A proposal for harmonizing clinical neurophysiology training in the Europe, Middle East and Africa Chapter of the International Federation of Clinical Neurophysiology.

There are significant differences in duration and intensity of clinical neurophysiology specialty training within the countries of the Europe, Middle East and Africa Chapter of the International Federation of Clinical Neurophysiology. We address these differences by proposing recommendations which may facilitate harmonisation of training and education within the Chapter. They arose from two workshops whose recommendations were then circulated widely within national societies in the Chapter for feedback and for consensus. The recommendations are applicable to clinical neurophysiology as a medical monospecialty and/or as a subspecialty (usually of neurology). We make a number of recommendations on governance and regulation of training, on the requirements for competence and the numbers of various examinations and tests performed by trainees, some under supervision. We also recommend a modular approach considering primary and complementary modules. Primary modules are electroencephalography, electromyography, nerve conduction studies and evoked potentials, while complementary ones include sleep analysis, intraoperative monitoring, small fibre testing, peripheral nerve and muscle ultrasound, intracortical recordings, and analysis of movement disorders. It is recommended that national examinations should include a variety of techniques to assess knowledge and judgement, practical skills, teamwork, communication skills, as well as safety and quality. The aim of the suggested recommendations is to harmonize clinical neurophysiology training in the member societies throughout the Chapter. It is realised that this may mean that the numbers for competence are aspirational for some, though ways to mitigate this, for instance through supranational training centres, are also discussed.

Multifunctional fibers enable modulation of cortical and deep brain activity during cognitive behavior in macaques.

Recording and modulating neural activity in vivo enables investigations of the neurophysiology underlying behavior and disease. However, there is a dearth of translational tools for simultaneous recording and localized receptor-specific modulation. We address this limitation by translating multifunctional fiber neurotechnology previously only available for rodent studies to enable cortical and subcortical neural recording and modulation in macaques. We record single-neuron and broader oscillatory activity during intracranial GABA infusions in the premotor cortex and putamen. By applying state-space models to characterize changes in electrophysiology, we uncover that neural activity evoked by a working memory task is reshaped by even a modest local inhibition. The recordings provide detailed insight into the electrophysiological effect of neurotransmitter receptor modulation in both cortical and subcortical structures in an awake macaque. Our results demonstrate a first-time application of multifunctional fibers for causal studies of neuronal activity in behaving nonhuman primates and pave the way for clinical translation of fiber-based neurotechnology.

A scoping review of current and emerging techniques for evaluation of peripheral nerve health, degeneration, and regeneration: part 1, neurophysiology.

Peripheral neuroregeneration research and therapeutic options are expanding exponentially. With this expansion comes an increasing need to reliably evaluate and quantify nerve health. Valid and responsive measures that can serve as biomarkers of the nerve status are essential for both clinical and research purposes for diagnosis, longitudinal follow-up, and monitoring the impact of any intervention. Furthermore, such biomarkers can elucidate regeneration mechanisms and open new avenues for research. Without these measures, clinical decision-making falls short, and research becomes more costly, time-consuming, and sometimes infeasible. As a companion to Part 2, which is focused on non-invasive imaging, Part 1 of this two-part scoping review systematically identifies and critically examines many current and emerging neurophysiological techniques that have the potential to evaluate peripheral nerve health, particularly from the perspective of regenerative therapies and research.

Modular, cement-free, customized headpost and connector-chamber implants for macaques.

BACKGROUND: Neurophysiological studies with awake macaques typically require chronic cranial implants. Headpost and connector-chamber implants are used to allow head stabilization and to house connectors of chronically implanted electrodes, respectively. NEW METHOD: We present long-lasting, modular, cement-free headpost implants made of titanium that consist of two pieces: a baseplate and a top part. The baseplate is implanted first, covered by muscle and skin and allowed to heal and osseointegrate for several weeks to months. The percutaneous part is added in a second, brief surgery. Using a punch tool, a perfectly round skin cut is achieved providing a tight fit around the implant without any sutures. We describe the design, planning and production of manually bent and CNC-milled baseplates. We also developed a remote headposting technique that increases handling safety. Finally, we present a modular, footless connector chamber that is implanted in a similar two-step approach and achieves a minimized footprint on the skull. RESULTS: Twelve adult male macaques were successfully implanted with a headpost and one with the connector chamber. To date, we report no implant failure, great headpost stability and implant condition, in four cases even more than 9 years post-implantation. COMPARISON WITH EXISTING METHODS: The methods presented here build on several related previous methods and provide additional refinements to further increase implant longevity and handling safety. CONCLUSIONS: Optimized implants can remain stable and healthy for at least 9 years and thereby exceed the typical experiment durations. This minimizes implant-related complications and corrective surgeries and thereby significantly improves animal welfare.

Invasive Computational Psychiatry.

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.

An implantable neurophysiology platform: Broadening research capabilities in free-living and non-traditional animals.

Animal-borne sensors that can record and transmit data ("biologgers") are becoming smaller and more capable at a rapid pace. Biologgers have provided enormous insight into the covert lives of many free-ranging animals by characterizing behavioral motifs, estimating energy expenditure, and tracking movement over vast distances, thereby serving both scientific and conservational endpoints. However, given that biologgers are usually attached externally, access to the brain and neurophysiological data has been largely unexplored outside of the laboratory, limiting our understanding of how the brain adapts to, interacts with, or addresses challenges of the natural world. For example, there are only a handful of studies in free-living animals examining the role of sleep, resulting in a wake-centric view of behavior despite the fact that sleep often encompasses a large portion of an animal's day and plays a vital role in maintaining homeostasis. The growing need to understand sleep from a mechanistic viewpoint and probe its function led us to design an implantable neurophysiology platform that can record brain activity and inertial data, while utilizing a wireless link to enable a suite of forward-looking capabilities. Here, we describe our design approach and demonstrate our device's capability in a standard laboratory rat as well as a captive fox squirrel. We also discuss the methodological and ethical implications of deploying this new class of device "into the wild" to fill outstanding knowledge gaps.

Preparation of Rat Sciatic Nerve for Ex Vivo Neurophysiology.

Ex vivo preparations enable the study of many neurophysiological processes in isolation from the rest of the body while preserving local tissue structure. This work describes the preparation of rat sciatic nerves for ex vivo neurophysiology, including buffer preparation, animal procedures, equipment setup and neurophysiological recording. This work provides an overview of the different types of experiments possible with this method. The outlined method aims to provide 6 h of stimulation and recording on extracted peripheral nerve tissue in tightly controlled conditions for optimal consistency in results. Results obtained using this method are A-fibre compound action potentials (CAP) with peak-to-peak amplitudes in the millivolt range over the entire duration of the experiment. CAP amplitudes and shapes are consistent and reliable, making them useful to test and compare new electrodes to existing models, or the effects of interventions on the tissue, such as the use of chemicals, surgical alterations, or neuromodulatory stimulation techniques. Both conventional commercially available cuff electrodes with platinum-iridium contacts and custom-made conductive elastomer electrodes were tested and gave similar results in terms of nerve stimulus strength-duration response.

The landscape of neurophysiological outcome measures in ALS interventional trials: A systematic review.

OBJECTIVE: We collated all interventional clinical trials in amyotrophic lateral sclerosis (ALS), which utilised at least one neurophysiological technique as a primary or secondary outcome measure. By identifying the strengths and limitations of these studies, we aim to guide study design in future trials. METHODS: We conducted and reported this systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Eight databases were searched from inception. In total, 703 studies were retrieved for screening and eligibility assessment. RESULTS: Dating back to 1986, 32 eligible interventional clinical trials were identified, recruiting a median of 30 patients per completed trial. The most widely employed neurophysiological techniques were electromyography, motor unit number estimation (including motor unit number index), neurophysiological index and transcranial magnetic stimulation (including resting motor threshold and short-interval intracortical inhibition). Almost 40% of trials reported a positive outcome with respect to at least one neurophysiological measure. The interventions targeted either ion channels, immune mechanisms or neuronal metabolic pathways. CONCLUSIONS: Neurophysiology offers many promising biomarkers that can be utilised as outcome measures in interventional clinical trials in ALS. When selecting the most appropriate technique, key considerations include methodological standardisation, target engagement and logistical burden. SIGNIFICANCE: Future trial design in ALS would benefit from a standardised, updated and easily accessible repository of neurophysiological outcome measures.

Seventy years since the invention of the averaging technique in Neurophysiology: Tribute to George Duncan Dawson / 70 anos da invenção da técnica de promediação na Neurofisiologia: Tributo a George Duncan Dawson

ABSTRACT In 1951, the physiologist George Duncan Dawson presented his work with the averaging of the signal in the evoked potentials (EPs), opening a new stage in the development of clinical neurophysiology. The authors present aspects of Professor Dawson's biography and a review of his work on the EPs and, mainly, the article reveals the new technique in detail that would allow the growth of the clinical application of the visual, auditory, and somatosensory EPs.

RESUMO Em 1951 o fisiologista George Duncan Dawson apresentou seu trabalho com a promediação de sinal nos potenciais evocados, abrindo uma nova etapa no desenvolvimento da neurofisiologia clínica. Os autores apresentam aspectos da biografia do professor Dawson e uma revisão de seus trabalhos sobre os potenciais evocados, principalmente do artigo que mostrava a nova técnica, que viria a permitir o crescimento da aplicação clínica dos potenciais evocados visual, auditivo e somatossensitivo.

Engineering brain assembloids to interrogate human neural circuits.

The development of neural circuits involves wiring of neurons locally following their generation and migration, as well as establishing long-distance connections between brain regions. Studying these developmental processes in the human nervous system remains difficult because of limited access to tissue that can be maintained as functional over time in vitro. We have previously developed a method to convert human pluripotent stem cells into brain region-specific organoids that can be fused and integrated to form assembloids and study neuronal migration. In contrast to approaches that mix cell lineages in 2D cultures or engineer microchips, assembloids leverage self-organization to enable complex cell-cell interactions, circuit formation and maturation in long-term cultures. In this protocol, we describe approaches to model long-range neuronal connectivity in human brain assembloids. We present how to generate 3D spheroids resembling specific domains of the nervous system and then how to integrate them physically to allow axonal projections and synaptic assembly. In addition, we describe a series of assays including viral labeling and retrograde tracing, 3D live imaging of axon projection and optogenetics combined with calcium imaging and electrophysiological recordings to probe and manipulate the circuits in assembloids. The assays take 3-4 months to complete and require expertise in stem cell culture, imaging and electrophysiology. We anticipate that these approaches will be useful in deciphering human-specific aspects of neural circuit assembly and in modeling neurodevelopmental disorders with patient-derived cells.

Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies.

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule that overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and electromyographic data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contain both non-negative and unconstrained variables.NEW & NOTEWORTHY The mixed matrix factorization (MMF) is a novel method for extracting kinematic-muscular synergies. The previous state of the art algorithm (NMFpn) factorizes separately positive and rectified negative waveforms; the MMF instead employs a gradient descent method to factorize mixed kinematic unconstrained data and muscular non-negative data. MMF achieves perfect reconstruction on noiseless data, improving the NMFpn. MMF shows promising applicability on real data.

A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity.

The effective decoding of movement from non-invasive electroencephalography (EEG) is essential for informing several therapeutic interventions, from neurorehabilitation robots to neural prosthetics. Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered by the gross classes of motor tasks in the currently available datasets, which are solvable even with network architectures that do not require specialized design considerations. Moreover, the weak association with the underlying neurophysiology limits the generalizability of modern networks for EEG inference. Here, we present a neurophysiologically interpretable 3-dimensional convolutional neural network (3D-CNN) that captured the spatiotemporal dependencies in brain areas that get co-activated during movement. The 3D-CNN received topography-preserving EEG inputs, and predicted complex components of hand movements performed on a plane using a back-drivable rehabilitation robot, namely (a) the reaction time (RT) for responding to stimulus (slow or fast), (b) the mode of movement (active or passive, depending on whether there was an assistive force provided by the apparatus), and (c) the orthogonal directions of the movement (left, right, up, or down). We validated the 3D-CNN on a new dataset that we acquired from an in-house motor experiment, where it achieved average leave-one-subject-out test accuracies of 79.81%, 81.23%, and 82.00% for RT, active vs. passive, and direction classifications, respectively. Our proposed method outperformed the modern 2D-CNN architecture by a range of 1.1% to 6.74% depending on the classification task. Further, we identified the EEG sensors and time segments crucial to the classification decisions of the network, which aligned well with the current neurophysiological knowledge on brain activity in motor planning and execution tasks. Our results demonstrate the importance of biological relevance in networks for an accurate decoding of EEG, suggesting that the real-time classification of other complex brain activities may now be within our reach.

Seventy years since the invention of the averaging technique in Neurophysiology: Tribute to George Duncan Dawson.

OBJECTIVE: In 1951, the physiologist George Duncan Dawson presented his work with the averaging of the signal in the evoked potentials (EPs), opening a new stage in the development of clinical neurophysiology. The authors present aspects of Professor Dawson's biography and a review of his work on the EPs and, mainly, the article reveals the new technique in detail that would allow the growth of the clinical application of the visual, auditory, and somatosensory EPs.

Simultaneous analyses of clutch coupling and actin polymerization in dendritic spines of rodent hippocampal neurons during chemical LTP.

Dendritic spine enlargement by synaptic activation is thought to increase synaptic efficacy underlying learning and memory. This process requires forces generated by actin polymerization and actin-adhesion coupling (clutch coupling). Here, we describe a protocol to monitor actin filament retrograde flow and actin polymerization within spines using a standard epi-fluorescence microscope. In combination with chemical long-term potentiation, this protocol allows us to quantify clutch coupling efficiency and actin polymerization rate, which are essential variables for generating forces for activity-dependent spine enlargement. For complete details on the use and execution of this protocol, please refer to Kastian et al. (2021).

Evaluation and resolution of many challenges of neural spike sorting: a new sorter.

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth data sets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth data sets and suggest postsorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses.NEW & NOTEWORTHY Electrophysiological recordings from multiple neurons across multiple channels pose great difficulty for spike sorting of single neurons. We propose methods that improve the ability to determine the number of individual neurons present in a recording and resolve near-simultaneous spike events from single neurons. We use ground-truth data sets to demonstrate the pros and cons of several current sorting algorithms and suggest strategies for determining the accuracy of spike sorting when ground-truth data are not available.

An open-source application for the standardized burst identification from the integrated muscle sympathetic neurogram.

Muscle sympathetic nerve activity (MSNA) can be acquired from humans using the technique of microneurography. The resulting integrated neurogram displays pulse-synchronous bursts of sympathetic activity, which undergoes processing for standard MSNA metrics including burst frequency, height, area, incidence, total activity, and latency. The procedure for detecting bursts of MSNA and calculating burst metrics is tedious and differs widely among laboratories worldwide. We sought to develop an open-source, cross-platform web application that provides a standardized approach for burst identification and a tool to increase research reproducibility for those measuring MSNA. We compared the performance of this web application against a manual scoring approach under conditions of rest, chemoreflex activation (n = 9, 20-min isocapnic hypoxia), and metaboreflex activation (n = 13, 2-min isometric handgrip exercise and 4-min postexercise circulatory occlusion). The intraclass correlation coefficient (ICC) indicated good to strong agreement between scoring approaches for burst frequency (ICC = 0.92-0.99), incidence (ICC = 0.94-0.99), height (ICC = 0.76-0.88), total activity (ICC = 0.85-0.99), and latency (ICC = 0.97-0.99). Agreement with burst area was poor to moderate (ICC = 0.04-0.67) but changes in burst area were similar with chemoreflex and metaboreflex activation. Scoring using the web application was highly efficient and provided data visualization tools that expedited data processing and the analysis of MSNA. We recommend the open-source web application be adopted by the community for the analysis of MSNA.NEW & NOTEWORTHY The basic analysis of muscle sympathetic nerve activity (MSNA) requires the identification of pulse-synchronous bursts from the integrated neurogram before standard MSNA metrics can be quantified. This process is a time-consuming task requiring an experienced microneurographer to visually identify and manually label bursts. We developed an open-source, cross-platform application permitting a standardized approach for sympathetic burst identification and present the performance of this application against a manual scorer under basal conditions and during sympathoexcitatory stresses.

Over the rainbow: Guidelines for meaningful use of colour maps in neurophysiology.

Visualization of complex data is commonplace in neurophysiology research. Here, we highlight specific perceptual issues related to the ongoing misuse of variations of the rainbow colour scheme, with a particular emphasis on time-frequency decompositions in electrophysiology as an illustrative example. We review the risks of biased interpretation of neurophysiological data in this context, and provide guidelines to improve the use of colour maps to visualise complex, multidimensional data in neurophysiology research.

Brief segments of neurophysiological activity enable individual differentiation.

Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual's neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.

Minhee Analysis Package: an integrated software package for detection and management of spontaneous synaptic events.

To understand the information encoded in a connection between the neurons, postsynaptic current (PSC) has been widely measured as a primary index of synaptic strength in the field of neurophysiology. Although several automatic detection methods for PSCs have been proposed to simplify a workflow in the analysis, repetitive steps such as quantification and management of PSC data should be still performed with much effort. Here, we present Minhee Analysis Package, an integrated standalone software package that is capable of detecting, sorting, and quantifying PSC data. First, we developed a stepwise exploratory algorithm to detect PSC and validated our detection algorithm using the simulated and experimental data. We also described all the features and examples of the package so that users can use and follow them properly. In conclusion, our software package is expected to improve the convenience and efficiency of neurophysiologists to analyze PSC data by simplifying the workflow from detection to quantification. Minhee Analysis Package is freely available to download from http://www.github.com/parkgilbong/Minhee_Analysis_Pack .