Biology subjects

Svantesson, M.

Publications and source records attributed to Svantesson, M..

4 recordsLinked to original sources

bioRxiv2023

The role of cutaneous Aβ fibers in human nocifensive functions: Nerve block study on painful withdrawal reflex responses

The nociceptive withdrawal reflex (NWR) is a protective limb withdrawal response triggered by painful stimuli, used to assess spinal nociceptive excitability. Conventionally, the NWR is understood as having two reflex responses: a short-latency A{beta}-mediated response, considered tactile, and a longer-latency A{delta}-mediated response, considered nociceptive. However, nociceptors with conduction velocities similar to A{beta} tactile afferents have been identified in human skin. In this study, we investigated the effect of a preferential conduction block of A{beta} fibers on pain perception and NWR signaling evoked by intradermal electrical stimulation in healthy participants. We recorded a total of 198 NWR responses in the intact condition, and no dual reflex responses occurred within our latency bandwidth (50-150 ms). The current intensity required to evoke the NWR was magnitude higher than the perceptual pain threshold, indicating that NWR did not occur before pain was felt. In the block condition, when the A{beta}-mediated tuning fork sensation was lost while A{delta}-mediated nonpainful cooling was still detectable (albeit reduced), we observed that the reflex was abolished. Further, short-latency electrical pain intensity at pre-block thresholds was greatly reduced, with any residual pain sensation having a longer latency. Although electrical pain was unaffected at suprathreshold current intensities, the reflex could not be evoked despite a two-fold increase in the pre-block current intensity and a five-fold increase in the pre-block pulse duration. These observations lend support to the possible involvement of A{beta}-fiber inputs in pain and reflex signaling.

neuroscience↗

bioRxiv2022

Get a new perspective on EEG: Convolutional neural network encoders for parametric t-SNE.

Backgroundt-distributed stochastic neighbor embedding (t-SNE) is a method for reducing high-dimensional data to a low-dimensional representation and is mostly used for visualizing data. In parametric t-SNE, a neural network learns to reproduce this mapping. When used for EEG analysis, the data is usually first transformed into a set of features, but it is not known which features are optimal. New methodThe principle of t-SNE was used to train convolutional neural network (CNN) encoders to learn to produce both a high- and a low-dimensional representation, eliminating the need for feature engineering. A simple neighbor distribution based on ranked distances was used for the high-dimensional representation instead of the traditional normal distribution. To evaluate the method, the Temple University EEG Corpus was used to create three datasets with distinct EEG characters: 1) wakefulness and sleep, 2) interictal epileptiform discharges, and 3) seizure activity. ResultsThe CNN encoders for the three datasets produced low-dimensional representations of the datasets with a global and local structure that conformed well to the EEG characters and generalized to new data. Comparison to existing methodsCompared to parametric t-SNE for either a short-time Fourier transforms or wavelet representation of the datasets, the developed CNN encoders performed equally well but generally produced a higher degree of clustering. ConclusionsThe developed principle is promising and could be further developed to create general tools for exploring relations in EEG data, e.g., visual summaries of recordings, and trends for continuous EEG monitoring. It might also be used to generate features for other types of machine learning.

neuroscience↗

bioRxiv2022

Investigations into an overlooked early component of painful nociceptive withdrawal reflex responses in humans

IntroductionThe role of pain as a warning system necessitates a rapid transmission of information from the periphery for the execution of appropriate motor responses. The nociceptive withdrawal reflex (NWR) is a physiological response to protect the limb from a painful stimulus and is often considered an objective measure of spinal nociceptive excitability. The NWR is commonly defined by its latency in the presumed Ad-fiber range consistent with the canonical view that "fast pain" is signaled by Ad nociceptors. We recently demonstrated that human skin is equipped with ultrafast (A{beta} range) nociceptors. Here, we investigated the short-latency component of the reflex and explored the relationship between reflex latency and pain perception. MethodsWe revisited our earlier work on NWR measurements in which, following convention, only those reflex responses were selected that were in the presumed Ad range (taken to be latencies [≥]90 ms in that study). In our current analysis, we expanded the time window to search for shorter latency responses and compared those with pain ratings. ResultsIn both cohorts, we found an abundance of recordings with short-latency reflex responses. In nearly 90% of successful recordings, only single reflex responses (not dual) were seen which allowed us to compare pain ratings to reflex latencies. We found that shorter latency reflexes were just as painful as those in the conventional latency range. DiscussionWe found a preponderance of short-latency painful reflex responses. Based on this finding, we suggest that short-latency responses must be considered in future studies. We predict these might be signaled by the ultrafast nociceptors, warranting further investigation.

neuroscience↗

bioRxiv2020

Virtual EEG-electrodes: Convolutional neural networks as a method for upsampling or restoring channels

BackgroundIn clinical practice, EEGs are assessed visually. For practical reasons, recordings often need to be performed with a reduced number of electrodes and artifacts make assessment difficult. To circumvent these obstacles, different interpolation techniques can be utilized. These techniques usually perform better for higher electrode densities and values interpolated at areas far from electrodes can be unreliable. Using a method that learns the statistical distribution of the cortical electrical fields and predicts values may yield better results. New MethodGenerative networks based on convolutional layers were trained to upsample from 4 or 14 channels or to dynamically restore single missing channels to recreate 21 channel EEGs. 5,144 hours of data from 1,385 subjects of the Temple University Hospital EEG database were used for training and evaluating the networks. Comparison with Existing MethodThe results were compared to spherical spline interpolation. Several statistical measures were used as well as a visual evaluation by board certified clinical neurophysiologists. Overall, the generative networks performed significantly better. There was no difference between real and network generated data in the number of examples assessed as artificial by experienced EEG interpreters whereas for data generated by interpolation, the number was significantly higher. In addition, network performance improved with increasing number of included subjects, with the greatest effect seen in the range 5 - 100 subjects. ConclusionsUsing neural networks to restore or upsample EEG signals is a viable alternative to interpolation methods.

neuroscience↗