13 March 2017 Identifying cognitive impairment in type 2 diabetes with functional connectivity: a multivariate pattern analysis of resting state fMRI data
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Abstract
Previous researches have shown that type 2 diabetes mellitus (T2DM) is associated with an increased risk of cognitive impairment. Early detection of brain abnormalities at the preclinical stage can be useful for developing preventive interventions to abate cognitive decline. We aimed to investigate the whole-brain resting-state functional connectivity (RSFC) patterns of T2DM patients between 90 regions of interest (ROIs) based on the RS-fMRI data, which can be used to test the feasibility of identifying T2DM patients with cognitive impairment from other T2DM patients. 74 patients were recruited in this study and multivariate pattern analysis was utilized to assess the prediction performance. Elastic net was firstly used to select the key features for prediction, and then a linear discrimination model was constructed. 23 RSFCs were selected and it achieved the performance with classification accuracy of 90.54% and areas under the receiver operating characteristic curve (AUC) of 0.944 using ten-fold cross-validation. The results provide strong evidence that functional interactions of brain regions undergo notable alterations between T2DM patients with cognitive impairment or not. By analyzing the RSFCs that were selected as key features, we found that most of them involved the frontal or temporal. We speculated that cognitive impairment in T2DM patients mainly impacted these two lobes. Overall, the present study indicated that RSFCs undergo notable alterations associated with the cognitive impairment in T2DM patients, and it is possible to predicted cognitive impairment early with RSFCs.
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Zhenyu Liu, Xingwei Cui, Zhenchao Tang, Di Dong, Yali Zang, Jie Tian, "Identifying cognitive impairment in type 2 diabetes with functional connectivity: a multivariate pattern analysis of resting state fMRI data", Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101370N (13 March 2017); doi: 10.1117/12.2254062; https://doi.org/10.1117/12.2254062
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