用机器学习进行精神分裂症的神经解剖学分类
Ibrahim Sungur1, Simay Selek2, Kaan Keskin1
1Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey; SoCAT Lab, Department of Psychiatry, School of Medicine, Ege University, Izmir, Turkey.
Psychiatry research. Neuroimaging
|September 4, 2025
概括
机器学习在精神分裂症患者中发现了两种不同的神经解剖学亚型. 这些亚型的特点是不同的灰质量,显示出个性化治疗策略的潜力.
科学领域:
- 神经成像
- 精神病学
- 计算神经科学
背景情况:
- 精神分裂症在临床和神经生物学表现上表现出显著的异质性.
- 了解这种异质性对于开发向治疗至关重要.
研究的目的:
- 使用数据驱动的机器学习方法识别精神分裂症的神经解剖学亚型.
- 描述与这些亚型相关的独特结构性大脑模式.
主要方法:
- 对222名参与者的结构性MRI数据 (136名精神分裂症患者,86名对照患者) 的分析.
- 应用HYDRA (异质性通过差别分析),一个半监督的机器学习算法.
- 基于Voxel的形态测量 (VBM) 用于与健康对照进行亚型比较.
主要成果:
- 发现了两种不同的神经解剖学的精神分裂症亚型.
- 小型1:在皮质区域 (岛,带状,额头,部) 广泛分布的下部灰质体积.
- 亚型2:与对照组和亚型1相比,皮层下体积增加
结论:
- 机器学习可以有效地揭示精神分裂症的结构异质性.
- 已识别的亚型为神经解剖学亚型提供了精细的框架.
- 不同的亚型可以促进个性化治疗和提高研究精度.
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