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Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models
Open accessAbstract
Importance: Schizophrenia and bipolar disorder are severe and complex brain disorders characterized by substantial clinical and biological heterogeneity. However, case-control studies often ignore such heterogeneity through their focus on the average patient, which may be the core reason for a lack of robust biomarkers indicative of an individual's treatment response and outcome. Objectives: To investigate the degree to which case-control analyses disguise interindividual differences in brain structure among patients with schizophrenia and bipolar disorder and to map the brain alterations linked to these disorders at the level of individual patients. Design, Setting, and Participants: This study used cross-sectional, T1-weighted magnetic resonance imaging data from participants recruited for the Thematically Organized Psychosis study from October 27, 2004, to October 17, 2012. Data were reanalyzed in 2017 and 2018. Patients were recruited from inpatient and outpatient clinics in the Oslo area of Norway, and healthy individuals from the same catchment area were drawn from the national population registry. Main Outcomes and Measures: Interindividual differences in brain structure among patients with schizophrenia and bipolar disorder. Voxel-based morphometry maps were computed, which were used for normative modeling to map the range of interindividual differences in brain structure. Results: This study included 218 patients with schizophrenia spectrum disorders (mean [SD] age, 30 [9.3] years; 126 [57.8%] male), of whom 163 had schizophrenia (mean [SD] age, 31 [8.7] years; 105 [64.4%] male) and 190 had bipolar disorder (mean [SD] age, 34 [11.3] years; 79 [41.6%] male), and 256 healthy individuals (mean [SD] age, 34 [9.5] years; 140 [54.7%] male). At the level of the individual, deviations from the normative model were frequent in both disorders but highly heterogeneous. Overlap of more than 2% among patients was observed in only a few loci, primarily in frontal, temporal, and cerebellar regions. The proportion of alterations was associated with diagnosis and cognitive and clinical characteristics within clinical groups. Patients with schizophrenia, on average, had significantly reduced gray matter in frontal regions, cerebellum, and temporal cortex. In patients with bipolar disorder, mean deviations were primarily present in cerebellar regions. Conclusions and Relevance: This study found that group-level differences disguised biological heterogeneity and interindividual differences among patients with the same diagnosis. This finding suggests that the idea of the average patient is a noninformative construct in psychiatry that falls apart when mapping abnormalities at the level of the individual patient. This study presents a workable route toward precision medicine in psychiatry.
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In the citing paper
“To characterize heterogeneity of schizophrenia and bipolar disorder nested within age and sex, investigators applied normative modeling to 474 participants from the Thematically Organized Psychosis study (61).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [61]
In the citing paper
“The in- vestigators created normative models where age and sex predicted structural magnetic resonance imaging measures and then examined patient variation by diagnosis.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [61]
In the citing paper
“They found that patients with the same or no diagnosis varied dramatically under the normative model, suggesting that bipolar/schizo- phrenia brain structural heterogeneity is nested within typical heterogeneity.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [61]
In the citing paper
“Newer hybrid models, such as normative modeling (19,59–61) or the functional random forest (FRF) (18,62), may overcome these limitations and enable new inferences from psychopathology studies.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [61]
In the citing paper
“Several studies incorporated normative models to address comorbidity and heterogeneity in clinical and nonclinical co- horts (19,60,61).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [61]
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Metadata
- Authors
- Wolfers, T., Doan, N. T., Kaufmann, T., Alnæs, D., Moberget, T., Agartz, I., Buitelaar, J. K., Ueland, T., Melle, I., Franke, B., Andreassen, O. A., Beckmann, C. F., Westlye, L. T., & Marquand, A. F.
- Affiliations
- Radboud University Nijmegen (NL)
- Oslo University Hospital (NO)
- Radboud University Medical Center (NL)
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Corresponding author
- Wolfers, Thomas
- ORCID
- Venue
- JAMA Psychiatry
- Publisher
- American Medical Association (AMA)
- Published
- 2018-11-01
- Volume
- 75
- Issue
- 11
- Pages
- 1146
- ISSN
- 2168-622X (ISSN-L)
- PMC
- PMC6248110
- OpenAlex
- W2896481393
- Cited by
- 508
- References
- 45
- Citations by year
- 2026: 77 · 2025: 92 · 2024: 65 · 2023: 62 · 2022: 62 · 2021: 60 · 2020: 47 · 2019: 35 · 2018: 7
- FWCI
- 29.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Psychiatry (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Wellcome Trust (098369/Z/12/Z)
- National Institute of Biomedical Imaging and Bioengineering (U54EB020403)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Social Sciences › Arts and Humanities › Philosophy › Mental Health and Psychiatry
- Keywords
- schizophrenia, bipolar disorder, gray matter volume, brain structure, schizophrenia spectrum disorders, cerebellum, frontal cortex, temporal cortex, treatment response biomarkers, psychosis spectrum disorders, neuroimaging biomarkers
- MeSH terms
- Adult, Aging, Bipolar Disorder, Brain Mapping, Cross-Sectional Studies, Female, Humans, Male, Phenotype, Schizophrenia, Case-Control Studies, Young Adult, Neuroimaging, White Matter, Gray Matter
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