Effects of neural synchrony on surface EEG

BDNF-TrkB signaling orchestrates the buildup process of local sleep

Sleep debt accumulates during wakefulness, leading to increased slow wave activity (SWA) during sleep, an encephalographic marker for sleep need. The use-dependent demands of prior wakefulness increase sleep SWA locally. However, the circuitry and molecular identity of this "local sleep" remain unclear. Using pharmacology and optogenetic perturbations together with transcriptomics, we find that cortical brain-derived neurotrophic factor (BDNF) regulates SWA via the activation of tyrosine kinase B (TrkB) receptor and cAMP-response element-binding protein (CREB). We map BDNF/TrkB-induced sleep SWA to layer 5 (L5) pyramidal neurons of the cortex, independent of neuronal firing per se. Using mathematical modeling, we here propose a model of how BDNF's effects on synaptic strength can increase SWA in ways not achieved through increased firing alone. Proteomic analysis further reveals that TrkB activation enriches ubiquitin and proteasome subunits. Together, our study reveals that local SWA control is mediated by BDNF-TrkB-CREB signaling in L5 excitatory cortical neurons.

Network resonance and the auditory steady state response

The auditory steady state response (ASSR) arises when periodic sounds evoke stable responses in auditory networks that reflect the acoustic characteristics of the stimuli, such as the amplitude of the sound envelope. Larger for some stimulus rates than others, the ASSR in the human electroencephalogram (EEG) is notably maximal for sounds modulated in amplitude at 40 Hz. To investigate the local circuit underpinnings of the large ASSR to 40 Hz amplitude-modulated (AM) sounds, we acquired skull EEG and local field potential (LFP) recordings from primary auditory cortex (A1) in the rat during the presentation of 20, 30, 40, 50, and 80 Hz AM tones. 40 Hz AM tones elicited the largest ASSR from the EEG acquired above auditory cortex and the LFP acquired from each cortical layer in A1. The large ASSR in the EEG to 40 Hz AM tones was not due to larger instantaneous amplitude of the signals or to greater phase alignment of the LFP across the cortical layers. Instead, it resulted from decreased latency variability (or enhanced temporal consistency) of the 40 Hz response. Statistical models indicate the EEG signal was best predicted by LFPs in either the most superficial or deep cortical layers, suggesting deep layer coordinators of the ASSR. Overall, our results indicate that the recruitment of non-uniform but more temporally consistent responses across A1 layers underlie the larger ASSR to amplitude-modulated tones at 40 Hz.

Graded decisions in the human brain

Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.

Development of the Alpha Rhythm Is Linked to Visual White Matter Pathways and Visual Detection Performance

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5-21 years old) and showed that maturation of the optic radiation specifically accounts for developmental changes of alpha frequency. Behavioral analyses also confirmed that variations of alpha frequency are related to maturational changes in visual perception. The present findings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

Validation of transparent and flexible neural implants for simultaneous electrophysiology, functional imaging, and optogenetics

The combination of electrophysiology and neuroimaging methods allows the simultaneous measurement of electrical activity signals with calcium dynamics from single neurons to neuronal networks across distinct brain regions in vivo. While traditional electrophysiological techniques are limited by photo-induced artefacts and optical occlusion for neuroimaging, different types of transparent neural implants have been proposed to resolve these issues. However, reproducing proposed solutions is often challenging and it remains unclear which approach offers the best properties for long-term chronic multimodal recordings. We therefore created a streamlined fabrication process to produce, and directly compare, two types of transparent surface micro-electrocorticography (μECoG) implants: nano-mesh gold structures (m-μECoGs) versus a combination of solid gold interconnects and PEDOT:PSS-based electrodes (pp-μECoGs). Both implants allowed simultaneous multimodal recordings but pp-μECoGs offered the best overall electrical, electrochemical, and optical properties with negligible photo-induced artefacts to light wavelengths of interest. Showing functional chronic stability for up to four months, pp-μECoGs also allowed the simultaneous functional mapping of electrical and calcium neural signals upon visual and tactile stimuli during widefield imaging. Moreover, recordings during two-photon imaging showed no visible signal attenuation and enabled the correlation of network dynamics across brain regions to individual neurons located directly below the transparent electrical contacts.

Oscillatory and non-oscillatory brain activity reflects fear expression in an immediate and delayed fear extinction task

Fear extinction is pivotal for inhibiting fear responding to former threat-predictive stimuli. In rodents, short intervals between fear acquisition and extinction impair extinction recall compared to long intervals. This is called Immediate Extinction Deficit (IED). Importantly, human studies of the IED are sparse and its neurophysiological correlates have not been examined in humans. We, therefore, investigated the IED by recording electroencephalography (EEG), skin conductance responses (SCRs), an electrocardiogram (ECG), and subjective ratings of valence and arousal. Forty male participants were randomly assigned to extinction learning either 10 min after fear acquisition (immediate extinction) or 24 h afterward (delayed extinction). Fear and extinction recall were assessed 24 h after extinction learning. We observed evidence for an IED in SCR responses, but not in the ECG, subjective ratings, or in any assessed neurophysiological marker of fear expression. Irrespective of extinction timing (immediate vs. delayed), fear conditioning caused a tilt of the non-oscillatory background spectrum with decreased low-frequency power (<30 Hz) for threat-predictive stimuli. When controlling for this tilt, we observed a suppression of theta and alpha oscillations to threat-predictive stimuli, especially pronounced during fear acquisition. In sum, our data show that delayed extinction might be partially advantageous over immediate extinction in reducing sympathetic arousal (as assessed via SCR) to former threat-predictive stimuli. However, this effect was limited to SCR responses since all other fear measures were not affected by extinction timing. Additionally, we demonstrate that oscillatory and non-oscillatory activity is sensitive to fear conditioning, which has important implications for fear conditioning studies examining neural oscillations.

Amygdala and hippocampus dialogue with neocortex during human sleep and wakefulness

ABSTRACT

Previous studies have described synchronic electroencephalographic (EEG) patterns of the background activity that is characteristic of several vigilance states.

STUDY OBJECTIVES

To explore whether the background synchronous activity of the amygdala-hippocampal-neocortical circuit is modified during sleep in the delta, theta, alpha, sigma, beta, and gamma bands characteristic of each sleep state.

METHODS

By simultaneously recording intracranial and noninvasive scalp EEG (10-20 system) in epileptic patients who were candidates for neurosurgery, we explored synchronous activity among the amygdala, hippocampus, and neocortex during wakefulness (W), Non-Rapid Eye Movement (NREM), and Rapid-Eye Movement (REM) sleep.

RESULTS

Our findings reveal that hippocampal-cortical synchrony in the sleep spindle frequencies was spread across the cortex and was higher during NREM versus W and REM, whereas the amygdala showed punctual higher synchronization with the temporal lobe. Contrary to expectations, delta synchrony between the amygdala and frontal lobe and between the hippocampus and temporal lobe was higher during REM than NREM. Gamma and alpha showed higher synchrony between limbic structures and the neocortex during wakefulness versus sleep, while synchrony among deep structures showed a mixed pattern. On the one hand, amygdala-hippocampal synchrony resembled cortical activity (i.e. higher gamma and alpha synchrony in W); on the other, it showed its own pattern in slow frequency oscillations.

CONCLUSIONS

This is the first study to depict diverse patterns of synchronic interaction among the frequency bands during distinct vigilance states in a broad human brain circuit with direct anatomical and functional connections that play a crucial role in emotional processes and memory.

Universal Lifespan Trajectories of Source-Space Information Flow Extracted from Resting-State MEG Data

Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18-60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength, and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed me to estimate the probability distribution of the DoCM independently of phase and amplitude. The results support earlier observations of a posterior-to-anterior information flow for phase dynamics in {α1, α2, β, γ} and an opposite flow (anterior to posterior) in θ. Amplitude dynamics reveal posterior-to-anterior information flow in {α1, α2, γ}, a sensory-motor β-oriented pattern, and an anterior-to-posterior pattern in {δ, θ}. The DoCM between intra- and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {δ, θ, α1}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of the probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence, reaching a peak in adulthood, and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported.

Neurofeedback for the Education of Children with ADHD and Specific Learning Disorders: A Review

Neurofeedback (NF) is a type of biofeedback in which an individual’s brain activity is measured and presented to them to support self-regulation of ongoing brain oscillations and achieve specific behavioral and neurophysiological outcomes. NF training induces changes in neurophysiological circuits that are associated with behavioral changes. Recent evidence suggests that the NF technique can be used to train electrical brain activity and facilitate learning among children with learning disorders. Toward this aim, this review first presents a generalized model for NF systems, and then studies involving NF training for children with disorders such as dyslexia, attention-deficit/hyperactivity disorder (ADHD), and other specific learning disorders such as dyscalculia and dysgraphia are reviewed. The discussion elaborates on the potential for translational applications of NF in educational and learning settings with details. This review also addresses some issues concerning the role of NF in education, and it concludes with some solutions and future directions. In order to provide the best learning environment for children with ADHD and other learning disorders, it is critical to better understand the role of NF in educational settings. The review provides the potential challenges of the current systems to aid in highlighting the issues undermining the efficacy of current systems and identifying solutions to address them. The review focuses on the use of NF technology in education for the development of adaptive teaching methods and the best learning environment for children with learning disabilities.

Theta but not beta activity is modulated by freedom of choice during action selection

Large-scale neurophysiological markers of action competition have been almost exclusively investigated in the context of instructed choices, hence it remains unclear whether these markers also apply to free choices. This study aimed to compare the specific brain dynamics underlying instructed and free decisions. Electroencephalography (EEG) was recorded while 31 participants performed a target selection task; the choice relied either on stimulus-response mappings (instructed) or on participants' preferences (free). Choice difficulty was increased by introducing distractors in the informative stimulus in instructed choices, and by presenting targets with similar motor costs in free choices. Results revealed that increased decision difficulty was associated with higher reaction times (RTs) in instructed choices and greater choice uncertainty in free choices. Midfrontal EEG theta (4-8 Hz) power increased with difficulty in instructed choices, but not in free choices. Although sensorimotor beta (15-30 Hz) power was correlated with RTs, it was not significantly influenced by choice context or difficulty. These results suggest that midfrontal theta power may specifically increase with difficulty in externally-driven choices, whereas sensorimotor beta power may be predictive of RTs in both externally- and internally-driven choices.

Aperiodic electrophysiological activity in preterm infants is linked to subsequent autism risk

Approximately 7% of preterm infants receive an autism spectrum disorder (ASD) diagnosis. Yet, there is a significant gap in the literature in identifying prospective markers of neurodevelopmental risk in preterm infants. The present study examined two electroencephalography (EEG) parameters during infancy, absolute EEG power and aperiodic activity of the power spectral density (PSD) slope, in association with subsequent autism risk and cognitive ability in a diverse cohort of children born preterm in South Africa. Participants were 71 preterm infants born between 25 and 36 weeks gestation (34.60 ± 2.34 weeks). EEG was collected during sleep between 39 and 41 weeks postmenstrual age adjusted (40.00 ± 0.42 weeks). The Bayley Scales of Infant Development and Brief Infant Toddler Social Emotional Assessment (BITSEA) were administered at approximately 3 years of age adjusted (34 ± 2.7 months). Aperiodic activity, but not the rhythmic oscillatory activity, at multiple electrode sites was associated with subsequent increased autism risk on the BITSEA at three years of age. No associations were found between the PSD slope or absolute EEG power and cognitive development. Our findings highlight the need to examine potential markers of subsequent autism risk in high-risk populations other than infants at familial risk.

Neonatal quantitative electroencephalography and long-term outcomes: a systematic review

AIM

To evaluate quantitative electroencephalogram (EEG) measures as predictors of long-term neurodevelopmental outcome in infants with a postconceptional age below 46 weeks, including typically developing infants born at term, infants with heterogeneous underlying pathologies, and infants born preterm.

METHOD

A comprehensive search was performed using PubMed, Embase, and Web of Science from study inception up to 8th January 2021. Studies that examined associations between neonatal quantitative EEG measures, based on conventional and amplitude-integrated EEG, and standardized neurodevelopmental outcomes at 2 years of age or older were reviewed. Significant associations between neonatal quantitative EEG and long-term outcome measures were grouped into one or more of the following categories: cognitive outcome; motor outcome; composite scores; and other standardized outcome assessments.

RESULTS

Twenty-four out of 1740 studies were included. Multiple studies showed that conventional EEG-based absolute power in the delta, theta, alpha, and beta frequency bands and conventional and amplitude-integrated EEG-related amplitudes were positively associated with favourable long-term outcome across several domains, including cognition and motor performance. Furthermore, a lower presence of discontinuous background pattern was also associated with favourable outcomes. However, interpretation of the results is limited by heterogeneity in study design and populations.

INTERPRETATION

Neonatal quantitative EEG measures may be used as prognostic biomarkers to identify those infants who will develop long-term difficulties and who might benefit from early interventions.

Laminar microcircuitry of visual cortex producing attention-associated electric fields

Cognitive operations are widely studied by measuring electric fields through EEG and ECoG. However, despite their widespread use, the neural circuitry giving rise to these signals remains unknown because the functional architecture of cortical columns producing attention-associated electric fields has not been explored. Here, we detail the laminar cortical circuitry underlying an attention-associated electric field measured over posterior regions of the brain in humans and monkeys. First, we identified visual cortical area V4 as one plausible contributor to this attention-associated electric field through inverse modeling of cranial EEG in macaque monkeys performing a visual attention task. Next, we performed laminar neurophysiological recordings on the prelunate gyrus and identified the electric-field-producing dipoles as synaptic activity in distinct cortical layers of area V4. Specifically, activation in the extragranular layers of cortex resulted in the generation of the attention-associated dipole. Feature selectivity of a given cortical column determined the overall contribution to this electric field. Columns selective for the attended feature contributed more to the electric field than columns selective for a different feature. Last, the laminar profile of synaptic activity generated by V4 was sufficient to produce an attention-associated signal measurable outside of the column. These findings suggest that the top-down recipient cortical layers produce an attention-associated electric field that can be measured extracortically with the relative contribution of each column depending upon the underlying functional architecture.

A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex

Synchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.

A ketogenic diet affects brain volume and metabolome in juvenile mice

Ketogenic diet (KD) is a high-fat and low-carbohydrate therapy for medically intractable epilepsy, and its applications in other neurological conditions, including those occurring in children, have been increasingly tested. However, how KD affects childhood neurodevelopment, a highly sensitive and plastic process, is not clear. In this study, we explored structural, metabolic, and functional consequences of a brief treatment of a strict KD (weight ratio of fat to carbohydrate plus protein is approximately 6.3:1) in naive juvenile mice of different inbred strains, using a multidisciplinary approach. Systemic measurements using magnetic resonance imaging revealed that unexpectedly, the volumes of most brain structures in KD-fed mice were about 90% of those in mice of the same strain but fed a standard diet. The reductions in volumes were nonselective, including different regions throughout the brain, the ventricles, and the white matter. The relative volumes of different brain structures were unaltered. Additionally, as KD is a metabolism-based treatment, we performed untargeted metabolomic profiling to explore potential means by which KD affected brain growth and to identify metabolic changes in the brain. We found that brain metabolomic profile was significantly impacted by KD, through both distinct and common pathways in different mouse strains. To explore whether the volumetric and metabolic changes induced by this KD treatment were associated with functional consequences, we recorded spontaneous EEG to measure brain network activity. Results demonstrated limited alterations in EEG patterns in KD-fed animals. In addition, we observed that cortical levels of brain-derived neurotrophic factor (BDNF), a critical molecule in neurodevelopment, did not change in KD-fed animals. Together, these findings indicate that a strict KD could affect volumetric development and metabolic profile of the brain in inbred juvenile mice, while global network activities and BDNF signaling in the brain were mostly preserved. Whether the volumetric and metabolic changes are related to any core functional consequences during neurodevelopment and whether they are also observed in humans need to be further investigated. In addition, our results indicate that certain outcomes of KD are specific to the individual mouse strains tested, suggesting that the physiological profiles of individuals may need to be examined to maximize the clinical benefit of KD.

Increased scale-free and aperiodic neural activity during sensorimotor integration-a novel facet in Tourette syndrome

Tourette syndrome is a common neurodevelopmental disorder defined by multiple motor and phonic tics. Tics in Tourette syndrome resemble spontaneously occurring movements in healthy controls and are therefore sometimes difficult to distinguish from these. Tics may in fact be mis-interpreted as a meaningful action, i.e. a signal with social content, whereas they lack such information and could be conceived a surplus of action or 'motor noise'. These and other considerations have led to a 'neural noise account' of Tourette syndrome suggesting that the processing of neural noise and adaptation of the signal-to-noise ratio during information processing is relevant for the understanding of Tourette syndrome. So far, there is no direct evidence for this. Here, we tested the 'neural noise account' examining 1/f noise, also called scale-free neural activity as well as aperiodic activity, in n = 74 children, adolescents and adults with Tourette syndrome and n = 74 healthy controls during task performance using EEG data recorded during a sensorimotor integration task. In keeping with results of a previous study in adults with Tourette syndrome, behavioural data confirmed that sensorimotor integration was also stronger in this larger Tourette syndrome cohort underscoring the relevance of perceptual-action processes in this disorder. More importantly, we show that 1/f noise and aperiodic activity during sensorimotor processing is increased in patients with Tourette syndrome supporting the 'neural noise account'. This implies that asynchronous/aperiodic neural activity during sensorimotor integration is stronger in patients with Tourette syndrome compared to healthy controls, which is probably related to abnormalities of GABAergic and dopaminergic transmission in these patients. Differences in 1/f noise and aperiodic activity between patients with Tourette syndrome and healthy controls were driven by high-frequency oscillations and not lower-frequency activity currently discussed to be important in the pathophysiology of tics. This and the fact that Bayesian statistics showed that there is evidence for the absence of a correlation between neural noise and clinical measures of tics, suggest that increased 1/f noise and aperiodic activity are not directly related to tics but rather represents a novel facet of Tourette syndrome.

Sevoflurane Increases Hippocampal Theta Oscillations and Impairs Memory Via TASK-3 Channels

Sevoflurane can induce memory impairment during clinical anesthesia; however, the underlying mechanisms are largely unknown. TASK-3 channels are one of the potential targets of sevoflurane. Accumulating evidence supports a negative role of intracranial theta rhythms (4–12 Hz) in memory formation. Here, we investigated whether TASK-3 channels contribute to sevoflurane-induced memory impairment by regulating hippocampal theta rhythms. In this study, the memory performance of mice was tested by contextual fear conditioning and inhibitory avoidance experiments. The hippocampal local field potentials (LFPs) were recorded from chronically implanted electrodes located in CA3 region. The results showed that sevoflurane concentration-dependently impaired the memory function of mice, as evidenced by the decreased time mice spent on freezing and reduced latencies for mice to enter the shock compartment. Our electrophysiological results revealed that sevoflurane also enhanced the spectral power of hippocampal LFPs (1–30 Hz), particularly in memory-related theta rhythms (4–12 Hz). These effects were mitigated by viral-mediated knockdown of TASK-3 channels in the hippocampal CA3 region. The knockdown of hippocampal TASK-3 channels significantly reduced the enhancing effect of sevoflurane on hippocampal theta rhythms and alleviated sevoflurane-induced memory impairment. Our data indicate that sevoflurane can increase hippocampal theta oscillations and impair memory function via TASK-3 channels.

Heterogenous electrophysiological responses of functionally distinct striatal subregions to circadian and sleep-related homeostatic processes

Basal Ganglia (BG) are a set of subcortical nuclei that are involved in the control of a wide variety of motor, cognitive and affective behaviors. Although many behavioral abnormalities associated with BG dysfunction overlap with the clinical picture precipitated by the lack of sleep, the impact of sleep alterations on neuronal activity in BG is unknown. Using wildtype C57BI mice, we investigated the circadian and sleep-related homeostatic modulation of neuronal activity in the 3 functional subdivisions of the striatum (i.e. sensorimotor, associative and limbic striatum). We found no circadian modulation of activity in both ventral and dorso-medial striatum while the dorso-lateral striatum displayed a significant circadian rhythm with increased firing rates during the subjective dark, active phase. By combining neuronal activity recordings with electroencephalogram (EEG) recordings, we found a strong modulation of neuronal activity by the nature of vigilance states with increased activity during wakefulness and rapid eye movement sleep relative to non-rapid eye movement sleep in all striatal subregions. Depriving animals of sleep for 6 hours induced significant, but heterogenous alterations in the neuronal activity across striatal subregions. Notably, these alterations lasted for up to 48 hours in the sensorimotor striatum and persisted even after the normalization of cortical EEG power densities. Our results show that vigilance and sleep states as well as their disturbances significantly affect neuronal activity within the striatum. We propose that these changes in neuronal activity underlie both the well-established links between sleep alterations and several disorders involving BG dysfunction as well as the maladaptive changes in behavior induced in healthy subjects following sleep loss.

Elevated and Slowed EEG Oscillations in Patients with Post-Concussive Syndrome and Chronic Pain Following a Motor Vehicle Collision

(1) Background: Mild traumatic brain injury produces significant changes in neurotransmission including brain oscillations. We investigated potential quantitative electroencephalography biomarkers in 57 patients with post-concussive syndrome and chronic pain following motor vehicle collision, and 54 healthy nearly age- and sex-matched controls. (2) Methods: Electroencephalography processing was completed in MATLAB, statistical modeling in SPSS, and machine learning modeling in Rapid Miner. Group differences were calculated using current-source density estimation, yielding whole-brain topographical distributions of absolute power, relative power and phase-locking functional connectivity. Groups were compared using independent sample Mann–Whitney U tests. Effect sizes and Pearson correlations were also computed. Machine learning analysis leveraged a post hoc supervised learning support vector non-probabilistic binary linear kernel classification to generate predictive models from the derived EEG signatures. (3) Results: Patients displayed significantly elevated and slowed power compared to controls: delta (p = 0.000000, r = 0.6) and theta power (p < 0.0001, r = 0.4), and relative delta power (p < 0.00001) and decreased relative alpha power (p < 0.001). Absolute delta and theta power together yielded the strongest machine learning classification accuracy (87.6%). Changes in absolute power were moderately correlated with duration and persistence of symptoms in the slow wave frequency spectrum (<15 Hz). (4) Conclusions: Distributed increases in slow wave oscillatory power are concurrent with post-concussive syndrome and chronic pain.

Coordinated Reset Vibrotactile Stimulation Induces Sustained Cumulative Benefits in Parkinson's Disease

BACKGROUND

Abnormal synchronization of neuronal activity in dopaminergic circuits is related to motor impairment in Parkinson's disease (PD). Vibrotactile coordinated reset (vCR) fingertip stimulation aims to counteract excessive synchronization and induce sustained unlearning of pathologic synaptic connectivity and neuronal synchrony. Here, we report two clinical feasibility studies that examine the effect of regular and noisy vCR stimulation on PD motor symptoms. Additionally, in one clinical study (study 1), we examine cortical beta band power changes in the sensorimotor cortex. Lastly, we compare these clinical results in relation to our computational findings.

METHODS

Study 1 examines six PD patients receiving noisy vCR stimulation and their cortical beta power changes after 3 months of daily therapy. Motor evaluations and at-rest electroencephalographic (EEG) recordings were assessed off medication pre- and post-noisy vCR. Study 2 follows three patients for 6+ months, two of whom received daily regular vCR and one patient from study 1 who received daily noisy vCR. Motor evaluations were taken at baseline, and follow-up visits were done approximately every 3 months. Computationally, in a network of leaky integrate-and-fire (LIF) neurons with spike timing-dependent plasticity, we study the differences between regular and noisy vCR by using a stimulus model that reproduces experimentally observed central neuronal phase locking.

RESULTS

Clinically, in both studies, we observed significantly improved motor ability. EEG recordings observed from study 1 indicated a significant decrease in off-medication cortical sensorimotor high beta power (21-30 Hz) at rest after 3 months of daily noisy vCR therapy. Computationally, vCR and noisy vCR cause comparable parameter-robust long-lasting synaptic decoupling and neuronal desynchronization.

CONCLUSION

In these feasibility studies of eight PD patients, regular vCR and noisy vCR were well tolerated, produced no side effects, and delivered sustained cumulative improvement of motor performance, which is congruent with our computational findings. In study 1, reduction of high beta band power over the sensorimotor cortex may suggest noisy vCR is effectively modulating the beta band at the cortical level, which may play a role in improved motor ability. These encouraging therapeutic results enable us to properly plan a proof-of-concept study.

A neural noise account of Gilles de la Tourette syndrome

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A neural noise account on Tourette syndrome is conceptualized.

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We outline how neurophysiological methods can be used to test this account.

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The neural noise account may lead to novel treatment options.

Tics, often preceded by premonitory urges, are the clinical hallmark of Tourette syndrome. They resemble spontaneous movements, but are exaggerated, repetitive and appear misplaced in a given communication context. Given that tics often go unnoticed, it has been suggested that they represent a surplus of action, or motor noise. In this conceptual position paper, we propose that tics and urges, but also patterns of the cognitive profile in Tourette syndrome might be explained by the principle of processing of neural noise and adaptation to it during information processing. We review evidence for this notion in the light of Tourette pathophysiology and outline why neurophysiological and imaging approaches are central to examine a possibly novel view on Tourette syndrome. We discuss how neurophysiological data at multiple levels of inspections, i.e., from local field potentials using intra-cranial recording to scalp-measured EEG data, in combination with imaging approaches, can be used to examine the neural noise account in Tourette syndrome. We outline what signal processing methods may be suitable for that. We argue that, as a starting point, the analysis of 1/f neural noise or scale-free activity may be suitable to investigate the role of neural noise and its adaptation during information processing in Tourette syndrome. We outline, how the neural noise perspective, if substantiated by further neurophysiological studies and re-analyses of existing data, may pave the way to novel interventions directly targeting neural noise levels and patterns in Tourette syndrome.

High-dose ethanol intoxication decreases 1/f neural noise or scale-free neural activity in the resting state

Binge drinking is a frequent phenomenon in many western societies and has been associated with an increased risk of developing alcohol use disorder later in life. Yet, the effects of high-dose alcohol intoxication on neurophysiological processes are still quite poorly understood. This is particularly the case given that neurophysiological brain activity not only contains recurring (oscillatory) patterns of activity, but also a significant fraction of "scale-free" or arrhythmic dynamics referred to as 1/f type activity, pink noise, or 1/f neural noise. Neurobiological considerations suggest that it should be modulated by alcohol intoxication. To investigate this assumption, we collected resting state EEG data from n = 23 healthy young male subjects in a crossover design, where each subject was once tested sober and once tested while intoxicated (mean breath alcohol concentration of 1.1 permille ±0.2). Analyses of the 1/f neural dynamics showed that ethanol intoxication decreased resting state 1/f neural noise, as compared with a sober state. The effects were strongest when the eyes were closed and particularly reliable in the beta frequency band. Given that the dynamics of the beta band have been shown to strongly depend on GABA_A receptor neural transmission, this finding nicely aligns with the fact that ethanol increases GABAergic signaling. The study reveals a currently unreported effect of binge drinking on neurophysiological dynamics, which likely revealed a higher sensitivity for ethanol effects than most commonly considered measures of power in neural oscillations. Implications and applicability of these findings are discussed.

The Effect of Transcranial Pulsed Current Stimulation at 4 and 75 Hz on Electroencephalography Theta and High Gamma Band Power: A Pilot Study

Introduction: Transcranial pulsed current stimulation (tPCS) is an emerging noninvasive brain stimulation technique that has shown significant effects on cortical excitability. To date, electrophysiological measures of the efficiency of monophasic tPCS have not been reported. Objective: We aimed to explore the effects of monophasic anodal and cathodal-tPCS (a-tPCS/c-tPCS) at theta (4 Hz) and gamma (75 Hz) frequencies on theta and high gamma electroencephalography (EEG) oscillatory power. Methods: In a single-blind, randomized, sham-controlled crossover design, 15 healthy participants were randomly assigned into 5 experimental sessions in which they received a-PCS/c-tPCS at 4 and 75 Hz or sham stimulation over the left primary motor cortex (M1) for 15 min at an intensity of 1.5 mA. Changes in theta and high gamma oscillatory power were recorded at baseline, immediately after, and 30 min after stimulation using EEG at rest with eyes open. Results: a-tPCS at 4 Hz showed a significant increase in theta power compared with sham, whereas c-tPCS at 4 Hz had no significant effect on theta power. a-tPCS at 75 Hz produced no changes in high gamma power compared with sham. Importantly, c-tPCS at 75 Hz led to a significant reduction in high gamma power compared with baseline, as well as compared with c-tPCS at 4 Hz and sham stimulation. Conclusion: The results demonstrate the modulation of oscillatory brain activity by monophasic tPCS, and highlight the need for future studies on a larger scale to confirm these initial findings. Impact statement Transcranial pulsed current stimulation (tPCS) is a novel brain stimulation technique. Recently, tPCS has been introduced to directly modulate brain oscillations by applying pulsatile current over the target brain area. Using both anodal and cathodal monophasic tPCS at theta and gamma frequencies, we demonstrate the ability of the stimulation to modulate brain activity. The present findings are the first direct electroencephalography evidence of an interaction between tPCS and ongoing oscillatory activity in the human motor cortex. Our work recommends tPCS as a tool for investigating human brain oscillations and open more studies in this area.

Localization of deep brain activity with scalp and subdural EEG

To what extent electrocorticography (ECoG) and electroencephalography (scalp EEG) differ in their capability to locate sources of deep brain activity is far from evident. Compared to EEG, the spatial resolution and signal-to-noise ratio of ECoG is superior but its spatial coverage is more restricted, as is arguably the volume of tissue activity effectively measured from. Moreover, scalp EEG studies are providing evidence of locating activity from deep sources such as the hippocampus using high-density setups during quiet wakefulness. To address this question, we recorded a multimodal dataset from 4 patients with refractory epilepsy during quiet wakefulness. This data comprises simultaneous scalp, subdural and depth EEG electrode recordings. The latter was located in the hippocampus or insula and provided us with our "ground truth" for source localization of deep activity. We applied independent component analysis (ICA) for the purpose of separating the independent sources in theta, alpha and beta frequency band activity. In all patients subdural- and scalp EEG components were observed which had a significant zero-lag correlation with one or more contacts of the depth electrodes. Subsequent dipole modeling of the correlating components revealed dipole locations that were significantly closer to the depth electrodes compared to the dipole location of non-correlating components. These findings support the idea that components found in both recording modalities originate from neural activity in close proximity to the depth electrodes. Sources localized with subdural electrodes were ~70% closer to the depth electrode than sources localized with EEG with an absolute improvement of around ~2cm. In our opinion, this is not a considerable improvement in source localization accuracy given that, for clinical purposes, ECoG electrodes were implanted in close proximity to the depth electrodes. Furthermore, the ECoG grid attenuates the scalp EEG, due to the electrically isolating silastic sheets in which the ECoG electrodes are embedded. Our results on dipole modeling show that the deep source localization accuracy of scalp EEG is comparable to that of ECoG. SIGNIFICANCE STATEMENT: Deep and subcortical regions play an important role in brain function. However, as joint recordings at multiple spatial scales to study brain function in humans are still scarce, it is still unresolved to what extent ECoG and EEG differ in their capability to locate sources of deep brain activity. To the best of our knowledge, this is the first study presenting a dataset of simultaneously recorded EEG, ECoG and depth electrodes in the hippocampus or insula, with a focus on non-epileptiform activity (quiet wakefulness). Furthermore, we are the first study to provide experimental findings on the comparison of source localization of deep cortical structures between invasive and non-invasive brain activity measured from the cortical surface.

Differential Generation of Saccade, Fixation, and Image-Onset Event-Related Potentials in the Human Mesial Temporal Lobe

Event-related potentials (ERPs) are a commonly used electrophysiological signature for studying mesial temporal lobe (MTL) function during visual memory tasks. The ERPs associated with the onset of visual stimuli (image-onset) and eye movements (saccades and fixations) provide insights into the mechanisms of their generation. We hypothesized that since eye movements and image-onset provide MTL structures with salient visual information, perhaps they both engage similar neural mechanisms. To explore this question, we used intracranial electroencephalographic data from the MTLs of 11 patients with medically refractory epilepsy who participated in a visual search task. We characterized the electrophysiological responses of MTL structures to saccades, fixations, and image-onset. We demonstrated that the image-onset response is an evoked/additive response with a low-frequency power increase. In contrast, ERPs following eye movements appeared to arise from phase resetting of higher frequencies than the image-onset ERP. Intriguingly, this reset was associated with saccade onset and not termination (fixation), suggesting it is likely the MTL response to a corollary discharge, rather than a response to visual stimulation. We discuss the distinct mechanistic underpinnings of these responses which shed light on the underlying neural circuitry involved in visual memory processing.

Multi-spectral oscillatory dynamics serving directed and divided attention

Attention-related amplification of neural representations of external stimuli has been well documented in the visual domain, however, research concerning the oscillatory dynamics of such directed attention is relatively sparse in humans. Specifically, it is unknown which spectrally-specific neural responses are mainly impacted by the direction and division of attention, as well as whether the effects of attention on these oscillations are spatially disparate. In this study, we use magnetoencephalography and a visual-somatosensory oddball task to investigate the whole-brain oscillatory dynamics of directed (Experiment 1; N = 26) and divided (Experiment 2; N = 34) visual attention. Sensor-level data were transformed into the time-frequency domain and significant responses from baseline were imaged using a frequency-resolved beamformer. We found that multi-spectral cortical oscillations were stronger when attention was sustained in the visual space and that these effects exhibited informative spatial distributions that differed by frequency. More specifically, we found stronger frontal theta (4–8 Hz), frontal and occipital alpha (8–14 Hz), occipital beta (16–22 Hz), and frontal gamma (74–84 Hz) responses when visual attention was sustained than when it was directed away from the visual domain. Similarly, in the divided attention condition, we observed stronger fronto-parietal theta activity and temporo-parietal alpha and beta oscillations when visual attention was sustained toward the visual stimuli than divided between the visual and somatosensory domains. Investigating how attentional gain is implemented in the human brain is essential for better understanding how this process is degraded in disease, and may provide useful targets for future therapies.

Interaction between steady-state visually evoked potentials at nearby flicker frequencies

Steady-state visually evoked potential (SSVEP) studies routinely employ simultaneous presentation of two temporally modulated stimuli, with SSVEP amplitude modulations serving to index top-down cognitive processes. However, the nature of SSVEP amplitude modulations as a function of competing temporal frequency (TF) has not been systematically studied, especially in relation to the normalization framework which has been extensively used to explain visual responses to multiple stimuli. We recorded spikes and local field potential (LFP) from the primary visual cortex (V1) as well as EEG from two awake macaque monkeys while they passively fixated plaid stimuli with components counterphasing at different TFs. We observed asymmetric SSVEP response suppression by competing TFs (greater suppression for lower TFs), which further depended on the relative orientations of plaid components. A tuned normalization model, adapted to SSVEP responses, provided a good account of the suppression. Our results provide new insights into processing of temporally modulated visual stimuli.

Theta Oscillations in Human Memory

Theta frequency (4-8 Hz) fluctuations of the local field potential have long been implicated in learning and memory. Human studies of episodic memory, however, have provided mixed evidence for theta's role in successful learning and remembering. Re-evaluating these conflicting findings leads us to conclude that: (i) successful memory is associated both with increased narrow-band theta oscillations and a broad-band tilt of the power spectrum; (ii) theta oscillations specifically support associative memory, whereas the spectral tilt reflects a general index of activation; and (iii) different cognitive contrasts (generalized versus specific to memory), recording techniques (invasive versus noninvasive), and referencing schemes (local versus global) alter the balance between the two phenomena to make one or the other more easily detectable.

What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective

Selective attention plays an essential role in information acquisition and utilization from the environment. In the past 50 years, research on selective attention has been a central topic in cognitive science. Compared with unimodal studies, crossmodal studies are more complex but necessary to solve real-world challenges in both human experiments and computational modeling. Although an increasing number of findings on crossmodal selective attention have shed light on humans' behavioral patterns and neural underpinnings, a much better understanding is still necessary to yield the same benefit for intelligent computational agents. This article reviews studies of selective attention in unimodal visual and auditory and crossmodal audiovisual setups from the multidisciplinary perspectives of psychology and cognitive neuroscience, and evaluates different ways to simulate analogous mechanisms in computational models and robotics. We discuss the gaps between these fields in this interdisciplinary review and provide insights about how to use psychological findings and theories in artificial intelligence from different perspectives.

Neurophysiological basis of contrast dependent BOLD orientation tuning

Recent work in early visual cortex of humans has shown that the BOLD signal exhibits contrast dependent orientation tuning, with an inverse oblique effect (oblique > cardinal) at high contrast and a horizontal effect (vertical > horizontal) at low contrast. This finding is at odds with decades of neurophysiological research demonstrating contrast invariant orientation tuning in primate visual cortex, yet the source of this discrepancy is unclear. We hypothesized that contrast dependent BOLD orientation tuning may arise due to contrast dependent influences of feedforward (FF) and feedback (FB) synaptic activity, indexed through gamma and alpha rhythms, respectively. To quantify this, we acquired EEG and BOLD in healthy humans to generate and compare orientation tuning curves across all neural frequency bands with BOLD. As expected, BOLD orientation selectivity in V1 was contrast dependent, preferring oblique orientations at high contrast and vertical at low contrast. On the other hand, EEG orientation tuning was contrast invariant, though frequency-specific, with an inverse-oblique effect in the gamma band (FF) and a horizontal effect in the alpha band (FB). Therefore, high-contrast BOLD orientation tuning closely matched FF activity, while at low contrast, BOLD best resembled FB orientation tuning. These results suggest that contrast dependent BOLD orientation tuning arises due to the reduced contribution of FF input to overall neurophysiological activity at low contrast, shifting BOLD orientation tuning towards the orientation preferences of FB at low contrast.

Brain-computer interfaces in neurologic rehabilitation practice

The brain-computer interfaces (BCIs) for neurologic rehabilitation are based on the assumption that by retraining the brain to specific activities, an ultimate improvement of function can be expected. In this chapter, we review the present status, key determinants, and future directions of the clinical use of BCI in neurorehabilitation. The recent advancements in noninvasive BCIs as a therapeutic tool to promote functional motor recovery by inducing neuroplasticity are described, focusing on stroke as it represents the major cause of long-term motor disability. The relevance of recent findings on BCI use in spinal cord injury beyond the control of neuroprosthetic devices to restore motor function is briefly discussed. In a dedicated section, we examine the potential role of BCI technology in the domain of cognitive function recovery by instantiating BCIs in the long history of neurofeedback and some emerging BCI paradigms to address cognitive rehabilitation are highlighted. Despite the knowledge acquired over the last decade and the growing number of studies providing evidence for clinical efficacy of BCI in motor rehabilitation, an exhaustive deployment of this technology in clinical practice is still on its way. The pipeline to translate BCI to clinical practice in neurorehabilitation is the subject of this chapter.

Brain Rhythms During Sleep and Memory Consolidation: Neurobiological Insights

Sleep can benefit memory consolidation. The characterization of brain regions underlying memory consolidation during sleep, as well as their temporal interplay, reflected by specific patterns of brain electric activity, is surfacing. Here, we provide an overview of recent concepts and results on the mechanisms of sleep-related memory consolidation. The latest studies strongly impacting future directions of research in this field are highlighted.

The asynchronous state's relation to large-scale potentials in cortex

Understanding the relation between large-scale potentials (M/EEG) and their underlying neural activity can improve the precision of research and clinical diagnosis. Recent insights into cortical dynamics highlighted a state of strongly reduced spike count correlations, termed the asynchronous state (AS). The AS has received considerable attention from experimenters and theorists alike, regarding its implications for cortical dynamics and coding of information. However, how reconcilable are these vanishing correlations in the AS with large-scale potentials such as M/EEG observed in most experiments? Typically the latter are assumed to be based on underlying correlations in activity, in particular between subthreshold potentials. We survey the occurrence of the AS across brain states, regions, and layers and argue for a reconciliation of this seeming disparity: large-scale potentials are either observed, first, at transitions between cortical activity states, which entail transient changes in population firing rate, as well as during the AS, and, second, on the basis of sufficiently large, asynchronous populations that only need to exhibit weak correlations in activity. Cells with no or little spiking activity can contribute to large-scale potentials via their subthreshold currents, while they do not contribute to the estimation of spiking correlations, defining the AS. Furthermore, third, the AS occurs only within particular cortical regions and layers associated with the currently selected modality, allowing for correlations at other times and between other areas and layers.

Monkey EEG links neuronal color and motion information across species and scales

It remains challenging to relate EEG and MEG to underlying circuit processes and comparable experiments on both spatial scales are rare. To close this gap between invasive and non-invasive electrophysiology we developed and recorded human-comparable EEG in macaque monkeys during visual stimulation with colored dynamic random dot patterns. Furthermore, we performed simultaneous microelectrode recordings from 6 areas of macaque cortex and human MEG. Motion direction and color information were accessible in all signals. Tuning of the non-invasive signals was similar to V4 and IT, but not to dorsal and frontal areas. Thus, MEG and EEG were dominated by early visual and ventral stream sources. Source level analysis revealed corresponding information and latency gradients across cortex. We show how information-based methods and monkey EEG can identify analogous properties of visual processing in signals spanning spatial scales from single units to MEG - a valuable framework for relating human and animal studies.

Working Memory Load-related Theta Power Decreases in Dorsolateral Prefrontal Cortex Predict Individual Differences in Performance

Holding information in working memory (WM) is an active and effortful process that is accompanied by sustained load-dependent changes in oscillatory brain activity. These proportional power increases are often reported in EEG studies recording theta over frontal midline sites. Intracranial recordings, however, yield mixed results, depending on the brain area being recorded from. We recorded intracranial EEG with depth electrodes in 13 patients with epilepsy who were performing a Sternberg WM task. Here, we investigated patterns of theta power changes as a function of memory load during maintenance in three areas critical for WM: dorsolateral prefrontal cortex (DLPFC), dorsal ACC (dACC), and hippocampus. Theta frequency power in both hippocampus and dACC increased during maintenance. In contrast, theta frequency power in the DLPFC decreased during maintenance, and this decrease was proportional to memory load. Only the power decreases in DLPFC, but not the power increases in hippocampus and dACC, were predictive of behavior in a given trial. The extent of the load-related theta power decreases in the DLPFC in a given participant predicted a participant's RTs, revealing that DLPFC theta explains individual differences in WM ability between participants. Together, these data reveal a pattern of theta power decreases in the DLPFC that is predictive of behavior and that is opposite of that in other brain areas. This result suggests that theta band power changes serve different cognitive functions in different brain areas and specifically that theta power decreases in DLPFC have an important role in maintenance of information.

The Modulation of Neural Noise Underlies the Effectiveness of Methylphenidate Treatment in Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Various lines of research suggest that the stability of neural processes is low in attention-deficit/hyperactivity disorder (ADHD). Considering overarching neural principles, this lack of stability relates to increased levels of neural noise. However, no study has directly examined neural noise in ADHD. Likewise, it is unknown whether the modulation of neural noise reflects a mechanistic link as to why methylphenidate (MPH) is effective in treating impulsivity in ADHD.

METHODS

We compared neural noise between 29 juvenile patients with ADHD and 32 healthy control subjects and examined the effects of MPH. We examined 1/f neural noise of electroencephalogram data collected while participants performed a response inhibition (Go/NoGo) task.

RESULTS

Specific during NoGo trials, children with ADHD showed more neural noise than healthy control subjects. This was especially the case with regard to the theta frequency band, which is very closely related to cognitive control. MPH treatment reduced neural noise in ADHD to the level of healthy control subjects. Correlational analyses showed a direct relationship between decreases in neural noise and increases in behavioral performance. Mechanistically, this can be explained by the MPH-induced increase in dopaminergic neurotransmission that enhances the signal-to-noise ratio in neural networks and thus reduces neural noise.

CONCLUSIONS

This study is the first to demonstrate increased (pink) neural noise in patients with ADHD and its reduction through MPH treatment. The study reveals an important mechanistic link as to why MPH is effective in treating impulsivity in ADHD.

Cortical Electrocorticogram (ECoG) Is a Local Signal

Electrocorticogram (ECoG), obtained by low-pass filtering the brain signal recorded from a macroelectrode placed on the cortex, is extensively used to find the seizure focus in drug-resistant epilepsy and is of growing importance in cognitive and brain-machine-interfacing studies. To accurately estimate the epileptogenic cortex or to make inferences about cognitive processes, it is important to determine the "spatial spread" of ECoG (i.e., the extent of cortical tissue that contributes to its activity). However, the ECoG spread is currently unknown; even the spread of local field potential (LFP) obtained from microelectrodes is debated, with estimates ranging from a few hundred micrometers to several millimeters. Spatial spread can be estimated by measuring the receptive field (RF) and multiplying by the cortical magnification factor, but this method overestimates the spread because RF size gets inflated due to several factors. This issue can be partially addressed using a model that compares the RFs of two measures, such as LFP and multi-unit activity (MUA). To use this approach for ECoG, we designed a customized array containing both microelectrodes and ECoG electrodes to simultaneously map MUA, LFP, and ECoG RFs from the primary visual cortex of awake monkeys (three female Macaca radiata). The spatial spread of ECoG was surprisingly local (diameter ∼3 mm), only 3 times that of the LFP. Similar results were obtained using a model to simulate ECoG as a sum of LFPs of varying electrode sizes. Our results further validate the use of ECoG in clinical and basic cognitive research.SIGNIFICANCE STATEMENT Brains signals capture different attributes of the neural network depending on the size and location of the recording electrode. Electrocorticogram (ECoG), obtained by placing macroelectrodes (typically 2-3 mm diameter) on the exposed surface of the cortex, is widely used by neurosurgeons to identify the source of seizures in drug-resistant epileptic patients. The brain area responsible for seizures is subsequently surgically removed. Accurate estimation of the epileptogenic cortex and its removal requires the estimation of spatial spread of ECoG. Here, we estimated the spatial spread of ECoG in five behaving monkeys using two different approaches. Our results suggest that ECoG is a local signal (diameter of ∼3 mm), which can provide a useful tool for clinical, cognitive neuroscience, and brain-machine-interfacing applications.

On the interrelation of 1/f neural noise and norepinephrine system activity during motor response inhibition

Several lines of evidence suggest that there is a close interrelation between the degree of noise in neural circuits and the activity of the norepinephrine (NE) system, yet the precise nexus between these aspects is far from being understood during human information processing and cognitive control in particular. We examine this nexus during response inhibition in n = 47 healthy participants. Using high-density EEG recordings, we estimate neural noise by calculating "1/f noise" of those data and integrate these EEG parameters with pupil diameter data as an established indirect index of NE system activity. We show that neural noise is reduced when cognitive control processes to inhibit a prepotent/automated response are exerted. These neural noise variations were confined to the theta frequency band, which has also been shown to play a central role during response inhibition and cognitive control. There were strong positive correlations between the 1/f neural noise parameter and the pupil diameter data within the first 250 ms after the Nogo stimulus presentation at centro-parietal electrode sites. No such correlations were evident during automated responding on Go trials. Source localization analyses using standardized low-resolution brain electromagnetic tomography show that inferior parietal areas are activated in this time period in Nogo trials. The data suggest an interrelation of NE system activity and neural noise within early stages of information processing associated with inferior parietal areas when cognitive control processes are required. The data provide the first direct evidence for the nexus between NE system activity and the modulation of neural noise during inhibitory control in humans. NEW & NOTEWORTHY This is the first study showing that there is a nexus between norepinephrine system activity and the modulation of neural noise or scale-free neural activity during inhibitory control in humans. It does so by integrating pupil diameter data with analysis of EEG neural noise.

Addressing the Language Binding Problem With Dynamic Functional Connectivity During Meaningful Spoken Language Comprehension

During speech, how does the brain integrate information processed on different timescales and in separate brain areas so we can understand what is said? This is the language binding problem. Dynamic functional connectivity (brief periods of synchronization in the phase of EEG oscillations) may provide some answers. Here we investigate time and frequency characteristics of oscillatory power and phase synchrony (dynamic functional connectivity) during speech comprehension. Twenty adults listened to meaningful English sentences and non-sensical “Jabberwocky” sentences in which pseudo-words replaced all content words, while EEG was recorded. Results showed greater oscillatory power and global connectivity strength (mean phase lag index) in the gamma frequency range (30–80 Hz) for English compared to Jabberwocky. Increased power and connectivity relative to baseline was also seen in the theta frequency range (4–7 Hz), but was similar for English and Jabberwocky. High-frequency gamma oscillations may reflect a mechanism by which the brain transfers and integrates linguistic information so we can extract meaning and understand what is said. Slower frequency theta oscillations may support domain-general processing of the rhythmic features of speech. Our findings suggest that constructing a meaningful representation of speech involves dynamic interactions among distributed brain regions that communicate through frequency-specific functional networks.

Propofol-induced Changes in α-β Sensorimotor Cortical Connectivity

BACKGROUND

Anesthetics are believed to alter functional connectivity across brain regions. However, network-level analyses of anesthesia, particularly in humans, are sparse. The authors hypothesized that propofol-induced loss of consciousness results in functional disconnection of human sensorimotor cortices underlying the loss of volitional motor responses.

METHODS

The authors recorded local field potentials from sensorimotor cortices in patients with Parkinson disease (N = 12) and essential tremor (N = 7) undergoing deep brain stimulation surgery, before and after propofol-induced loss of consciousness. Local spectral power and interregional connectivity (coherence and imaginary coherence) were evaluated separately across conditions for the two populations.

RESULTS

Propofol anesthesia caused power increases for frequencies between 2 and 100 Hz across the sensorimotor cortices and a shift of the dominant spectral peak in α and β frequencies toward lower frequencies (median ± SD peak frequency: 24.5 ± 2.6 Hz to 12.8 ± 2.3 Hz in Parkinson disease; 13.8 ± 2.1 Hz to 12.1 ± 1.0 Hz in essential tremor). Despite local increases in power, sensorimotor cortical coherence was suppressed with propofol in both cohorts, specifically in β frequencies (18 to 29 Hz) for Parkinson disease and α and β (10 to 48 Hz) in essential tremor.

CONCLUSIONS

The decrease in functional connectivity between sensory and motor cortices, despite an increase in local spectral power, suggests that propofol causes a functional disconnection of cortices with increases in autonomous activity within cortical regions. This pattern occurs across diseases evaluated, suggesting that these may be generalizable effects of propofol in patients with movement disorders and beyond. Sensorimotor network disruption may underlie anesthetic-induced loss of volitional control.

Neurofeedback Tunes Scale-Free Dynamics in Spontaneous Brain Activity

Brain oscillations exhibit long-range temporal correlations (LRTCs), which reflect the regularity of their fluctuations: low values representing more random (decorrelated) while high values more persistent (correlated) dynamics. LRTCs constitute supporting evidence that the brain operates near criticality, a state where neuronal activities are balanced between order and randomness. Here, healthy adults used closed-loop brain training (neurofeedback, NFB) to reduce the amplitude of alpha oscillations, producing a significant increase in spontaneous LRTCs post-training. This effect was reproduced in patients with post-traumatic stress disorder, where abnormally random dynamics were reversed by NFB, correlating with significant improvements in hyperarousal. Notably, regions manifesting abnormally low LRTCs (i.e., excessive randomness) normalized toward healthy population levels, consistent with theoretical predictions about self-organized criticality. Hence, when exposed to appropriate training, spontaneous cortical activity reveals a residual capacity for "self-tuning" its own temporal complexity, despite manifesting the abnormal dynamics seen in individuals with psychiatric disorder. Lastly, we observed an inverse-U relationship between strength of LRTC and oscillation amplitude, suggesting a breakdown of long-range dependence at high/low synchronization extremes, in line with recent computational models. Together, our findings offer a broader mechanistic framework for motivating research and clinical applications of NFB, encompassing disorders with perturbed LRTCs.

Variability and Predictors of Response to Continuous Theta Burst Stimulation: A TMS-EEG Study

Continuous theta-burst stimulation (cTBS) is a repetitive transcranial magnetic stimulation paradigm reported to decrease the excitability of the stimulated cortical area and which is thought to reflect a form of inhibitory synaptic plasticity. However, since its introduction, the effect of cTBS has shown a remarkable variability in its effects, which are often quantified by measuring the amplitude of motor evoked potentials (MEPs). Part of this inconsistency in experimental results might be due to an intrinsic variability of TMS effects caused by genetic or neurophysiologic factors. However, it is also possible that MEP only reflect the excitability of a sub-population of output neurons; resting EEG power and measures combining TMS and electroencephalography (TMS-EEG) might represent a more thorough reflection of cortical excitability. The aim of the present study was to verify the robustness of several predictors of cTBS response, such as I wave recruitment and baseline MEP amplitude, and to test cTBS after-effects on multiple neurophysiologic measurements such as MEP, resting EEG power, local mean field power (LMFP), TMS-related spectral perturbation (TRSP), and inter-trial phase clustering (ITPC). As a result, we were not able to confirm either the expected decrease of MEP amplitude after cTBS or the ability of I wave recruitment and MEP amplitude to predict the response to cTBS. Resting EEG power, LMFP, TRSP, and ITPC showed a more consistent trend toward a decrease after cTBS. Overall, our data suggest that the effect of cTBS on corticospinal excitability is variable and difficult to predict with common electrophysiologic markers, while its effect might be clearer when probed with combined TMS and EEG.

Circadian and Homeostatic Modulation of Multi-Unit Activity in Midbrain Dopaminergic Structures

Although the link between sleep disturbances and dopamine (DA)-related neurological and neuropsychiatric disorders is well established, the impact of sleep alterations on neuronal activity of midbrain DA-ergic structures is currently unknown. Here, using wildtype C57Bl mice, we investigated the circadian- and sleep-related modulation of electrical neuronal activity in midbrain ventral-tegmental-area (VTA) and substantia nigra (SN). We found no significant circadian modulation of activity in SN while VTA displayed a low amplitude but significant circadian modulation with increased firing rates during the active phase. Combining neural activity recordings with electroencephalogram (EEG) recordings revealed a strong vigilance state dependent modulation of neuronal activity with increased activity during wakefulness and rapid eye movement sleep relative to non-rapid eye movement sleep in both SN and VTA. Six-hours of sleep deprivation induced a significant depression of neuronal activity in both areas. Surprisingly, these alterations lasted for up to 48 hours and persisted even after the normalization of cortical EEG waves. Our results show that sleep and sleep disturbances significantly affect neuronal activity in midbrain DA structures. We propose that these changes in neuronal activity underlie the well-known relationship between sleep alterations and several disorders involving dysfunction of the DA circuitry such as addiction and depression.

Stimulus-evoked changes in cerebral vessel diameter: A study in healthy humans

The high metabolic demand of neuronal tissue, coupled with its relatively low energy storage capacity, requires that increases in neuronal activation are quickly matched with increased blood flow to ensure efficient supply of oxygen and nutrients to the tissue. For this to occur, dilation of nearby arterioles must be coordinated with the dilation of larger upstream feeding arteries. As it stands, the exact spatial extent of such dilation in humans is unknown. Using non-invasive time-of-flight magnetic resonance angiography in healthy participants, we developed an automatic methodology for reconstructing cerebral arterial vessels and quantifying their diameter on a voxel-by-voxel basis. Specifically, we isolated the posterior cerebral artery (PCA) supplying each occipital lobe and quantified its vasodilation induced by visual stimulation. Stimulus-induced changes were strongest (∼30%) near primary visual cortex and progressively decreased in a non-linear fashion as a function of distance. Surprisingly, weak - albeit significant - changes (∼2%) were observed ∼70 mm from the visual cortex. This demonstrates that visual stimulation modulates vascular tone along the bulk of the PCA segment, and thus may have important implications for our understanding of functional hyperemia in healthy and diseased states.

Superficial Slow Rhythms Integrate Cortical Processing in Humans

The neocortex is composed of six anatomically and physiologically specialized layers. It has been proposed that integration of activity across cortical areas is mediated anatomically by associative connections terminating in superficial layers, and physiologically by slow cortical rhythms. However, the means through which neocortical anatomy and physiology interact to coordinate neural activity remains obscure. Using laminar microelectrode arrays in 19 human participants, we found that most EEG activity is below 10-Hz (delta/theta) and generated by superficial cortical layers during both wakefulness and sleep. Cortical surface grid, grid-laminar, and dual-laminar recordings demonstrate that these slow rhythms are synchronous within upper layers across broad cortical areas. The phase of this superficial slow activity is reset by infrequent stimuli and coupled to the amplitude of faster oscillations and neuronal firing across all layers. These findings support a primary role of superficial slow rhythms in generating the EEG and integrating cortical activity.

Deviant Processing in the Primary Somatosensory Cortex

Stimulus-specific adaptation (SSA) to repetitive stimulation has been proposed to separate behaviorally relevant features from a stream of continuous sensory information. However, the exact mechanisms giving rise to SSA and cortical deviance detection are not well understood. We therefore used an oddball paradigm and multicontact electrodes to characterize single-neuron and local field potential responses to various deviant stimuli across the rat somatosensory cortex. Changing different single-whisker stimulus features evoked robust SSA in individual cortical neurons over a wide range of stimulus repetition rates (0.25-80 Hz). Notably, SSA was weakest in the granular input layer and significantly stronger in the supra- and infragranular layers, suggesting that a major part of SSA is generated within cortex. Moreover, we found a small subset of neurons in the granular layer with a deviant-specific late response, occurring roughly 200 ms after stimulus offset. This late deviant response exhibited true-deviance detection properties that were not explained by depression of sensory inputs. Our results show that deviant responses are actively amplified within cortex and contain an additional late component that is sensitive for context-specific sensory deviations. This strongly implicates deviance detection as a feature of intracortical stimulus processing beyond simple sensory input depression.

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the neurogenesis of EEG is still very limited. Local field potentials (LFPs) recorded via a multi-laminar microelectrode can provide a more detailed account of simultaneous neural activity across different cortical layers in the neocortex, but the technique is invasive. Combining EEG and LFP measurements in a pre-clinical model can greatly enhance understanding of the neural mechanisms involved in the generation of EEG signals, and facilitate the derivation of a more realistic and biologically accurate mathematical model of EEG. A simple procedure for acquiring concurrent and co-localized EEG and multi-laminar LFP signals in the anesthetized rodent is presented here. We also investigated whether EEG signals were significantly affected by a burr hole drilled in the skull for the insertion of a microelectrode. Our results suggest that the burr hole has a negligible impact on EEG recordings.