Less Is Better: Single-Digit Brain Functional Connections Predict T2DM and T2DM-Induced Cognitive Impairment

Haotian Qian; Dongxue Qin; Shouliang Qi; Yueyang Teng; Chen Li; Yudong Yao; Jianlin Wu

doi:10.3389/fnins.2020.588684

Less Is Better: Single-Digit Brain Functional Connections Predict T2DM and T2DM-Induced Cognitive Impairment

Front Neurosci. 2021 Jan 11:14:588684. doi: 10.3389/fnins.2020.588684. eCollection 2020.

Authors

Haotian Qian¹, Dongxue Qin², Shouliang Qi^{1

3}, Yueyang Teng¹, Chen Li¹, Yudong Yao⁴, Jianlin Wu⁵

Affiliations

¹ College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
² Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
³ Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
⁴ Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, United States.
⁵ Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.

Abstract

Type 2 diabetes mellitus (T2DM) leads to a higher risk of brain damage and adversely affects cognition. The underlying neural mechanism of T2DM-induced cognitive impairment (T2DM-CI) remains unclear. This study proposes to identify a small number of dysfunctional brain connections as imaging biomarkers, distinguishing between T2DM-CI, T2DM with normal cognition (T2DM-NC), and healthy controls (HC). We have recruited 22 T2DM-CI patients, 31 T2DM-NC patients, and 39 HCs. The structural Magnetic Resonance Imaging (MRI) and resting state fMRI images are acquired, and neuropsychological tests are carried out. Amplitude of low frequency fluctuations (ALFF) is analyzed to identify impaired brain regions implicated with T2DM and T2DM-CI. The functional network is built and all connections connected to impaired brain regions are selected. Subsequently, L₁-norm regularized sparse canonical correlation analysis and sparse logistic regression are used to identify discriminative connections and Support Vector Machine is trained to realize three two-category classifications. It is found that single-digit dysfunctional connections predict T2DM and T2DM-CI. For T2DM-CI versus HC, T2DM-NC versus HC, and T2DM-CI versus T2DM-NC, the number of connections is 6, 7, and 5 and the area under curve (AUC) can reach 0.912, 0.901, and 0.861, respectively. The dysfunctional connection is mainly related to Default Model Network (DMN) and long-distance links. The strength of identified connections is significantly different among groups and correlated with cognitive assessment score (p < 0.05). Via ALFF analysis and further feature selection algorithms, a small number of dysfunctional brain connections can be identified to predict T2DM and T2DM-CI. These connections might be the imaging biomarkers of T2DM-CI and targets of intervention.

Keywords: cognitive impairment; functional connectivity; machine learning; resting state fMRI; type 2 diabetes mellitus.