Spatial–Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

30 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)10591-10605
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Volume35
Issue number8
Online published17 Feb 2023
Publication statusPublished - Aug 2024

Abstract

Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial–temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data. In particular, a guided co-attention (GCA) module is developed to model the intermodal interactions of spatial and temporal signal patterns. A novel sliding cluster attention module is designed to address global feature dependency of self-attention mechanism in fMRI time series. Comprehensive experimental results demonstrate that our STCAL model can achieve competitive accuracies of 73.0 ± 4.5%, 72.0 ± 3.8%, and 72.5 ± 4.2% on the ABIDE I, ABIDE II, and ADHD-200 datasets, respectively. Moreover, the potential for feature pruning based on the co-attention scores is validated by the simulation experiment. The clinical interpretation analysis of STCAL can allow medical professionals to concentrate on the discriminative regions of interest and key time frames from fMRI data.

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Research Area(s)

  • Attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), co-attention learning, computer-aided diagnosis (CAD), discriminative localization, functional magnetic resonance imaging (fMRI)

Citation Format(s)

Spatial–Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data. / Liu, Rui; Huang, Zhi-An; Hu, Yao et al.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 35, No. 8, 08.2024, p. 10591-10605.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review