No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
HUBIN — Human Brain Informatics: a database project on schizophrenia
... This study included data from 1,217 individuals diagnosed with schizophrenia, 301 individuals diagnosed with bipolar disorder I, and 1,543 unrelated healthy volunteers from the following datasets, many previously described in the literature; the Functional Imaging Biomedical Information Research Network study (FBIRN 3; multiple sites in the USA) (Potkin et al., 2009), the Center of Biomedical Research Excellence study (COBRE; Albuquerque, NM, USA) (Gupta et al., 2015), the Bipolar and Schizophrenia Network for Intermediate Phenotypes 1 study (B-SNIP 1; multiple sites in the USA) (Meda et al., 2015), the MIND Clinical Imaging Consortium study (MCIC; Albuquerque, NM, USA) (Gollub et al., 2013), the Northwestern University Schizophrenia Data study (NW; Chicago, IL, USA) (Wang et al., 2013), the Human Brain Informatics (HUBIN; Stockholm, Sweden) (Hall et al., 2002), Thematic Organized Psychosis [(TOP) research; Oslo, Norway] (Ringen et al., 2008;Rimol et al., 2012), Olin (Olin Center for Neuropsychiatric Research) (Yao et al., 2017), the Maryland Psychiatric Research Center (MPRC, Baltimore, MD, USA) (Kochunov et al., 2016(Kochunov et al., , 2017, and from the Centre for Addiction and Mental Health (CAMH, Toronto, Canada) (Hawco et al., 2019). A diagnosis of SZ was confirmed by the Structured Clinical Interview for Diagnosis (SCID) for Diagnostic and Statistical Manual of Mental Health 4th Edition (DSM-IV or DSM-IV TR) as part of each study site's protocol. ...
Background
Structural neuroimaging studies have identified similarities in the brains of individuals diagnosed with schizophrenia (SZ) and bipolar I disorder (BP), with overlap in regions of gray matter (GM) deficits between the two disorders. Recent studies have also shown that the symptom phenotypes associated with SZ and BP may allow for a more precise categorization than the current diagnostic criteria. In this study, we sought to identify GM alterations that were unique to each disorder and whether those alterations were also related to unique symptom profiles.
Materials and methods
We analyzed the GM patterns and clinical symptom presentations using independent component analysis (ICA), hierarchical clustering, and n-way biclustering in a large ( N ∼ 3,000), merged dataset of neuroimaging data from healthy volunteers (HV), and individuals with either SZ or BP.
Results
Component A showed a SZ and BP < HV GM pattern in the bilateral insula and cingulate gyrus. Component B showed a SZ and BP < HV GM pattern in the cerebellum and vermis. There were no significant differences between diagnostic groups in these components. Component C showed a SZ < HV and BP GM pattern bilaterally in the temporal poles. Hierarchical clustering of the PANSS scores and the ICA components did not yield new subgroups. N-way biclustering identified three unique subgroups of individuals within the sample that mapped onto different combinations of ICA components and symptom profiles categorized by the PANSS but no distinct diagnostic group differences.
Conclusion
These multivariate results show that diagnostic boundaries are not clearly related to structural differences or distinct symptom profiles. Our findings add support that (1) BP tend to have less severe symptom profiles when compared to SZ on the PANSS without a clear distinction, and (2) all the gray matter alterations follow the pattern of SZ < BP < HV without a clear distinction between SZ and BP.
ResearchGate has not been able to resolve any references for this publication.