Topic: spontaneous behavior

Experimental work across a variety of species has demonstrated that spontaneously generated behaviors are robustly correlated to variation in neural activity within the cerebral cortex. Indeed, functional magnetic resonance imaging (fMRI) data suggest that functional connectivity in cortical networks varies across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these studies generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior typically observed i

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

Experimental work across a variety of species has demonstrated that spontaneously generated behaviors are robustly correlated to variation in neural activity within the cerebral cortex. Indeed, functional magnetic resonance imaging (fMRI) data suggest that functional connectivity in cortical networks varies across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these studies generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior typically observed i

Uncovering how a stream of sensory inputs maps onto a sequence of actions is crucial for understanding the nervous system. However, behavior is shaped not only by recent sensory stimuli but also by longer-term experiences, internal neuronal dynamics, and physiological states. As a result, the same experimental conditions can result in variable behaviors. We study the role of internal states in zebrafish larvae behavior, leveraging a rich dataset of three million movements recorded during spontaneous behavior, photomotor responses, optomotor responses, and visually driven escape in the zebraf

Alzheimer's disease (AD) is a progressive neurological disorder characterized by amyloid accumulation and behavioral changes across preclinical and clinical stages. Although AD is currently incurable, emerging machine-learning (ML) approaches are facilitating novel insights into disease etiology and treatment mechanisms. Our study utilized and built upon two open-source ML platforms, DeepLabCut (Mathis et al. 2018) and Variational Animal Motion Encoding (VAME) (Luxem et al. 2022), to track and analyze naturalistic behavior in App knock-in (KI) and 5xFAD transgenic mouse models of AD. New human