Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering

Yixin Ji; Godfrey Pearlson; Juan Bustillo; Peter Kochunov; Jessica A Turner; Rongtao Jiang; Wei Shao; Xiao Zhang; Zening Fu; Kaicheng Li; Zhaowen Liu; Xijia Xu; Daoqiang Zhang; Shile Qi; Vince D Calhoun

doi:10.1016/j.schres.2023.12.013

Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering

Schizophr Res. 2024 Feb:264:130-139. doi: 10.1016/j.schres.2023.12.013. Epub 2023 Dec 20.

Authors

Yixin Ji¹, Godfrey Pearlson², Juan Bustillo³, Peter Kochunov⁴, Jessica A Turner⁵, Rongtao Jiang⁶, Wei Shao¹, Xiao Zhang⁷, Zening Fu⁸, Kaicheng Li⁹, Zhaowen Liu¹⁰, Xijia Xu¹¹, Daoqiang Zhang¹², Shile Qi¹³, Vince D Calhoun¹⁴

Affiliations

¹ College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
² Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA.
³ Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA.
⁴ Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
⁵ Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA.
⁶ Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA.
⁷ Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.
⁸ Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
⁹ Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
¹⁰ Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹¹ Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.
¹² College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China. Electronic address: dqzhang@nuaa.edu.cn.
¹³ College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China. Electronic address: shile.qi@nuaa.edu.cn.
¹⁴ Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Department of Electrical and Computer Engineering, Georgia Tech University, Atlanta, GA, USA.

PMID: 38128344
DOI: 10.1016/j.schres.2023.12.013

Abstract

Background: Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences.

Methods: 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives.

Results: Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms.

Conclusions: These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.

Keywords: Individual-specific brain network; Multi-site clustering; Psychosis; Subtype identification.

MeSH terms

Bipolar Disorder* / psychology
Brain / diagnostic imaging
Family / psychology
Humans
Magnetic Resonance Imaging
Psychotic Disorders* / diagnostic imaging
Psychotic Disorders* / genetics
Schizophrenia* / diagnostic imaging
Schizophrenia* / genetics

Abstract

MeSH terms

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