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Invertible Network for Classification and Biomarker Selection for ASD.

Juntang Zhuang1, Nicha C Dvornek2, Xiaoxiao Li1

  • 1Biomedical Engineering, Yale University, New Haven, CT USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 11, 2020
PubMed
Summary

Researchers developed an interpretable deep learning method using invertible networks to identify autism spectrum disorder (ASD) biomarkers from fMRI data. This approach effectively classifies ASD and reveals key brain connectivity patterns linked to its severity.

Keywords:
ASDbiomarkerinvertible networkregression

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Identifying reliable biomarkers for autism spectrum disorder (ASD) is essential for understanding its underlying mechanisms.
  • Deep learning models show promise in classifying ASD using functional magnetic resonance imaging (fMRI) data, but often lack interpretability.
  • The 'black-box' nature of conventional deep learning hinders biomarker discovery and understanding of model predictions.

Purpose of the Study:

  • To develop a novel, interpretable deep learning method for ASD classification using fMRI data.
  • To identify robust biomarkers for ASD by analyzing brain connectivity patterns.
  • To leverage invertible networks to overcome the interpretability limitations of traditional deep learning models.

Main Methods:

  • Utilized invertible neural networks to classify ASD based on fMRI-derived connectivity matrices.
  • Developed a method to explicitly determine decision boundaries and project data points, enabling 'explanation' of model decisions.
  • Defined an 'importance' measure, weighting explanations by the gradient of predictions with respect to the input, for biomarker identification.

Main Results:

  • The proposed invertible network method achieved effective classification of ASD.
  • Biomarker identification based on the defined importance measure was validated through a regression task.
  • Using the top 10% of identified important edges resulted in lower regression error for 6 different ASD severity scores compared to using all edges.

Conclusions:

  • Invertible networks offer an effective and interpretable approach for ASD classification using fMRI data.
  • The method successfully identifies reliable biomarkers associated with ASD.
  • This interpretable deep learning framework facilitates the discovery of neurobiological markers for autism spectrum disorder.