Pablo Andrés-Camazón, Covadonga M Diaz-Caneja, Ram Ballem, Jiayu Chen, Vince D Calhoun, Armin Iraji
{"title":"Neurobiology-based cognitive biotypes using multi-scale intrinsic connectivity networks in psychotic disorders.","authors":"Pablo Andrés-Camazón, Covadonga M Diaz-Caneja, Ram Ballem, Jiayu Chen, Vince D Calhoun, Armin Iraji","doi":"10.1038/s41537-025-00593-2","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74758,"journal":{"name":"Schizophrenia (Heidelberg, Germany)","volume":"11 1","pages":"45"},"PeriodicalIF":3.0000,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Schizophrenia (Heidelberg, Germany)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1038/s41537-025-00593-2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
引用次数: 0
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.