Applications of resting-state fMRI and machine learning in cognitive impairment in type 2 diabetes mellitus: A scoping review

Meta-Radiology Pub Date : 2025-03-11 DOI:10.1016/j.metrad.2025.100136

Zhizhong Sun , Zidong Cao , Limin Ge , Yifan Li , Haoming Huang , Mingrui Li , Shijun Qiu

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Abstract

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.

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静息状态功能磁共振成像和机器学习在2型糖尿病认知功能障碍中的应用综述

2型糖尿病（T2DM）是一种常见的代谢紊乱，会对认知功能产生不利影响，并增加神经退行性疾病的风险。本文综述了利用机器学习技术评估2型糖尿病患者脑功能变化的最新进展，重点是认知障碍（CI）。通过对主要医学数据库的全面搜索，我们确定并评估了六项使用静息状态功能MRI （rs-fMRI）和机器学习分类器分析T2DM患者大脑连接模式的研究。我们的分析表明，机器学习方法可以有效区分有和没有CI的T2DM患者，揭示与认知能力下降相关的异常功能连接模式。这些发现表明，机器学习与神经影像学相结合在指导早期干预和治疗策略方面具有很好的初步发现，其目标是减轻T2DM患者的CI并改善临床结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Meta-Radiology

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