Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity.
Acta Psychiatr Scand
; 138(6): 571-580, 2018 12.
Article
em En
| MEDLINE
| ID: mdl-30242828
ABSTRACT
OBJECTIVE:
Structural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross-sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings' reproducibility.METHOD:
We propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross-site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first-episode patients.RESULTS:
Machine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first-episode psychosis patients (73% accuracy).CONCLUSION:
These results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Esquizofrenia
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Processamento de Imagem Assistida por Computador
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Imageamento por Ressonância Magnética
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Substância Cinzenta
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Aprendizado de Máquina
Tipo de estudo:
Clinical_trials
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Diagnostic_studies
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Prognostic_studies
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Ano de publicação:
2018
Tipo de documento:
Article