Published online Feb 15, 2022. doi: 10.4239/wjd.v13.i2.110
Peer-review started: August 23, 2021
First decision: December 4, 2021
Revised: December 10, 2021
Accepted: January 6, 2022
Article in press: January 6, 2022
Published online: February 15, 2022
Processing time: 169 Days and 19.2 Hours
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.
To investigate the utility of LSFC for predicting cognitive impairment related to T2DM more accurately and reliably.
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.
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.
LSFC provides neuroimaging-based information that may be useful in detecting MCI early and accurately in patients with T2DM.
Core Tip: Large-scale functional connectivity (LSFC) patterns show unique characteristics. Abnormal LSFC patterns have been observed in patients with dementia or mild cognitive impairment. Patients with diabetes may develop mild cognitive impairment that could potentially progress to dementia. We assessed the applicability of LSFC-related discriminative features to predict the cognitive level of patients with type 2 diabetes mellitus using a connectome-based predictive modeling and support vector machine. We found that the application of these two techniques, based on LSFC patterns, to predict neurocognitive abilities, can complement conventional neurocognitive assessments and aid the management of type 2 diabetes mellitus.