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Recording from the human brain at the spatiotemporal resolution of action potentials provides critical insight into mechanisms of higher cognitive functions and neuropsychiatric disease that is challenging to derive from animal models. Here, organic materials and conformable electronics are employed to create an integrated neural interface device compatible with minimally invasive neurosurgical procedures and geared toward chronic implantation on the surface of the human brain. Data generated with these devices enable identification and characterization of individual, spatially distribute human cortical neurons in the absence of any tissue penetration (n = 229 single units). Putative single‐units are effectively clustered, and found to possess features characteristic of pyramidal cells and interneurons, as well as identifiable microcircuit interactions. Human neurons exhibit consistent phase modulation by oscillatory activity and a variety of population coupling responses. The parameters are furthermore established to optimize the yield and quality of single‐unit activity from the cortical surface, enhancing the ability to investigate human neural network mechanisms without breaching the tissue interface and increasing the information that can be safely derived from neurophysiological monitoring. [ABSTRACT FROM AUTHOR]

The use of transcutaneous electrical spinal stimulation (TSS) to modulate sensorimotor networks after neurological insult has garnered much attention from both researchers and clinicians in recent years. Although many different stimulation paradigms have been reported, the interlimb effects of these neuromodulation techniques have been little studied. The effects of multisite TSS on interlimb sensorimotor function are of particular interest in the context of neurorehabilitation, as these networks have been shown to be important for functional recovery after neurological insult. The present study utilized a condition-test paradigm to investigate the effects of interenlargement TSS on spinal motor excitability in both cervical and lumbosacral motor pools. Additionally, comparison was made between the conditioning effects of lumbosacral and cervical TSS and peripheral stimulation of the fibular nerve and ulnar nerve, respectively. In 16/16 supine, relaxed participants, facilitation of spinally evoked motor responses (sEMRs) in arm muscles was seen in response to lumbosacral TSS or fibular nerve stimulation, whereas facilitation of sEMRs in leg muscles was seen in response to cervical TSS or ulnar nerve stimulation. The decreased latency between TSS-and peripheral nerve-evoked conditioning implicates interlimb networks in the observed facilitation of motor output. The results demonstrate the ability of multisite TSS to engage interlimb networks, resulting in the bidirectional influence of cervical and lumbosacral motor output. The engagement of interlimb networks via TSS of the cervical and lumbosacral enlargements represents a feasible method for engaging spinal sensorimotor networks in clinical populations with compromised motor function. NEW & NOTEWORTHY Bidirectional interlimb modulation of spinal motor excitability can be evoked by transcutaneous spinal stimulation over the cervical and lumbosacral enlargements. Multisite transcutaneous spinal stimulation engages spinal sensorimotor networks thought to be important in the recovery of function after spinal cord injury. [ABSTRACT FROM AUTHOR]

Summary: Cognition depends on rapid and robust communication between neural circuits spanning different brain areas. We investigated the mesoscopic network of cortico-cortical interactions in the human brain in an extensive dataset consisting of 6,024 h of intracranial field potential recordings from 4,142 electrodes in 48 subjects. We evaluated communication between brain areas at the network level across different frequency bands. The interaction networks were validated against known anatomical measurements and neurophysiological interactions in humans and monkeys. The resulting human brain interactome is characterized by a broad and spatially specific, dynamic, and extensive network. The physiological interactome reveals small-world properties, which we conjecture might facilitate efficient and reliable information transmission. The interaction dynamics correlate with the brain sleep/awake state. These results constitute initial steps toward understanding how the interactome orchestrates cortical communication and provide a reference for future efforts assessing how dysfunctional interactions may lead to mental disorders.

Objective . Our goal is to decode firing patterns of single neurons in the left ventralis intermediate nucleus (Vim) of the thalamus, related to speech production, perception, and imagery. For realistic speech brain-machine interfaces (BMIs), we aim to characterize the amount of thalamic neurons necessary for high accuracy decoding. Approach . We intraoperatively recorded single neuron activity in the left Vim of eight neurosurgical patients undergoing implantation of deep brain stimulator or RF lesioning during production, perception and imagery of the five monophthongal vowel sounds. We utilized the Spade decoder, a machine learning algorithm that dynamically learns specific features of firing patterns and is based on sparse decomposition of the high dimensional feature space. Main results . Spade outperformed all algorithms compared with, for all three aspects of speech: production, perception and imagery, and obtained accuracies of 100%, 96%, and 92%, respectively (chance level: 20%) based on pooling together neurons across all patients. The accuracy was logarithmic in the amount of neurons for all three aspects of speech. Regardless of the amount of units employed, production gained highest accuracies, whereas perception and imagery equated with each other. Significance . Our research renders single neuron activity in the left Vim a promising source of inputs to BMIs for restoration of speech faculties for locked-in patients or patients with anarthria or dysarthria to allow them to communicate again. Our characterization of how many neurons are necessary to achieve a certain decoding accuracy is of utmost importance for planning BMI implantation., (Creative Commons Attribution license.)

Imagine you were asked to investigate the workings of an engine, but to do so without ever opening the hood. Now imagine the engine fueled the human mind. This is the challenge faced by cognitive neuroscientists worldwide aiming to understand the neural bases of our psychological functions. Luckily, human ingenuity comes to the rescue. Around the same time as the Society for Neuroscience was being established in the 1960s, the first tools for measuring the human brain at work were becoming available. Noninvasive human brain imaging and neurophysiology have continued developing at a relentless pace ever since. In this 50 year anniversary, we reflect on how these methods have been changing our understanding of how brain supports mind. [ABSTRACT FROM AUTHOR]

Rapid and flexible interpretation of conflicting sensory inputs in the context of current goals is a critical component of cognitive control that is orchestrated by frontal cortex. The relative roles of distinct subregions within frontal cortex are poorly understood. To examine the dynamics underlying cognitive control across frontal regions, we took advantage of the spatiotemporal resolution of intracranial recordings in epilepsy patients while subjects resolved color-word conflict. We observed differential activity preceding the behavioral responses to conflict trials throughout frontal cortex; this activity was correlated with behavioral reaction times. These signals emerged first in anterior cingulate cortex (ACC) before dorsolateral prefrontal cortex (dlPFC), followed by medial frontal cortex (mFC) and then by orbitofrontal cortex (OFC). These results disassociate the frontal subregions based on their dynamics, and suggest a temporal hierarchy for cognitive control in human cortex.

Cognitive control involves flexibly combining multiple sensory inputs with task-dependent goals during decision making. Several tasks involving conflicting sensory inputs and motor outputs have been proposed to examine cognitive control, including the Stroop, Flanker, and multi-source interference task. Because these tasks have been studied independently, it remains unclear whether the neural signatures of cognitive control reflect abstract control mechanisms or specific combinations of sensory and behavioral aspects of each task. To address these questions, we record invasive neurophysiological signals from 16 patients with pharmacologically intractable epilepsy and compare neural responses within and between tasks. Neural signals differ between incongruent and congruent conditions, showing strong modulation by conflicting task demands. These neural signals are mostly specific to each task, generalizing within a task but not across tasks. These results highlight the complex interplay between sensory inputs, motor outputs, and task demands underlying cognitive control processes. [Display omitted] • Neural signals reveal incongruency during multiple cognitive control tasks • The neural circuits that detect conflict generalize within a task • The neural mechanisms underlying cognitive control are not task invariant • Cognitive control relies on specific combinations of sensory inputs and motor outputs Cognitive control involves flexibly routing information according to current goals. Xiao et al. demonstrate that the neural mechanisms underlying cognitive control generalize across different conditions within a task but are not task invariant. Instead, the neural signatures of conflict monitoring reflect task-dependent combinations of sensory inputs and motor outputs. [ABSTRACT FROM AUTHOR]

Learning novel sequences constitutes an important example of declarative memory formation. Performance in sequence learning tasks improves with repetition and involves forming temporal associations over scales of seconds to minutes. To further understand the neural circuits underlying declarative sequence learning over trials, we tracked changes in intracranial field potentials from 1142 electrodes in fourteen human subjects while they learned the temporal-order of multiple sequences over trials through repeated recall. We observed a decrease in power in the gamma frequency band (30-100Hz) in the recall phase during the course of learning over trials, particularly in areas within the temporal lobe. Gamma power was directly correlated with the improvement in recall performance and was reset when learning new sequences. The decrease in gamma frequency band amplitudes over trials may reflect the need for plasticity early in the learning process combined with relatively reduced levels of plasticity required to maintain learned memory traces.

Visual recognition takes a small fraction of a second and relies on the cascade of signals along the ventral visual stream. Given the rapid path through multiple processing steps between photoreceptors and higher visual areas, information must progress from stage to stage very quickly. This rapid progression of information suggests that fine temporal details of the neural response may be important to the brain's encoding of visual signals. We investigated how changes in the relative timing of incoming visual stimulation affect the representation of object information by recording intracranial field potentials along the human ventral visual stream while subjects recognized objects whose parts were presented with varying asynchrony. Visual responses along the ventral stream were sensitive to timing differences as small as 17 ms between parts. In particular, there was a strong dependency on the temporal order of stimulus presentation, even at short asynchronies. From these observations we infer that the neural representation of complex information in visual cortex can be modulated by rapid dynamics on scales of tens of milliseconds. [ABSTRACT FROM AUTHOR]

Learning novel sequences constitutes an example of declarative memory formation, involving conscious recall of temporal events. Performance in sequence learning tasks improves with repetition and involves forming temporal associations over scales of seconds to minutes. To further understand the neural circuits underlying declarative sequence learning over trials, we tracked changes in intracranial field potentials (IFPs) recorded from 1142 electrodes implanted throughout temporal and frontal cortical areas in 14 human subjects, while they learned the temporal-order of multiple sequences of images over trials through repeated recall. We observed an increase in power in the gamma frequency band (30-100 Hz) in the recall phase, particularly in areas within the temporal lobe including the parahippocampal gyrus. The degree of this gamma power enhancement decreased over trials with improved sequence recall. Modulation of gamma power was directly correlated with the improvement in recall performance. When presenting new sequences, gamma power was reset to high values and decreased again after learning. These observations suggest that signals in the gamma frequency band may play a more prominent role during the early steps of the learning process rather than during the maintenance of memory traces. [ABSTRACT FROM AUTHOR]

Sensory signals must be interpreted in the context of goals and tasks. To detect a target in an image, the brain compares input signals and goals to elicit the correct behavior. We examined how target detection modulates visual recognition signals by recording intracranial field potential responses from 776 electrodes in 10 epileptic human subjects. We observed reliable differences in the physiological responses to stimuli when a cued target was present versus absent. Goal-related modulation was particularly strong in the inferior temporal and fusiform gyri, two areas important for object recognition. Target modulation started after 250 ms post stimulus, considerably after the onset of visual recognition signals. While broadband signals exhibited increased or decreased power, gamma frequency power showed predominantly increases during target presence. These observations support models where task goals interact with sensory inputs via top-down signals that influence the highest echelons of visual processing after the onset of selective responses. [ABSTRACT FROM AUTHOR]

Highlights: [•] New generation of cognitive-motor BMIs, focusing on three BMI types. [•] Speech brain–machine interfaces – reconstructing speech from neuronal activity. [•] Direct object control – controlling object movement without mimicking hand movements. [•] Decoding internal processes: sensory information and decision making. [ABSTRACT FROM AUTHOR]

Abstract: Many currently accepted notions of motor control originate from a few seminal concepts developed in the latter half of the 19th century (see ). The goal of this review is to retrace some current ideas about motor control back to the thought of three French neurologists of Hospital of the Salpetrière hospital in Paris during the latter half of the 19th century and early 20th century (): Guillaume Duchenne de Boulogne (1806–1875), Jean-Martin Charcot (1825–1893), and . A common theoretical and methodological thread unites these three men as Charcot was taught neurology by Duchenne, and Babinski was trained by Charcot. The influential concepts developed by these pioneering French neurologists have been neglected for nearly a century and only rediscovered recently. We intend to highlight how these astute clinicians used their meticulous clinical observations of patients to reveal novel and original perspectives of motor co-ordination. Between 1850 and 1930, all three men played a major role in developing and shaping the entire field of normal and pathological motor control in addition to making important contributions to three major scientific issues; the centralist view of muscle sense, the emerging concept of muscle synergy in voluntary movements and in locomotion and finally the specific role of the cerebellum in muscle synergy. The important contributions of these men will be considered in the context of other significant schools of neurology from other countries. Finally, the concept of cerebellar asynergy as proposed by Babinski anticipated the development of the internal models which much later were able to provide a theoretical basis for understanding the mechanism of learned motor co-ordination involving the cerebellum. [Copyright &y& Elsevier]

Abstract-Human intracranial microwire recordings have typically had poor signal-to-noise ratios (SNRs), often below 10 dB. The physiological signal source is a fixed-amplitude one; thus, SNR must be improved by reducing either noise or interference. An understanding of the interference sources, how they are coupled to the recording system, and their relative magnitudes is needed to improve SNR. We measured potentially interfering sources in a controlled laboratory model of microwire recordings. Specifically considered were interference from power lines, fluorescent lights, radio transmitters, and other nearby electrical devices. In the presence of typical mismatches in impedance (100 kΩ) and loop area (30 cm[sup2]), the greatest sources of interference are capacitive coupling to power lines (11.4μVrins), capacitive coupling to fluorescent lights (9.7 μVrms), and nonpower line capacitive interference (8.6 /LVrrns). The model and techniques employed here to study human microwire recordings may also be applied to other neurophysiological recordings. [ABSTRACT FROM AUTHOR]

Abstract: ‘Common Electrode’ microneurography (i.e. stimulating a nerve and recording return afferent neural activity through the same microelectrode facilitates investigation of linkages between sensory and motor nervous systems in humans. Currently there is no commercial product designed specifically to conduct common electrode microneurography experiments. However, such experiments would advance investigations in several key areas including spinal injury research. In this paper, we report on the successful production and testing (on a human subject) of an integrated amplifier built specifically for this purpose. The amplifier was built using commercially available components to allow for both easy and economical manufacture. In particular, we report on the design requirements and outline our chosen design solutions. The amplifier handles low-level neural signals amidst large 50Hz interference, with protection against potentially high stimulation voltages of over 100V dc, with minimal cross-coupling of rapid stimulus pulses onto the high gain amplifier''s input, and a short ‘blocking’ time between stimulation and recording. The amplifier also includes necessary filters, selectable gains and internal stimulator triggering circuits to provide a simple, integrated solution for common electrode operation on human subjects. [Copyright &y& Elsevier]

TMS is a non-invasive tool for measuring neural conduction and processing time, activation thresholds, facilitation and inhibition in brain cortex, and neural connections in humans. It is used to study motor, visual, somatosensory, and cognitive functions. TMS does not appear to cause long-term adverse neurological, cardiovascular, hormonal, motor, sensory, or cognitive effects in healthy subjects. Single-pulse (<1 Hz) TMS is safe in normal subjects. High frequency, high-intensity repetitive TMS (rTMS) can elicit seizures even in normal subjects. Safety guidelines for using rTMS have been published. [Copyright &y& Elsevier]

Cognition depends on rapid and robust communication between neural circuits spanning different brain areas. We investigated the mesoscopic network of cortico-cortical interactions in the human brain in an extensive dataset consisting of 6,024 h of intracranial field potential recordings from 4,142 electrodes in 48 subjects. We evaluated communication between brain areas at the network level across different frequency bands. The interaction networks were validated against known anatomical measurements and neurophysiological interactions in humans and monkeys. The resulting human brain interactome is characterized by a broad and spatially specific, dynamic, and extensive network. The physiological interactome reveals small-world properties, which we conjecture might facilitate efficient and reliable information transmission. The interaction dynamics correlate with the brain sleep/awake state. These results constitute initial steps toward understanding how the interactome orchestrates cortical communication and provide a reference for future efforts assessing how dysfunctional interactions may lead to mental disorders. [Display omitted] • Recorded continuous intracranial field potentials for 5 days in 48 human subjects • Characterized functional mesoscopic interactome assessed by pairwise coherence • Validated methods using anatomical and physiological interactions in monkeys • Human functional interactome shows small-world graph and changes with brain state Cognition relies on rapid and robust communication between brain areas. Wang et al. leverage multi-day intracranial field potential recordings to characterize the human mesoscopic functional interactome. They validated the methods using monkey anatomical and physiological data. The human interactome reveals small-world properties and is modulated by sleep versus awake state. [ABSTRACT FROM AUTHOR]

Objective . The goal of this study is to decode the electrical activity of single neurons in the human subthalamic nucleus (STN) to infer the speech features that a person articulated, heard or imagined. We also aim to evaluate the amount of subthalamic neurons required for high accuracy decoding suitable for real-life speech brain-machine interfaces (BMI). Approach . We intraoperatively recorded single-neuron activity in the STN of 21 neurosurgical patients with Parkinson's disease undergoing implantation of deep brain stimulator while patients produced, perceived or imagined the five monophthongal vowel sounds. Our decoder is based on machine learning algorithms that dynamically learn specific features of the speech-related firing patterns. Main results . In an extensive comparison of algorithms, our sparse decoder ('SpaDe'), based on sparse decomposition of the high dimensional neuronal feature space, outperformed the other algorithms in all three conditions: production, perception and imagery. For speech production, our algorithm, Spade, predicted all vowels correctly (accuracy: 100%; chance level: 20%). For perception accuracy was 96%, and for imagery: 88%. The accuracy of Spade showed a linear behavior in the amount of neurons for the perception data, and even faster for production or imagery. Significance . Our study demonstrates that the information encoded by single neurons in the STN about the production, perception and imagery of speech is suitable for high-accuracy decoding. It is therefore an important step towards BMIs for restoration of speech faculties that bears an enormous potential to alleviate the suffering of completely paralyzed ('locked-in') patients and allow them to communicate again with their environment. Moreover, our research indicates how many subthalamic neurons may be necessary to achieve each level of decoding accuracy, which is of supreme importance for a neurosurgeon planning the implantation of a speech BMI., (© 2021 IOP Publishing Ltd.)

Background: Our previous study found degradation to subthalamic neuronal encoding of speech features in Parkinson disease (PD) patients suffering from speech disorders., Objective: To find how timing of speech-related neuronal firing changes in PD patients with speech disorders compared to PD patients without speech disorders., Methods: During the implantation of deep brain stimulator (DBS), we recorded the activity of single neurons in the subthalamic nucleus (STN) of 18 neurosurgical patients with PD while they articulated, listened to, or imagined articulation of 5 vowel sounds, each following a beep. We compared subthalamic activity of PD patients with (n = 10) vs without speech disorders., Results: In this comparison, patients with speech disorders had longer reaction times and shorter lengths of articulation. Their speech-related neuronal activity preceding speech onset (planning) was delayed relative to the beep, but the time between this activity and the emission of speech sound was similar. Notwithstanding, speech-related neuronal activity following the onset of speech (feedback) was delayed when computed relative to the onset. Only in these patients was the time lag of planning neurons significantly correlated with the reaction time. Neuronal activity in patients with speech disorders was delayed during imagined articulation of vowel sounds but earlier during speech perception., Conclusion: Our findings indicate that longer reaction times in patients with speech disorders are due to STN or earlier activity of the speech control network. This is a first step in locating the source(s) of PD delays within this network and is therefore of utmost importance for future treatment of speech disorders., (© Congress of Neurological Surgeons 2021.)

The article discusses how transcranial direct current stimulation (tDCS) can have positive therapeutic effects for patients with various neurological and psychiatric conditions. It tells us how non-linear dose-response relationship for tDCS is investigated and how tDCS effects are being made in the combination of transcranial magnetic stimulation and tDCS with neuroimaging modalities such as functional magnetic resonance imaging, positron emission tomography and electroencephalography.

Rapid and flexible learning during behavioral choices is critical to our daily endeavors and constitutes a hallmark of dynamic reasoning. An important paradigm to examine flexible behavior involves learning new arbitrary associations mapping visual inputs to motor outputs. We conjectured that visuomotor rules are instantiated by translating visual signals into actions through dynamic interactions between visual, frontal and motor cortex. We evaluated the neural representation of such visuomotor rules by performing intracranial field potential recordings in epilepsy subjects during a rule-learning delayed match-to-behavior task. Learning new visuomotor mappings led to the emergence of specific responses associating visual signals with motor outputs in 3 anatomical clusters in frontal, anteroventral temporal and posterior parietal cortex. After learning, mapping selective signals during the delay period showed interactions with visual and motor signals. These observations provide initial steps towards elucidating the dynamic circuits underlying flexible behavior and how communication between subregions of frontal, temporal, and parietal cortex leads to rapid learning of task-relevant choices., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Rapid and flexible interpretation of conflicting sensory inputs in the context of current goals is a critical component of cognitive control that is orchestrated by frontal cortex. The relative roles of distinct subregions within frontal cortex are poorly understood. To examine the dynamics underlying cognitive control across frontal regions, we took advantage of the spatiotemporal resolution of intracranial recordings in epilepsy patients while subjects resolved color-word conflict. We observed differential activity preceding the behavioral responses to conflict trials throughout frontal cortex; this activity was correlated with behavioral reaction times. These signals emerged first in anterior cingulate cortex (ACC) before dorsolateral prefrontal cortex (dlPFC), followed by medial frontal cortex (mFC) and then by orbitofrontal cortex (OFC). These results disassociate the frontal subregions based on their dynamics, and suggest a temporal hierarchy for cognitive control in human cortex.

We use rules to extend learned behavior beyond specific instances to general scenarios. The prefrontal cortex (PFC) is thought to play an important role in representing rules, as evidenced by subjects who have difficulty in following rules after PFC damage and by animal studies demonstrating rule sensitivity of individual PFC neurons. How rules are instantiated at the single-neuronal level in the human brain, however, remains unclear. Here, we recorded from individual neurons in the human dorsolateral prefrontal cortex (DLPFC) as subjects performed a task in which they evaluated pairs of images using either of 2 abstract rules. We find that DLPFC neurons selectively encoded these rules while carrying little information about the subjects' responses or the sensory cues used to guide their decisions.

Recording human neurophysiological data in the teaching laboratory generally requires expensive instrumentation. From our experience in developing inexpensive equipment used in teaching neurophysiology laboratory exercises, we offer a strategy for the development of affordable and safe recording of human neurophysiological parameters. There are many resources available to guide the design and construction of electronic equipment that will record human biopotentials. An important consideration is subject safety, and the electrical characteristics of any equipment must meet strict galvanic isolation standards. Wireless data gathering offers the most complete isolation from 120VAC current. As an example, we present a homemade electrocardiogram recording circuit using only inexpensive and readily available components. We outline the feasibility of constructing equipment that meets the needs of the student laboratory for good data collection, and we consider the obstacles likely to be encountered in these projects. If students actively participate in the equipment design and construction, the process can also be a teaching tool. Students may gain a deeper understanding of the human neurobiology by making the electronic data acquisition and its presentation more transparent.