Resting-state EEG recorded with gel-based vs. consumer dry electrodes: spectral characteristics and across-device correlations

Introduction

Recordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes.

Methods

Here we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the PSBD Headband, the PSBD Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications.

Results

Distinctive characteristics of signal quality were found, with the PSBD Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The PSBD Headphones were more prone to low-frequency artifacts compared to the PSBD Headband, yet recorded modulations in the alpha power and had a strong alignment with the NVX at the higher EEG frequencies. The Muse Headband had several limitations in signal quality.

Discussion

We suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications, the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighed against the capacity of each given system.

[Cerebral persistent primitive arteries. Clinical case of combination with intracranial aneurysm and review of the literature]

Cerebral persistent primitive arteries are uncommon and associated with cerebrovascular diseases, like cerebral aneurysms. They can cause vertebrobasilar ischemia and neuropathy of the cranial nerves. The authors present a patient with trigeminal artery associated with giant partially thrombosed cavernous internal cerebral artery aneurysm.

Communities in C. elegans connectome through the prism of non-backtracking walks

The fundamental relationship between the mesoscopic structure of neuronal circuits and organismic functions they subserve is one of the major challenges in contemporary neuroscience. Formation of structurally connected modules of neurons enacts the conversion from single-cell firing to large-scale behaviour of an organism, highlighting the importance of their accurate profiling in the data. While connectomes are typically characterized by significant sparsity of neuronal connections, recent advances in network theory and machine learning have revealed fundamental limitations of traditionally used community detection approaches in cases where the network is sparse. Here we studied the optimal community structure in the structural connectome of Caenorhabditis elegans, for which we exploited a non-conventional approach that is based on non-backtracking random walks, virtually eliminating the sparsity issue. In full agreement with the previous asymptotic results, we demonstrated that non-backtracking walks resolve the ground truth annotation into clusters on stochastic block models (SBM) with the size and density of the connectome better than the spectral methods related to simple random walks. Based on the cluster detectability threshold, we determined that the optimal number of modules in a recently mapped connectome of C. elegans is 10, which precisely corresponds to the number of isolated eigenvalues in the spectrum of the non-backtracking flow matrix. The discovered communities have a clear interpretation in terms of their functional role, which allows one to discern three structural compartments in the worm: the Worm Brain (WB), the Worm Movement Controller (WMC), and the Worm Information Flow Connector (WIFC). Broadly, our work provides a robust network-based framework to reveal mesoscopic structures in sparse connectomic datasets, paving way to further investigation of connectome mechanisms for different functions.

An olfactory-based Brain-Computer Interface: electroencephalography changes during odor perception and discrimination

Brain-Computer Interfaces (BCIs) are devices designed for establishing communication between the central nervous system and a computer. The communication can occur through different sensory modalities, and most commonly visual and auditory modalities are used. Here we propose that BCIs can be expanded by the incorporation of olfaction and discuss the potential applications of such olfactory BCIs. To substantiate this idea, we present results from two olfactory tasks: one that required attentive perception of odors without any overt report, and the second one where participants discriminated consecutively presented odors. In these experiments, EEG recordings were conducted in healthy participants while they performed the tasks guided by computer-generated verbal instructions. We emphasize the importance of relating EEG modulations to the breath cycle to improve the performance of an olfactory-based BCI. Furthermore, theta-activity could be used for olfactory-BCI decoding. In our experiments, we observed modulations of theta activity over the frontal EEG leads approximately 2 s after the inhalation of an odor. Overall, frontal theta rhythms and other types of EEG activity could be incorporated in the olfactory-based BCIs which utilize odors either as inputs or outputs. These BCIs could improve olfactory training required for conditions like anosmia and hyposmia, and mild cognitive impairment.

An experimental paradigm for studying EEG correlates of olfactory discrimination

Electroencephalography (EEG) correlates of olfaction are of fundamental and practical interest for many reasons. In the field of neural technologies, olfactory-based brain-computer interfaces (BCIs) represent an approach that could be useful for neurorehabilitation of anosmia, dysosmia and hyposmia. While the idea of a BCI that decodes neural responses to different odors and/or enables odor-based neurofeedback is appealing, the results of previous EEG investigations into the olfactory domain are rather inconsistent, particularly when non-primary processing of olfactory signals is concerned. Here we developed an experimental paradigm where EEG recordings are conducted while a participant executes an olfaction-based instructed-delay task. We utilized an olfactory display and a sensor of respiration to deliver odors in a strictly controlled fashion. We showed that with this approach spatial and spectral EEG properties could be analyzed to assess neural processing of olfactory stimuli and their conversion into a motor response. We conclude that EEG recordings are suitable for detecting active processing of odors. As such they could be integrated in a BCI that strives to rehabilitate olfactory disabilities or uses odors for hedonistic purposes.

Exoskeleton gait training with spinal cord neuromodulation

Neuromodulating the locomotor network through spinal cord electrical stimulation (SCES) is effective for restoring function in individuals with gait deficits. However, SCES alone has limited effectiveness without concurrent locomotor function training that enhances activity-dependent plasticity of spinal neuronal networks by sensory feedback. This mini review discusses recent developments in using combined interventions, such as SCES added to exoskeleton gait training (EGT). To develop personalized therapies, it is crucial to assess the state of spinal circuitry through a physiologically relevant approach that identifies individual characteristics of spinal cord function to develop person-specific SCES and EGT. The existing literature suggests that combining SCES and EGT to activate the locomotor network can have a synergistic rehabilitative effect on restoring walking abilities, somatic sensation, and cardiovascular and bladder function in paralyzed individuals.

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.

Time-Dependent Effect of Sciatic Nerve Injury on Rat Plasma Lipidome

Neuropathic pain is a condition affecting the quality of life of a substantial part of the population, but biomarkers and treatment options are still limited. While this type of pain is caused by nerve damage, in which lipids play key roles, lipidome alterations related to nerve injury remain poorly studied. Here, we assessed blood lipidome alterations in a common animal model, the rat sciatic nerve crush injury. We analyzed alterations in blood lipid abundances between seven rats with nerve injury (NI) and eight control (CL) rats in a time-course experiment. For these rats, abundances of 377 blood lipid species were assessed at three distinct time points: immediately after, two weeks, and five weeks post injury. Although we did not detect significant differences between NI and CL at the first two time points, 106 lipids were significantly altered in NI five weeks post injury. At this time point, we found increased levels of triglycerides (TGs) and lipids containing esterified palmitic acid (16:0) in the blood plasma of NI animals. Lipids containing arachidonic acid (20:4), by contrast, were significantly decreased after injury, aligning with the crucial role of arachidonic acid reported for NI. Taken together, these results indicate delayed systematic alterations in fatty acid metabolism after nerve injury, potentially reflecting nerve tissue restoration dynamics.

Interaction of 5-[4'-(N-Methyl-1,3-benzimidazol-2-yl)phenyl]-10,15,20-tri-(N-methyl-3'-pyridyl)porphyrin Triiodide with SARS-CoV-2 Spike Protein

The results of experimental studies of the interaction of the S-protein with a monohetaryl-substituted porphyrin containing a benzimidazole residue are presented. It has been revealed that the S-protein forms high-affinity complexes with the specified porphyrin. The porphyrin binding by the SARS-CoV-2 S-protein has proceeded stepwise; at the first stage, the driving force of the complexation is electrostatic interaction between the surface negatively charged regions of the protein and cationic substituents of the porphyrin. At the second stage, the target complex of the S-protein with the porphyrin is formed. It has been established that the introduction of 5-[4'-(N-methyl-1,3-benzimidazol-2-yl)phenyl]-10,15,20-tri-(N-methyl-3'-pyridyl)porphyrin triiodide into a solution of the S-protein complex with the angiotensin-converting enzyme leads to the replacement of the latter with the porphyrin. Displacement of the angiotensin-converting enzyme from the complex with the S-protein under the action of 5-[4'-(N-methyl-1,3-benzimidazol-2-yl)phenyl]-10,15,20-tri-(N-methyl-3'-pyridyl)porphyrin triiodide is the experimental evidence for the porphyrin binding at the receptor-binding domain of the S-protein.

Capturing spike train temporal pattern with wavelet average coefficient for brain machine interface

Motor brain machine interfaces (BMIs) directly link the brain to artificial actuators and have the potential to mitigate severe body paralysis caused by neurological injury or disease. Most BMI systems involve a decoder that analyzes neural spike counts to infer movement intent. However, many classical BMI decoders (1) fail to take advantage of temporal patterns of spike trains, possibly over long time horizons; (2) are insufficient to achieve good BMI performance at high temporal resolution, as the underlying Gaussian assumption of decoders based on spike counts is violated. Here, we propose a new statistical feature that represents temporal patterns or temporal codes of spike events with richer description-wavelet average coefficients (WAC)-to be used as decoder input instead of spike counts. We constructed a wavelet decoder framework by using WAC features with a sliding-window approach, and compared the resulting decoder against classical decoders (Wiener and Kalman family) and new deep learning based decoders ( Long Short-Term Memory) using spike count features. We found that the sliding-window approach boosts decoding temporal resolution, and using WAC features significantly improves decoding performance over using spike count features.

Generating artificial sensations with spinal cord stimulation in primates and rodents

For patients who have lost sensory function due to a neurological injury such as spinal cord injury (SCI), stroke, or amputation, spinal cord stimulation (SCS) may provide a mechanism for restoring somatic sensations via an intuitive, non-visual pathway. Inspired by this vision, here we trained rhesus monkeys and rats to detect and discriminate patterns of epidural SCS. Thereafter, we constructed psychometric curves describing the relationship between different SCS parameters and the animal's ability to detect SCS and/or changes in its characteristics. We found that the stimulus detection threshold decreased with higher frequency, longer pulse-width, and increasing duration of SCS. Moreover, we found that monkeys were able to discriminate temporally- and spatially-varying patterns (i.e. variations in frequency and location) of SCS delivered through multiple electrodes. Additionally, sensory discrimination of SCS-induced sensations in rats obeyed Weber's law of just-noticeable differences. These findings suggest that by varying SCS intensity, temporal pattern, and location different sensory experiences can be evoked. As such, we posit that SCS can provide intuitive sensory feedback in neuroprosthetic devices.

Decoding and interpreting cortical signals with a compact convolutional neural network

OBJECTIVE

Brain-computer interfaces (BCIs) decode information from neural activity and send it to external devices. The use of Deep Learning approaches for decoding allows for automatic feature engineering within the specific decoding task. Physiologically plausible interpretation of the network parameters ensures the robustness of the learned decision rules and opens the exciting opportunity for automatic knowledge discovery.

APPROACH

We describe a compact convolutional network-based architecture for adaptive decoding of electrocorticographic (ECoG) data into finger kinematics. We also propose a novel theoretically justified approach to interpreting the spatial and temporal weights in the architectures that combine adaptation in both space and time. The obtained spatial and frequency patterns characterizing the neuronal populations pivotal to the specific decoding task can then be interpreted by fitting appropriate spatial and dynamical models.

MAIN RESULTS

We first tested our solution using realistic Monte-Carlo simulations. Then, when applied to the ECoG data from Berlin BCI competition IV dataset, our architecture performed comparably to the competition winners without requiring explicit feature engineering. Using the proposed approach to the network weights interpretation we could unravel the spatial and the spectral patterns of the neuronal processes underlying the successful decoding of finger kinematics from an ECoG dataset. Finally we have also applied the entire pipeline to the analysis of a 32-channel EEG motor-imagery dataset and observed physiologically plausible patterns specific to the task.

SIGNIFICANCE

We described a compact and interpretable CNN architecture derived from the basic principles and encompassing the knowledge in the field of neural electrophysiology. For the first time in the context of such multibranch architectures with factorized spatial and temporal processing we presented theoretically justified weights interpretation rules. We verified our recipes using simulations and real data and demonstrated that the proposed solution offers a good decoder and a tool for investigating motor control neural mechanisms.

Short-delay neurofeedback facilitates training of the parietal alpha rhythm

OBJECTIVE

Feedback latency was shown to be a critical parameter in a range of applications that imply learning. The therapeutic effects of neurofeedback (NFB) remain controversial. We hypothesized that often encountered unreliable results of NFB intervention could be associated with large feedback latency values that are often uncontrolled and may preclude the efficient learning.

APPROACH

We engaged our subjects into a parietal alpha power unpregulating paradigm faciliated by visual neurofeedback based on the invidually extracted envelope of the alpha-rhythm at P4 electrode. NFB was displayed either as soon as EEG envelope was processed, or with an extra 250 or 500-ms delay. The feedback training consisted of 15 two-minute long blocks interleaved with 15s pauses. We have also recorded two minute long baselines immediately before and after the training.

MAIN RESULTS

The time course of NFB-induced changes in the alpha rhythm power clearly depended on NFB latency, as shown with the adaptive Neyman test. NFB had a strong effect on the alpha-spindle incidence rate, but not on their duration or amplitude. The sustained changes in alpha activity measured after the completion of NFB training were negatively correlated to latency, with the maximum change for the shortest tested latency and no change for the longest.

SIGNIFICANCE

Here we for the first time show that visual NFB of parietal electroencephalographic (EEG) alpha-activity is efficient only when delivered to human subjects at short latency, which guarantees that NFB arrives when an alpha spindle is still ongoing. Such a considerable effect of NFB latency on the alpha-activity temporal structure could explain some of the previous inconsistent results, where latency was neither controlled nor documented. Clinical practitioners and manufacturers of NFB equipment should add latency to their specifications while enabling latency monitoring and supporting short-latency operations.

Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)

Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.

Generating Artificial Sensations with Spinal Cord Stimulation in Primates and Rodents.. [DOI: 10.1101/2020.05.09.085647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

For patients who have lost sensory function due to a neurological injury such as spinal cord injury (SCI), stroke, or amputation, spinal cord stimulation (SCS) may provide a mechanism for restoring somatic sensations via an intuitive, non-visual pathway. Inspired by this vision, here we trained rhesus monkeys and rats to detect and discriminate patterns of epidural SCS. Thereafter, we constructed psychometric curves describing the relationship between different SCS parameters and the animal’s ability to detect SCS and/or changes in its characteristics. We found that the stimulus detection threshold decreased with higher frequency, longer pulse-width, and increasing duration of SCS. Moreover, we found that monkeys were able to discriminate temporally- and spatially-varying patterns (i.e. variations in frequency and location) of SCS delivered through multiple electrodes. Additionally, sensory discrimination of SCS-induced sensations in rats obeyed Weber’s law of just noticeable differences. These findings suggest that by varying SCS intensity, temporal pattern, and location different sensory experiences can be evoked. As such, we posit that SCS can provide intuitive sensory feedback in neuroprosthetic devices.

Decoding Movement From Electrocorticographic Activity: A Review

Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.

Creating a neuroprosthesis for active tactile exploration of textures

Intracortical microstimulation (ICMS) of the primary somatosensory cortex (S1) can produce percepts that mimic somatic sensation and, thus, has potential as an approach to sensorize prosthetic limbs. However, it is not known whether ICMS could recreate active texture exploration-the ability to infer information about object texture by using one's fingertips to scan a surface. Here, we show that ICMS of S1 can convey information about the spatial frequencies of invisible virtual gratings through a process of active tactile exploration. Two rhesus monkeys scanned pairs of visually identical screen objects with the fingertip of a hand avatar-controlled first via a joystick and later via a brain-machine interface-to find the object with denser virtual gratings. The gratings consisted of evenly spaced ridges that were signaled through individual ICMS pulses generated whenever the avatar's fingertip crossed a ridge. The monkeys learned to interpret these ICMS patterns, evoked by the interplay of their voluntary movements and the virtual textures of each object, to perform a sensory discrimination task. Discrimination accuracy followed Weber's law of just-noticeable differences (JND) across a range of grating densities; a finding that matches normal cutaneous sensation. Moreover, 1 monkey developed an active scanning strategy where avatar velocity was integrated with the ICMS pulses to interpret the texture information. We propose that this approach could equip upper-limb neuroprostheses with direct access to texture features acquired during active exploration of natural objects.

Human Brain/Cloud Interface

The Internet comprises a decentralized global system that serves humanity's collective effort to generate, process, and store data, most of which is handled by the rapidly expanding cloud. A stable, secure, real-time system may allow for interfacing the cloud with the human brain. One promising strategy for enabling such a system, denoted here as a "human brain/cloud interface" ("B/CI"), would be based on technologies referred to here as "neuralnanorobotics." Future neuralnanorobotics technologies are anticipated to facilitate accurate diagnoses and eventual cures for the ∼400 conditions that affect the human brain. Neuralnanorobotics may also enable a B/CI with controlled connectivity between neural activity and external data storage and processing, via the direct monitoring of the brain's ∼86 × 109 neurons and ∼2 × 1014 synapses. Subsequent to navigating the human vasculature, three species of neuralnanorobots (endoneurobots, gliabots, and synaptobots) could traverse the blood-brain barrier (BBB), enter the brain parenchyma, ingress into individual human brain cells, and autoposition themselves at the axon initial segments of neurons (endoneurobots), within glial cells (gliabots), and in intimate proximity to synapses (synaptobots). They would then wirelessly transmit up to ∼6 × 1016 bits per second of synaptically processed and encoded human-brain electrical information via auxiliary nanorobotic fiber optics (30 cm3) with the capacity to handle up to 1018 bits/sec and provide rapid data transfer to a cloud based supercomputer for real-time brain-state monitoring and data extraction. A neuralnanorobotically enabled human B/CI might serve as a personalized conduit, allowing persons to obtain direct, instantaneous access to virtually any facet of cumulative human knowledge. Other anticipated applications include myriad opportunities to improve education, intelligence, entertainment, traveling, and other interactive experiences. A specialized application might be the capacity to engage in fully immersive experiential/sensory experiences, including what is referred to here as "transparent shadowing" (TS). Through TS, individuals might experience episodic segments of the lives of other willing participants (locally or remote) to, hopefully, encourage and inspire improved understanding and tolerance among all members of the human family.

NFBLab-A Versatile Software for Neurofeedback and Brain-Computer Interface Research

Neurofeedback (NFB) is a real-time paradigm, where subjects learn to volitionally modulate their own brain activity recorded with electroencephalographic (EEG), magnetoencephalographic (MEG) or other functional brain imaging techniques and presented to them via one of sensory modalities: visual, auditory or tactile. NFB has been proposed as an approach to treat neurological conditions and augment brain functions. Although the early NFB studies date back nearly six decades ago, there is still much debate regarding the efficiency of this approach and the ways it should be implemented. Partly, the existing controversy is due to suboptimal conditions under which the NFB training is undertaken. Therefore, new experimental tools attempting to provide optimal or close to optimal training conditions are needed to further exploration of NFB paradigms and comparison of their effects across subjects and training days. To this end, we have developed open-source NFBLab, a versatile, Python-based software for conducting NFB experiments with completely reproducible paradigms and low-latency feedback presentation. Complex experimental protocols can be configured using the GUI and saved in NFBLab's internal XML-based language that describes signal processing tracts, experimental blocks and sequences including randomization of experimental blocks. NFBLab implements interactive modules that enable individualized EEG/MEG signal processing tracts specification using spatial and temporal filters for feature selection and artifacts removal. NFBLab supports direct interfacing to MNE-Python software to facilitate source-space NFB based on individual head models and properly tailored individual inverse solvers. In addition to the standard algorithms for extraction of brain rhythms dynamics from EEG and MEG data, NFBLab implements several novel in-house signal processing algorithms that afford significant reduction in latency of feedback presentation and may potentially improve training effects. The software also supports several standard BCI paradigms. To interface with external data acquisition devices NFBLab employs Lab Streaming Layer protocol supported by the majority of EEG vendors. MEG devices are interfaced through the Fieldtrip buffer.

Place Cell-Like Activity in the Primary Sensorimotor and Premotor Cortex During Monkey Whole-Body Navigation

Primary motor (M1), primary somatosensory (S1) and dorsal premotor (PMd) cortical areas of rhesus monkeys previously have been associated only with sensorimotor control of limb movements. Here we show that a significant number of neurons in these areas also represent body position and orientation in space. Two rhesus monkeys (K and M) used a wheelchair controlled by a brain-machine interface (BMI) to navigate in a room. During this whole-body navigation, the discharge rates of M1, S1, and PMd neurons correlated with the two-dimensional (2D) room position and the direction of the wheelchair and the monkey head. This place cell-like activity was observed in both monkeys, with 44.6% and 33.3% of neurons encoding room position in monkeys K and M, respectively, and the overlapping populations of 41.0% and 16.0% neurons encoding head direction. These observations suggest that primary sensorimotor and premotor cortical areas in primates are likely involved in allocentrically representing body position in space during whole-body navigation, which is an unexpected finding given the classical hierarchical model of cortical processing that attributes functional specialization for spatial processing to the hippocampal formation.

Navigation Patterns and Scent Marking: Underappreciated Contributors to Hippocampal and Entorhinal Spatial Representations?

According to the currently prevailing theory, hippocampal formation constructs and maintains cognitive spatial maps. Most of the experimental evidence for this theory comes from the studies on navigation in laboratory rats and mice, typically male animals. While these animals exhibit a rich repertoire of behaviors associated with navigation, including locomotion, head movements, whisking, sniffing, raring and scent marking, the contribution of these behavioral patterns to the hippocampal spatially-selective activity has not been sufficiently studied. Instead, many publications have considered animal position in space as the major variable that affects the firing of hippocampal place cells and entorhinal grid cells. Here we argue that future work should focus on a more detailed examination of different behaviors exhibited during navigation to better understand the mechanism of spatial tuning in hippocampal neurons. As an inquiry in this direction, we have analyzed data from two datasets, shared online, containing recordings from rats navigating in square and round arenas. Our analyses revealed patchy navigation patterns, evident from the spatial maps of animal position, velocity and acceleration. Moreover, grid cells available in the datasets exhibited similar periodicity as the navigation parameters. These findings indicate that activity of grid cells could affect navigation parameters and/or vice versa. Additionally, we speculate that scent marks left by navigating animals could contribute to neuronal responses while rats and mice sniff their environment; the act of sniffing could modulate neuronal discharges even in virtual visual environments. Accordingly, we propose that future experiments should contain additional controls for navigation patterns, whisking, sniffing and maps composed of scent marks.

A novel paraplegia model in awake behaving macaques

Lower limb paralysis from spinal cord injury (SCI) or neurological disease carries a poor prognosis for recovery and remains a large societal burden. Neurophysiological and neuroprosthetic research have the potential to improve quality of life for these patients; however, the lack of an ethical and sustainable nonhuman primate model for paraplegia hinders their advancement. Therefore, our multidisciplinary team developed a way to induce temporary paralysis in awake behaving macaques by creating a fully implantable lumbar epidural catheter-subcutaneous port system that enables easy and reliable targeted drug delivery for sensorimotor blockade. During treadmill walking, aliquots of 1.5% lidocaine with 1:200,000 epinephrine were percutaneously injected into the ports of three rhesus macaques while surface electromyography (EMG) recorded muscle activity from their quadriceps and gastrocnemii. Diminution of EMG amplitude, loss of voluntary leg movement, and inability to bear weight were achieved for 60-90 min in each animal, followed by a complete recovery of function. The monkeys remained alert and cooperative during the paralysis trials and continued to take food rewards, and the ports remained functional after several months. This technique will enable recording from the cortex and/or spinal cord in awake behaving nonhuman primates during the onset, maintenance, and resolution of paraplegia for the first time, thus opening the door to answering basic neurophysiological questions about the acute neurological response to spinal cord injury and recovery. It will also negate the need to permanently injure otherwise high-value research animals for certain experimental paradigms aimed at developing and testing neural interface decoding algorithms for patients with lower extremity dysfunction.NEW & NOTEWORTHY A novel implantable lumbar epidural catheter-subcutaneous port system enables targeted drug delivery and induction of temporary paraplegia in awake, behaving nonhuman primates. Three macaques displayed loss of voluntary leg movement for 60-90 min after injection of lidocaine with epinephrine, followed by a full recovery. This technique for the first time will enable ethical live recording from the proximal central nervous system during the acute onset, maintenance, and resolution of paraplegia.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

EXiO-A Brain-Controlled Lower Limb Exoskeleton for Rhesus Macaques

Recent advances in the field of brain-machine interfaces (BMIs) have demonstrated enormous potential to shape the future of rehabilitation and prosthetic devices. Here, a lower-limb exoskeleton controlled by the intracortical activity of an awake behaving rhesus macaque is presented as a proof-of-concept for a locomotorBMI. A detailed description of the mechanical device, including its innovative features and first experimental results, is provided. During operation, BMI-decoded position and velocity are directly mapped onto the bipedal exoskeleton's motions, which then move the monkey's legs as the monkey remains physicallypassive. To meet the unique requirements of such an application, the exoskeleton's features include: high output torque with backdrivable actuation, size adjustability, and safe user-robot interface. In addition, a novel rope transmission is introduced and implemented. To test the performance of the exoskeleton, a mechanical assessment was conducted, which yielded quantifiable results for transparency, efficiency, stiffness, and tracking performance. Usage under both brain control and automated actuation demonstrates the device's capability to fulfill the demanding needs of this application. These results lay the groundwork for further advancement in BMI-controlled devices for primates including humans.

Mechatronic Wearable Exoskeletons for Bionic Bipedal Standing and Walking: A New Synthetic Approach

During the last few years, interest has been growing to mechatronic and robotic technologies utilized in wearable powered exoskeletons that assist standing and walking. The available literature includes single-case reports, clinical studies conducted in small groups of subjects, and several recent systematic reviews. These publications have fulfilled promotional and marketing objectives but have not yet resulted in a fully optimized, practical wearable exoskeleton. Here we evaluate the progress and future directions in this field from a joint perspective of health professionals, manufacturers, and consumers. We describe the taxonomy of existing technologies and highlight the main improvements needed for the development and functional optimization of the practical exoskeletons.

Neostriatal Neuronal Activity Correlates Better with Movement Kinematics under Certain Rewards

This study investigated how the activity of neostriatal neurons is related to the kinematics of movement when monkeys performed visually and vibratory cued wrist extensions and flexions. Single-unit recordings of 142/236 neostriatal neurons showed pre-movement activity (PMA) in a reaction time task with unpredictable reward. Monkeys were pseudo-randomly (75%) rewarded for correct performance. A regression model was used to determine whether the correlation between neostriatal neuronal activity and the kinematic variables (position, velocity, and acceleration) of wrist movement changes as a function of reward contingency, sensory cues, and movement direction. The coefficients of determination (CoD) representing the proportion of the variance in neuronal activity explained by the regression model on a trial by trial basis, together with their temporal occurrences (time of best regression/correlation, ToC) were compared across sensory modality, movement direction, and reward contingency. The best relationship (correlation) between neuronal activity and movement kinematic variables, given by the average coefficient of determination (CoD), was: (a) greater during trials in which rewards were certain, called “A” trials, as compared with those in which reward was uncertain called (“R”) trials, (b) greater during flexion (Flex) trials as compared with extension (Ext) trials, and (c) greater during visual (VIS) cued trials than during vibratory (VIB) cued trials, for the same type of trial and the same movement direction. These results are consistent with the hypothesis that predictability of reward for correct performance is accompanied by faster linkage between neostriatal PMA and the vigor of wrist movement kinematics. Furthermore, the results provide valuable insights for building an upper-limb neuroprosthesis.

Neurofeedback Therapy for Enhancing Visual Attention: State-of-the-Art and Challenges

We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention.

Insights into Seeing and Grasping: Distinguishing the Neural Correlates of Perception and Action

Vision contributes to both perception and visuomotor control, and it has been suggested that many higher brain structures specialize in one or the other function. An alternative view, presented here, is that most higher brain areas participate in both visuomotor and perceptual functions. In the anterior frontal cortex, for example, the activity of one population of neurons reflects perceptual reports about a visual stimulus, whereas the activity of an intermingled population reflects movements aimed at the same stimulus. Similarly, posterior parietal and inferior temporal areas appear to function in both visual perception and visuomotor control. Visuomotor signals in higher order cortical areas could contribute to the perception of one’s own action. They also might reflect the existence of two systems for visual information processing: one stressing accuracy for the control of movement and the other generating hypotheses about the world.

An automatic experimental apparatus to study arm reaching in New World monkeys

BACKGROUND

Several species of the New World monkeys have been used as experimental models in biomedical and neurophysiological research. However, a method for controlled arm reaching tasks has not been developed for these species.

NEW METHOD

We have developed a fully automated, pneumatically driven, portable, and reconfigurable experimental apparatus for arm-reaching tasks suitable for these small primates.

RESULTS

We have utilized the apparatus to train two owl monkeys in a visually-cued arm-reaching task. Analysis of neural recordings demonstrates directional tuning of the M1 neurons.

COMPARISON WITH EXISTING METHOD(S)

Our apparatus allows automated control, freeing the experimenter from manual experiments.

CONCLUSION

The presented apparatus provides a valuable tool for conducting neurophysiological research on New World monkeys.

Computing Arm Movements with a Monkey Brainet

Traditionally, brain-machine interfaces (BMIs) extract motor commands from a single brain to control the movements of artificial devices. Here, we introduce a Brainet that utilizes very-large-scale brain activity (VLSBA) from two (B2) or three (B3) nonhuman primates to engage in a common motor behaviour. A B2 generated 2D movements of an avatar arm where each monkey contributed equally to X and Y coordinates; or one monkey fully controlled the X-coordinate and the other controlled the Y-coordinate. A B3 produced arm movements in 3D space, while each monkey generated movements in 2D subspaces (X-Y, Y-Z, or X-Z). With long-term training we observed increased coordination of behavior, increased correlations in neuronal activity between different brains, and modifications to neuronal representation of the motor plan. Overall, performance of the Brainet improved owing to collective monkey behaviour. These results suggest that primate brains can be integrated into a Brainet, which self-adapts to achieve a common motor goal.

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.

Signal-independent timescale analysis (SITA) and its application for neural coding during reaching and walking

What are the relevant timescales of neural encoding in the brain? This question is commonly investigated with respect to well-defined stimuli or actions. However, neurons often encode multiple signals, including hidden or internal, which are not experimentally controlled, and thus excluded from such analysis. Here we consider all rate modulations as the signal, and define the rate-modulations signal-to-noise ratio (RM-SNR) as the ratio between the variance of the rate and the variance of the neuronal noise. As the bin-width increases, RM-SNR increases while the update rate decreases. This tradeoff is captured by the ratio of RM-SNR to bin-width, and its variations with the bin-width reveal the timescales of neural activity. Theoretical analysis and simulations elucidate how the interactions between the recovery properties of the unit and the spectral content of the encoded signals shape this ratio and determine the timescales of neural coding. The resulting signal-independent timescale analysis (SITA) is applied to investigate timescales of neural activity recorded from the motor cortex of monkeys during: (i) reaching experiments with Brain-Machine Interface (BMI), and (ii) locomotion experiments at different speeds. Interestingly, the timescales during BMI experiments did not change significantly with the control mode or training. During locomotion, the analysis identified units whose timescale varied consistently with the experimentally controlled speed of walking, though the specific timescale reflected also the recovery properties of the unit. Thus, the proposed method, SITA, characterizes the timescales of neural encoding and how they are affected by the motor task, while accounting for all rate modulations.

A brain-machine interface enables bimanual arm movements in monkeys

Brain-machine interfaces (BMIs) are artificial systems that aim to restore sensation and movement to paralyzed patients. So far, BMIs have enabled only one arm to be moved at a time. Control of bimanual arm movements remains a major challenge. We have developed and tested a bimanual BMI that enables rhesus monkeys to control two avatar arms simultaneously. The bimanual BMI was based on the extracellular activity of 374 to 497 neurons recorded from several frontal and parietal cortical areas of both cerebral hemispheres. Cortical activity was transformed into movements of the two arms with a decoding algorithm called a fifth-order unscented Kalman filter (UKF). The UKF was trained either during a manual task performed with two joysticks or by having the monkeys passively observe the movements of avatar arms. Most cortical neurons changed their modulation patterns when both arms were engaged simultaneously. Representing the two arms jointly in a single UKF decoder resulted in improved decoding performance compared with using separate decoders for each arm. As the animals' performance in bimanual BMI control improved over time, we observed widespread plasticity in frontal and parietal cortical areas. Neuronal representation of the avatar and reach targets was enhanced with learning, whereas pairwise correlations between neurons initially increased and then decreased. These results suggest that cortical networks may assimilate the two avatar arms through BMI control. These findings should help in the design of more sophisticated BMIs capable of enabling bimanual motor control in human patients.

Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys

Advances in techniques for recording large-scale brain activity contribute to both the elucidation of neurophysiological principles and the development of brain-machine interfaces (BMIs). Here we describe a neurophysiological paradigm for performing tethered and wireless large-scale recordings based on movable volumetric three-dimensional (3D) multielectrode implants. This approach allowed us to isolate up to 1,800 units per animal and simultaneously record the extracellular activity of close to 500 cortical neurons, distributed across multiple cortical areas, in freely behaving rhesus monkeys. The method is expandable, in principle, to thousands of simultaneously recorded channels. It also allows increased recording longevity (5 consecutive years), and recording of a broad range of behaviors, e.g. social interactions, and BMI paradigms in freely moving primates. We propose that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research, while providing a framework for the development and testing of clinically relevant neuroprostheses.

[Characteristics of astheniс disorders in adolescents]

A clinical and epidemiological follow-up study of symptoms in asthenic disorders of neurotic level in 56 male adolescents has been conducted. Two clinical variants of asthenic states, including somato-autonomic (32 patients) and affective (24 patients), are defined. The clinical structure of these syndromes and their changes during a one year follow-up and treatment are described. Positive and negative factors that influence the development and prognosis of asthenic states are determined.

[Premorbid border-line mental disorders in adolescents and young-aged people]

The aim of the study was to determine the predictive value of some types of premorbid state for the development of border-line mental diseases (neurotic disorders and personality disorders). We examined 579 school students and 523 university students. The study comprised two stages: primary examination and follow-up (2 years). Some forms of premorbid mental disorders and their impact on the development of border-line mental diseases were described. The predictive value of different premorbid disorders was estimated.

Time-dependent statistical and correlation properties of neural signals during handwriting

To elucidate the cortical control of handwriting, we examined time-dependent statistical and correlational properties of simultaneously recorded 64-channel electroencephalograms (EEGs) and electromyograms (EMGs) of intrinsic hand muscles. We introduced a statistical method, which offered advantages compared to conventional coherence methods. In contrast to coherence methods, which operate in the frequency domain, our method enabled us to study the functional association between different neural regions in the time domain. In our experiments, subjects performed about 400 stereotypical trials during which they wrote a single character. These trials provided time-dependent EMG and EEG data capturing different handwriting epochs. The set of trials was treated as a statistical ensemble, and time-dependent correlation functions between neural signals were computed by averaging over that ensemble. We found that trial-to-trial variability of both the EMGs and EEGs was well described by a log-normal distribution with time-dependent parameters, which was clearly distinguished from the normal (Gaussian) distribution. We found strong and long-lasting EMG/EMG correlations, whereas EEG/EEG correlations, which were also quite strong, were short-lived with a characteristic correlation durations on the order of 100 ms or less. Our computations of correlation functions were restricted to the spectral range (13–30 Hz) of EEG signals where we found the strongest effects related to handwriting. Although, all subjects involved in our experiments were right-hand writers, we observed a clear symmetry between left and right motor areas: inter-channel correlations were strong if both channels were located over the left or right hemispheres, and 2–3 times weaker if the EEG channels were located over different hemispheres. Although we observed synchronized changes in the mean energies of EEG and EMG signals, we found that EEG/EMG correlations were much weaker than EEG/EEG and EMG/EMG correlations. The absence of strong correlations between EMG and EEG signals indicates that (i) a large fraction of the EEG signal includes electrical activity unrelated to low-level motor variability; (ii) neural processing of cortically-derived signals by spinal circuitry may reduce the correlation between EEG and EMG signals.

Reprogramming movements: extraction of motor intentions from cortical ensemble activity when movement goals change

The ability to inhibit unwanted movements and change motor plans is essential for behaviors of advanced organisms. The neural mechanisms by which the primate motor system rejects undesired actions have received much attention during the last decade, but it is not well understood how this neural function could be utilized to improve the efficiency of brain-machine interfaces (BMIs). Here we employed linear discriminant analysis (LDA) and a Wiener filter to extract motor plan transitions from the activity of ensembles of sensorimotor cortex neurons. Two rhesus monkeys, chronically implanted with multielectrode arrays in primary motor (M1) and primary sensory (S1) cortices, were overtrained to produce reaching movements with a joystick toward visual targets upon their presentation. Then, the behavioral task was modified to include a distracting target that flashed for 50, 150, or 250 ms (25% of trials each) followed by the true target that appeared at a different screen location. In the remaining 25% of trials, the initial target stayed on the screen and was the target to be approached. M1 and S1 neuronal activity represented both the true and distracting targets, even for the shortest duration of the distracting event. This dual representation persisted both when the monkey initiated movements toward the distracting target and then made corrections and when they moved directly toward the second, true target. The Wiener filter effectively decoded the location of the true target, whereas the LDA classifier extracted the location of both targets from ensembles of 50–250 neurons. Based on these results, we suggest developing real-time BMIs that inhibit unwanted movements represented by brain activity while enacting the desired motor outcome concomitantly.

High-side digitally current controlled biphasic bipolar microstimulator

Electrical stimulation of nervous tissue has been extensively used as both a tool in experimental neuroscience research and as a method for restoring of neural functions in patients suffering from sensory and motor disabilities. In the central nervous system, intracortical microstimulation (ICMS) has been shown to be an effective method for inducing or biasing perception, including visual and tactile sensation. ICMS also holds promise for enabling brain-machine-brain interfaces (BMBIs) by directly writing information into the brain. Here we detail the design of a high-side, digitally current-controlled biphasic, bipolar microstimulator, and describe the validation of the device in vivo. As many applications of this technique, including BMBIs, require recording as well as stimulation, we pay careful attention to isolation of the stimulus channels and parasitic current injection. With the realized device and standard recording hardware-without active artifact rejection-we are able to observe stimulus artifacts of less than 2 ms in duration.

Virtual active touch using randomly patterned intracortical microstimulation

Intracortical microstimulation (ICMS) has promise as a means for delivering somatosensory feedback in neuroprosthetic systems. Various tactile sensations could be encoded by temporal, spatial, or spatiotemporal patterns of ICMS. However, the applicability of temporal patterns of ICMS to artificial tactile sensation during active exploration is unknown, as is the minimum discriminable difference between temporally modulated ICMS patterns. We trained rhesus monkeys in an active exploration task in which they discriminated periodic pulse-trains of ICMS (200 Hz bursts at a 10 Hz secondary frequency) from pulse trains with the same average pulse rate, but distorted periodicity (200 Hz bursts at a variable instantaneous secondary frequency). The statistics of the aperiodic pulse trains were drawn from a gamma distribution with mean inter-burst intervals equal to those of the periodic pulse trains. The monkeys distinguished periodic pulse trains from aperiodic pulse trains with coefficients of variation 0.25 or greater. Reconstruction of movement kinematics, extracted from the activity of neuronal populations recorded in the sensorimotor cortex concurrent with the delivery of ICMS feedback, improved when the recording intervals affected by ICMS artifacts were removed from analysis. These results add to the growing evidence that temporally patterned ICMS can be used to simulate a tactile sense for neuroprosthetic devices.

Cortical correlates of fitts' law

Fitts’ law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts’ law has been extensively studied in ergonomics and has guided the design of human–computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control.