A new algorithm for drift compensation in multi-unit recordings of action potentials in peripheral autonomic nerves over time

A ganglionic intestinointestinal reflex activated by acute noxious challenge

To investigate noxious stimulation-responsive neural circuits that could influence the gut, we recorded from intestinally directed (efferent) nerve filaments dissected from mesenteric nerves close to the small intestine in anesthetized rats. These exhibited baseline multiunit activity that was almost unaffected by vagotomy (VagX) and reduced only slightly by cutting the splanchnic nerves. The activity was halved by hexamethonium (Hex) treatment. When an adjacent gut segment received an intraluminal stimulus 2,4,6-trinitrobenzenesulfonate (TNBS) in 30% ethanol, mesenteric efferent nerve activity increased for more than 1 h. The increased activity was almost unaffected by bilateral vagotomy or splanchnic nerve section, indicating a lack of central nervous involvement, but it was 60% reduced by hexamethonium. Spike sorting discriminated efferent single and predominantly single-unit spike trains that responded to TNBS, were unaffected by splachnectomy but were silenced by hexamethonium. After noxious stimulation of one segment, the adjacent segment showed no evidence of suppression of gut motility or vasoconstriction. We conclude that luminal application of a noxious stimulus to the small intestine activates an entirely peripheral, intestinointestinal reflex pathway. This pathway involves enteric intestinofugal neurons that excite postganglionic sympathetic neurons via a nicotinic synapse. We suggest that the final sympathetic efferent neurons that respond to a tissue damaging stimulus are distinct from vasoconstrictor, secretomotor, and motility inhibiting neurons.NEW & NOTEWORTHY An intraluminal noxious chemical stimulus applied to one segment of small intestine increased mesenteric efferent nerve activity to an adjacent segment. This was identified as a peripheral ganglionic reflex that did not require vagal or spinal connections. Hexamethonium blocked most, but not all, ongoing and reflex mesenteric efferent activity. The prevertebral sympathetic efferent neurons that are activated likely affect inflammatory and immune functions of other gut segments.

Introducing the STReaC (Spike Train Response Classification) toolbox

Background

This work presents a toolbox that implements methodology for automated classification of diverse neural responses to optogenetic stimulation or other changes in conditions, based on spike train recordings.

New Method

The toolbox implements what we call the Spike Train Response Classification algorithm (STReaC), which compares measurements of activity during a baseline period with analogous measurements during a subsequent period to identify various responses that might result from an event such as introduction of a sustained stimulus. The analyzed response types span a variety of patterns involving distinct time courses of increased firing, or excitation, decreased firing, or inhibition, or combinations of these. Excitation (inhibition) is identified from a comparative analysis of the spike density function (interspike interval function) for the baseline period relative to the corresponding function for the response period.

Results

The STReaC algorithm as implemented in this toolbox provides a user-friendly, tunable, objective methodology that can detect a variety of neuronal response types and associated subtleties. We demonstrate this with single-unit neural recordings of rodent substantia nigra pars reticulata (SNr) during optogenetic stimulation of the globus pallidus externa (GPe).

Comparison with existing methods

In several examples, we illustrate how the toolbox classifies responses in situations in which traditional methods (spike counting and visual inspection) either fail to detect a response or provide a false positive.

Conclusions

The STReaC toolbox provides a simple, efficient approach for classifying spike trains into a variety of response types defined relative to a period of baseline spiking.

Decoding Neuropathic Pain: Can We Predict Fluctuations of Propagation Speed in Stimulated Peripheral Nerve?

To understand neural encoding of neuropathic pain, evoked and resting activity of peripheral human C-fibers are studied via microneurography experiments. Before different spiking patterns can be analyzed, spike sorting is necessary to distinguish the activity of particular fibers of a recorded bundle. Due to single-electrode measurements and high noise contamination, standard methods based on spike shapes are insufficient and need to be enhanced with additional information. Such information can be derived from the activity-dependent slowing of the fiber propagation speed, which in turn can be assessed by introducing continuous "background" 0.125-0.25 Hz electrical stimulation and recording the corresponding responses from the fibers. Each fiber's speed propagation remains almost constant in the absence of spontaneous firing or additional stimulation. This way, the responses to the "background stimulation" can be sorted by fiber. In this article, we model the changes in the propagation speed resulting from the history of fiber activity with polynomial regression. This is done to assess the feasibility of using the developed models to enhance the spike shape-based sorting. In addition to human microneurography data, we use animal in-vitro recordings with a similar stimulation protocol as higher signal-to-noise ratio data example for the models.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Closed-Loop Vagus Nerve Stimulation for the Treatment of Cardiovascular Diseases: State of the Art and Future Directions

The autonomic nervous system exerts a fine beat-to-beat regulation of cardiovascular functions and is consequently involved in the onset and progression of many cardiovascular diseases (CVDs). Selective neuromodulation of the brain-heart axis with advanced neurotechnologies is an emerging approach to corroborate CVDs treatment when classical pharmacological agents show limited effectiveness. The vagus nerve is a major component of the cardiac neuroaxis, and vagus nerve stimulation (VNS) is a promising application to restore autonomic function under various pathological conditions. VNS has led to encouraging results in animal models of CVDs, but its translation to clinical practice has not been equally successful, calling for more investigation to optimize this technique. Herein we reviewed the state of the art of VNS for CVDs and discuss avenues for therapeutic optimization. Firstly, we provided a succinct description of cardiac vagal innervation anatomy and physiology and principles of VNS. Then, we examined the main clinical applications of VNS in CVDs and the related open challenges. Finally, we presented preclinical studies that aim at overcoming VNS limitations through optimization of anatomical targets, development of novel neural interface technologies, and design of efficient VNS closed-loop protocols.

Both high fat and high carbohydrate diets impair vagus nerve signaling of satiety

Obesity remains prevalent in the US. One potential treatment is vagus nerve stimulation (VNS), which activates the sensory afferents innervating the stomach that convey stomach volume and establish satiety. However, current VNS approaches and stimulus optimization could benefit from additional understanding of the underlying neural response to stomach distension. In this study, obesity-prone Sprague Dawley rats consumed a standard, high-carbohydrate, or high-fat diet for several months, leading to diet-induced obesity in the latter two groups. Under anesthesia, the neural activity in the vagus nerve was recorded with a penetrating microelectrode array while the stomach was distended with an implanted balloon. Vagal tone during distension was compared to baseline tone prior to distension. Responses were strongly correlated with stomach distension, but the sensitivity to distension was significantly lower in animals that had been fed the nonstandard diets. The results indicate that both high fat and high carbohydrate diets impair vagus activity.