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Neurophysiological signals are often noisy, non-sinusoidal, and consist of transient bursts. Extraction and analysis of oscillatory features (such as waveform shape and cross-frequency coupling) in such datasets remains difficult. This limits our understanding of brain dynamics and its functional importance. Here, we develop Iterated Masking Empirical Mode Decomposition (itEMD), a method designed to decompose noisy and transient single channel data into relevant oscillatory modes in a flexible, fully data-driven way without the need for manual tuning. Based on Empirical Mode Decomposition (EMD), this technique can extract single-cycle waveform dynamics through phase-aligned instantaneous frequency. We test our method by extensive simulations across different noise, sparsity, and non-sinusoidality conditions. We find itEMD significantly improves the separation of data into distinct non-sinusoidal oscillatory components and robustly reproduces waveform shape across a wide range of relevant parameters. We further validate the technique on multi-modal, multi-species electrophysiological data. Our itEMD extracts known rat hippocampal theta waveform asymmetry and identifies subject-specific human occipital alpha without any prior assumptions about the frequencies contained in the signal. Notably, it does so with significantly less mode mixing compared to existing EMD-based methods. By reducing mode mixing and simplifying interpretation of EMD results, itEMD will enable new analyses into functional roles of neural signals in behaviour and disease.New & NoteworthyWe introduce a novel, data-driven method to identify oscillations in neural recordings. This approach is based on Empirical Mode Decomposition and reduces mixing of components, one of its main problems. The technique is validated and compared with existing methods using simulations and real data. We show our method better extracts oscillations and their properties in highly noisy and non-sinusoidal datasets.Running HeadDecomposition of data into non-sinusoidal oscillatory modes

Spectrum estimators that make use of averaging across time segments are ubiquitous across neuroscience. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling and significance testing via non-parametric statistics.We apply the approach to a first-level EEG resting-state dataset alternating between eyes open and eyes closed resting-state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole-head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multi-level analysis of power spectra, with adaptive covariance and confound modelling.

Beta oscillations in human sensorimotor cortex are hallmark signatures of healthy and pathological movement. In single trials, beta oscillations include bursts of intermittent, transient periods of high-power activity. These burst events have been linked to a range of sensory and motor processes, but their precise spatial, spectral, and temporal structure remains unclear. Specifically, a role for beta burst activity in information coding and communication suggests spatiotemporal patterns, or travelling wave activity, along specific anatomical gradients. We here show in human magnetoencephalography recordings that burst activity in sensorimotor cortex occurs in planar spatiotemporal wave-like patterns that dominate along two axes either parallel or perpendicular to the central sulcus. Moreover, we find that the two propagation directions are characterised by distinct anatomical and physiological features. Finally, our results suggest that sensorimotor beta bursts occurring before and after a movement share the same generator but can be distinguished by their anatomical, spectral and spatiotemporal characteristics, indicating distinct functional roles.

Neural oscillations are thought to play a central role in orchestrating activity states between distant neural populations. For example, during isometric contraction, 13–30 Hz beta activity becomes phase coupled between the motor cortex and the contralateral muscle. This and related observations have led to the proposal that beta activity and connectivity sustain stable cognitive and motor states – or the ‘status quo’ – in the brain. Recently, however, beta activity at the single-trial level has been shown to be short-lived – though so far this has been reported for regional beta activity in tasks without sustained motor demands. Here, we measured magnetoencephalography (MEG) and electromyography (EMG) in 18 human participants performing a sustained isometric contraction (gripping) task. If cortico-muscular beta connectivity is directly responsible for sustaining a stable motor state, then beta activity within single trials should be (or become) sustained in this context. In contrast, we found that motor beta activity and connectivity with the downstream muscle were transient. Moreover, we found that sustained motor requirements did not prolong beta-event duration in comparison to rest. These findings suggest that neural synchronisation between the brain and the muscle involves short ‘bursts’ of frequency-specific connectivity, even when task demands – and motor behaviour – are sustained.

Neuronal oscillations in different frequency bands have been linked to a wide variety of cognitive functions, and may even be a fundamental mechanism of inter-regional communication. For this reason, manipulation of oscillatory activity via brain stimulation is a central goal in neuroscience research. However, the vast majority of studies characterise oscillatory activity solely in terms of amplitude and frequency. Oscillations can also be characterised by their waveform shape; the degree to which they resemble or deviate from sinusoids. Here we exploit Empirical Mode Decomposition (EMD), a novel method that allows quantification of oscillatory waveform shape. We show for the first time that transcranial direct current stimulation (tDCS) alters the waveform shape of gamma oscillatory activity in the visual cortex. Notably, changes in waveform shape were limited to one half of the phase cycle; anodal stimulation led to a relatively slower, and cathodal to a relatively faster, descending half-wave. tDCS is generally believed to affect cortical excitability via alteration of resting membrane potential. Interestingly, simulations of altered cortical excitability in a gamma-generating neuronal population indicated the waveform shape changes observed experimentally likely stem from stimulation of pyramidal neurons. These findings have implications for understanding the neural consequences of tDCS at the level of neuronal population phenomena such as cortical oscillations and underscore the importance of waveform shape as an important feature of neuronal oscillations.

Non-sinusoidal waveform is emerging as an important feature of neuronal oscillations. However, the role of single cycle shape dynamics in rapidly unfolding brain activity remains unclear. Here, we develop an analytical framework that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency. We show how phase-alignment, a process of projecting cycles into a regularly sampled phase-grid space, makes it possible to compare cycles of different durations and shapes. ‘Normalised shapes’ can then be constructed with high temporal detail whilst accounting for differences in both duration and amplitude. We find that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data. Notably, in local field potential recordings of mouse hippocampal CA1, we find that theta oscillations have a stereotyped slow-descending slope in the cycle-wise average, yet exhibiting high variability on a cycle-by-cycle basis. We show how Principal Components Analysis allows identification of motifs of theta cycle waveform that have distinct associations to cycle amplitude, cycle duration and animal movement speed. By allowing investigation into oscillation shape at high temporal resolution, this analytical framework will open new lines of enquiry into how neuronal oscillations support moment-by-moment information processing and integration in brain networks.

Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks. ispartof: NEUROIMAGE vol:185 pages:72-82 ispartof: location:United States status: published

Gamma activity (γ, >30 Hz) is universally demonstrated across brain regions and species. However, the physiological basis and functional role of γ sub-bands (slow-γ, mid-γ, fast-γ) have been predominantly studied in rodent hippocampus; γ activity in the human neocortex is much less well understood.Here we combined neuroimaging and non-invasive brain stimulation to examine the properties of γ activity sub-bands in the primary motor cortex (M1), and their relationship to both local GABAergic activity and to motor learning. In 33 healthy individuals, we quantified movement-related γ activity in M1 using magnetoencephalography, assessed GABAergic signaling using transcranial magnetic stimulation (TMS), and estimated motor learning via a serial reaction time task.We characterised two distinct γ sub-bands (slow-γ, mid-γ) which show movement-related increase in activity during unilateral index finger movements and are characterised by distinct temporal-spectral-spatial profiles. Bayesian correlation analysis revealed strong evidence for a positive relationship between slow-γ (∼30-60Hz) peak frequency and endogenous GABA signalling during movement preparation (as assessed using the TMS-metric short interval intracortical inhibition). There was also moderate evidence for a relationship between power of the movement-related mid-γ activity (60-90Hz) and motor learning. These relationships were neurochemically- and frequency-specific.These data provide new insights into the neurophysiological basis and functional roles of γ activity in human M1 and allow the development of a new theoretical framework for γ activity in the human neocortex.Significance StatementGamma (γ) activity is ubiquitous in the brain, yet our understanding of the mechanisms and function of γ activity in the human neocortex, and particularly in the human motor cortex, is limited. Using a multimodal approach, we characterised two patterns of movement-related γ activity in the human motor cortex (slow-γ and mid-γ), with different spatial, temporal and spectral properties. Slow-γ peak frequency was correlated to local GABA-A activity, whereas mid-gamma power predicted performance in a subsequent motor learning task. Based on these findings and previous research, we propose a theoretical framework to explain how human motor cortical γ activities may arise and their potential role in plasticity and motor learning, providing new hypotheses to be tested in future studies.Key PointsWe combined neuroimaging (i.e. MEG) and non-invasive brain stimulation (i.e. TMS) to examine the properties of γ activity sub-bands in the primary motor cortex.Two distinct γ sub-bands (slow-γ, mid-γ) show a movement-related increase in activity during finger movements and are characterised by distinct temporal-spectral-spatial profiles.We found strong evidence for a positive relationship between slow-γ (∼30-60Hz) peak frequency and endogenous GABA signalling during movement preparation (as assessed using the TMS-metric short interval intracortical inhibition).

At any given moment our sensory systems receive multiple, often rhythmic, inputs from the environment. Processing of temporally structured events in one sensory modality can guide both behavioural and neural processing of events in other sensory modalities, but whether this occurs remains unclear. Here, we used human electroencephalography (EEG) to test the cross-modal influences of a continuous auditory frequency-modulated (FM) sound on visual perception and visual cortical activity. We report systematic fluctuations in perceptual discrimination of brief visual stimuli in line with the phase of the FM sound. We further show that this rhythmic modulation in visual perception is related to an accompanying rhythmic modulation of neural activity recorded over visual areas. Importantly, in our task, perceptual and neural visual modulations occurred without any abrupt and salient onsets in the energy of the auditory stimulation and without any rhythmic structure in the visual stimulus. As such, the results provide a critical validation for the existence and functional role of cross-modal entrainment and demonstrates its utility for organising the perception of multisensory stimulation in the natural environment.Significance StatementOur sensory environment is filled with rhythmic structures that are often multi-sensory in nature. Here we show that the alignment of neural activity to the phase of an auditory frequency-modulated sound has cross-modal consequences for vision: yielding systematic fluctuations in perceptual discrimination of brief visual stimuli that is mediated by accompanying rhythmic modulation of neural activity recorded over visual areas. These cross-modal effects on visual neural activity and perception occurred without any abrupt and salient onsets in the energy of the auditory stimulation and without any rhythmic structure in the visual stimulus. The current work shows that continuous auditory fluctuations in the natural environment can provide a pacing signal for neural activity and perception across the senses.

Neural oscillations are thought to play a central role in orchestrating activity states between distant neural populations. In humans, long-range neural connectivity has been particularly well characterised for 13-30 Hz beta activity which becomes phase coupled between the motor cortex and the contralateral muscle during isometric contraction. Based on this and related observations, beta activity and connectivity have been linked to sustaining stable cognitive and motor states – or the ‘status quo’ – in the brain. Recently, however, beta activity has been shown to be short-lived, as opposed to sustained – though so far this has been reported for regional beta activity in tasks without sustained motor demands. Here, we measured magnetoencephalography (MEG) and electromyography (EMG) in 18 human participants performing an isometric-contraction (gripping) task designed to yield sustained behavioural output. If cortico-muscular beta connectivity is directly responsible for sustaining a stable motor state, then beta activity should be (or become) sustained in this context. In contrast, we found that beta activity and connectivity with the downstream muscle were transient, even when participants engaged in sustained gripping. Moreover, we found that sustained motor requirements did not prolong beta-event duration in comparison to rest. These findings suggest that long-range neural synchronisation may entail short ‘bursts’ of frequency-specific connectivity, even when task demands – and behaviour – are sustained.HighlightsTrial-average 13-30 Hz beta activity and connectivity with the muscle appear sustained during stable motor behaviourSingle-trial beta activity and connectivity are short-lived, even when motor behaviour is sustainedSustained task demands do not prolong beta-event duration in comparison to resting state

Highlights • We applied Hidden Markov Model (HMM) to concurrent MEG and intracranial EEG (iEEG). • Epilepsy cohort HMM analysis yielded states similar to those of healthy subjects. • iEEG power correlated with the time course of HMM states. • Functional clusters of iEEG electrodes agreed with those based on spatial location. • Our pipeline can be used for group analysis of concurrent MEG and invasive data., Background Intracranial electroencephalography (iEEG) recordings are used for clinical evaluation prior to surgical resection of the focus of epileptic seizures and also provide a window into normal brain function. A major difficulty with interpreting iEEG results at the group level is inconsistent placement of electrodes between subjects making it difficult to select contacts that correspond to the same functional areas. Recent work using time delay embedded hidden Markov model (HMM) applied to magnetoencephalography (MEG) resting data revealed a distinct set of brain states with each state engaging a specific set of cortical regions. Here we use a rare group dataset with simultaneously acquired resting iEEG and MEG to test whether there is correspondence between HMM states and iEEG power changes that would allow classifying iEEG contacts into functional clusters. Methods Simultaneous MEG-iEEG recordings were performed at rest on 11 patients with epilepsy whose intracranial electrodes were implanted for pre-surgical evaluation. Pre-processed MEG sensor data was projected to source space. Time delay embedded HMM was then applied to MEG time series. At the same time, iEEG time series were analyzed with time-frequency decomposition to obtain spectral power changes with time. To relate MEG and iEEG results, correlations were computed between HMM probability time courses of state activation and iEEG power time course from the mid contact pair for each electrode in equally spaced frequency bins and presented as correlation spectra for the respective states and iEEG channels. Association of iEEG electrodes with HMM states based on significant correlations was compared to that based on the distance to peaks in subject-specific state topographies. Results Five HMM states were inferred from MEG. Two of them corresponded to the left and the right temporal activations and had a spectral signature primarily in the theta/alpha frequency band. All the electrodes had significant correlations with at least one of the states (p

Language and reading acquisitions are strongly associated with a child’s socioeconomic status (SES). There are a number of potential explanations for this relationship. We explore one potential explanation—a child’s SES is associated with how children discriminate word-like sounds (i.e., phonological processing), a foundational skill for reading acquisition. Magnetoencephalography data from a sample of 71 children (aged 6 years and 11 months–12 years and 3 months), during a passive auditory oddball task containing word and nonword deviants, were used to test “where” (which sensors) and “when” (at what time) any association may occur. We also investigated associations between cognition, education, and this neurophysiological response. We report differences in the neural processing of word and nonword deviant tones at an early N200 component (likely representing early sensory processing) and a later P300 component (likely representing attentional and/or semantic processing). More interestingly we found “parental subjective” SES (the parents rating of their own relative affluence) was convincingly associated with later responses, but there were no significant associations with equivalized income. This suggests that the SES as rated by their parents is associated with underlying phonological detection skills. Furthermore, this correlation likely occurs at a later time point in information processing, associated with semantic and attentional processes. In contrast, household income is not significantly associated with these skills. One possibility is that the subjective assessment of SES is more impactful on neural mechanisms of phonological processing than the less complex and more objective measure of household income.

Long-range communication through the motor system is thought to be facilitated by phase coupling between neural activity in the 15–30 Hz beta range. During periods of sustained muscle contraction (grip), such coupling is manifest between motor cortex and the contralateral forearm muscles—measured as the cortico-muscular coherence. We examined alterations in cortico-muscular coherence in individuals with Parkinson’s disease, while equating grip strength between individuals with Parkinson’s disease (off their medication) and healthy control participants. We show a marked reduction in beta cortico-muscular coherence in the Parkinson’s disease group, even though the grip strength was comparable between the two groups. Moreover, the reduced cortico-muscular coherence was related to motor symptoms, so that individuals with lower cortico-muscular coherence also displayed worse motor symptoms. These findings highlight the cortico-muscular coherence as a simple, effective and clinically relevant neural marker of Parkinson’s disease pathology, with the potential to aid monitoring of disease progression and the efficacy of novel treatments for Parkinson’s disease., Zokaei et al. report reduced cortico-muscular coherence—coupling between cortex and muscle—in individuals with Parkinson’s disease compared to controls, during well-equated muscle contraction between the two groups. This reduction is related to motor symptoms in patients. This measure can provide a sensitive and objective measure of disease-related motor deficits., Graphical Abstract Graphical Abstract

Increasing efforts are being made to understand the role of intermittent, transient, high-power burst events of neural activity. These events have a temporal, spectral, and spatial domain. Here, we argue that considering all three domains is crucial to fully reveal the functional relevance of these events in health and disease.

It is well established that antibiotic treatment selects for resistance in pathogenic bacteria. However, the evolutionary responses of pathogen populations to antibiotic treatment during infections remain poorly resolved, especially in acute infections. Here we map the evolutionary responses to treatment in high definition through genomic and phenotypic characterization of >100 isolates from a patient with P. aeruginosa pneumonia. Antibiotic therapy (meropenem, colistin) caused a rapid crash of the P. aeruginosa population in the lung, but this decline was followed by the spread of meropenem resistance mutations that restrict antibiotic uptake (oprD) or modify LPS biosynthesis (wbpM). Low fitness strains with high-level meropenem resistance (oprD) were then replaced by high fitness strains with ‘anti-resistance’ mutations in the MexAB-OprM efflux pump, causing a rapid decline in resistance to both meropenem and a collateral loss of resistance to a broad spectrum of antibiotics. In contrast, we did not observe any evolutionary responses to antibiotic treatment in the intestinal population of P. aeruginosa. Carbapenem antibiotics are key to the treatment of infections caused by Gram negative pathogens, and our work highlights the ability of natural selection to drive both the rapid rise and fall of carbapenem resistance during acute infections.

The spatial and spectral structure of oscillatory networks in the brain provide a readout of the underlying neuronal function. Within and between subject variability in these networks can be highly informative but also poses a considerable analytic challenge. Here, we describe a method that simultaneously estimate spectral and spatial network structure without assumptions about either feature distorting estimation of the other. This enables analyses exploring how variability in the frequency and spatial structure of oscillatory networks might vary both across the brain and across individuals. The method performs a modal decomposition of an autoregressive model to describe the oscillatory signals present within a time-series based on their peak frequency and damping time. Moreover, an alternate mathematical formulation for the system transfer function can be written in terms of these oscillatory modes; describing the spatial topography and network structure of each component. We define a set of Spatio-Spectral Eigenmodes (SSEs) from these parameters to provide a parsimonious description of oscillatory networks. Crucially, the SSEs preserve the rich between-subject variability and are constructed without pre-averaging within specified frequency bands or limiting analyses to single channels or regions. After validating the method on simulated data, we explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide variability in peak frequency and network structure of alpha oscillations and reveal a distinction between occipital ‘high-frequency alpha’ and parietal ‘low-frequency alpha’. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person’s behavioural, cognitive or clinical state

When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants’ experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain’s response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.

Understanding the role of Tau protein aggregation in the pathogenesis of Alzheimer's disease is critical for the development of new Tau-based therapeutic strategies to slow or prevent dementia. We tested the hypothesis that Tau pathology is associated with functional organization of widespread neurophysiological networks. We used electro-magnetoencephalography with [18F]AV-1451 PET scanning to quantify Tau-dependent network changes. Using a graph theoretical approach to brain connectivity, we quantified nodal measures of functional segregation, centrality, and the efficiency of information transfer and tested them against levels of [18F]AV-1451. Higher Tau burden in early Alzheimer's disease was associated with a shift away from the optimal small-world organization and a more fragmented network in the beta and gamma bands, whereby parieto-occipital areas were disconnected from the anterior parts of the network. Similarly, higher Tau burden was associated with decreases in both local and global efficiency, especially in the gamma band. The results support the translational development of neurophysiological “signatures” of Alzheimer's disease, to understand disease mechanisms in humans and facilitate experimental medicine studies., Highlights • Alzheimer tau pathology linked to neurophysiological impairments in humans. • Higher Tau burden is linked to disconnection of posterior parts of the brain. • Higher Tau burden is linked to declines in local and global efficiency in gamma. • Eigenvector centrality of the occipital lobe shows declines in only 6 months.

Neural oscillations dominate electrophysiological measures of macroscopic brain activity and fluctuations in these rhythms offer an insightful window on cortical excitation, inhibition, and connectivity. However, in recent years the ‘classical’ picture of smoothly varying oscillations has been challenged by the idea that many ‘oscillations’ may actually be formed from the recurrence of punctate high-amplitude bursts in activity, whose spectral composition intersects the traditionally defined frequency ranges (e.g. alpha/beta band). This finding offers a new interpretation of measurable brain activity, however neither the methodological means to detect bursts, nor their link to other findings (e.g. connectivity) have been settled. Here, we use a new approach to detect bursts in magnetoencephalography (MEG) data. We show that a time-delay embedded Hidden Markov Model (HMM) can be used to delineate single-region bursts which are in agreement with existing techniques. However, unlike existing techniques, the HMM looks for specific spectral patterns in timecourse data. We characterise the distribution of burst duration, frequency of occurrence and amplitude across the cortex in resting state MEG data. During a motor task we show how the movement related beta decrease and post movement beta rebound are driven by changes in burst occurrence. Finally, we show that the beta band functional connectome can be derived using a simple measure of burst overlap, and that coincident bursts in separate regions correspond to a period of heightened coherence. In summary, this paper offers a new methodology for burst identification and connectivity analysis which will be important for future investigations of neural oscillations.

Objectives The neural activity of the primary motor cortex is variably synchronised with contralateral peripheral electromyographic signals, which is thought to facilitate long-range communication through the motor system. Such corticomuscular coherence (CMC) is typically observed in the beta-band (15–30 Hz) range during steady force production. We aimed to measure pathological alteration to CMC resulting from ALS. Methods CMC was appraised during a forearm grip task in 17 ALS patients contrasted against age-matched healthy controls. An exploratory comparison with a group of asymptomatic ALS gene carriers and neuropathy disease mimics was also undertaken. Neural signals were acquired by whole-head magnetoencephalography and localised via structural MRI to the motor cortices. Results During light voluntary muscular contraction, beta-band CMC was significantly reduced in ALS patients compared to healthy controls. Propagation of motoric beta rhythms across the cortical hemispheres was also shown to be impaired in ALS patients. CMC was preserved in the asymptomatic gene carrier and did not distinguish ALS patients from neuropathy mimics. Conclusion Functional connectivity metrics reveal an ALS-related decrease in both corticomuscular and interhemispheric communication during bilateral grip force production. Significance MEG-derived beta oscillation coupling may be a potential biomarker of motor system dysfunction in ALS, against which to measure future therapeutic efficacy.

Highlights • A data-driven modal decomposition describes oscillations by their resonant frequency, damping time and network structure. • We show that the full multivariate transfer function can be rewritten as a linear superposition of these modes. • These modal coordinates factorise oscillatory systems without pre-specification of frequency bands or regions of interest. • Using these modes, we find a spatial gradient in alpha peak frequency between Occipital and Parietal cortex . • This gradient is highly variable between participants, showing shifts in spatial structure and peak frequency., Between subject variability in the spatial and spectral structure of oscillatory networks can be highly informative but poses a considerable analytic challenge. Here, we describe a data-driven modal decomposition of a multivariate autoregressive model that simultaneously identifies oscillations by their peak frequency, damping time and network structure. We use this decomposition to define a set of Spatio-Spectral Eigenmodes (SSEs) providing a parsimonious description of oscillatory networks. We show that the multivariate system transfer function can be rewritten in these modal coordinates, and that the full transfer function is a linear superposition of all modes in the decomposition. The modal transfer function is a linear summation and therefore allows for single oscillatory signals to be isolated and analysed in terms of their spectral content, spatial distribution and network structure. We validate the method on simulated data and explore the structure of whole brain oscillatory networks in eyes-open resting state MEG data from the Human Connectome Project. We are able to show a wide between participant variability in peak frequency and network structure of alpha oscillations and show a distinction between occipital ’high-frequency alpha’ and parietal ’low-frequency alpha’. The frequency difference between occipital and parietal alpha components is present within individual participants but is partially masked by larger between subject variability; a 10Hz oscillation may represent the high-frequency occipital component in one participant and the low-frequency parietal component in another. This rich characterisation of individual neural phenotypes has the potential to enhance analyses into the relationship between neural dynamics and a person’s behavioural, cognitive or clinical state.

Beta and gamma oscillations are the dominant oscillatory activity in the human motor cortex (M1). However, their physiological basis and precise functional significance remain poorly understood. Here, we used transcranial magnetic stimulation (TMS) to examine the physiological basis and behavioral relevance of driving beta and gamma oscillatory activity in the human M1 using transcranial alternating current stimulation (tACS). tACS was applied using a sham-controlled crossover design at individualized intensity for 20 min and TMS was performed at rest (before, during, and after tACS) and during movement preparation (before and after tACS). We demonstrated that driving gamma frequency oscillations using tACS led to a significant, duration-dependent decrease in local resting-state GABAAinhibition, as quantified by short interval intracortical inhibition. The magnitude of this effect was positively correlated with the magnitude of GABAAdecrease during movement preparation, when gamma activity in motor circuitry is known to increase. In addition, gamma tACS-induced change in GABAAinhibition was closely related to performance in a motor learning task such that subjects who demonstrated a greater increase in GABAAinhibition also showed faster short-term learning. The findings presented here contribute to our understanding of the neurophysiological basis of motor rhythms and suggest that tACS may have similar physiological effects to endogenously driven local oscillatory activity. Moreover, the ability to modulate local interneuronal circuits by tACS in a behaviorally relevant manner provides a basis for tACS as a putative therapeutic intervention.SIGNIFICANCE STATEMENTGamma oscillations have a vital role in motor control. Using a combined tACS-TMS approach, we demonstrate that driving gamma frequency oscillations modulates GABAAinhibition in the human motor cortex. Moreover, there is a clear relationship between the change in magnitude of GABAAinhibition induced by tACS and the magnitude of GABAAinhibition observed during task-related synchronization of oscillations in inhibitory interneuronal circuits, supporting the hypothesis that tACS engages endogenous oscillatory circuits. We also show that an individual's physiological response to tACS is closely related to their ability to learn a motor task. These findings contribute to our understanding of the neurophysiological basis of motor rhythms and their behavioral relevance and offer the possibility of developing tACS as a therapeutic tool.