Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data

• URL: http://arxiv.org/abs/2508.11672v1
• Date: Thu, 07 Aug 2025 23:36:52 GMT
• Title: Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
• Authors: Zixia Zhou, Junyan Liu, Wei Emma Wu, Ruogu Fang, Sheng Liu, Qingyue Wei, Rui Yan, Yi Guo, Qian Tao, Yuanyuan Wang, Md Tauhidul Islam, Lei Xing,
• Abstract summary: This paper introduces a generalizable unsupervised deep manifold learning for exploration of neurocognitive and behavioral patterns.<n>The proposed Brain-dynamic Convolutional-Network-based Embedding (BCNE) seeks to capture the brain-state trajectories by deciphering temporospatial correlations within the data.<n>The results, both visual and quantitative, reveal a diverse array of intriguing and interpretable patterns.
• Score: 29.522638794625536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic brain data, teeming with biological and functional insights, are becoming increasingly accessible through advanced measurements, providing a gateway to understanding the inner workings of the brain in living subjects. However, the vast size and intricate complexity of the data also pose a daunting challenge in reliably extracting meaningful information across various data sources. This paper introduces a generalizable unsupervised deep manifold learning for exploration of neurocognitive and behavioral patterns. Unlike existing methods that extract patterns directly from the input data as in the existing methods, the proposed Brain-dynamic Convolutional-Network-based Embedding (BCNE) seeks to capture the brain-state trajectories by deciphering the temporospatial correlations within the data and subsequently applying manifold learning to this correlative representation. The performance of BCNE is showcased through the analysis of several important dynamic brain datasets. The results, both visual and quantitative, reveal a diverse array of intriguing and interpretable patterns. BCNE effectively delineates scene transitions, underscores the involvement of different brain regions in memory and narrative processing, distinguishes various stages of dynamic learning processes, and identifies differences between active and passive behaviors. BCNE provides an effective tool for exploring general neuroscience inquiries or individual-specific patterns.

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