Identifying Individualized Functional Brain Networks: An Unsupervised Deep Temporal Attention Based Autoencoder Model
Identifying functional brain networks (FBNs) under naturalistic stimuli is crucial for understanding the brain function. While numerous studies have employed deep learning methods to model neural responses, most of them overlook self-attention mechanism of brain and the temporal dependencies in neur...
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| Published in: | Proceedings (International Symposium on Biomedical Imaging) pp. 1 - 5 |
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| Main Authors: | , , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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27.05.2024
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| ISSN: | 1945-8452 |
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| Abstract | Identifying functional brain networks (FBNs) under naturalistic stimuli is crucial for understanding the brain function. While numerous studies have employed deep learning methods to model neural responses, most of them overlook self-attention mechanism of brain and the temporal dependencies in neural activity. In this study, we proposed an unsupervised deep temporal attention based autoencoder model (DTA-AE) that aimed to model brain function in a compact, standardized latent space, thus representing complex brain activities via dense embedding vectors. Our method employed self-attention mechanisms to establish inter-regional temporal dependencies and simulate brain's attention allocation mechanism. Experimental results demonstrated that reliable and distinctive FBNs can be extracted. Specifically, the fingerprint analysis revealed that the identified FBNs can capture the individualized characteristics, enabling accurate subject recognition. Furthermore, temporal features exhibited good temporal consistency across subjects demonstrated by the inter-subject correlation (ISC) analysis, further confirming the effectiveness of the proposed method. |
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| AbstractList | Identifying functional brain networks (FBNs) under naturalistic stimuli is crucial for understanding the brain function. While numerous studies have employed deep learning methods to model neural responses, most of them overlook self-attention mechanism of brain and the temporal dependencies in neural activity. In this study, we proposed an unsupervised deep temporal attention based autoencoder model (DTA-AE) that aimed to model brain function in a compact, standardized latent space, thus representing complex brain activities via dense embedding vectors. Our method employed self-attention mechanisms to establish inter-regional temporal dependencies and simulate brain's attention allocation mechanism. Experimental results demonstrated that reliable and distinctive FBNs can be extracted. Specifically, the fingerprint analysis revealed that the identified FBNs can capture the individualized characteristics, enabling accurate subject recognition. Furthermore, temporal features exhibited good temporal consistency across subjects demonstrated by the inter-subject correlation (ISC) analysis, further confirming the effectiveness of the proposed method. |
| Author | Ding, Zhenqing Yin, Song Wang, Kexin Ren, Yudan Li, Xiao He, Xiaowei Liu, Zhengyang |
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| Snippet | Identifying functional brain networks (FBNs) under naturalistic stimuli is crucial for understanding the brain function. While numerous studies have employed... |
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| SubjectTerms | Brain modeling Correlation Deep learning FBN identification Feature extraction Fingerprint recognition Individualized FBNs Naturalistic fMRI Neural activity Self-attention mechanisms Vectors |
| Title | Identifying Individualized Functional Brain Networks: An Unsupervised Deep Temporal Attention Based Autoencoder Model |
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