Decoding behavior from global cerebrovascular activity using neural networks.

ABSTRACT: Functional Ultrasound (fUS) provides spatial and temporal frames of the vascular activity in the brain with high resolution and sensitivity in behaving animals. The large amount of resulting data is underused at present due to the lack of appropriate tools to visualize and interpret such signals. Here we show that neural networks can be trained to leverage the richness of information available in fUS datasets to reliably determine behavior, even from a single fUS 2D image after appropriate training. We illustrate the potential of this method with two examples: determining if a rat is moving or static and decoding the animal's sleep/wake state in a neutral environment. We further demonstrate that our method can be transferred to new recordings, possibly in other animals, without additional

SUBMITTER: Berthon B 

PROVIDER: S-EPMC9981746 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature