Emergent epileptiform activity in spinal sensory circuits drives ectopic bursting in afferent axons and sensory dysfunction after cord injury.

ABSTRACT: Spinal cord injury leads to hyperexcitability and dysfunction in spinal sensory processing. As hyperexcitable circuits can become epileptiform, we explored whether such activity emerges in a thoracic spinal cord injury (SCI) contusion model of neuropathic pain. Recordings from spinal sensory axons in multiple below-lesion segmental dorsal roots demonstrated that SCI facilitated the emergence of spontaneous ectopic burst spiking in afferent axons, which were correlated across multiple adjacent dorsal roots. Burst frequency correlated with behavioral mechanosensitivity. The same bursting events were recruited by afferent stimulation, and timing interactions with ongoing spontaneous bursts revealed that recruitment was limited by a prolonged post-burst refractory period. Ectopic bursting in afferent axons was driven by GABAA receptor activation, presumably by conversion of subthreshold GABAergic interneuronal presynaptic axoaxonic inhibitory actions to suprathreshold spiking. Collectively, the emergence of stereotyped bursting circuitry with hypersynchrony, sensory input activation, post-burst refractory period, and reorganization of connectivity represent defining features of an epileptiform network. Indeed, these same features were reproduced in naive animals with the convulsant 4-aminopyridine (fampridine). We conclude that spinal cord injury promotes the emergence of epileptiform activity in spinal sensory networks that promote profound corruption of sensory signaling. This includes hyperexcitability and bursting by ectopic spiking in afferent axons that propagate bidirectionally by reentrant central and peripheral projections as well as sensory circuit hypoexcitability during the burst refractory period. More broadly, the work links circuit hyperexcitability to epileptiform circuit emergence, further strengthening it as a conceptual basis to understand features of sensory dysfunction and neuropathic pain.

Single-pericyte nanomechanics measured by contraction cytometry.

Pericytes line the microvasculature throughout the body and play a key role in regulating blood flow by constricting and dilating vessels. However, the biophysical mechanisms through which pericytes transduce microenvironmental chemical and mechanical cues to mediate vessel diameter, thereby impacting oxygen and nutrient delivery, remain largely unknown. This knowledge gap is clinically relevant as numerous diseases are associated with the aberrant contraction of pericytes, which are unusually susceptible to injury. Here, we report the development of a high-throughput hydrogel-based pericyte contraction cytometer that quantifies single-cell contraction forces from murine and human pericytes in different microvascular microenvironments and in the presence of competing vasoconstricting and vasodilating stimuli. We further show that murine pericyte survival in hypoxia is mediated by the mechanical microenvironment and that, paradoxically, pre-treating pericytes to reduce contraction increases hypoxic cell death. Moreover, using the contraction cytometer as a drug-screening tool, we found that cofilin-1 could be applied extracellularly to release murine pericytes from hypoxia-induced contractile rigor mortis and, therefore, may represent a novel approach for mitigating the long-lasting decrease in blood flow that occurs after hypoxic injury.

GABAergic synaptic scaling is triggered by changes in spiking activity rather than AMPA receptor activation.

Homeostatic plasticity represents a set of mechanisms that are thought to recover some aspect of neural function. One such mechanism called AMPAergic scaling was thought to be a likely candidate to homeostatically control spiking activity. However, recent findings have forced us to reconsider this idea as several studies suggest AMPAergic scaling is not directly triggered by changes in spiking. Moreover, studies examining homeostatic perturbations in vivo have suggested that GABAergic synapses may be more critical in terms of spiking homeostasis. Here, we show results that GABAergic scaling can act to homeostatically control spiking levels. We found that perturbations which increased or decreased spiking in cortical cultures triggered multiplicative GABAergic upscaling and downscaling, respectively. In contrast, we found that changes in AMPA receptor (AMPAR) or GABAR transmission only influence GABAergic scaling through their indirect effect on spiking. We propose that GABAergic scaling represents a stronger candidate for spike rate homeostat than AMPAergic scaling.

BRAND: a platform for closed-loop experiments with deep network models.

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.Significance.By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.