Cited by 148Schizophrenia research and treatmentFunctional Brain Connectivity StudiesDementia and Cognitive Impairment Research

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Elucidating a Magnetic Resonance Imaging-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms

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Sun, Daqiang, van Erp, Theo G.M., Thompson, Paul M., Bearden, Carrie E., Daley, Melita, Kushan, Leila, Hardt, Molly E., Nuechterlein, Keith H., Toga, Arthur W., Cannon, Tyrone D.·2009·Biological Psychiatry

DOI 10.1016/j.biopsych.2009.07.019PMC PMC3192809

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Abstract

Background No objective diagnostic biomarkers or laboratory tests have yet been developed for psychotic illness. Magnetic resonance imaging (MRI) studies consistently find significant abnormalities in multiple brain structures in psychotic patients relative to healthy control subjects, but these abnormalities show substantial overlap with anatomic variation that is in the normal range and therefore nondiagnostic. Recently, efforts have been made to discriminate psychotic patients from healthy individuals using machine-learning-based pattern classification methods on MRI data.Methods Three-dimensional cortical gray matter density (GMD) maps were generated for 36 patients with recent-onset psychosis and 36 sex- and age-matched control subjects using a cortical pattern matching method. Between-group differences in GMD were evaluated. Second, the sparse multinomial logistic regression classifier included in the Multivariate Pattern Analysis in Python machine-learning package was applied to the cortical GMD maps to discriminate psychotic patients from control subjects.Results Patients showed significantly lower GMD, particularly in prefrontal, cingulate, and lateral temporal brain regions. Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation.Conclusions These results suggest that even at the early stage of illness, psychotic patients present distinct patterns of regional cortical gray matter changes that can be discriminated from the normal pattern. These findings indicate that we can detect complex patterns of brain abnormality in early stages of psychotic illness, which has critical implications for early identification and intervention in individuals at ultra-high risk for developing psychosis/schizophrenia.

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Authors: Sun, D., van Erp, T. G. M., Thompson, P. M., Bearden, C. E., Daley, M., Kushan, L., Hardt, M. E., Nuechterlein, K. H., Toga, A. W., & Cannon, T. D.
Affiliations: University of California, Los Angeles (US)
Corresponding author: Cannon, Tyrone D.
ORCID: Sun, Daqiang
Thompson, Paul M.
Bearden, Carrie E.
Kushan, Leila
Nuechterlein, Keith H.
Toga, Arthur W.
Cannon, Tyrone D.
Venue: Biological Psychiatry
Publisher: Elsevier BV
Published: 2009-12
Volume: 66
Issue: 11
Pages: 1055-1060
ISSN: 0006-3223 (ISSN-L)
DOI: 10.1016/j.biopsych.2009.07.019
PMC: PMC3192809
OpenAlex: W2039397313
Cited by: 148
References: 37
Citations by year: 2026: 1 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 5 · 2021: 9 · 2020: 8 · 2019: 5 · 2018: 8 · 2017: 12 · 2016: 10 · 2015: 16 · 2014: 12 · 2013: 18 · 2012: 13
FWCI: 8.25
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Biological Psychiatry (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: U.S. Public Health Service
National Institute on Aging (R01AG020098, R56AG020098, AG020098)
National Institute of Mental Health (MH037705, K23MH079028, R01MH065079, R01MH037705, P50MH066286, MH65079)
National Institute of Biomedical Imaging and Bioengineering (R01EB008432, R01EB008281, R01EB007813, EB008281, EB 007813, EB008432)
National Center for Research Resources (P41RR013642, U54RR021813)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD 050735, R01HD050735)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Schizophrenia research and treatment
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords: psychosis, schizophrenia, probabilistic brain atlas, multivariate pattern analysis, sparse multinomial logistic regression, recent-onset psychosis, prefrontal cortex, cingulate cortex, lateral temporal cortex, clinical high risk for psychosis, early detection of psychosis
MeSH terms: Adult, Algorithms, Atlases as Topic, Brain Mapping, Cerebral Cortex, Female, Humans, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Predictive Value of Tests, Psychotic Disorders, Statistics as Topic, Biomarkers

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Elucidating a Magnetic Resonance Imaging-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms

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