A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants

Weizheng Yan; Godfrey D Pearlson; Zening Fu; Xinhui Li; Armin Iraji; Jiayu Chen; Jing Sui; Nora D Volkow; Vince D Calhoun

doi:10.1016/j.biopsych.2023.09.017

A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants

Biol Psychiatry. 2024 Apr 1;95(7):699-708. doi: 10.1016/j.biopsych.2023.09.017. Epub 2023 Sep 26.

Authors

Weizheng Yan¹, Godfrey D Pearlson², Zening Fu³, Xinhui Li⁴, Armin Iraji³, Jiayu Chen³, Jing Sui⁵, Nora D Volkow⁶, Vince D Calhoun⁷

Affiliations

¹ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia; National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, Maryland. Electronic address: weizheng.yan@nih.gov.
² Department of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, Connecticut.
³ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia.
⁴ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia.
⁵ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
⁶ National Institute on Alcohol Abuse and Alcoholism, Laboratory of Neuroimaging, National Institutes of Health, Bethesda, Maryland.
⁷ Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia. Electronic address: vince.calhoun@ece.gatech.edu.

PMID: 37769983
PMCID: PMC10942727 (available on 2025-04-01)
DOI: 10.1016/j.biopsych.2023.09.017

Abstract

Background: Accurate psychiatric risk assessment requires biomarkers that are both stable and adaptable to development. Functional network connectivity (FNC), which steadily reconfigures over time, potentially contains abundant information to assess psychiatric risks. However, the absence of suitable analytical methodologies has constrained this area of investigation.

Methods: We investigated the brainwide risk score (BRS), a novel FNC-based metric that contrasts the relative distances of an individual's FNC to that of psychiatric disorders versus healthy control references. To generate group-level disorder and healthy control references, we utilized a large brain imaging dataset containing 5231 total individuals diagnosed with schizophrenia, autism spectrum disorder, major depressive disorder, and bipolar disorder and their corresponding healthy control individuals. The BRS metric was employed to assess the psychiatric risk in 2 new datasets: Adolescent Brain Cognitive Development (ABCD) Study (n = 8191) and Human Connectome Project Early Psychosis (n = 170).

Results: The BRS revealed a clear, reproducible gradient of FNC patterns from low to high risk for each psychiatric disorder in unaffected adolescents. We found that low-risk ABCD Study adolescent FNC patterns for each disorder were strongly present in over 25% of the ABCD Study participants and homogeneous, whereas high-risk patterns of each psychiatric disorder were strongly present in about 1% of ABCD Study participants and heterogeneous. The BRS also showed its effectiveness in predicting psychosis scores and distinguishing individuals with early psychosis from healthy control individuals.

Conclusions: The BRS could be a new image-based tool for assessing psychiatric vulnerability over time and in unaffected individuals, and it could also serve as a potential biomarker, facilitating early screening and monitoring interventions.

Keywords: ABCD; Biomarker; Functional network connectivity; HCP; Psychiatric risk score.

MeSH terms

Adolescent
Autism Spectrum Disorder* / diagnostic imaging
Biomarkers
Brain / diagnostic imaging
Depressive Disorder, Major*
Humans
Magnetic Resonance Imaging / methods
Mental Disorders*
Risk Factors

Substances

Biomarkers

Abstract

MeSH terms

Substances

Grants and funding