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- Title
Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.
- Authors
Bo Qiu; Qianqian Wang; Xizhi Li; Wenyang Li; Wei Shao; Mingliang Wang
- Abstract
Computer aided diagnosis methods play an important role in Attention Deficit Hyperactivity Disorder (ADHD) identification. Dynamic functional connectivity (dFC) analysis has been widely used for ADHD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), which can help capture abnormalities of brain activity. However, most existing dFC-based methods only focus on dependencies between two adjacent timestamps, ignoring global dynamic evolution patterns. Furthermore, the majority of these methods fail to adaptively learn dFCs. In this paper, we propose an adaptive spatial-temporal neural network (ASTNet) comprising three modules for ADHD identification based on rs-fMRI time series. Specifically, we first partition rs-fMRI time series into multiple segments using non-overlapping sliding windows. Then, adaptive functional connectivity generation (AFCG) is used to model spatial relationships among regions-of-interest (ROIs) with adaptive dFCs as input. Finally, we employ a temporal dependency mining (TDM) module which combines local and global branches to capture global temporal dependencies from the spatially-dependent pattern sequences. Experimental results on the ADHD-200 dataset demonstrate the superiority of the proposed ASTNet over competing approaches in automated ADHD classification.
- Subjects
COMPUTER-aided diagnosis; FUNCTIONAL magnetic resonance imaging; ATTENTION-deficit hyperactivity disorder; FUNCTIONAL connectivity; DIAGNOSIS methods
- Publication
Frontiers in Neuroscience, 2024, p1
- ISSN
1662-4548
- Publication type
Article
- DOI
10.3389/fnins.2024.1394234