Biology subjects

Burdge, J.

Publications and source records attributed to Burdge, J..

5 recordsLinked to original sources

bioRxiv2025

Neuropathic pain drives time-dependent reorganization of corticostriatal circuits

Chronic pain fundamentally alters sensorimotor integration and motivated behaviors, yet the neural mechanisms underlying this transition remain poorly understood. The striatum, composed of dopamine receptor type 1 (D1)- and type 2 (D2)-expressing spiny projection neurons (SPN), integrates cortical sensory and motor inputs to coordinate movement and motivation, making it a critical candidate for mediating pain-induced behavioral adaptations. Although spinal and cortical pain circuits are well-characterized in limited phases of pain, how corticostriatal pathways and distinct striatal cell populations contribute to the transition from acute to chronic pain states remains unclear. Here we show that neuropathic pain, after spared nerve injury in mice, produces temporally distinct, cell-type-specific changes in striatal SPN activity and corticostriatal plasticity that evolve across acute to chronic pain phases. D1 SPNs exhibit smaller amplitude and slower calcium signals during acute pain stages that persist through early chronic phases, while D2 SPNs show delayed response timing during later chronic stages, but also stimulus-specific alterations in neural activity throughout acute and chronic pain states. Critically, primary somatosensory cortex inputs to D2 SPNs develop depressing synapses specifically during intermediate chronic pain phases ([~]25 days post-injury) that disappear during more severe chronic stages (>3 months), suggesting a failed compensatory mechanism. These findings reveal that striatal circuits undergo dynamic, time-dependent reorganization after peripheral injury, with D1 and D2 pathways contributing distinct temporal signatures to pain-related behavior. The identification of critical windows of striatal plasticity provides new targets for therapeutic interventions that could prevent or reverse chronic pain states by modulating specific corticostriatal circuits during vulnerable transition periods.

neuroscience↗

bioRxiv2024

Remote automated delivery of mechanical stimuli coupled to brain recordings in behaving mice

The canonical framework for testing pain and mechanical sensitivity in rodents is manual delivery of stimuli to the paw. However, this approach is time consuming, produces variability in results, requires significant training, and is ergonomically unfavorable to the experimenter. To circumvent limitations in manual delivery of stimuli, we have created a device called the ARM (Automated Reproducible Mechano-stimulator). Built using a series of linear stages, cameras, and stimulus holders, the ARM is more accurate at hitting the desired target, delivers stimuli faster, and decreases variability in delivery of von Frey hair filaments. We demonstrate that the ARM can be combined with traditional measurements of pain behavior and automated machine-learning based pipelines. Importantly, the ARM enables remote testing of mice with experimenters outside the testing room. Using remote testing, we found that mice habituated more quickly when an experimenter was not present and experimenter presence leads to significant sex-dependent differences in paw withdrawal and pain associated behaviors. Lastly, to demonstrate the utility of the ARM for neural circuit dissection of pain mechanisms, we combined the ARM with cellular-resolved microendoscopy in the amygdala, linking stimulus, behavior, and brain activity of amygdala neurons that encode negative pain states. Taken together, the ARM improves speed, accuracy, and robustness of mechanical pain assays and can be combined with automated pain detection systems and brain recordings to map central control of pain.

neuroscience↗

bioRxiv2020

A machine-vision approach for automated pain measurement at millisecond timescales

Objective and automatic measurement of pain in mice remains a barrier for discovery in both basic and translational neuroscience. Here we capture rapid paw kinematics during pain behavior in mice with high-speed videography and automated paw tracking with machine and deep learning approaches. Our statistical software platform, PAWS (Pain Assessment at Withdrawal Speeds), uses a univariate projection of paw position over time to automatically quantify fast paw dynamics at the onset of paw withdrawal and also lingering pain-related behaviors such as paw guarding and shaking. Applied to innocuous and noxious stimuli across six inbred mouse strains, a linear discriminant analysis reveals a two-dimensional subspace that separates painful from non-painful stimuli on one axis, and further distinguishes the severity of pain on the second axis. Automated paw tracking combined with PAWS reveals behaviorally-divergent mouse strains that display hypo- and hyper-sensitivity to mechanical stimuli. To demonstrate the efficacy of PAWS for detecting hypersensitivity to noxious stimuli, we chemogenetically activated pain-aversion neurons in the amygdala, which further separated the behavioral representation of pain-related behaviors along a low-dimensional path. Taken together, this automated pain quantification approach should increase the ease and objectivity of collecting rigorous behavioral data, and it is compatible with other neural circuit dissection tools for determining the mouse pain state.

neuroscience↗

bioRxiv2018

High-Speed Imaging of Paw Withdrawal Reflex to Objectively Assess Pain State in Mice

Rodents are often used for studying chronic pain mechanisms and developing new pain therapeutics, but objectively determining the animals pain state is a major challenge. To improve the precision of using reflexive withdrawal behaviors for interpreting the mouse pain state, we adopted high-speed videography to capture sub-second movement features of mice upon hind paw stimulation. We identified several parameters that are significantly different between behaviors evoked by innocuous and noxious stimuli, and combined them to map the mouse pain state through statistical modeling and machine learning. To test the utility of this approach, we determined the pain state triggered by von Frey hairs (VFHs) and optogenetic activation of two nociceptor populations. Our method reliably assesses the \"pain-like\" probability for each mouse paw withdrawal reflex under all scenarios, highlighting the improved precision of using this high resolution behavior-centered composite methodology to determine the mouse pain state from reflexive withdrawal assays.

neuroscience↗

bioRxiv2017

Sparse genetic tracing reveals regionally specific functional organization of mammalian nociceptors

The human distal limbs have a high spatial acuity for noxious stimuli but a low density of pain-sensing neurites. To elucidate mechanisms underlying the pain fovea, we sparsely traced non-peptidergic nociceptors across the body using a newly generated MrgprDCreERT2 mouse line. We found that mouse plantar paw skin also has a low density of MrgprD+ neurites, and individual arbors in different locations are comparable in size. Surprisingly, the central arbors of plantar paw and trunk innervating nociceptors have distinct morphologies in the spinal cord. This regional difference is well correlated with a heightened signal transmission for plantar paw circuits, as revealed by both spinal cord slice recordings and behavior assays. Taken together, our results elucidate a novel somatotopic functional organization of the mammalian pain system and suggest that regional central arbor structure could facilitate the magnification of plantar paw regions to contribute to the pain fovea.

neuroscience↗