Large-scale functional connectivity predicts cognitive impairment related to type 2 diabetes mellitus

Abstract

BACKGROUND Large-scale functional connectivity (LSFC) patterns in the brain have unique intrinsic characteristics. Abnormal LSFC patterns have been found in patients with dementia, as well as in those with mild cognitive impairment (MCI), and these patterns predicted their cognitive performance. It has been reported that patients with type 2 diabetes mellitus (T2DM) may develop MCI that could progress to dementia. We investigated whether we could adopt LSFC patterns as discriminative features to predict the cognitive function of patients with T2DM, using connectome-based predictive modeling (CPM) and a support vector machine. AIM To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably. METHODS Resting-state functional magnetic resonance images were derived from 42 patients with T2DM and 24 healthy controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Patients with T2DM were divided into two groups, according to the presence (T2DM-C; n = 16) or absence (T2DM-NC; n = 26) of MCI. Brain regions were marked using Harvard Oxford (HOA-112), automated anatomical labeling (AAL-116), and 264-region functional (Power-264) atlases. LSFC biomarkers for predicting MoCA scores were identified using a new CPM technique. Subsequently, we used a support vector machine based on LSFC patterns for among-group differentiation. The area under the receiver operating characteristic curve determined the appearance of the classification. RESULTS CPM could predict the MoCA scores in patients with T2DM (Pearson’s correlation coefficient between predicted and actual MoCA scores, r = 0.32, P =0.0066 [HOA-112 atlas]; r = 0.32, P =0.0078 [AAL-116 atlas]; r = 0.42, P =0.0038 [Power-264 atlas]), indicating that LSFC patterns represent cognition-level measures in these patients. Positive (anti-correlated) LSFC networks based on the Power-264 atlas showed the best predictive performance; moreover, we observed new brain regions of interest associated with T2DM-related cognition. The area under the receiver operating characteristic curve values (T2DM-NC group vs. T2DM-C group) were 0.65-0.70, with LSFC matrices based on HOA-112 and Power-264 atlases having the highest value (0.70). Most discriminative and attractive LSFCs were related to the default mode network, limbic system, and basal ganglia. CONCLUSION LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.

Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity

“…Notably, this study is the first to apply whole-brain FC for detecting T2DM-MCI using the XGBoost model. Our model yields better classification performance (87.91% accuracy) than that of previous studies 21,22 . Using only 23 patients with T2DM and CI, Chen et al 21 used highorder FC for differentiating healthy controls from patients with T2DM and CI (79.17% accuracy) and patients with T2DM without CI (59.62% accuracy).…”

“…With only 16 T2DM-MCI, Shi et al employed large-scale FC to predict MoCA scores with a connectome-based predictive model and support vector machine, achieving AUC values (T2DM-NCI vs. T2DM-MCI) of 0.65-0.70, which was significantly lower than that obtained by our method (0.82 in AUC). Moreover, our sample size was larger than those of previous studies 21,22,33 , including 199 participants in total. T2DM is typically related to an increased risk of CI and dementia.…”

“…Region-specific FC provides useful features for T2DM-MCI classification. However, existing T2DM-MCI classification methods based on FC have limited accuracy of less than 70.0% 21,22 . Therefore, further effort is needed to improve the specificity and accuracy.…”