Intraneural cyst of the peroneal nerve in child

This article presents an observation of an intraneural cyst of the peroneal nerve in a 16-year-old boy after a knee injury. Surgical treatment of an intraneural cyst of the peroneal nerve was performed 9 months after the appearance of peroneal nerve neuropathy. One month after the operation, the peroneal muscle strength increased from 2 to 4 points on the MRC scale; positive dynamics after the operation was also noted according to the data of electroneuromyography and ultrasound examination. Children often observed intraneural cyst of the peroneal nerve at the knee (90% of cases among all sites intraneural cysts). For diff erential diagnosis with compression-ischemic neuropathy and nerve cysts, clinical and neurophysiological data should be supplemented by ultrasound and/or MRI examination. Early diagnosis and surgical treatment are critical to the full recovery of motor and sensory function.

Understanding Harmonic Structures Through Instantaneous Frequency

The analysis of harmonics and non-sinusoidal waveform shape in neurophysiological data is growing in importance. However, a precise definition of what constitutes a harmonic is lacking. In this paper, we propose a rigorous definition of when to consider signals to be in a harmonic relationship based on an integer frequency ratio, constant phase, and a well-defined joint instantaneous frequency. We show this definition is linked to extrema counting and Empirical Mode Decomposition (EMD). We explore the mathematics of our definition and link it to results from analytic number theory. This naturally leads to us to define two classes of harmonic structures, termed strong and weak, with different extrema behaviour. We validate our framework using both simulations and real data. Specifically, we look at the harmonics structure in the FitzHugh-Nagumo model and the non-sinusoidal hippocampal theta oscillation in rat local field potential data. We further discuss how our definition helps to address mode splitting in EMD. A clear understanding of when harmonics are present in signals will enable a deeper understanding of the functional and clinical roles of non-sinusoidal neural oscillations.

Distinct neurocognitive bases for social trait judgments of faces in autism spectrum disorder

Autism spectrum disorder (ASD) is characterized by difficulties in social processes, interactions, and communication. Yet, the neurocognitive bases underlying these difficulties are unclear. Here, we triangulated the trans-diagnostic approach to personality, social trait judgments of faces, and neurophysiology to investigate (1) the relative position of autistic traits in a comprehensive social-affective personality space and (2) the distinct associations between the social-affective personality dimensions and social trait judgment from faces in individuals with ASD and neurotypical individuals. We collected personality and facial judgment data from a large sample of online participants (N = 89 self-identified ASD; N = 308 neurotypical controls). Factor analysis with 33 sub-scales of 10 social-affective personality questionnaires identified a 4-dimensional personality space. This analysis revealed that ASD and control participants did not differ significantly along the personality dimensions of empathy and prosociality, antisociality, or social agreeableness. However, the associations between these dimensions and judgments of facial trustworthiness and warmth differed across groups. Neurophysiological data also indicated that ASD and control participants might rely on distinct neuronal representations for judging trustworthiness and warmth from faces. These results suggest that the atypical association between social-affective personality and social trait judgment from faces may contribute to the social and affective difficulties associated with ASD.

Sudomotor dysfunction in patients with gluten neuropathy

Background and aim Gluten neuropathy (GN) is a common neurological manifestation of gluten sensitivity (GS), characterized by serological evidence of GS, while other risk factors for developing neuropathy are absent. The degree of small fiber dysfunction in GN has not been studied in depth to date. Small fiber involvement may lead to pain, thermal perception abnormalities, and sweat gland dysfunction. Sudomotor innervation refers to the cholinergic innervation of the sympathetic nervous system through small fibers in the sweat glands. The aim of our study was to assess the sudomotor function of GN patients. Methods Patients with GN were recruited. Clinical and neurophysiological data were obtained. HLA-DQ genotyping was performed. The skin electrochemical conductance (ESC) was measured with SUDOSCANTM. Results Thirty-two patients (25 males, mean age 69.5±10.2 years) were recruited. Thirteen patients (40.6%) had abnormal sudomotor function of the hands. Sixteen patients (50%) had abnormal sudomotor function of the feet. Twenty-one patients (65.6%) had abnormal sudomotor function of either the hands or feet. Sudomotor dysfunction did not correlate with the type of neuropathy (length-dependent neuropathy or sensory ganglionopathy), gluten-free diet adherence, severity of neuropathy, and duration of disease or HLA-DQ genotype. No differences in the ESC were found between patients with painful and patients with painless GN. Conclusion Sudomotor dysfunction affects two-thirds of patients with GN. The lack of correlation between pain and sudomotor dysfunction suggests different patterns of small fiber involvement in patients with GN.

P-sort: an open-source software for cerebellar neurophysiology

Algorithms that perform spike sorting depend on waveforms to cluster spikes. However, a cerebellar Purkinje-cell produces two types of spikes; simple and complex spikes. A complex spike coincides with the suppression of generating simple spikes. Here, we recorded neurophysiological data from three species and developed a spike analysis software named P-sort that relies on this statistical property to improve both the detection and the attribution of simple and complex spikes in the cerebellum.

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.

Microstimulation Is a Promising Approach in Achieving Better Lead Placement in Subthalamic Nucleus Deep Brain Stimulation Surgery

Background: The successful application of subthalamic nucleus (STN) deep brain stimulation (DBS) surgery relies mostly on optimal lead placement, whereas the major challenge is how to precisely localize STN. Microstimulation, which can induce differentiating inhibitory responses between STN and substantia nigra pars reticulata (SNr) near the ventral border of STN, has indicated a great potential of breaking through this barrier.Objective: This study aims to investigate the feasibility of localizing the boundary between STN and SNr (SSB) using microstimulation and promote better lead placement.Methods: We recorded neurophysiological data from 41 patients undergoing STN-DBS surgery with microstimulation in our hospital. Trajectories with typical STN signal were included. Microstimulation was applied near the bottom of STN to determine SSB, which was validated by the imaging reconstruction of DBS leads.Results: In most trajectories with microstimulation (84.4%), neuronal firing in STN could not be inhibited by microstimulation, whereas in SNr long inhibition was observed following microstimulation. The success rate of localizing SSB was significantly higher in trajectories with microstimulation than those without. Moreover, results from imaging reconstruction and intraoperative neurological assessments demonstrated better lead location and higher therapeutic effectiveness in trajectories with microstimulation and accurately identified SSB.Conclusion: Microstimulation on microelectrode recording is an effective approach to localize the SSB. Our data provide clinical evidence that microstimulation can be routinely employed to achieve better lead placement.

Decreased preparatory activation and inattention to cues suggest lower activation of proactive cognitive control among high procrastinating students

Procrastination is a voluntary delay in completing an important task while being aware that this behavior may lead to negative outcomes. It has been shown that an increased tendency to procrastinate is associated with deficits in some aspects of cognitive control. However, none of the previous studies investigated these dysfunctions through the lenses of the Dual Mechanisms Framework, which differentiates proactive and reactive modes of control. The present study was designed to fill this gap, using behavioral and neurophysiological assessment during the completion of the AX-Continuous Performance Task (AX-CPT) by high (HP) and low (LP) procrastinating students (N = 139). Behavioral results indicated that HP (vs. LP) were characterized by increased attentional fluctuations (higher reaction time variability) and reduction in some indices of proactive cognitive control (lower d’-context and A-cue bias, but similar PBIs). Furthermore, the neurophysiological data showed that HP, compared with LP, allocated less attentional resources (lower P3b) to cues that help to predict the correct responses to upcoming probes. They also responded with reduced preparatory activity (smaller CNV) after cues presentation. The two groups did not differ in neural responses linked to conflict detection and inhibition (similar N2 and P3a). Obtained findings indicate that HP might present deficits in some cognitive functions that are essential for effective proactive control engagement, along with preserved levels of reactive cognitive control. In the present paper, we discuss the potential neural and cognitive mechanisms responsible for the observed effects.