Neurons interact in networks distributed throughout the brain. While much effort has focused on whole-brain calcium imaging, advances in genetically encoded voltage indicators raise the question of whether it might be possible to image neuronal voltage across entire brains. Achieving this requires a microscope with high volumetric imaging rates and signal-to-noise ratio. Here we present a remote-scanning light-sheet microscope capable of imaging genetically encoded voltage indicator-expressing neurons distributed throughout much of the brain of larval zebrafish at a volumetric rate of 200.8 Hz. We measured voltage traces from approximately one-quarter of all brain neurons. We found that neurons firing at different times during a sequence occupied different locations: visually evoked sequences mapped across the optic tectum, whereas stimulus-independent bursts were mapped across the cerebellum and medulla. Imaging voltage of neurons distributed in many brain regions may open new frontiers for understanding fundamental neural system operations.

Glucagon-like peptide 1 receptor agonists (GLP1RAs) effectively reduce body weight and improve metabolic outcomes; however, established peptide-based therapies require injections and are complex to manufacture. Small-molecule GLP1RAs promise oral bioavailability and scalable manufacturing, but their selective binding to human versus rodent receptors has limited mechanistic studies. Here we developed humanized GLP1R mouse models to investigate how small-molecule GLP1RAs influence feeding behaviour. We found that these compounds regulate both homeostatic and hedonic feeding through parallel neural circuits. Beyond engaging canonical hypothalamic and hindbrain networks that control metabolic homeostasis, GLP1RAs recruit a discrete population of Glp1r-expressing neurons in the central amygdala, which selectively suppress the consumption of palatable foods by reducing dopamine release in the nucleus accumbens. Stimulating these central amygdalar neurons curtails hedonic feeding, whereas targeted deletion of the receptor in this cell population specifically diminishes the anorectic efficacy of GLP1RAs for reward-driven intake. These findings identify a neural circuit through which small-molecule GLP1RAs modulate reward processing, with implications for the treatment of substance-use disorder and binge eating.

The integration of synaptic inputs is a fundamental function of neurons. In the traditional model, excitatory inputs are summed at the soma to generate action potentials. However, how synaptic inputs are integrated by dendrites in vivo remains poorly explored. We used intravital two-photon dendritic imaging with a genetically encoded voltage indicator (accelerated sensor of action potentials 5) together with somatic whole-cell patch clamp recordings to investigate how synaptic depolarizations are transferred to the soma in pyramidal neurons of the mouse somatosensory cortex. We studied the integration of synaptic inputs under spontaneous and sensory-evoked conditions, as well as following electrical and optogenetic stimulation. In all cases, while multiple inputs evoked measurable depolarizations in the cell body, isolated synaptic potentials were strongly attenuated. Our results suggest that isolated synaptic inputs have a minimal contribution to somatic depolarization, whereas coincident inputs within short temporal windows are more effective, indicating a regime of dendritic integration that favors coincident or clustered neuronal activity in cortical networks.

Recent advances in the field of Voltage Imaging, with a special focus on new constructs and novel implementations.

Basal Ganglia Advances is a collection highlighting research on the structure, function, and disorders of the basal ganglia. It features studies spanning neuroscience, clinical insights, and computational models, serving as a hub for advances in movement, cognition, and behavior.

Work related to place tuning, spatial navigation, orientation and direction. Mainly includes articles on connectivity in the hippocampus, retrosplenial cortex, and related areas.

Neurons interact in networks distributed throughout the brain. While much effort has focused on whole-brain calcium imaging, advances in genetically encoded voltage indicators raise the question of whether it might be possible to image neuronal voltage across entire brains. Achieving this requires a microscope with high volumetric imaging rates and signal-to-noise ratio. Here we present a remote-scanning light-sheet microscope capable of imaging genetically encoded voltage indicator-expressing neurons distributed throughout much of the brain of larval zebrafish at a volumetric rate of 200.8 Hz. We measured voltage traces from approximately one-quarter of all brain neurons. We found that neurons firing at different times during a sequence occupied different locations: visually evoked sequences mapped across the optic tectum, whereas stimulus-independent bursts were mapped across the cerebellum and medulla. Imaging voltage of neurons distributed in many brain regions may open new frontiers for understanding fundamental neural system operations.

The integration of synaptic inputs is a fundamental function of neurons. In the traditional model, excitatory inputs are summed at the soma to generate action potentials. However, how synaptic inputs are integrated by dendrites in vivo remains poorly explored. We used intravital two-photon dendritic imaging with a genetically encoded voltage indicator (accelerated sensor of action potentials 5) together with somatic whole-cell patch clamp recordings to investigate how synaptic depolarizations are transferred to the soma in pyramidal neurons of the mouse somatosensory cortex. We studied the integration of synaptic inputs under spontaneous and sensory-evoked conditions, as well as following electrical and optogenetic stimulation. In all cases, while multiple inputs evoked measurable depolarizations in the cell body, isolated synaptic potentials were strongly attenuated. Our results suggest that isolated synaptic inputs have a minimal contribution to somatic depolarization, whereas coincident inputs within short temporal windows are more effective, indicating a regime of dendritic integration that favors coincident or clustered neuronal activity in cortical networks.

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions. This field, known as explainable AI (XAI), aims to help clinicians interrogate, interpret, and critically evaluate AI predictions by identifying factors associated with model outputs. In this educational and practical review, we provide an accessible overview of XAI tailored for practicing radiologists and physicians. We cover the major categories of explanation methods, including saliency maps, perturbation-based and feature-attribution approaches, concept- based methods, and example-based reasoning, as well as uncertainty quantification as a complementary approach for assessing prediction reliability, along with common misconceptions and emerging regulatory obligations. We aim to make XAI easier for healthcare professionals to understand, as effective oversight of AI tools has become a core competency for the modern radiologist.