Modulations in high-density EEG during the suppression of phantom-limb pain with neurostimulation in upper limb amputees.

Phantom limb pain (PLP) is a distressing and persistent sensation that occurs after the amputation of a limb. While medication-based treatments have limitations and adverse effects, neurostimulation is a promising alternative approach whose mechanism of action needs research, including electroencephalographic (EEG) recordings for the assessment of cortical manifestation of PLP relieving effects. Here we collected and analyzed high-density EEG data in 3 patients (P01, P02, and P03). Peripheral nerve stimulation suppressed PLP in P01 but was ineffective in P02. In contrast, transcutaneous electrical nerve stimulation was effective in P02. In P03, spinal cord stimulation was used to suppress PLP. Changes in EEG oscillatory components were analyzed using spectral analysis and Petrosian fractal dimension. With these methods, changes in EEG spatio-spectral components were found in the theta, alpha, and beta bands in all patients, with these effects being specific to each individual. The changes in the EEG patterns were found for both the periods when PLP level was stationary and the periods when PLP was gradually changing after neurostimulation was turned on or off. Overall, our findings align with the proposed roles of brain rhythms in thalamocortical dysrhythmia or disruption of cortical excitation and inhibition which has been linked to neuropathic pain. The individual differences in the observed effects could be related to the specifics of each patient's treatment and the unique spectral characteristics in each of them. These findings pave the way to the closed-loop systems for PLP management where neurostimulation parameters are adjusted based on EEG-derived markers.

Opposite effects of rapid auditory stimulation on tetanized and non-tetanized tone of adjacent frequency: Mismatch negativity study.

Our study describes the effects of sensory tetanization on neurophysiological and behavioral measures in humans linking cellular studies of long-term potentiation with high-level brain processes. Rapid (every 75ms) presentation of pure tone (1020 Hz, 50ms) for 2 minutes was preceded and followed by oddball blocks that contained the same stimulus presented as deviant (probability of 5-10%) interspersed with standard (80-90%) and deviant tones (5-10%) of adjacent frequencies (1000 and 980Hz, respectively). Mismatch negativity (MMN) component in response to tetanized tone (1020Hz), while being similar to MMN for non-tetanized tone before tetanization, became larger than that after tetanization, pointing to the increase in cortical differentiation of these tones. However, this differentiation was partially due to the MMN decrease after tetanization for tones adjacent to tetanized frequency, suggesting the influence of lateral inhibition to this effect. Although MMN correlated with tone discriminability in a psychophysical task, the behavioral improvement after tetanization was not statistically detectable. To conclude, short-term auditory tetanization affects cortical representation of tones that are not limited to the tetanized stimuli.

Peripheral nerve stimulation enables somatosensory feedback while suppressing phantom limb pain in transradial amputees.

To simultaneously treat phantom limb pain (PLP) and restore somatic sensations using peripheral nerve stimulation (PNS), two bilateral transradial amputees were implanted with stimulating electrodes in the proximity of the medial, ulnar and radial nerves. Application of PNS evoked tactile and proprioceptive sensations in the phantom hand. Both patients learned to determine the shape of invisible objects by scanning a computer tablet with a stylus while receiving feedback based on PNS or transcutaneous electrical nerve stimulation (TENS). Оne patient learned to use PNS as feedback from the prosthetic hand that grasped objects of different sizes. PNS abolished PLP completely in one patient and reduced it by 40-70% in the other. We suggest incorporating PNS and/or TENS in active tasks to reduce PLP and restore sensations in amputees.

An adaptive paradigm for detecting the individual duration of the preparatory period in the choice reaction time task.

According to the sequential stage model, the selection and the execution of a motor response are two distinct independent processes. Here, we propose a new adaptive paradigm for identifying the individual duration of the response preparatory period based on the motor reaction time (RT) data. The results are compared using the paradigm with constant values of the preparatory period. Two groups of participants performed on either an easy (Group 1) or a hard (Group 2) response selection task with two types of stimuli based on the preparatory period parameters: (1) stimuli with a constant preparatory period duration of 0 or 1200 ms and (2) stimuli with adaptive preparatory period durations. Our analysis showed an increase in the duration of the response selection process as a function of increasing task complexity when using both paradigms with constant and adaptive values of the preparatory period duration. We conclude that the adaptive paradigm proposed in the current paper has several important advantages over the constant paradigm in terms of measuring the response accuracy while being equally efficiently in capturing other critical response parameters.

Fast parametric curve matching (FPCM) for automatic spike detection.

Objective. Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers an attractive way to localize epileptogenic cortical structures for surgery planning purposes. Interictal spike detection in lengthy multichannel data is a daunting task that is still often performed manually. This frequently limits such an analysis to a small portion of the data which renders the appropriate risks of missing the potentially epileptogenic region. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, none of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM).Approach. Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to verbalized text-book like descriptions of the expected interictal event morphology allow us to accomplish the spike detection task.Main results. The results of simulations mimicking typical low SNR scenarios show the robustness and high receiver operating characteristic AUC values of the FPCM method as compared to the spike detection performed using more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to rat ECoG data, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist.Significance. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of an arbitrary target morphology, it is likely to complement the existing arsenal of means for analysis of noisy interictal datasets.

Generalization of sustained neurophysiological effects of short-term auditory 13-Hz stimulation to neighbouring frequency representation in humans.

A fuller understanding of the effects of auditory tetanization in humans would inform better language and sensory learning paradigms; however, there are still unanswered questions. Here, we probe sustained changes in the event-related potentials (ERPs) to 1020- and 980-Hz tones following a rapid presentation of 1020-Hz tone (every 75 ms, 13.3 Hz, tetanization). Consistent with some previous studies, we revealed the increase in the P2 ERP component after tetanization. Contrary to some other studies, we did not observe the expected N1 increase after tetanization even in the identical experimental sequence. We detected a significant N1 decrease after tetanization. Expanding previous research, we showed that P2 increase and N1 decrease are not specific to the stimulus type (tetanized 1020 Hz and non-tetanized 980 Hz), suggesting the generalizability of tetanization effect to the not-stimulated auditory tones, at least to those of the neighbouring frequency. The ERPs' tetanization effects were observed for at least 30 min-the most prolonged interval examined, consistent with the duration of long-term potentiation, LTP. In addition, the tetanization effects were detectable in the blocks where the participants watched muted videos, an experimental setting that can be easily used in children and other challenging groups. Thus, auditory 13-Hz stimulation affects brain processing of tones including those of neighbouring frequencies.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography.

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts' involvement. As also revealed by our study, experts' opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts' knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.