Oxytocin-mediated social preference and socially reinforced reward learning in the miniature fish Danionella cerebrum.

Integrative studies of diverse neuronal networks that govern social behavior are hindered by a lack of methods to record neural activity comprehensively across the entire brain. The recent development of the miniature fish Danionella cerebrum as a model organism offers one potential solution, as the small size and optical transparency of these animals make it possible to visualize circuit activity throughout the nervous system. Here, we establish the feasibility of using Danionella as a model for social behavior and socially reinforced learning by showing that adult fish exhibit strong affiliative tendencies and that social interactions can serve as the reinforcer in an appetitive conditioning paradigm.

Automated Assessment of Right Atrial Pressure From Ultrasound Videos Using Machine Learning.

Background: Early recognition of volume overload is essential for heart failure patients. Volume overload can often be easily treated if caught early but causes significant morbidity if unrecognized and allowed to progress. Intravascular volume status can be assessed by ultrasound-based estimation of right atrial pressure (RAP), but the availability of this diagnostic modality is limited by the need for experienced physicians to accurately interpret these scans.

Genetics of Cardiac Aging Implicate Organ-Specific Variation.

Heart structure and function change with age, and the notion that the heart may age faster for some individuals than for others has driven interest in estimating cardiac age acceleration. However, current approaches have limited feature richness (heart measurements; radiomics) or capture extraneous data and therefore lack cardiac specificity (deep learning [DL] on unmasked chest MRI). These technical limitations have been a barrier to efforts to understand genetic contributions to age acceleration.

Automated Assessment of Cardiac Systolic Function From Coronary Angiograms With Video-Based Artificial Intelligence Algorithms.

Importance: Understanding left ventricular ejection fraction (LVEF) during coronary angiography can assist in disease management.

Objective: To develop an automated approach to predict LVEF from left coronary angiograms.

Design, Setting, And Participants: This was a cross-sectional study with external validation using patient data from December 12, 2012, to December 31, 2019, from the University of California, San Francisco (UCSF).

Deep learning augmented ECG analysis to identify biomarker-defined myocardial injury.

Chest pain is a common clinical complaint for which myocardial injury is the primary concern and is associated with significant morbidity and mortality. To aid providers' decision-making, we aimed to analyze the electrocardiogram (ECG) using a deep convolutional neural network (CNN) to predict serum troponin I (TnI) from ECGs. We developed a CNN using 64,728 ECGs from 32,479 patients who underwent ECG within 2 h prior to a serum TnI laboratory result at the University of California, San Francisco (UCSF).

Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective.

Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data. Cardiovascular medicine is particularly well poised to take advantage of these ML advances, due to the widespread digitization of medical data and the large number of diagnostic tests used to evaluate cardiovascular disease. Various ML approaches have successfully been applied to cardiovascular tests and diseases to automate interpretation, accurately perform measurements, and, in some cases, predict novel diagnoses from less invasive tests, effectively expanding the utility of more widely accessible diagnostic tests.

Regenerated interneurons integrate into locomotor circuitry following spinal cord injury.

Whereas humans and other adult mammals lack the ability to regain locomotor function after spinal cord injury, zebrafish are able to recover swimming behavior even after complete spinal cord transection. We have previously shown that zebrafish larvae regenerate lost spinal cord neurons within 9 days post-injury (dpi), but it is unknown whether these neurons are physiologically active or integrate into functional circuitry. Here we show that genetically defined premotor interneurons are regenerated in injured spinal cord segments as functional recovery begins.

Hypothalamic Dopamine Neurons Control Sensorimotor Behavior by Modulating Brainstem Premotor Nuclei in Zebrafish.

Dopamine (DA)-producing neurons are critically involved in the production of motor behaviors in multiple circuits that are conserved from basal vertebrates to mammals. Although there is increasing evidence that DA neurons in the hypothalamus play a locomotor role, their precise contributions to behavior and the circuit mechanisms by which they are achieved remain unclear. Here, we demonstrate that tyrosine-hydroxylase-2-expressing (th2+) DA neurons in the zebrafish hypothalamus fire phasic bursts of activity to acutely promote swimming and modulate audiomotor behaviors on fast timescales.

Zebrafish oxytocin neurons drive nocifensive behavior via brainstem premotor targets.

Animals have evolved specialized neural circuits to defend themselves from pain- and injury-causing stimuli. Using a combination of optical, behavioral and genetic approaches in the larval zebrafish, we describe a novel role for hypothalamic oxytocin (OXT) neurons in the processing of noxious stimuli. In vivo imaging revealed that a large and distributed fraction of zebrafish OXT neurons respond strongly to noxious inputs, including the activation of damage-sensing TRPA1 receptors.

Motor Behavior Mediated by Continuously Generated Dopaminergic Neurons in the Zebrafish Hypothalamus Recovers after Cell Ablation.

Postembryonic neurogenesis has been observed in several regions of the vertebrate brain, including the dentate gyrus and rostral migratory stream in mammals, and is required for normal behavior [1-3]. Recently, the hypothalamus has also been shown to undergo continuous neurogenesis as a way to mediate energy balance [4-10]. As the hypothalamus regulates multiple functional outputs, it is likely that additional behaviors may be affected by postembryonic neurogenesis in this brain structure.

Transgenic FingRs for Live Mapping of Synaptic Dynamics in Genetically-Defined Neurons.

Tools for genetically-determined visualization of synaptic circuits and interactions are necessary to build connectomics of the vertebrate brain and to screen synaptic properties in neurological disease models. Here we develop a transgenic FingR (fibronectin intrabodies generated by mRNA display) technology for monitoring synapses in live zebrafish. We demonstrate FingR labeling of defined excitatory and inhibitory synapses, and show FingR applicability for dissecting synapse dynamics in normal and disease states.