神经影像学
人工智能
符号
静息状态功能磁共振成像
注意缺陷多动障碍
功能磁共振成像
机器学习
计算机科学
心理学
模式识别(心理学)
神经科学
数学
精神科
算术
作者
Rui Liu,Zhi-An Huang,Yao Hu,Zexuan Zhu,Ka‐Chun Wong,Kay Chen Tan
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2023-02-17
卷期号:35 (8): 10591-10605
被引量:26
标识
DOI:10.1109/tnnls.2023.3243000
摘要
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 $\pm$ 4.5%, 72.0 $\pm$ 3.8%, and 72.5 $\pm$ 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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