Precise 3D Localization of Intracerebral Implants with a simple Brain Clearing Method.

Determining the localization of intracerebral implants in rodent brain stands as a critical final step in most physiological and behaviroral studies, especially when targeting deep brain nuclei. Conventional histological approaches, reliant on manual estimation through sectioning and slice examination, are error-prone, potentially complicating data interpretation. Leveraging recent advances in tissue-clearing techniques and light-sheet fluorescence microscopy, we introduce a method enabling virtual brain slicing in any orientation, offering precise implant localization without the limitations of traditional tissue sectioning. To illustrate the method's utility, we present findings from the implantation of linear silicon probes into the midbrain interpeduncular nucleus (IPN) of anesthetized transgenic mice expressing chanelrhodopsin-2 and enhanced yellow fluorescent protein under the choline acetyltransferase (ChAT) promoter/enhancer regions (ChAT-Chr2-EYFP mice). Utilizing a fluorescent dye applied to the electrode surface, we visualized both the targeted area and the precise localization, enabling enhanced inter-subject comparisons. Three dimensional (3D) brain renderings, presented effortlessly in video format across various orientations, showcase the versatility of this approach.

FABEL: Forecasting Animal Behavioral Events with Deep Learning-Based Computer Vision.

Behavioral neuroscience aims to provide a connection between neural phenomena and emergent organism-level behaviors. This requires perturbing the nervous system and observing behavioral outcomes, and comparing observed post-perturbation behavior with predicted counterfactual behavior and therefore accurate behavioral forecasts. In this study we present FABEL, a deep learning method for forecasting future animal behaviors and locomotion trajectories from historical locomotion alone. We train an offline pose estimation network to predict animal body-part locations in behavioral video; then sequences of pose vectors are input to deep learning time-series forecasting models. Specifically, we train an LSTM network that predicts a future food interaction event in a specified time window, and a Temporal Fusion Transformer that predicts future trajectories of animal body-parts, which are then converted into probabilistic label forecasts. Importantly, accurate prediction of food interaction provides a basis for neurobehavioral intervention in the context of compulsive eating. We show promising results on forecasting tasks between 100 milliseconds and 5 seconds timescales. Because the model takes only behavioral video as input, it can be adapted to any behavioral task and does not require specific physiological readouts. Simultaneously, these deep learning models may serve as extensible modules that can accommodate diverse signals, such as in-vivo fluorescence imaging and electrophysiology, which may improve behavior forecasts and elucidate invervention targets for desired behavioral change.

A master regulator of opioid reward in the ventral prefrontal cortex.

In addition to their intrinsic rewarding properties, opioids can also evoke aversive reactions that protect against misuse. Cellular mechanisms that govern the interplay between opioid reward and aversion are poorly understood. We used whole-brain activity mapping in mice to show that neurons in the dorsal peduncular nucleus (DPn) are highly responsive to the opioid oxycodone. Connectomic profiling revealed that DPn neurons innervate the parabrachial nucleus (PBn). Spatial and single-nuclei transcriptomics resolved a population of PBn-projecting pyramidal neurons in the DPn that express µ-opioid receptors (µORs). Disrupting µOR signaling in the DPn switched oxycodone from rewarding to aversive and exacerbated the severity of opioid withdrawal. These findings identify the DPn as a key substrate for the abuse liability of opioids.

Hypothalamic CRF neurons facilitate brain reward function.

Aversive stimuli activate corticotropin-releasing factor (CRF)-expressing neurons in the paraventricular nucleus of hypothalamus (PVNCRF neurons) and other brain stress systems to facilitate avoidance behaviors. Appetitive stimuli also engage the brain stress systems, but their contributions to reward-related behaviors are less well understood. Here, we show that mice work vigorously to optically activate PVNCRF neurons in an operant chamber, indicating a reinforcing nature of these neurons. The reinforcing property of these neurons is not mediated by activation of the hypothalamic-pituitary-adrenal (HPA) axis. We found that PVNCRF neurons send direct projections to the ventral tegmental area (VTA), and selective activation of these projections induced robust self-stimulation behaviors, without activation of the HPA axis. Similar to the PVNCRF cell bodies, self-stimulation of PVNCRF-VTA projection was dramatically attenuated by systemic pretreatment of CRF receptor 1 or dopamine D1 receptor (D1R) antagonist and augmented by corticosterone synthesis inhibitor metyrapone, but not altered by dopamine D2 receptor (D2R) antagonist. Furthermore, we found that activation of PVNCRF-VTA projections increased c-Fos expression in the VTA dopamine neurons and rapidly triggered dopamine release in the nucleus accumbens (NAc), and microinfusion of D1R or D2R antagonist into the NAc decreased the self-stimulation of these projections. Together, our findings reveal an unappreciated role of PVNCRF neurons and their VTA projections in driving reward-related behaviors, independent of their core neuroendocrine functions. As activation of PVNCRF neurons is the final common path for many stress systems, our study suggests a novel mechanism underlying the positive reinforcing effect of stressful stimuli.