Biology subjects

Heinricher, M. M.

Publications and source records attributed to Heinricher, M. M..

3 recordsLinked to original sources

bioRxiv2026

Glucocorticoid - endocannabinoid crosstalk in the ventrolateral periaqueductal gray (vlPAG) promotes pain resolution

Inflammation is a primary response to injury. Here we show that inflammation plays a critical role in engaging the endocannabinoid system in the ventrolateral periaqueductal gray (vlPAG) to activate the descending pain modulatory circuit to inhibit pain. Inflammation-induced increases in corticosterone activate glucocorticoid receptors to increase synthesis of 2-arachidonylglycerol (2-AG). Retrograde transmission of 2-AG stimulates presynaptic cannabinoid 1 receptors to inhibit GABA release in the vlPAG, producing anti-hyperalgesia. Conversely, blocking both glucocorticoid and cannabinoid receptor activity impairs recovery from hyperalgesia, highlighting the beneficial role of endocannabinoid signaling in pain resolution. However, this system is tightly regulated and over-stimulation of glucocorticoid receptors with corticosterone results in cannabinoid 1 receptor desensitization. In addition, cannabinoid receptors are more susceptible to desensitization in inflamed rats and rapidly desensitize in response to exogenous cannabinoid receptor agonists. Thus, there is a narrow therapeutic window for cannabinoid drugs in the context of inflammatory pain. These findings indicate that cannabinoid agonists should be used with caution in the context of inflammation to avoid CB1R desensitization, and that exploiting glucocorticoid-endocannabinoid interactions is a promising strategy to optimize cannabinoid-based therapies for inflammatory pain.

neuroscience↗

bioRxiv2025

Multi-timescale Rhythmic Dynamics in Rostral Ventromedial Medulla Neurons

Effective pain therapies increasingly target neural circuits that regulate nociceptive processing; yet, how descending control systems regulate pain across time remains poorly understood. Because pain regulation must coordinate rapid defensive responses with slower fluctuations in physiological state, these neural circuits are likely to operate across multiple timescales. However, whether such dynamics exist in brainstem pain-control circuits remains largely unknown. Here, we investigated this question in populations of rostral ventromedial medullary (RVM) pain-modulating neurons. The RVM contains ON- and OFF-cells that exert descending control over spinal nociceptive transmission, regulating pain sensitivity and behaviors. By integrating neuronal recordings with probabilistic modeling, we show that unstimulated and stimulus-driven conditions give rise to distinct timescales of ON- and OFF-cell dynamics. During noxious stimulation, we find that population responses undergo rapid activation followed by superimposed slow and fast recovery dynamics over tens of seconds. In contrast, the same neurons exhibit quasi-periodic fluctuations in firing activity on the order of minutes in the absence of stimulation. Gaussian-process models show that these slow dynamics are statistically predictable from past activity, indicating structured temporal organization beyond stimulus-evoked responses. Taken together, these results indicate that descending pain-control circuits exhibit structured dynamics spanning rapid pain-related signaling and slower fluctuations associated with ongoing physiological state. SignificancePain regulation requires coordination between rapid defensive responses and slower changes in physiological state, yet how these processes are integrated in the brain remains unclear. We show that neurons in a key brainstem pain-control center, the rostral ventromedial medulla, operate across multiple timescales. Using neuronal recordings and computational modeling, we find that these neurons exhibit both fast responses to painful stimuli and slow, structured fluctuations in ongoing activity. These results demonstrate that descending pain control is temporally organized beyond immediate stimulus-evoked responses. This provides a framework for understanding how pain is regulated over time.

neuroscience↗

bioRxiv2024

Comparative analysis of spike-sorters in large-scale brainstem recordings

Recent technological advancements in high-density multi-channel electrodes have made it possible to record large numbers of neurons from previously inaccessible regions. While the performance of automated spike-sorters has been assessed in recordings from cortex, dentate gyrus, and thalamus, the most effective and efficient approach for spike-sorting can depend on the target region due to differing morphological and physiological characteristics. We therefore assessed the performance of five commonly used sorting packages, Kilosort3, MountainSort5, Tridesclous, SpyKING CIRCUS, and IronClust, in recordings from the rostral ventromedial medulla, a region that has been characterized using single-electrode recordings but that is essentially unexplored at the high-density network level. As demonstrated in other brain regions, each sorter produced unique results. Manual curation preferentially eliminated units detected by only one sorter. Kilosort3 and IronClust required the least curation while maintaining the largest number of units, whereas SpyKING CIRCUS and MountainSort5 required substantial curation. Tridesclous consistently identified the smallest number of units. Nonetheless, all sorters successfully identified classically defined RVM physiological cell types. These findings suggest that while the level of manual curation needed may vary across sorters, each can extract meaningful data from this deep brainstem site. Significance StatementHigh-density multichannel recording probes that can access deep brainstem structures have only recently become commercially available, but the performance of open-source spike-sorting packages applied to recordings from these regions has not yet been evaluated. The present findings demonstrate that Kilosort3, MountainSort5, Tridesclous, SpyKING CIRCUS, and IronClust can all be reasonably used to identify units in a deep brainstem structure, the rostral ventromedial medulla (RVM). However, manual curation of the output was essential for all sorters. Importantly, all sorters identified the known, physiologically defined RVM cell classes, confirming their utility for deep brainstem recordings. Our findings provide suggestions for processing parameters to use for brainstem recordings and highlight considerations when using high-density silicon probes in the brainstem.

neuroscience↗