Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation

Dajiang Lei, Tao Zhang, Yue Wu, Weisheng Li, Xinwei Li

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Abstract: Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group comparison. To address these limitations, we propose a new approach to FBNs construction called the deep unrolling-based spatial constraint representation (DUSCR) model and integrate it with a convolutional classifier to create an end-to-end framework for ASD recognition. Specifically, the model spatial constraint representation (SCR) is solved using a proximal gradient descent algorithm, and we unroll it into deep networks using the deep unrolling algorithm. Classification is then performed using a convolutional prototype learning model. We evaluated the effectiveness of the proposed method on the ABIDE I dataset and observed a significant improvement in model performance and classification accuracy. Graphical abstract: The resting state fMRI images are preprocessed into time series data and 3D coordinates of each region of interest. The data are fed into the DUSCR model, a model for building functional brain networks using deep learning instead of traditional models, that we propose, and then the outputs are fed into the convolutional classifier with prototype learning to determine whether the patient has ASD disease.

Original languageEnglish
Pages (from-to)2829-2842
Number of pages14
JournalMedical and Biological Engineering and Computing
Issue number11
Publication statusPublished - Nov 2023
Externally publishedYes


  • Autism spectrum disorder
  • Classification
  • Deep unrolling
  • Diagnosis-oriented
  • Functional brain networks


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