Topic: VAME

Behavioral manifestations define neurological disorders, but our knowledge of disease-induced behavioral alterations is incomplete and largely limited to standard domain-specific behavioral approaches. New humanized App knock-in (KI) models of Alzheimer’s disease (AD) mimic disease mechanisms better than transgenic overexpression, but fail to display consistent behavioral alterations in standard behavioral tests despite severe AD-related neuropathological changes. To address this technological barrier in AD modeling, we used the machine learning platforms DeepLabCut and VAME to test the hypoth

Undirected behavior reflects cognitive functions and provides insights for diagnosing psychiatric conditions such as bipolar disorder (BD) (McReynolds, 1962). Open-field animal behaviors have been well-studied for this purpose; however, a corresponding human subject paradigm is still lacking, and quantifying complex spontaneous human behaviors is challenging. Here, we demonstrate a semi-supervised model to quantify undirected human behavior, differentiate subtle hallmark behavioral features of BD, and create a natural language generative model to provide nuanced interpretations of behaviors wi

The ability to switch between different behaviors is essential to all animals’ survival. The selection of a behavior is guided by multiple factors, such as sensory inputs, internal states, and memory, which suggests that regions across the brain are involved in the decision to switch between them. While whole-brain information is necessary to investigate the neural basis of behavioral switching, brain-wide imaging in behaving mice has proved challenging so far. We employed functional Ultrasound imaging (fUS) to record large-scale neural dynamics in head-fixed mice while simultaneously tracking