ArticleNeuroImage. Clinical2014
Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: a support vector machine learning approach.
Article in NeuroImage. Clinical, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 9 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- Assessing the prevalence of cognitive impairment among Australians with schizophrenia: A systematic review.The Australian and New Zealand journal of psychiatry · 2026Pooled it
- Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data.Frontiers in psychiatry · 2026Pooled it
- Estimating Multimodal Structural Brain Variability in Schizophrenia Spectrum Disorders: A Worldwide ENIGMA Study.The American journal of psychiatry · 2025Pooled it
- Magnetic resonance imaging-based machine learning classification of schizophrenia spectrum disorders: a meta-analysis.Psychiatry and clinical neurosciences · 2024Pooled it
- Brain Morphological Characteristics of Cognitive Subgroups of Schizophrenia-Spectrum Disorders and Bipolar Disorder: A Systematic Review with Narrative Synthesis.Neuropsychology review · 2023Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- A Systematic Review of Studies Reporting Data-Driven Cognitive Subtypes across the Psychosis Spectrum.Neuropsychology review · 2020Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Heterogeneity and Homogeneity of Regional Brain Structure in Schizophrenia: A Meta-analysis.JAMA psychiatry · 2017Pooled it
- Volume Alterations in Thalamic Subnuclei in Parkinson's Disease Dementia and Machine Learning-Based Prediction of Diagnosis and Severity.Brain and behavior · 2026Article
- Subtyping schizophrenia via machine learning by using structural neuroimaging.Translational psychiatry · 2025Review
- Neuroimaging Data Informed Mood and Psychosis Diagnosis Using an Ensemble Deep Multimodal Framework.Human brain mapping · 2025Article
- Cognitive subgroups of affective and non-affective psychosis show differences in medication and cortico-subcortical brain networks.Scientific reports · 2024Article
- Transdiagnostic subgroups of cognitive impairment in early affective and psychotic illness.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Article
- Prevalence of cognitive impairments and strengths in the early course of psychosis and depression.Psychological medicine · 2023Article
- Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP).Scientific reports · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Structural MRI-Based Schizophrenia Classification Using Autoencoders and 3D Convolutional Neural Networks in Combination with Various Pre-Processing Techniques.Brain sciences · 2022Article
- A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning.Frontiers in neuroscience · 2022Article
- Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Heterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia - 'cognitive deficit' and 'cognitively spared' - in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features.
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