A SMARTR workflow for multi-ensemble atlas mapping and brain-wide network analysis.

In the last decade, activity-dependent strategies for labelling multiple immediate early gene (IEG) ensembles in mice have generated unprecedented insight into the mechanisms of memory encoding, storage, and retrieval. However, few strategies exist for brain-wide mapping of multiple ensembles, including their overlapping population, and none incorporate capabilities for downstream network analysis. Here, we introduce a scalable workflow to analyze traditionally coronally-sectioned datasets produced by activity-dependent tagging systems. Intrinsic to this pipeline is simple multi-ensemble atlas registration and statistical testing in R (SMARTR), an R package which wraps mapping capabilities with functions for statistical analysis and network visualization. We demonstrate the versatility of SMARTR by mapping the ensembles underlying the acquisition and expression of learned helplessness (LH), a robust stress model. Applying network analysis, we find that exposure to inescapable shock (IS), compared to context training (CT), results in decreased centrality of regions engaged in spatial and contextual processing and higher influence of regions involved in somatosensory and affective processing. During LH expression, the substantia nigra emerges as a highly influential region which shows a functional reversal following IS, indicating a possible regulatory function of motor activity during helplessness. We also report that IS results in a robust decrease in reactivation activity across a number of cortical, hippocampal, and amygdalar regions, indicating suppression of ensemble reactivation may be a neurobiological signature of LH. These results highlight the emergent insights uniquely garnered by applying our analysis approach to multiple ensemble datasets and demonstrate the strength of our workflow as a hypothesis-generating toolkit.

The Dorsal Column Nuclei Scale Mechanical Sensitivity in Naive and Neuropathic Pain States.

Tactile perception relies on reliable transmission and modulation of low-threshold information as it travels from the periphery to the brain. During pathological conditions, tactile stimuli can aberrantly engage nociceptive pathways leading to the perception of touch as pain, known as mechanical allodynia. Two main drivers of peripheral tactile information, low-threshold mechanoreceptors (LTMRs) and postsynaptic dorsal column neurons (PSDCs), terminate in the brainstem dorsal column nuclei (DCN). Activity within the DRG, spinal cord, and DCN have all been implicated in mediating allodynia, yet the DCN remains understudied at the cellular, circuit, and functional levels compared to the other two. Here, we show that the gracile nucleus (Gr) of the DCN mediates tactile sensitivity for low-threshold stimuli and contributes to mechanical allodynia during neuropathic pain in mice. We found that the Gr contains local inhibitory interneurons in addition to thalamus-projecting neurons, which are differentially innervated by primary afferents and spinal inputs. Functional manipulations of these distinct Gr neuronal populations resulted in bidirectional changes to tactile sensitivity, but did not affect noxious mechanical or thermal sensitivity. During neuropathic pain, silencing Gr projection neurons or activating Gr inhibitory neurons was able to reduce tactile hypersensitivity, and enhancing inhibition was able to ameliorate paw withdrawal signatures of neuropathic pain, like shaking. Collectively, these results suggest that the Gr plays a specific role in mediating hypersensitivity to low-threshold, innocuous mechanical stimuli during neuropathic pain, and that Gr activity contributes to affective, pain-associated phenotypes of mechanical allodynia. Therefore, these brainstem circuits work in tandem with traditional spinal circuits underlying allodynia, resulting in enhanced signaling of tactile stimuli in the brain during neuropathic pain.

Mapping the neuroethological signatures of pain, analgesia, and recovery in mice.

Ongoing pain is driven by the activation and modulation of pain-sensing neurons, affecting physiology, motor function, and motivation to engage in certain behaviors. The complexity of the pain state has evaded a comprehensive definition, especially in non-verbal animals. Here, in mice, we used site-specific electrophysiology to define key time points corresponding to peripheral sensitivity in acute paw inflammation and chronic knee pain models. Using supervised and unsupervised machine learning tools, we uncovered sensory-evoked coping postures unique to each model. Through 3D pose analytics, we identified movement sequences that robustly represent different pain states and found that commonly used analgesics do not return an animal's behavior to a pre-injury state. Instead, these analgesics induce a novel set of spontaneous behaviors that are maintained even after resolution of evoked pain behaviors. Together, these findings reveal previously unidentified neuroethological signatures of pain and analgesia at heightened pain states and during recovery.