Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high‐order functional connectivity

• Published in: Human brain mapping Vol. 42; no. 14; pp. 4671 - 4684
• Main Authors: Chen, Yuna, Zhou, Zhen, Liang, Yi, Tan, Xin, Li, Yifan, Qin, Chunhong, Feng, Yue, Ma, Xiaomeng, Mo, Zhanhao, Xia, Jing, Zhang, Han, Qiu, Shijun, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.10.2021
• Subjects: Adult; Aged; Brain; Brain mapping; Cerebellum - diagnostic imaging; Cerebellum - physiopathology; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - physiopathology; Classification; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - etiology; Cognitive Dysfunction - physiopathology; cognitive impairment; Connectome - methods; Dementia disorders; Diabetes; Diabetes Complications - classification; Diabetes Complications - diagnostic imaging; Diabetes Complications - etiology; Diabetes Complications - physiopathology; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - classification; Diabetes Mellitus, Type 2 - complications; Diabetes Mellitus, Type 2 - diagnostic imaging; Disease; dynamic functional connectivity; Experiments; Female; Functional magnetic resonance imaging; Glucose; Hospitals; Humans; Impairment; Insulin; Learning algorithms; Machine Learning; Magnetic Resonance Imaging; Male; Medical research; Memory; Middle Aged; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Neural networks; Neuroimaging; Patients; resting‐state brain networks; Statistical analysis; Support vector machines; Synchronization; Time synchronization; type 2 diabetes mellitus; type 2 diabetes mellitus; dynamic functional connectivity; resting-state brain networks; cognitive impairment; machine learning
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25575

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM‐related dementia is still less understood. Recent resting‐state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM‐CI). However, they mainly used a mass‐univariate statistical analysis that was not suitable to reveal the altered FC “pattern” in T2DM‐CI, due to lower sensitivity. In this study, we proposed to use high‐order FC to reveal the abnormal connectomics pattern in T2DM‐CI with a multivariate, machine learning‐based strategy. We also investigated whether such patterns were different between T2DM‐CI and T2DM without cognitive impairment (T2DM‐noCI) to better understand T2DM‐induced cognitive impairment, on 23 T2DM‐CI and 27 T2DM‐noCI patients, as well as 50 healthy controls (HCs). We first built the large‐scale high‐order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM‐CI (as well as T2DM‐noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM‐CI versus HC differentiation, but only 59.62% in T2DM‐noCI versus HC classification. We found abnormal high‐order FC patterns in T2DM‐CI compared to HC, which was different from that in T2DM‐noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM‐induced cognitive impairment. The results help to better understand the changes in the central neural system due to T2DM. We used high‐order functional connectivity to reveal the abnormal connectomics pattern in T2DM with cognitive impairment with a multivariate, machine learning‐based strategy. We also investigated whether such patterns were different between T2DM with cognitive impairment and T2DM without cognitive impairment to better understand T2DM‐induced cognitive impairment. Our study is well suited for publication in Human Brain Mapping as we used this method is highly advanced and desirable for extensive applications in the future. Meanwhile, it is of great help for standardizing the methodology and boosting clinical applications of the functional imaging‐based machine learning with improved reproducibility, generalizability, and interpretability.