Modeling Alzheimers’ Disease Progression from Multi-task and Self-supervised Learning Perspective with Brain Networks

Liang, Wei; Zhang, Kai; Cao, Peng; Zhao, Pengfei; Liu, Xiaoli; Yang, Jinzhu; Zaiane, Osmar R.

doi:10.1007/978-3-031-43907-0_30

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Several dynamic functional analysis methods have recently been proposed for brain disease classification (Wang et al, 2019b , 2023 ; Yan et al, 2019 ; Gadgil et al, 2020 ; Lin et al, 2022 ; Liu et al, 2022 ; Liang et al, 2023 ). For example, Wang et al ( 2019b ) proposed a spatial-temporal convolutional-recurrent neural network (STNet) for Alzheimer's disease progression prediction using rs-fMRI time series.…”

Section: Related Workmentioning

confidence: 99%

“…Then, three layers of ST-GC units were used to perform spatial graph convolution, followed by a fully connected layer for final prediction. Liang et al ( 2023 ) proposed a self-supervised multi-task learning model for detecting AD progression, in which a masked map auto-encoder and temporal contrast learning were jointly pre-trained to capture the structural and evolutionary features of longitudinal brain networks. Liu et al ( 2022 ) proposed a method based on nested residual convolutional denoising autoencoder (NRCDAE) and convolutional gated recurrent unit (GRU) for ADHD diagnosis.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive spatial-temporal neural network for ADHD identification using functional fMRI

Qiu,

Wang,

et al. 2024

Front. Neurosci.

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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.

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