基于表面的皮质厚度和化映射与数据驱动的预测在糖尿病前期认知障碍
Juan Wu1, Qi Wang2, Feifei Sun2
1Department of Radiology, Chengdu Qingbaijiang District People's Hospital, Chengdu, Sichuan, China.
IBRO neuroscience reports
|February 25, 2026
概括
糖尿病前期与大脑变化有关,包括皮质厚度降低和化,这与早期认知能力下降有关. 机器学习模型对早期检测这些与糖尿病前期相关的神经效应充满希望.
科学领域:
- 神经科学是一个神经科学.
- 放射学 放射学是一门学科.
- 内分泌学 在内分泌学.
背景情况:
- 糖尿病前期,是2型糖尿病的前体,涉及血糖水平升高.
- 糖尿病前期对大脑结构的影响,特别是皮质形态,仍然在很大程度上未被探索.
- 研究与糖尿病前期相关的皮质变化对于了解早期神经功能障碍至关重要.
研究的目的:
- 研究糖尿病前期患者皮质厚度和变的变化.
- 为了确定这些结构性大脑变化是否可以预测认知表现.
- 探索机器学习在识别糖尿病前期相关认知衰退方面的实用性.
主要方法:
- 从48名糖尿病前期患者和42名健康对照人群获得T1加权的MRI扫描.
- 使用FreeSurfer进行了基于表面的形态测量分析 (皮层厚度,局部旋转指数 - LGI).
- 使用神经心理测试 (MMSE,MoCA,TMT A/B) 和机器学习 (PyCaret) 来进行分析.
主要成果:
- 患有糖尿病前期的个体表现出左下环 (ITG) 皮质厚度减少和左前中心环LGI.
- 糖尿病前期组在TMT的A和B部分表现较差.
- 左ITG皮质厚度与TMT-B性能负相关;机器学习模型实现了0.87 AUC和0.80准确度.
结论:
- 在糖尿病前期,可以观察到ITG和前中心的皮层变化.
- 这些结构变化与早期认知障碍有关.
- 结合神经成像和人工智能,有可能在糖尿病前期相关的认知衰退中进行早期检测和干预.
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