Mining neural connectivity via spatiotemporal features of neural calcium activity data

文献类型： 外文期刊

第一作者： Yuan, Ye

作者： Yuan, Ye;Du, Yuheng;Wen, Wentao;Liu, Jian;Xin, Kuankuan;Liu, Hongjiang;Li, Zhaoyu;Zhang, Amin;Yang, Shiyan;Fang, Tao

作者机构：

关键词： C. elegans; Connectome reconstruction; Graph neural networks; Long short-term memory networks; Whole-brain calcium imaging

期刊名称：EXPERT SYSTEMS WITH APPLICATIONS （ 影响因子：7.5； 五年影响因子：7.8 ）

ISSN： 0957-4174

年卷期： 2026 年 295 卷

页码：

收录情况： SCI

摘要： Reconstructing the mesoscopic brain connectome is key to understanding the principles of biological neural connectivity, thereby shedding light on how brain functions emerge. However, biological brains typically contain hundreds of millions of neurons and synapses, making large-scale reconstruction difficult using traditional techniques such as tracer labeling and microscopic imaging. So far, only a few model organisms with simple nervous systems, such as C. elegans and zebrafish, have had their connectome reconstructed. Here, we propose a novel method that combines graph neural networks (GNN) with long short-term memory (LSTM) networks to mine spatiotemporal features from neural calcium activity data. The features uniquely characterize neurons and synapses, enabling the prediction of neural connectivity. To validate the effectiveness of the method, we performed multiple whole-brain calcium imaging experiments on C. elegans and collected a large number of calcium activity data to create several datasets. Experiments on these datasets show that the method can reliably predict the connections of local neural circuits in C. elegans, achieving an average accuracy of approximately 0.75, outperforming existing methods. It is anticipated to offer a simpler and data-driven approach for reconstructing connectome in more complex nervous systems.

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