A1 Journal article (refereed)
Aberrant brain functional networks in type 2 diabetes mellitus : A graph theoretical and support-vector machine approach (2022)


Lin, 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. Frontiers in Human Neuroscience, 16, Article 974094. https://doi.org/10.3389/fnhum.2022.974094


JYU authors or editors


Publication details

All authors or editorsLin, Lin; Zhang, Jindi; Liu, Yutong; Hao, Xinyu; Shen, Jing; Yu, Yang; Xu, Huashuai; Cong, Fengyu; Li, Huanjie; Wu, Jianlin

Journal or seriesFrontiers in Human Neuroscience

eISSN1662-5161

Publication year2022

Publication date12/10/2022

Volume16

Article number974094

PublisherFrontiers Media SA

Publication countrySwitzerland

Publication languageEnglish

DOIhttps://doi.org/10.3389/fnhum.2022.974094

Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/84210

Publication is parallel publishedhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597867/


Abstract

Objective: 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.


Keywordsadult-onset diabetescognitive skillsbiomarkersneural networks (biology)cerebral cortexmagnetic resonance imagingmachine learning

Free keywordstype 2 diabetes mellitus; cognitive function; auditory cortex; resting-state MRI; support vector machine; topological properties


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-30-04 at 20:06