Identifying functional brain networks from spatial-temporal wide-field calcium imaging data via a recurrent autoencoder

Xiaohui Zhang; Eric C. Landsness; Joseph P. Culver; Mark A. Anastasio

doi:10.1117/12.2626317

28 April 2022 Identifying functional brain networks from spatial-temporal wide-field calcium imaging data via a recurrent autoencoder

Xiaohui Zhang, Eric C. Landsness, Joseph P. Culver, Mark A. Anastasio

Author Affiliations +

Proceedings Volume PC11946, Neural Imaging and Sensing 2022; PC1194612 (2022) https://doi.org/10.1117/12.2626317
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

Exploring functional brain networks (FBNs) from wide-field calcium imaging (WFCI) data is important to understand the functional architecture and organization of the brain. In the study, an unsupervised deep learning method is implemented for identifying FBNs from WFCI data. Specifically, a recurrent autoencoder is adapted to extract spatial-temporal latent embeddings of brain activity followed by use of ordinary least square regression to establish the corresponding function brain networks. Spatial similarities are shared between FBNs estimated from learned embeddings and those derived by seed-based correlation method. The proposed method allows investigations about the effect of spatial-temporal calcium dynamics on FBNs.

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Xiaohui Zhang, Eric C. Landsness, Joseph P. Culver, and Mark A. Anastasio "Identifying functional brain networks from spatial-temporal wide-field calcium imaging data via a recurrent autoencoder", Proc. SPIE PC11946, Neural Imaging and Sensing 2022, PC1194612 (28 April 2022); https://doi.org/10.1117/12.2626317

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