Applications of resting-state fMRI and machine learning in cognitive impairment in type 2 diabetes mellitus: A scoping review - Zhizhong Sun; Zidong Cao; Limin Ge; Yifan Li; Haoming Huang; Mingrui Li; Shijun Qiu (Meta-Radiology)
Type 2 Diabetes Mellitus (T2DM) is a common metabolic disorder that adversely affects cognitive function and heightens the risk of neurodegenerative diseases. This review examines cutting-edge developments in utilizing machine learning techniques to assess brain function changes in T2DM patients, with a focus on cognitive impairment (CI). Through a comprehensive search across major medical databases, we identified and evaluated six studies that used resting-state functional MRI (rs-fMRI) and machine learning classifiers to analyze brain connectivity patterns in T2DM patients. Our analysis indicates that machine learning methods can effectively distinguish between T2DM patients with and without CI, revealing abnormal functional connectivity patterns linked to cognitive decline. These findings suggest that machine learning combined with neuroimaging holds promising initial findings for guiding early interventions and treatment strategies, with the goal of mitigating CI in T2DM patients and improving clinical outcomes.