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Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale

Walter H L Pinaya1,2,3, Andrea Mechelli3, João R Sato1

  • 1Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil.

Human Brain Mapping
|October 13, 2018
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Summary
This summary is machine-generated.

This study introduces a deep autoencoder model to analyze brain structure differences in schizophrenia and autism spectrum disorder. The novel approach offers insights into neuroanatomical deviations without requiring large datasets.

Keywords:
autism spectrum disordercomputational psychiatrydeep autoencoderdeep learningschizophreniastructural MRI

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Machine learning (ML) is increasingly used for neuroanatomical alteration research in brain disorders.
  • Existing ML models face criticism for large data requirements and lack of interpretability (black box problem).
  • A need exists for alternative ML approaches to investigate subtle neuroanatomical changes in neuropsychiatric conditions.

Purpose of the Study:

  • To propose and validate a novel deep autoencoder (DAE) approach for assessing neuroanatomical deviations in brain disorders.
  • To overcome limitations of traditional ML models, such as large sample size needs and lack of data insight.
  • To investigate neuroanatomical differences in schizophrenia and autism spectrum disorder using a DAE-based normative model.

Main Methods:

  • A deep autoencoder (DAE) model was trained on structural magnetic resonance imaging (sMRI) data from 1,113 healthy individuals to create a normative model.
  • The DAE model was applied to two independent datasets comprising 263 patients with schizophrenia and autism spectrum disorder (ASD).
  • Total and regional neuroanatomical deviation scores were estimated for individual patients relative to the normative model.

Main Results:

  • The DAE model successfully generated distinct total neuroanatomical deviation values for schizophrenia and ASD groups compared to their respective controls (p < 0.005).
  • The model identified unique patterns of regional neuroanatomical deviations specific to each disorder.
  • These findings align with established neuroimaging literature on schizophrenia and ASD.

Conclusions:

  • The deep autoencoder (DAE) offers a flexible and promising framework for quantifying neuroanatomical deviations in neuropsychiatric populations.
  • This approach provides valuable insights into disease-specific patterns of brain alterations.
  • The DAE method addresses limitations of traditional ML, enhancing the study of brain-based disorders.