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基于神经生物学的认知生物型:使用多尺度内在连接网络研究精神障碍

Neurobiology-based cognitive biotypes using multi-scale intrinsic connectivity networks in psychotic disorders.

作者信息

Andrés-Camazón Pablo, Diaz-Caneja Covadonga M, Ballem Ram, Chen Jiayu, Calhoun Vince D, Iraji Armin

机构信息

Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain.

Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (Georgia State University, Georgia Institute of Technology, Emory University), Atlanta, GA, USA.

出版信息

Schizophrenia (Heidelb). 2025 Mar 19;11(1):45. doi: 10.1038/s41537-025-00593-2.

Abstract

Understanding neurobiology and developing effective interventions for cognitive dysfunction in psychotic disorders remain elusive. Insufficient knowledge about the biological heterogeneity of cognitive dysfunction hinders progress. We aimed to identify subgroups of patients with psychosis and distinct patterns of functional brain alterations related to cognition (cognitive biotypes). We analyzed B-SNIP consortium data (2 270 participants including participants with psychotic disorders, relatives, and controls, 55% females). We used reference-informed independent component analysis with the standardized and fully automated framework NeuroMark and the 100k multi-scale intrinsic connectivity networks (ICN) template to obtain subject-specific ICNs and whole-brain functional network connectivity (FNC). FNC features associated with cognitive performance were identified using multivariate joint analysis. K-means clustering identified patient subgroups based on these features. Two biotypes with different functional brain alteration patterns were identified. Relative to controls, biotype 1 exhibited hypoconnectivity in cerebellar-subcortical and somatomotor-visual networks and worse cognitive performance. Biotype 2 exhibited hyperconnectivity in somatomotor-subcortical networks, hypoconnectivity in somatomotor-high cognitive processing networks, and better-preserved cognitive performance. Demographic, clinical, cognitive, and FNC characteristics of biotypes were consistent in discovery and replication sets and in relatives. 76.56% of relatives were assigned to a psychosis biotype, of those, 70.12% were to the same biotype as their affected family members. These findings suggest two distinctive psychosis-related cognitive biotypes with differing functional brain patterns shared with their relatives. Instead of traditional diagnosis, patient stratification based on these biotypes may help optimize future research and identify biological targets for the treatment of cognitive dysfunction in psychosis.

摘要

了解神经生物学并开发针对精神障碍认知功能障碍的有效干预措施仍然困难重重。对认知功能障碍的生物学异质性认识不足阻碍了进展。我们旨在识别精神病患者亚组以及与认知相关的不同功能性脑改变模式(认知生物型)。我们分析了B-SNIP联盟的数据(2270名参与者,包括患有精神障碍的参与者、亲属和对照组,55%为女性)。我们使用基于参考信息的独立成分分析,采用标准化且完全自动化的NeuroMark框架和100k多尺度内在连接网络(ICN)模板,以获得个体特异性ICN和全脑功能网络连接性(FNC)。使用多变量联合分析确定与认知表现相关的FNC特征。基于这些特征,通过K均值聚类确定患者亚组。识别出两种具有不同功能性脑改变模式的生物型。相对于对照组,生物型1在小脑-皮质下和躯体运动-视觉网络中表现为连接不足,认知表现较差。生物型2在躯体运动-皮质下网络中表现为连接过度,在躯体运动-高级认知处理网络中表现为连接不足,且认知表现保留较好。生物型的人口统计学、临床、认知和FNC特征在发现集、复制集以及亲属中具有一致性。76.56%的亲属被归为精神病生物型,其中70.12%与受影响的家庭成员属于同一生物型。这些发现表明存在两种与精神病相关的独特认知生物型,其具有不同的功能性脑模式,且亲属也有类似模式。基于这些生物型进行患者分层,而非传统诊断,可能有助于优化未来研究,并确定治疗精神病认知功能障碍的生物学靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8023/11923125/4fce88349653/41537_2025_593_Fig1_HTML.jpg

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