grant number: 82071911) and from the Dalian Science and Technology Innovation Fund (2021JJ12SN38).
dc.type.okmA1


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