Aloysius Y. T. Low

Bio: Aloysius Y. T. Low is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Deep cerebellar nuclei & Forelimb. The author has an hindex of 2, co-authored 2 publications receiving 52 citations. Previous affiliations of Aloysius Y. T. Low include University of Warwick & Nanyang Technological University.

Reverse-translational identification of a cerebellar satiation network.

Abstract: The brain is the seat of body weight homeostasis. However, our inability to control the increasing prevalence of obesity highlights a need to look beyond canonical feeding pathways to broaden our understanding of body weight control1–3. Here we used a reverse-translational approach to identify and anatomically, molecularly and functionally characterize a neural ensemble that promotes satiation. Unbiased, task-based functional magnetic resonance imaging revealed marked differences in cerebellar responses to food in people with a genetic disorder characterized by insatiable appetite. Transcriptomic analyses in mice revealed molecularly and topographically -distinct neurons in the anterior deep cerebellar nuclei (aDCN) that are activated by feeding or nutrient infusion in the gut. Selective activation of aDCN neurons substantially decreased food intake by reducing meal size without compensatory changes to metabolic rate. We found that aDCN activity terminates food intake by increasing striatal dopamine levels and attenuating the phasic dopamine response to subsequent food consumption. Our study defines a conserved satiation centre that may represent a novel therapeutic target for the management of excessive eating, and underscores the utility of a ‘bedside-to-bench’ approach for the identification of neural circuits that influence behaviour. Activity in anterior deep cerebellar nuclei reduces food consumption in mice without reducing metabolic rate, potentially identifying a therapeutic target for disorders involving excessive eating.

The cerebellum as a neuronal machine

Abstract: Summary form only given. The exceedingly regular and intricate neuroanatomy of the vertebrate cerebellar cortex has prompted many speculations about its function. The author reviews and assesses models for the functioning of cerebellar cortex, including those suggesting that it is: a timing device, a device enabling spatial navigation, or an associative memory store which is used in motor control. (1 page)

Modular output circuits of the fastigial nucleus for diverse motor and nonmotor functions of the cerebellar vermis

Abstract: The cerebellar vermis, long associated with axial motor control, has been implicated in a surprising range of neuropsychiatric disorders and cognitive and affective functions. Remarkably little is known, however, about the specific cell types and neural circuits responsible for these diverse functions. Here, using single-cell gene expression profiling and anatomical circuit analyses of vermis output neurons in the mouse fastigial (medial cerebellar) nucleus, we identify five major classes of glutamatergic projection neurons distinguished by gene expression, morphology, distribution, and input-output connectivity. Each fastigial cell type is connected with a specific set of Purkinje cells and inferior olive neurons and in turn innervates a distinct collection of downstream targets. Transsynaptic tracing indicates extensive disynaptic links with cognitive, affective, and motor forebrain circuits. These results indicate that diverse cerebellar vermis functions could be mediated by modular synaptic connections of distinct fastigial cell types with posturomotor, oromotor, positional-autonomic, orienting, and vigilance circuits.

Anipose: a toolkit for robust markerless 3D pose estimation

Abstract: Quantifying movement is critical for understanding animal behavior. Advances in computer vision now enable markerless tracking from 2D video, but most animals live and move in 3D. Here, we introduce Anipose, a Python toolkit for robust markerless 3D pose estimation. Anipose consists of four components: (1) a 3D calibration module, (2) filters to resolve 2D tracking errors, (3) a triangulation module that integrates temporal and spatial constraints, and (4) a pipeline for processing large numbers of videos. We evaluate Anipose on four datasets: a moving calibration board, fruit flies walking on a treadmill, mice reaching for a pellet, and humans performing various actions. Because Anipose is built on popular 2D tracking methods (e.g., DeepLabCut), users can expand their existing experimental setups to incorporate robust 3D tracking. We hope this open-source software and accompanying tutorials (www.anipose.org) will facilitate the analysis of 3D animal behavior and the biology that underlies it.