认知基本症状的生物特征标志着一种独特的神经发育途径导致精神分裂症
Nikolaos Koutsouleris1,2,3,4,5, Clara Vetter1, Madalina Buciuman1,4
1Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, 80336 Munich, Germany.
Brain : a journal of neurology
|March 13, 2026
概括
认知基本症状 (COGDIS) 显示出不同的大脑特征,预测精神分裂症风险,与超高风险 (UHR) 标准不同. 这项研究提供了一种生物知情的方法来理解精神病的脆弱性.
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:
- 目前对精神病的临床高风险 (CHR-P) 标准异质,阻碍了对精神分裂症的生物学预测.
- 超高风险 (UHR) 症状得到了优先考虑,而与精神分裂症核心特征一致的认知基本症状 (COGDIS) 仍然未得到充分利用.
研究的目的:
- 为了直接比较不同CHR-P定义 (COGDIS,UHR,MIXED) 与精神分裂症的大脑特征.
- 通过在结构性MRI数据上使用机器学习来评估他们的诊断,生物心理社会和预后资料.
主要方法:
- 应用机器学习对1425名患者 (CHR-P亚组,精神病,抑郁症) 和907名健康对照的结构性MRI数据进行了应用.
- 获得并比较COGDIS,UHR,MIXED和精神分裂症的诊断大脑特征.
- 从多基因,认知,暴露性和生物心理社会因素分析了可预测性.
主要成果:
- 该COGDIS签名将患者与对照区分开来,并与精神分裂症签名保持一致,显示共享的前端-parieto-perisylvian体积减少.
- UHR和MIXED的签名缺乏诊断特异性和与精神分裂症的相似性;UHR显示体积膨胀,MIXED显示混合模式.
- 根据COGDIS和精神分裂症的特征表达,可以预测多基因,认知和暴露性因素,从健康增加到精神分裂症.
- 根据COGDIS和精神分裂症的大脑得分,患者按功能障碍分层,而UHR得分预测了更好的结果.
结论:
- 精神病风险综合征表现出不同的大脑特征,具有不同的诊断特异性,生物心理社会信息性和预后价值.
- COGDIS与神经发育基础的脆弱性模式保持一致,与精神分裂症的认知失调维度相融合.
- 这支持了对精神病风险的生物知情再概念化,将COGDIS确定为精神分裂症的核心责任维度.
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