Multisite Simultaneous Neural Recording of Motor Pathway in Free-Moving Rats

Neural interfaces typically focus on one or two sites in the motoneuron system simultaneously due to the limitation of the recording technique, which restricts the scope of observation and discovery of this system. Herein, we built a system with various electrodes capable of recording a large spectrum of electrophysiological signals from the cortex, spinal cord, peripheral nerves, and muscles of freely moving animals. The system integrates adjustable microarrays, floating microarrays, and microwires to a commercial connector and cuff electrode on a wireless transmitter. To illustrate the versatility of the system, we investigated its performance for the behavior of rodents during tethered treadmill walking, untethered wheel running, and open field exploration. The results indicate that the system is stable and applicable for multiple behavior conditions and can provide data to support previously inaccessible research of neural injury, rehabilitation, brain-inspired computing, and fundamental neuroscience.

Learning positive social information reduces racial bias as indexed by N400 response

The present study used EEG/ERPs to detect the activation of implicit stereotypical representations associated to other-race (OR) people and the modulation of such activation through the previous presentation of positive vs. neutral social information. Electrophysiological signals were recorded in 40 Italian Caucasian participants, unaware of the overall study’s purpose. They were presented with 285 sentences that could either violate, non-violate (e.g., “the Roma girl was involved in a robbery) or be neutral with regard to stereotypical concepts concerning other-race people (e.g. Asians, Africans, Arabic). ERPs were time-locked to the terminal words. Prior to the sentence reading task, participants were exposed to a 10 minutes colourful video documentary. While the experimental group was presented a video containing images picturing other-race characters involved in “prestigious” activities that violated stereotypical negative assumptions (e.g. a black neurosurgeon leading a surgery team), the control group viewed a neutral documentary about flora and fauna. EEG signals were then recorded during the sentence reading task to explore whether the previous exposure to the experimental video could modulate the detection of incongruence in the sentences violating stereotypes, as marked by the N400 response. A fictitious task was adopted, consisted in detecting rare animal names. Indeed, only the control group showed a greater N400 response (350–550 ms) to words incongruent with ethnic stereotypes compared to congruent and neutral ones, thus suggesting the presence of a racial bias. No N400 response was found for the experimental group, suggesting a lack of negative expectation for OR individuals. The swLORETA inverse solution, performed on the prejudice-related N400 showed that the Inferior Temporal and the Superior and Middle Frontal Gyri were the strongest N400 intra-cortical sources. Regardless of the experimental manipulation, Congruent terminal words evoked a greater P300 response (500–600 ms) compared to incongruent and neutral ones and a late frontal positivity (650–800 ms) was found to be larger to sentences involving OR than own-race characters (either congruent or incongruent with the prejudice) thus possibly indicating bias-free perceptual in-group/out-group categorization processes. The data showed how it is possible to modulate a pre-existing racial prejudice (as reflected by N400 effect) through exposure to positive media-driven information about OR people. Further follow-up studies should determine the duration in time, and across contexts, of this modulatory effect.

Amplitude modulated transcranial alternating current stimulation (AM-TACS) efficacy evaluation via phosphene induction

Amplitude modulated transcranial alternating current stimulation (AM-tACS) is a novel method of electrostimulation which enables the recording of electrophysiological signals during stimulation, thanks to an easier removable stimulation artefact compared to classical electrostimulation methods. To gauge the neuromodulatory potential of AM-tACS, we tested its capacity to induce phosphenes as an indicator of stimulation efficacy. AM-tACS was applied via a two-electrode setup, attached on FpZ and below the right eye. AM-tACS waveforms comprised of different carrier (50 Hz, 200 Hz, 1000 Hz) and modulation frequencies (8 Hz, 16 Hz, 28 Hz) were administered with at maximum 2 mA peak-to-peak stimulation strength. TACS conditions in the same frequencies were used as a benchmark for phosphene induction. AM-tACS conditions using a 50 Hz carrier frequency were able to induce phosphenes, but with no difference in phosphene thresholds between modulation frequencies. AM-tACS using a 200 Hz or 1000 Hz carrier frequency did not induce phosphenes. TACS conditions induced phosphenes in line with previous studies. Stimulation effects of AM-tACS conditions were independent of amplitude modulation and instead relied solely on the carrier frequency. A possible explanation may be that AM-tACS needs higher stimulation intensities for its amplitude modulation to have a neuromodulatory effect.

Automated Robust Interpretation of Intraoperative Electrophysiological Signals – A Bayesian Deep Learning Approach

Intraoperative neurophysiological monitoring (IONM) is an essential tool during numerous surgical interventions to assess and monitor the functional integrity of neural structures at risk. A reliable signal interpretation is of importance to support medical staff by reducing manual evaluation. Deep learning (DL) techniques proved to be a robust tool for the analysis of neurophysiological data. The large amount of required manually labeled data as well as the lack of interpretability of the results however often limit the use of DL in medical scenarios. A possible way to tackle these obstacles is the utilization of Bayesian deep learning (BDL) methods. The modelling of uncertainties in the network parameters and the thereby possible quantification of predictive uncertainties allows both the identification of potential erroneous predictions as well as the targeted selection of informative signals in the context of active learning. To evaluate the applicability of BDL for the analysis of electrophysiological data as well as to increase the training efficiency by active learning, we implemented a multi-task Bayesian Convolutional Neural Network (BCNN) for the simultaneous classification of action potentials and the assessment of relevant signal characteristics (latency, maximum, minimum). We compare the results for electromyographical signals (EMG), containing in total approximately twelve thousand signals from 34 patients, with both a traditional non-Bayesian single-task and multi-task CNN. For all models, including the BCNN, we could achieve similar performances with detection rates over 97% accuracy. Further, we could improve training efficiency of the BCNN using pool-based active learning and therefore significantly reduce the required amount of manual labeling. The evaluated predictive uncertainties of the BCNN prove useful both for the efficient selection of informative signals in the context of active learning as well as the interpretation of the predictive posterior distribution and therefore trustworthiness of the classifications.

Electrophysiological biomarkers of behavioral dimensions from cross-species paradigms

There has been a fundamental failure to translate preclinically supported research into clinically efficacious treatments for psychiatric disorders. One of the greatest impediments toward improving this species gap has been the difficulty of identifying translatable neurophysiological signals that are related to specific behavioral constructs. Here, we present evidence from three paradigms that were completed by humans and mice using analogous procedures, with each task eliciting candidate a priori defined electrophysiological signals underlying effortful motivation, reinforcement learning, and cognitive control. The effortful motivation was assessed using a progressive ratio breakpoint task, yielding a similar decrease in alpha-band activity over time in both species. Reinforcement learning was assessed via feedback in a probabilistic learning task with delta power significantly modulated by reward surprise in both species. Additionally, cognitive control was assessed in the five-choice continuous performance task, yielding response-locked theta power seen across species, and modulated by difficulty in humans. Together, these successes, and also the teachings from these failures, provide a roadmap towards the use of electrophysiology as a method for translating findings from the preclinical assays to the clinical settings.

Analysis of electrogastroenterographic signals using the computer method

Electrogastroenterography is a field that studies the condition of the gastrointestinal tract. Electrophysiological signals contain artifacts that are not directly related to the activity of the organism, and the filtration of such artifacts is an important issue. Electrogastroenterography is a field that studies the condition of the gastrointestinal tract. This method is associated with radiography, ultrasound, endoscopy. In these methods, electrical signals are taken from the surface of the abdominal cavity. For this purpose, the issues of elimination of artifactors, processing and analysis of signals on the example of electrogastroenterograms for processing and measurement of electrophysiological signals were considered. Keywords:Electrogastroenterography, non-stationary signals, electrophysiological signals, computer processing, wavelet conversion, frequency modulation, wavelet Morle function