在精神分裂症-双极性障碍中绘制异质性和同质性的神经生理和分子概况
Fali Li1,2,3,4,5, Guangying Wang1,4, Sarah Genon6,7
1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Science advances
|November 14, 2025
概括
机器学习在精神疾病中发现了独特的大脑连接模式. 这种方法准确地区分精神分裂症,双相情感障碍和精神分裂情感障碍,为客观的诊断工具铺平了道路.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 机器学习 机器学习
背景情况:
- 精神疾病表现出显著的诊断异质性.
- 目前的诊断方法依赖于主观的症状评估.
- 功能连接 (FC) 改变与精神病障碍有关.
研究的目的:
- 应用一种机器学习框架,即常规直角基础提取 (COBE),到基于电脑图的功能连接 (FC).
- 用个性化和共享的FC子空间区分精神双极障碍 (PBD),精神分裂症 (SCZ) 和精神分裂情绪障碍 (SAD).
- 探索神经生物学基础,并确定潜在的药理学标.
主要方法:
- 使用COBE框架将电脑学衍生FC分解为个性化和共享的子空间.
- 在患有PBD,SCZ和SAD的患者中分析FC模式.
- 分子解码用于研究神经生物学差异.
- 在独立患者队列中的验证.
主要成果:
- 个性化FC有效捕捉疾病异质性,并预测症状严重程度.
- 共享FC揭示了诊断特异性的异常,在区分PBD,SCZ和SAD方面达到79.30%的准确性.
- 分子解码涉及疾病特异性神经生物学中的特定的血清系统和星球细胞.
- 结果在一个独立的队列中成功复制.
结论:
- COBE框架在分析精神病障碍的神经生理学和分子特征方面是有效的.
- 个性化和共享的FC子空间为疾病异质性和特异性提供了独特的见解.
- 结果表明PBD,SCZ和SAD的潜在疾病特异性药理标.
- 这种方法对开发客观,临床相关的精神疾病评估工具充满希望.
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