Functional subgroups of rat and human sensory neurons: a systematic review of electrophysiological properties

Sensory neurons are responsible for the generation and transmission of nociceptive signals from the periphery to the central nervous system. They encompass a broadly heterogeneous population of highly specialized neurons. The understanding of the molecular choreography of individual subpopulations is essential to understand physiological and pathological pain states. Recently, it became evident that species differences limit transferability of research findings between human and rodents in pain research. Thus, it is necessary to systematically compare and categorize the electrophysiological data gained from human and rodent dorsal root ganglia neurons (DRGs). In this systematic review, we condense the available electrophysiological data defining subidentities in human and rat DRGs. A systematic search on PUBMED yielded 30 studies on rat and 3 studies on human sensory neurons. Defined outcome parameters included current clamp, voltage clamp, cell morphology, pharmacological readouts, and immune reactivity parameters. We compare evidence gathered for outcome markers to define subgroups, offer electrophysiological parameters for the definition of neuronal subtypes, and give a framework for the transferability of electrophysiological findings between species. A semiquantitative analysis revealed that for rat DRGs, there is an overarching consensus between studies that C-fiber linked sensory neurons display a lower action potential threshold, higher input resistance, a larger action potential overshoot, and a longer afterhyperpolarization duration compared to other sensory neurons. They are also more likely to display an infliction point in the falling phase of the action potential. This systematic review points out the need of more electrophysiological studies on human sensory neurons.

Neural responses associated with attentional engagement and disengagement from threat in high socially anxious children: Evidence from temporal-spatial PCA

Background Cognitive models indicated that social anxiety disorder (SAD) would be caused and maintained by a biased attentional processing of threatening information. This study investigates whether socially anxious children may present impaired attentional engagement and disengagement from negative emotional faces, as well as their underlying event-related potential responses. Methods and findings Fifteen children with high levels of social anxiety (HSA; 9 boys; mean age = 9.99y; SD = 1.14) and twenty low socially anxious children (LSA; 16 boys; mean age = 10.47y; SD = 1.17) participated in a spatial cueing task in which they had to detect targets following neutral/disgusted faces in a valid or invalid location. No group effect was reported on reaction times [p>.05]. However, electrophysiological data showed lower P3a amplitude in HSA children compared with the LSA group when processing facial stimuli. They also reported larger N2 amplitudes for valid-disgusted targets and a larger P3a amplitude for the invalid-disgusted ones. Conclusion In terms of electrophysiological data, our results validated, the hypothesis of attentional disengagement difficulties in SAD children. We also confirm the idea that high levels of social anxiety are associated with cognitive control impairments and have a greater impact on the processing efficiency than on the performance effectiveness.

Automatic decomposition of electrophysiological data into distinct non-sinusoidal oscillatory modes

Neurophysiological signals are often noisy, non-sinusoidal, and consist of transient bursts. Extraction and analysis of oscillatory features (such as waveform shape and cross-frequency coupling) in such datasets remains difficult. This limits our understanding of brain dynamics and its functional importance. Here, we develop Iterated Masking Empirical Mode Decomposition (itEMD), a method designed to decompose noisy and transient single channel data into relevant oscillatory modes in a flexible, fully data-driven way without the need for manual tuning. Based on Empirical Mode Decomposition (EMD), this technique can extract single-cycle waveform dynamics through phase-aligned instantaneous frequency. We test our method by extensive simulations across different noise, sparsity, and non-sinusoidality conditions. We find itEMD significantly improves the separation of data into distinct non-sinusoidal oscillatory components and robustly reproduces waveform shape across a wide range of relevant parameters. We further validate the technique on multi-modal, multi-species electrophysiological data. Our itEMD extracts known rat hippocampal theta waveform asymmetry and identifies subject-specific human occipital alpha without any prior assumptions about the frequencies contained in the signal. Notably, it does so with significantly less mode mixing compared to existing EMD-based methods. By reducing mode mixing and simplifying interpretation of EMD results, itEMD will enable new analyses into functional roles of neural signals in behaviour and disease.

Revealing the Dynamic Nature of Amplitude Modulated Neural Entrainment With Holo-Hilbert Spectral Analysis

Patterns in external sensory stimuli can rapidly entrain neuronally generated oscillations observed in electrophysiological data. Here, we manipulated the temporal dynamics of visual stimuli with cross-frequency coupling (CFC) characteristics to generate steady-state visual evoked potentials (SSVEPs). Although CFC plays a pivotal role in neural communication, some cases reporting CFC may be false positives due to non-sinusoidal oscillations that can generate artificially inflated coupling values. Additionally, temporal characteristics of dynamic and non-linear neural oscillations cannot be fully derived with conventional Fourier-based analyses mainly due to trade off of temporal resolution for frequency precision. In an attempt to resolve these limitations of linear analytical methods, Holo-Hilbert Spectral Analysis (HHSA) was investigated as a potential approach for examination of non-linear and non-stationary CFC dynamics in this study. Results from both simulation and SSVEPs demonstrated that temporal dynamic and non-linear CFC features can be revealed with HHSA. Specifically, the results of simulation showed that the HHSA is less affected by the non-sinusoidal oscillation and showed possible cross frequency interactions embedded in the simulation without any a priori assumptions. In the SSVEPs, we found that the time-varying cross-frequency interaction and the bidirectional coupling between delta and alpha/beta bands can be observed using HHSA, confirming dynamic physiological signatures of neural entrainment related to cross-frequency coupling. These findings not only validate the efficacy of the HHSA in revealing the natural characteristics of signals, but also shed new light on further applications in analysis of brain electrophysiological data with the aim of understanding the functional roles of neuronal oscillation in various cognitive functions.

Fruit Herbivory Alters Plant Electrome: Evidence for Fruit-Shoot Long-Distance Electrical Signaling in Tomato Plants

The electrical activity of tomato plants subjected to fruit herbivory was investigated. The study aimed to test the hypothesis that tomato fruits transmit long-distance electrical signals to the shoot when subjected to herbivory. For such, time series classification by machine learning techniques and analyses related to the oxidative response were employed. Tomato plants (cv. “Micro-Tom”) were placed into a Faraday's cage and an electrode pair was inserted in the fruit's peduncle. Helicoverpa armigera caterpillars were placed on the fruit (either green and ripe) for 24 h. The time series were recorded before and after the fruit's exposure of the caterpillars. The plant material for chemical analyses was collected 24 and 48 h after the end of the acquisition of electrophysiological data. The time series were analyzed by the following techniques: Fast Fourier Transform (FFT), Wavelet Transform, Power Spectral Density (PSD), and Approximate Entropy. The following features from FFT, PSD, and Wavelet Transform were used for PCA (Principal Component Analysis): average, maximum and minimum value, variance, skewness, and kurtosis. Additionally, these features were used in Machine Learning (ML) analyses for looking for classifiable patterns between tomato plants before and after fruit herbivory. Also, we compared the electrome before and after herbivory in the green and ripe fruits. To evaluate an oxidative response in different organs, hydrogen peroxide, superoxide anion, catalase, ascorbate peroxidase, guaiacol peroxidase, and superoxide dismutase activity were evaluated in fruit and leaves. The results show with 90% of accuracy that the electrome registered in the fruit's peduncle before herbivory is different from the electrome during predation on the fruits. Interestingly, there was also a sharp difference in the electrome of the green and ripe fruits' peduncles before, but not during, the herbivory, which demonstrates that the signals generated by the herbivory stand over the others. Biochemical analysis showed that herbivory in the fruit triggered an oxidative response in other parts of the plant. Here, we demonstrate that the fruit perceives biotic stimuli and transmits electrical signals to the shoot of tomato plants. This study raises new possibilities for studies involving electrical signals in signaling and systemic response, as well as for the applicability of ML to classify electrophysiological data and its use in early diagnosis.