Spatial–Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 10591-10605 |
Journal / Publication | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 35 |
Issue number | 8 |
Online published | 17 Feb 2023 |
Publication status | Published - Aug 2024 |
Link(s)
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.
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
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.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 35, No. 8, 08.2024, p. 10591-10605.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review