Your browser doesn't support javascript.
loading
Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.
Pinaya, Walter H L; Mechelli, Andrea; Sato, João R.
Afiliação
  • Pinaya WHL; Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.
  • Mechelli A; Center for Engineering, Modeling and Applied Social Sciences, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.
  • Sato JR; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Hum Brain Mapp ; 40(3): 944-954, 2019 02 15.
Article em En | MEDLINE | ID: mdl-30311316
Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.
Assuntos
Palavras-chave
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Esquizofrenia / Encéfalo / Neuroimagem / Transtorno do Espectro Autista / Aprendizado Profundo Limite: Adult / Female / Humans / Male Idioma: En Revista: Hum Brain Mapp Assunto da revista: CEREBRO Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Esquizofrenia / Encéfalo / Neuroimagem / Transtorno do Espectro Autista / Aprendizado Profundo Limite: Adult / Female / Humans / Male Idioma: En Revista: Hum Brain Mapp Assunto da revista: CEREBRO Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos