Post-task responses following working memory and movement are driven by transient spectral bursts with similar characteristics.

The post-movement beta rebound has been studied extensively using magnetoencephalography (MEG) and is reliably modulated by various task parameters as well as illness. Our recent study showed that rebounds, which we generalise as "post-task responses" (PTRs), are a ubiquitous phenomenon in the brain, occurring across the cortex in theta, alpha, and beta bands. Currently, it is unknown whether PTRs following working memory are driven by transient bursts, which are moments of short-lived high amplitude activity, similar to those that drive the post-movement beta rebound. Here, we use three-state univariate hidden Markov models (HMMs), which can identify bursts without a priori knowledge of frequency content or response timings, to compare bursts that drive PTRs in working memory and visuomotor MEG datasets. Our results show that PTRs across working memory and visuomotor tasks are driven by pan-spectral transient bursts. These bursts have very similar spectral content variation over the cortex, correlating strongly between the two tasks in the alpha (R2 = .89) and beta (R2 = .53) bands. Bursts also have similar variation in duration over the cortex (e.g., long duration bursts occur in the motor cortex for both tasks), strongly correlating over cortical regions between tasks (R2 = .56), with a mean over all regions of around 300 ms in both datasets. Finally, we demonstrate the ability of HMMs to isolate signals of interest in MEG data, such that the HMM probability timecourse correlates more strongly with reaction times than frequency filtered power envelopes from the same brain regions. Overall, we show that induced PTRs across different tasks are driven by bursts with similar characteristics, which can be identified using HMMs. Given the similarity between bursts across tasks, we suggest that PTRs across the cortex may be driven by a common underlying neural phenomenon.

Moving magnetoencephalography towards real-world applications with a wearable system.

Imaging human brain function with techniques such as magnetoencephalography typically requires a subject to perform tasks while their head remains still within a restrictive scanner. This artificial environment makes the technique inaccessible to many people, and limits the experimental questions that can be addressed. For example, it has been difficult to apply neuroimaging to investigation of the neural substrates of cognitive development in babies and children, or to study processes in adults that require unconstrained head movement (such as spatial navigation). Here we describe a magnetoencephalography system that can be worn like a helmet, allowing free and natural movement during scanning. This is possible owing to the integration of quantum sensors, which do not rely on superconducting technology, with a system for nulling background magnetic fields. We demonstrate human electrophysiological measurement at millisecond resolution while subjects make natural movements, including head nodding, stretching, drinking and playing a ball game. Our results compare well to those of the current state-of-the-art, even when subjects make large head movements. The system opens up new possibilities for scanning any subject or patient group, with myriad applications such as characterization of the neurodevelopmental connectome, imaging subjects moving naturally in a virtual environment and investigating the pathophysiology of movement disorders.

Beyond the Beta Rebound: Post-Task Responses in Oscillatory Activity follow Cessation of Working Memory Processes.

Post-task responses (PTRs) are transitionary responses occurring for several seconds between the end of a stimulus/task and a period of rest. The most well-studied of these are beta band (13 - 30 Hz) PTRs in motor networks following movement, often called post-movement beta rebounds, which have been shown to differ in patients with schizophrenia and autism. Previous studies have proposed that beta PTRs reflect inhibition of task-positive networks to enable a return to resting brain activity, scaling with cognitive demand and reflecting cortical self-regulation. It is unknown whether PTRs are a phenomenon of the motor system, or whether they are a more general self-modulatory property of cortex that occur following cessation of higher cognitive processes as well as movement. To test this, we recorded magnetoencephalography (MEG) responses in 20 healthy participants to a working-memory task, known to recruit cortical networks associated with higher cognition. Our results revealed PTRs in the theta, alpha and beta bands across many regions of the brain, including the dorsal attention network (DAN) and lateral visual regions. These PTRs increased significantly (p < 0.05) in magnitude with working-memory load, an effect which is independent of oscillatory modulations occurring over the task period as well as those following individual stimuli. Furthermore, we showed that PTRs are functionally related to reaction times in left lateral visual (p < 0.05) and left parietal (p < 0.1) regions, while the oscillatory responses measured during the task period are not. Importantly, motor PTRs following button presses did not modulate with task condition, suggesting that PTRs in different networks are driven by different aspects of cognition. Our findings show that PTRs are not limited to motor networks but are widespread in regions which are recruited during the task. We provide evidence that PTRs have unique properties, scaling with cognitive load and correlating significantly with behaviour. Based on the evidence, we suggest that PTRs inhibit task-positive network activity to enable a transition to rest, however, further investigation is required to uncover their role in neuroscience and pathology.

Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study.

Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data-driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision-making for patients with intractable epilepsy.

Across the adult lifespan the ipsilateral sensorimotor cortex negative BOLD response exhibits decreases in magnitude and spatial extent suggesting declining inhibitory control.

Ipsilateral sensorimotor (iSM1) cortex negative BOLD responses (NBR) are observed to unilateral tasks and are thought to reflect a functionally relevant component of sensorimotor inhibition. Evidence suggests that sensorimotor inhibitory mechanisms degrade with age, along with aspects of motor ability and dexterity. However, understanding of age-related changes to NBR is restricted by limited comparisons between young vs old adults groups with relatively small samples sizes. Here we analysed a BOLD fMRI dataset (obtained from the CamCAN repository) of 581 healthy subjects, gender-balanced, sampled from the whole adult lifespan performing a motor response task to an audiovisual stimulus. We aimed to investigate how sensorimotor and default-mode NBR characteristics of magnitude, spatial extent and response shape alter at every decade of the aging process. We observed a linear decrease in iSM1 NBR magnitude across the whole lifespan, whereas the contralateral sensorimotor (cSM1) PBR magnitude was unchanged. An age-related decrease in the spatial extent of NBR and an increase in the ipsilateral positive BOLD response (PBR) was observed. This occurred alongside an increasing negative correlation between subject's iSM1 NBR and cSM1 PBR magnitude, reflecting a change in the balance between cortical excitation and inhibition. Conventional GLM analysis, using a canonical haemodynamic response (HR) function, showed disappearance of iSM1 NBR in subjects over 50 years of age. However, a deconvolution analysis showed that the shape of the iSM1 HR altered throughout the lifespan, with significantly delayed time-to-peak and decreased magnitude. The most significant decreases in iSM1 HR magnitude occurred in older age (>60 years) but the first changes in HR shape and timing occurred as early as 30 years, suggesting the possibility of separate mechanisms underlying these alterations. Reanalysis using data-driven HRs for each decade detected significant sensorimotor NBR into late older age, showing the importance of taking changes in HR morphology into account in fMRI aging studies. These results may reflect fMRI measures of the age-related decreases in transcollosal inhibition exerted upon ipsilateral sensorimotor cortex and alterations to the excitatory-inhibitory balance in the sensorimotor network.

Two Spatially Distinct Posterior Alpha Sources Fulfill Different Functional Roles in Attention.

Directing attention helps to extract relevant information and suppress distracters. Alpha brain oscillations (8-12 Hz) are crucial for this process, with power decreases facilitating processing of important information and power increases inhibiting brain regions processing irrelevant information. Evidence for this phenomenon arises from visual attention studies (Worden et al., 2000); however, the effect also exists in other modalities, including the somatosensory system (Haegens et al., 2011) and intersensory attention tasks (Foxe and Snyder, 2011). We investigated in human participants (10 females, 10 males) the role of alpha oscillations in focused (0/100%) versus divided (40/60%) attention, both across modalities (visual/somatosensory; Experiment 1) and within the same modality (visual domain: across hemifields; Experiment 2) while recording EEG over 128 scalp electrodes. In Experiment 1, participants divided their attention between visual and somatosensory modality to determine the temporal/spatial frequency of a target stimulus (vibrotactile stimulus/Gabor grating). In Experiment 2, participants divided attention between two visual hemifields to identify the orientation of a Gabor grating. In both experiments, prestimulus alpha power in visual areas decreased linearly with increasing attention to visual stimuli. In contrast, prestimulus alpha power in parietal areas was lower when attention was divided between modalities/hemifields compared with focused attention. These results suggest there are two alpha sources, one of which reflects the "visual spotlight of attention" and the other reflects attentional effort. To our knowledge, this is the first study to show that attention recruits two spatially distinct alpha sources in occipital and parietal brain regions, acting simultaneously but serving different functions in attention.SIGNIFICANCE STATEMENT Attention to one spatial location/sensory modality leads to power changes of alpha oscillations (∼10 Hz) with decreased power over regions processing relevant information and power increases to actively inhibit areas processing "to-be-ignored" information. Here, we used detailed source modeling to investigate EEG data recorded during separate unimodal (visual) and multimodal (visual and somatosensory) attention tasks. Participants either focused their attention on one modality/spatial location or directed it to both. We show for the first time two distinct alpha sources are active simultaneously but play different roles. A sensory (visual) alpha source was linearly modulated by attention representing the "visual spotlight of attention." By contrast, a parietal alpha source was modulated by attentional effort, showing lowest alpha power when attention was divided.

Post-stimulus beta responses are modulated by task duration.

Modulation of beta-band neural oscillations during and following movement is a robust marker of brain function. In particular, the post-movement beta rebound (PMBR), which occurs on movement cessation, has been related to inhibition and connectivity in the healthy brain, and is perturbed in disease. However, to realise the potential of the PMBR as a biomarker, its modulation by task parameters must be characterised and its functional role determined. Here, we used MEG to image brain electrophysiology during and after a grip-force task, with the aim to characterise how task duration, in the form of an isometric contraction, modulates beta responses. Fourteen participants exerted a 30% maximum voluntary grip-force for 2, 5 and 10 s. Our results showed that the amplitude of the PMBR is modulated by task duration, with increasing duration significantly reducing PMBR amplitude and increasing its time-to-peak. No variation in the amplitude of the movement related beta decrease (MRBD) with task duration was observed. To gain insight into what may underlie these trial-averaged results, we used a Hidden Markov Model to identify the individual trial dynamics of a brain network encompassing bilateral sensorimotor areas. The rapidly evolving dynamics of this network demonstrated similar variation with task parameters to the 'classical' rebound, and we show that the modulation of the PMBR can be well-described in terms of increased frequency of beta events on a millisecond timescale rather than modulation of beta amplitude during this time period. Our results add to the emerging picture that, in the case of a carefully controlled paradigm, beta modulation can be systematically controlled by task parameters and such control can reveal new information as to the processes that generate the average beta timecourse. These findings will support design of clinically relevant paradigms and analysis pipelines in future use of the PMBR as a marker of neuropathology.

The role of transient spectral 'bursts' in functional connectivity: A magnetoencephalography study.

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.

Relationships Between Neuronal Oscillatory Amplitude and Dynamic Functional Connectivity.

Event-related fluctuations of neural oscillatory amplitude are reported widely in the context of cognitive processing and are typically interpreted as a marker of brain "activity". However, the precise nature of these effects remains unclear; in particular, whether such fluctuations reflect local dynamics, integration between regions, or both, is unknown. Here, using magnetoencephalography, we show that movement induced oscillatory modulation is associated with transient connectivity between sensorimotor regions. Further, in resting-state data, we demonstrate a significant association between oscillatory modulation and dynamic connectivity. A confound with such empirical measurements is that increased amplitude necessarily means increased signal-to-noise ratio (SNR): this means that the question of whether amplitude and connectivity are genuinely coupled, or whether increased connectivity is observed purely due to increased SNR is unanswered. Here, we counter this problem by analogy with computational models which show that, in the presence of global network coupling and local multistability, the link between oscillatory modulation and long-range connectivity is a natural consequence of neural networks. Our results provide evidence for the notion that connectivity is mediated by neural oscillations, and suggest that time-frequency spectrograms are not merely a description of local synchrony but also reflect fluctuations in long-range connectivity.

The relationship between negative BOLD responses and ERS and ERD of alpha/beta oscillations in visual and motor cortex.

Previous work has investigated the electrophysiological origins of the intra-modal (within the stimulated sensory cortex) negative BOLD fMRI response (NBR, decrease from baseline) but little attention has been paid to the origin of cross-modal NBRs, those in a different sensory cortex. In the current study we use simultaneous EEG-fMRI recordings to assess the neural correlates of both intra- and cross-modal responses to left-hemifield visual stimuli and right-hand motor tasks, and evaluate the balance of activation and deactivation between the visual and motor systems. Within- and between-subject covariations of EEG and fMRI responses to both tasks are assessed to determine how patterns of event-related desynchronization/synchronisation (ERD/ERS) of alpha/beta frequency oscillations relate to the NBR in the two sensory cortices. We show that both visual and motor tasks induce intra-modal NBR and cross-modal NBR (e.g. visual stimuli evoked NBRs in both visual and motor cortices). In the EEG data, bilateral intra-modal alpha/beta ERD were consistently observed to both tasks, whilst the cross-modal EEG response varied across subjects between alpha/beta ERD and ERS. Both the mean cross-modal EEG and fMRI response amplitudes showed a small increase in magnitude with increasing task intensity. In response to the visual stimuli, subjects displaying cross-modal ERS of motor beta power displayed a significantly larger magnitude of cross-modal NBR in motor cortex. However, in contrast to the motor stimuli, larger cross-modal ERD of visual alpha power was associated with larger cross-modal visual NBR. Single-trial correlation analysis provided further evidence of relationship between EEG signals and the NBR, motor cortex beta responses to motor tasks were significantly negatively correlated with cross-modal visual cortex NBR amplitude, and positively correlated with intra-modal motor cortex PBR. This study provides a new body of evidence that the coupling between BOLD and low-frequency (alpha/beta) sensory cortex EEG responses extends to cross-modal NBR.

Wearable neuroimaging: Combining and contrasting magnetoencephalography and electroencephalography.

One of the most severe limitations of functional neuroimaging techniques, such as magnetoencephalography (MEG), is that participants must maintain a fixed head position during data acquisition. This imposes restrictions on the characteristics of the experimental cohorts that can be scanned and the experimental questions that can be addressed. For these reasons, the use of 'wearable' neuroimaging, in which participants can move freely during scanning, is attractive. The most successful example of wearable neuroimaging is electroencephalography (EEG), which employs lightweight and flexible instrumentation that makes it useable in almost any experimental setting. However, EEG has major technical limitations compared to MEG, and therefore the development of wearable MEG, or hybrid MEG/EEG systems, is a compelling prospect. In this paper, we combine and compare EEG and MEG measurements, the latter made using a new generation of optically-pumped magnetometers (OPMs). We show that these new second generation commercial OPMs, can be mounted on the scalp in an 'EEG-like' cap, enabling the acquisition of high fidelity electrophysiological measurements. We show that these sensors can be used in conjunction with conventional EEG electrodes, offering the potential for the development of hybrid MEG/EEG systems. We compare concurrently measured signals, showing that, whilst both modalities offer high quality data in stationary subjects, OPM-MEG measurements are less sensitive to artefacts produced when subjects move. Finally, we show using simulations that OPM-MEG offers a fundamentally better spatial specificity than EEG. The demonstrated technology holds the potential to revolutionise the utility of functional brain imaging, exploiting the flexibility of wearable systems to facilitate hitherto impractical experimental paradigms.

Exploring the relative efficacy of motion artefact correction techniques for EEG data acquired during simultaneous fMRI.

Simultaneous EEG-fMRI allows multiparametric characterisation of brain function, in principle enabling a more complete understanding of brain responses; unfortunately the hostile MRI environment severely reduces EEG data quality. Simply eliminating data segments containing gross motion artefacts [MAs] (generated by movement of the EEG system and head in the MRI scanner's static magnetic field) was previously believed sufficient. However recently the importance of removal of all MAs has been highlighted and new methods developed. A systematic comparison of the ability to remove MAs and retain underlying neuronal activity using different methods of MA detection and post-processing algorithms is needed to guide the neuroscience community. Using a head phantom, we recorded MAs while simultaneously monitoring the motion using three different approaches: Reference Layer Artefact Subtraction (RLAS), Moiré Phase Tracker (MPT) markers and Wire Loop Motion Sensors (WLMS). These EEG recordings were combined with EEG responses to simple visual tasks acquired on a subject outside the MRI environment. MAs were then corrected using the motion information collected with each of the methods combined with different analysis pipelines. All tested methods retained the neuronal signal. However, often the MA was not removed sufficiently to allow accurate detection of the underlying neuronal signal. We show that the MA is best corrected using the RLAS combined with post-processing using a multichannel, recursive least squares (M-RLS) algorithm. This method needs to be developed further to enable practical utility; thus, WLMS combined with M-RLS currently provides the best compromise between EEG data quality and practicalities of motion detection.

Addressing challenges of high spatial resolution UHF fMRI for group analysis of higher-order cognitive tasks: An inter-sensory task directing attention between visual and somatosensory domains.

Functional MRI at ultra-high field (UHF, ≥7 T) provides significant increases in BOLD contrast-to-noise ratio (CNR) compared with conventional field strength (3 T), and has been exploited for reduced field-of-view, high spatial resolution mapping of primary sensory areas. Applying these high spatial resolution methods to investigate whole brain functional responses to higher-order cognitive tasks leads to a number of challenges, in particular how to perform robust group-level statistical analyses. This study addresses these challenges using an inter-sensory cognitive task which modulates top-down attention at graded levels between the visual and somatosensory domains. At the individual level, highly focal functional activation to the task and task difficulty (modulated by attention levels) were detectable due to the high CNR at UHF. However, to assess group level effects, both anatomical and functional variability must be considered during analysis. We demonstrate the importance of surface over volume normalisation and the requirement of no spatial smoothing when assessing highly focal activity. Using novel group analysis on anatomically parcellated brain regions, we show that in higher cognitive areas (parietal and dorsal-lateral-prefrontal cortex) fMRI responses to graded attention levels were modulated quadratically, whilst in visual cortex and VIP, responses were modulated linearly. These group fMRI responses were not seen clearly using conventional second-level GLM analyses, illustrating the limitations of a conventional approach when investigating such focal responses in higher cognitive regions which are more anatomically variable. The approaches demonstrated here complement other advanced analysis methods such as multivariate pattern analysis, allowing UHF to be fully exploited in cognitive neuroscience.

Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: Implications for motion artefact correction.

Motion artefacts (MAs) are induced within EEG data collected simultaneously with fMRI when the subject's head rotates relative to the magnetic field. The effects of these artefacts have generally been ameliorated by removing periods of data during which large artefact voltages appear in the EEG traces. However, even when combined with other standard post-processing methods, this strategy does not remove smaller MAs which can dominate the neuronal signals of interest. A number of methods are therefore being developed to characterise the MA by measuring reference signals and then using these in artefact correction. These methods generally assume that the head and EEG cap, plus any attached sensors, form a rigid body which can be characterised by a standard set of six motion parameters. Here we investigate the motion of the head/EEG cap system to provide a better understanding of MAs. We focus on the reference layer artefact subtraction (RLAS) approach, as this allows measurement of a separate reference signal for each electrode that is being used to measure brain activity. Through a series of experiments on phantoms and subjects, we find that movement of the EEG cap relative to the phantom and skin on the forehead is relatively small and that this non-rigid body movement does not appear to cause considerable discrepancy in artefacts between the scalp and reference signals. However, differences in the amplitude of these signals is observed which may be due to differences in geometry of the system from which the reference signals are measured compared with the brain signals. In addition, we find that there is non-rigid body movement of the skull and skin which produces an additional MA component for a head shake, which is not present for a head nod. This results in a large discrepancy in the amplitude and temporal profile of the MA measured on the scalp and reference layer, reducing the efficacy of MA correction based on the reference signals. Together our data suggest that the efficacy of the correction of MA using any reference-based system is likely to differ for different types of head movement with head shake being the hardest to correct. This provides new information to inform the development of hardware and post-processing methods for removing MAs from EEG data acquired simultaneously with fMRI data.

Exploring the advantages of multiband fMRI with simultaneous EEG to investigate coupling between gamma frequency neural activity and the BOLD response in humans.

We established an optimal combination of EEG recording during sparse multiband (MB) fMRI that preserves high-resolution, whole-brain fMRI coverage while enabling broad-band EEG recordings which are uncorrupted by MRI gradient artefacts (GAs). We first determined the safety of simultaneous EEG recording during MB fMRI. Application of MB factor = 4 produced <1°C peak heating of electrode/hardware during 20 min of GE-EPI data acquisition. However, higher SAR sequences require specific safety testing, with greater heating observed using PCASL with MB factor = 4. Heating was greatest in the electrocardiogram channel, likely due to it possessing longest lead length. We investigated the effect of MB factor on the temporal signal-to-noise ratio for a range of GE-EPI sequences (varying MB factor and temporal interval between slice acquisitions). We found that, for our experimental purpose, the optimal acquisition was achieved with MB factor = 3, 3mm isotropic voxels, and 33 slices providing whole head coverage. This sequence afforded a 2.25 s duration quiet period (without GAs) in every 3 s TR. Using this sequence, we demonstrated the ability to record gamma frequency (55-80 Hz) EEG oscillations, in response to right index finger abduction, that are usually obscured by GAs during continuous fMRI data acquisition. In this novel application of EEG-MB fMRI to a motor task, we observed a positive correlation between gamma and BOLD responses in bilateral motor regions. These findings support and extend previous work regarding coupling between neural and hemodynamic measures of brain activity in humans and showcase the utility of EEG-MB fMRI for future investigations.

Decoding fMRI events in sensorimotor motor network using sparse paradigm free mapping and activation likelihood estimates.

Most functional MRI (fMRI) studies map task-driven brain activity using a block or event-related paradigm. Sparse paradigm free mapping (SPFM) can detect the onset and spatial distribution of BOLD events in the brain without prior timing information, but relating the detected events to brain function remains a challenge. In this study, we developed a decoding method for SPFM using a coordinate-based meta-analysis method of activation likelihood estimation (ALE). We defined meta-maps of statistically significant ALE values that correspond to types of events and calculated a summation overlap between the normalized meta-maps and SPFM maps. As a proof of concept, this framework was applied to relate SPFM-detected events in the sensorimotor network (SMN) to six motor functions (left/right fingers, left/right toes, swallowing, and eye blinks). We validated the framework using simultaneous electromyography (EMG)-fMRI experiments and motor tasks with short and long duration, and random interstimulus interval. The decoding scores were considerably lower for eye movements relative to other movement types tested. The average successful rate for short and long motor events were 77 ± 13% and 74 ± 16%, respectively, excluding eye movements. We found good agreement between the decoding results and EMG for most events and subjects, with a range in sensitivity between 55% and 100%, excluding eye movements. The proposed method was then used to classify the movement types of spontaneous single-trial events in the SMN during resting state, which produced an average successful rate of 22 ± 12%. Finally, this article discusses methodological implications and improvements to increase the decoding performance. Hum Brain Mapp 38:5778-5794, 2017. © 2017 Wiley Periodicals, Inc.

Modulation of post-movement beta rebound by contraction force and rate of force development.

Movement induced modulation of the beta rhythm is one of the most robust neural oscillatory phenomena in the brain. In the preparation and execution phases of movement, a loss in beta amplitude is observed [movement related beta decrease (MRBD)]. This is followed by a rebound above baseline on movement cessation [post movement beta rebound (PMBR)]. These effects have been measured widely, and recent work suggests that they may have significant importance. Specifically, they have potential to form the basis of biomarkers for disease, and have been used in neuroscience applications ranging from brain computer interfaces to markers of neural plasticity. However, despite the robust nature of both MRBD and PMBR, the phenomena themselves are poorly understood. In this study, we characterise MRBD and PMBR during a carefully controlled isometric wrist flexion paradigm, isolating two fundamental movement parameters; force output, and the rate of force development (RFD). Our results show that neither altered force output nor RFD has a significant effect on MRBD. In contrast, PMBR was altered by both parameters. Higher force output results in greater PMBR amplitude, and greater RFD results in a PMBR which is higher in amplitude and shorter in duration. These findings demonstrate that careful control of movement parameters can systematically change PMBR. Further, for temporally protracted movements, the PMBR can be over 7 s in duration. This means accurate control of movement and judicious selection of paradigm parameters are critical in future clinical and basic neuroscientific studies of sensorimotor beta oscillations. Hum Brain Mapp 37:2493-2511, 2016. © 2016 Wiley Periodicals, Inc.

Poststimulus undershoots in cerebral blood flow and BOLD fMRI responses are modulated by poststimulus neuronal activity.

fMRI is the foremost technique for noninvasive measurement of human brain function. However, its utility is limited by an incomplete understanding of the relationship between neuronal activity and the hemodynamic response. Though the primary peak of the hemodynamic response is modulated by neuronal activity, the origin of the typically negative poststimulus signal is poorly understood and its amplitude assumed to covary with the primary response. We use simultaneous recordings of EEG with blood oxygenation level-dependent (BOLD) and cerebral blood flow (CBF) fMRI during unilateral median nerve stimulation to show that the poststimulus fMRI signal is neuronally modulated. We observe high spatial agreement between concurrent BOLD and CBF responses to median nerve stimulation, with primary signal increases in contralateral sensorimotor cortex and primary signal decreases in ipsilateral sensorimotor cortex. During the poststimulus period, the amplitude and directionality (positive/negative) of the BOLD signal in both contralateral and ipsilateral sensorimotor cortex depends on the poststimulus synchrony of 8-13 Hz EEG neuronal activity, which is often considered to reflect cortical inhibition, along with concordant changes in CBF and metabolism. Therefore we present conclusive evidence that the fMRI time course represents a hemodynamic signature of at least two distinct temporal phases of neuronal activity, substantially improving understanding of the origin of the BOLD response and increasing the potential measurements of brain function provided by fMRI. We suggest that the poststimulus EEG and fMRI responses may be required for the resetting of the entire sensory network to enable a return to resting-state activity levels.

Comparison of functional thalamic segmentation from seed-based analysis and ICA.

Information flow between the thalamus and cerebral cortex is a crucial component of adaptive brain function, but the details of thalamocortical interactions in human subjects remain unclear. The principal aim of this study was to evaluate the agreement between functional thalamic network patterns, derived using seed-based connectivity analysis and independent component analysis (ICA) applied separately to resting state functional MRI (fMRI) data from 21 healthy participants. For the seed-based analysis, functional thalamic parcellation was achieved by computing functional connectivity (FC) between thalamic voxels and a set of pre-defined cortical regions. Thalamus-constrained ICA provided an alternative parcellation. Both FC analyses demonstrated plausible and comparable group-level thalamic subdivisions, in agreement with previous work. Quantitative assessment of the spatial overlap between FC thalamic segmentations, and comparison of each to a histological "gold-standard" thalamic atlas and a structurally-defined thalamic atlas, highlighted variations between them and, most notably, differences with both histological and structural results. Whilst deeper understanding of thalamocortical connectivity rests upon identification of features common to multiple non-invasive neuroimaging techniques (e.g. FC, structural connectivity and anatomical localisation of individual-specific nuclei), this work sheds further light on the functional organisation of the thalamus and the varying sensitivities of complementary analyses to resolve it.

Reference layer artefact subtraction (RLAS): a novel method of minimizing EEG artefacts during simultaneous fMRI.

Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after average artefact subtraction. Any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, as well as reducing the residual artefacts that can easily swamp signals from brain activity measured using current methods. Since these problems currently limit the utility of simultaneous EEG-fMRI, new approaches for reducing the magnitude and variability of the artefacts are required. One such approach is the use of an EEG cap that incorporates electrodes embedded in a reference layer that has similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads by time-varying field gradients, cardiac pulsation and subject movement are similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Taking the difference of the voltages in the reference and scalp channels will therefore reduce the artefacts, without affecting sensitivity to neuronal signals. Here, we test this approach by using a simple experimental realisation of the reference layer to investigate the artefacts induced on the leads attached to the reference layer and scalp and to evaluate the degree of artefact attenuation that can be achieved via reference layer artefact subtraction (RLAS). Through a series of experiments on phantoms and human subjects, we show that RLAS significantly reduces the gradient (GA), pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms AAS when motion is present in the removal of the GA and PA, while the combination of AAS and RLAS always produces higher artefact attenuation than AAS. Additionally, we demonstrate that RLAS greatly attenuates the unpredictable and highly variable MAs that are very hard to remove using post-processing methods.