Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach

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

doi:10.3389/fnhum.2022.974094

Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach

Front Hum Neurosci. 2022 Oct 12:16:974094. doi: 10.3389/fnhum.2022.974094. eCollection 2022.

Authors

Lin Lin^{1

2}, Jindi Zhang³, Yutong Liu³, Xinyu Hao^{3

4}, Jing Shen², Yang Yu⁵, Huashuai Xu⁴, Fengyu Cong³, Huanjie Li^{2

3}, Jianlin Wu²

Affiliations

¹ Graduate School, Tianjin Medical University, Tianjin, China.
² Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
³ School of Biomedical Engineering, Dalian University of Technology, Dalian, China.
⁴ Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.
⁵ Department of Endocrinology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.

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.

Keywords: auditory cortex; cognitive function; resting-state MRI; support vector machine; topological properties; type 2 diabetes mellitus.