High-frequency (600 Hz) population spikes in human EEG delineate thalamic and cortical fMRI activation sites

Relationship between high-frequency activity in the cortical sensory and the motor hand areas, and their myelin content

BACKGROUND

The human primary sensory (S1) and primary motor (M1) hand areas feature high-frequency neuronal responses. Electrical nerve stimulation evokes high-frequency oscillations (HFO) at around 650 Hz in the contralateral S1. Likewise, transcranial magnetic stimulation (TMS) of M1 can evoke a series of descending volleys in the corticospinal pathway that can be detected non-invasively with a paired-pulse TMS protocol, called short interval intracortical facilitation (SICF). SICF features several peaks of facilitation of motor evoked potentials in contralateral hand muscles, which are separated by inter-peak intervals resembling HFO rhythmicity.

HYPOTHESIS

In this study, we tested the hypothesis that the individual expressions of HFO and SICF are tightly related to each other and to the regional myelin content in the sensorimotor cortex.

METHODS

In 24 healthy volunteers, we recorded HFO and SICF, and, in a subgroup of 20 participants, we mapped the cortical myelin content using the ratio between the T1- and T2-weighted MRI signal as read-out.

RESULTS

The individual frequencies and magnitudes of HFO and SICF curves were tightly correlated: the intervals between the first and second peak of cortical HFO and SICF showed a positive linear relationship (r = 0.703, p < 0.001), while their amplitudes were inversely related (r = -0.613, p = 0.001). The rhythmicity, but not the magnitude of the high-frequency responses, was related to the cortical myelin content: the higher the cortical myelin content, the shorter the inter-peak intervals of HFO and SICF.

CONCLUSION

The results confirm a tight functional relationship between high-frequency responses in S1 (i.e., HFO) and M1 (i.e., as measured with SICF). They also establish a link between the degree of regional cortical myelination and the expression of high-frequency responses in the human sensorimotor cortex, giving further the opportunity to infer their generators.

EEG measures for clinical research in major vascular cognitive impairment: recommendations by an expert panel

Vascular contribution to cognitive impairment (VCI) and dementia is related to etiologies that may affect the neurophysiological mechanisms regulating brain arousal and generating electroencephalographic (EEG) activity. A multidisciplinary expert panel reviewed the clinical literature and reached consensus about the EEG measures consistently found as abnormal in VCI patients with dementia. As compared to cognitively unimpaired individuals, those VCI patients showed (1) smaller amplitude of resting state alpha (8-12 Hz) rhythms dominant in posterior regions; (2) widespread increases in amplitude of delta (< 4 Hz) and theta (4-8 Hz) rhythms; and (3) delayed N200/P300 peak latencies in averaged event-related potentials, especially during the detection of auditory rare target stimuli requiring participants' responses in "oddball" paradigms. The expert panel formulated the following recommendations: (1) the above EEG measures are not specific for VCI and should not be used for its diagnosis; (2) they may be considered as "neural synchronization" biomarkers to enlighten the relationships between features of the VCI-related cerebrovascular lesions and abnormalities in neurophysiological brain mechanisms; and (3) they may be tested in future clinical trials as prognostic biomarkers and endpoints of interventions aimed at normalizing background brain excitability and vigilance in wakefulness.

Noninvasive neuromagnetic single-trial analysis of human neocortical population spikes

Neuronal spiking is commonly recorded by invasive sharp microelectrodes, whereas standard noninvasive macroapproaches (e.g., electroencephalography [EEG] and magnetoencephalography [MEG]) predominantly represent mass postsynaptic potentials. A notable exception are low-amplitude high-frequency (∼600 Hz) somatosensory EEG/MEG responses that can represent population spikes when averaged over hundreds of trials to raise the signal-to-noise ratio. Here, a recent leap in MEG technology-featuring a factor 10 reduction in white noise level compared with standard systems-is leveraged to establish an effective single-trial portrayal of evoked cortical population spike bursts in healthy human subjects. This time-resolved approach proved instrumental in revealing a significant trial-to-trial variability of burst amplitudes as well as time-correlated (∼10 s) fluctuations of burst response latencies. Thus, ultralow-noise MEG enables noninvasive single-trial analyses of human cortical population spikes concurrent with low-frequency mass postsynaptic activity and thereby could comprehensively characterize cortical processing, potentially also in diseases not amenable to invasive microelectrode recordings.

Schrödinger filtering: a precise EEG despiking technique for EEG-fMRI gradient artifact

In EEG data acquired in the presence of fMRI, gradient-related spike artifacts contaminate the signal following the common preprocessing step of average artifact subtraction. Spike artifacts compromise EEG data quality since they overlap with the EEG signal in frequency, thereby confounding frequency-based inferences on activity. As well, spike artifacts can inflate or deflate correlations among time series, thereby confounding inferences on functional connectivity. We present Schrödinger filtering, which uses the Schrödinger equation to decompose the spike-containing input. The basis functions of the decomposition are localized and pulse-shaped, and selectively capture the various input peaks, with the spike components clustered at the beginning of the spectrum. Schrödinger filtering automatically subtracts the spike components from the data. On real and simulated data, we show that Schrödinger filtering (1) simultaneously accomplishes high spike removal and high signal preservation without affecting evoked activity, and (2) reduces spurious pairwise correlations in spontaneous activity. In these regards, Schrödinger filtering was significantly better than three other despiking techniques: median filtering, amplitude thresholding, and wavelet denoising. These results encourage the use of Schrödinger filtering in future EEG-fMRI pipelines, as well as in other spike-related applications (e.g., fMRI motion artifact removal or action potential extraction).

Concurrent electrophysiological and hemodynamic measurements of evoked neural oscillations in human visual cortex using sparsely interleaved fast fMRI and EEG

Electroencephalography (EEG) concurrently collected with functional magnetic resonance imaging (fMRI) is heavily distorted by the repetitive gradient coil switching during the fMRI acquisition. The performance of the typical template-based gradient artifact suppression method can be suboptimal because the artifact changes over time. Gradient artifact residuals also impede the subsequent suppression of ballistocardiography artifacts. Here we propose recording continuous EEG with temporally sparse fast fMRI (fast fMRI-EEG) to minimize the EEG artifacts caused by MRI gradient coil switching without significantly compromising the field-of-view and spatiotemporal resolution of fMRI. Using simultaneous multi-slice inverse imaging to achieve whole-brain fMRI with isotropic 5-mm resolution in 0.1 ​s, and performing these acquisitions once every 2 ​s, we have 95% of the duty cycle available to record EEG with substantially less gradient artifact. We found that the standard deviation of EEG signals over the entire acquisition period in fast fMRI-EEG was reduced to 54% of that in conventional concurrent echo-planar imaging (EPI) and EEG recordings (EPI-EEG) across participants. When measuring 15-Hz steady-state visual evoked potentials (SSVEPs), the baseline-normalized oscillatory neural response in fast fMRI-EEG was 2.5-fold of that in EPI-EEG. The functional MRI responses associated with the SSVEP delineated by EPI and fast fMRI were similar in the spatial distribution, the elicited waveform, and detection power. Sparsely interleaved fast fMRI-EEG provides high-quality EEG without substantially compromising the quality of fMRI in evoked response measurements, and has the potential utility for applications where the onset of the target stimulus cannot be precisely determined, such as epilepsy.

Cortical somatosensory evoked high-frequency (600Hz) oscillations predict absence of severe hypoxic encephalopathy after resuscitation

OBJECTIVE

Following cardiac arrest (CA), hypoxic encephalopathy (HE) frequently occurs and hence reliable neuroprognostication is crucial to decide on the extent of intensive care. Several investigations predict severe HE leading to persistent unresponsive wakefulness or death, with high specificity. Only few studies attempted to predict absence of severe HE. Cortical somatosensory evoked high-frequency (600Hz) oscillation (HFO) bursts indicate the presence of highly synchronized spiking activity in the primary somatosensory cortex. Since global neuronal damage characterizes severe HE preserved cortical HFOs may early exclude severe HE.

METHODS

We determined amplitudes of early and late HFO bursts in 302 comatose CA patients after median nerve somatosensory evoked potential (SSEPs) and clinical outcome upon intensive care unit discharge using the cerebral performance category (CPC) scale.

RESULTS

We detected significant early HFO bursts in 146 patients and late HFO bursts in 95 patients. Only one of 27 unresponsive wakefulness patients had a late HFO burst amplitude above 70nV and all seventeen patients who died despite higher amplitudes died from non-neurological causes.

CONCLUSIONS

High-frequency SSEP components can reliably be studied in comatose CA patients using standard equipment.

SIGNIFICANCE

Late HFO burst amplitudes above 70nV largely exclude severe HE incompatible with regaining consciousness.

Neuromelanin marks the spot: identifying a locus coeruleus biomarker of cognitive reserve in healthy aging

Leading a mentally stimulating life may build up a reserve of neural and mental resources that preserve cognitive abilities in late life. Recent autopsy evidence links neuronal density in the locus coeruleus (LC), the brain's main source of norepinephrine, to slower cognitive decline before death, inspiring the idea that the noradrenergic system is a key component of reserve (Robertson, I. H. 2013. A noradrenergic theory of cognitive reserve: implications for Alzheimer's disease. Neurobiol. Aging. 34, 298-308). Here, we tested this hypothesis using neuromelanin-sensitive magnetic resonance imaging to visualize and measure LC signal intensity in healthy younger and older adults. Established proxies of reserve, including education, occupational attainment, and verbal intelligence, were linearly correlated with LC signal intensity in both age groups. Results indicated that LC signal intensity was significantly higher in older than younger adults and significantly lower in women than in men. Consistent with the LC-reserve hypothesis, both verbal intelligence and a composite reserve score were positively associated with LC signal intensity in older adults. LC signal intensity was also more strongly associated with attentional shifting ability in older adults with lower cognitive reserve. Together these findings link in vivo estimates of LC neuromelanin signal intensity to cognitive reserve in normal aging.

State-dependencies of learning across brain scales

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.

Non-invasive single-trial EEG detection of evoked human neocortical population spikes

QUESTION

Human high-frequency (>400 Hz) components of somatosensory evoked potentials (hf-SEPs), which can be recorded non-invasively at the scalp, are generated by cortical population spikes, as inferred from microelectrode recordings in non-human primates. It is a critical limitation to broader neurophysiological study of hf-SEPs in that hundreds of responses have to be averaged to detect hf-SEPs reliably. Here, we establish a framework for detecting human hf-SEPs non-invasively in single trials.

METHODS

Spatio-temporal features were extracted from band-pass filtered (400-900 Hz) hf-SEPs by bilinear Common Spatio-Temporal Patterns (bCSTP) and then classified by a weighted Extreme Learning Machine (w-ELM). The effect of varying signal-to-noise ratio (SNR), number of trials, and degree of w-ELM re-weighting was characterized using surrogate data. For practical demonstration of the algorithm, median nerve hf-SEPs were recorded inside a shielded room in four subjects, spanning the hf-SEP signal-to-noise ratio characteristic for a larger population, utilizing a custom-built 29-channel low-noise EEG amplifier.

RESULTS

Using surrogate data, the SNR proved to be pivotal to detect hf-SEPs in single trials efficiently, with the trade-off between sensitivity and specificity of the algorithm being obtained by the w-ELM re-weighting parameter. In practice, human hf-SEPs were detected non-invasively in single trials with a sensitivity of up to 99% and a specificity of up to 97% in two subjects, even without any recourse to knowledge of stimulus timing. Matching with the results of the surrogate data analysis, these rates dropped to 62-79% sensitivity and 18-31% specificity in two subjects with lower SNR.

CONCLUSIONS

Otherwise buried in background noise, human high-frequency EEG components can be extracted from low-noise recordings. Specifically, refined supervised filter optimization and classification enables the reliable detection of single-trial hf-SEPs, representing non-invasive correlates of cortical population spikes.

SIGNIFICANCE

While low-frequency EEG reflects summed postsynaptic potentials, and thereby neuronal input, we suggest that high-frequency EEG (>400 Hz) can provide non-invasive access to the unaveraged output of neuronal computation, i.e., single-trial population spike activity evoked in the responsive neuronal ensemble.

Early somatosensory processing in individuals at risk for developing psychoses

Human cortical somatosensory evoked potentials (SEPs) allow an accurate investigation of thalamocortical and early cortical processing. SEPs reveal a burst of superimposed early (N20) high-frequency oscillations around 600 Hz. Previous studies reported alterations of SEPs in patients with schizophrenia. This study addresses the question whether those alterations are also observable in populations at risk for developing schizophrenia or bipolar disorders. To our knowledge to date, this is the first study investigating SEPs in a population at risk for developing psychoses. Median nerve SEPs were investigated using multichannel EEG in individuals at risk for developing bipolar disorders (n = 25), individuals with high-risk status (n = 59) and ultra-high-risk status for schizophrenia (n = 73) and a gender and age-matched control group (n = 45). Strengths and latencies of low- and high-frequency components as estimated by dipole source analysis were compared between groups. Low- and high-frequency source activity was reduced in both groups at risk for schizophrenia, in comparison to the group at risk for bipolar disorders. HFO amplitudes were also significant reduced in subjects with high-risk status for schizophrenia compared to healthy controls. These differences were accentuated among cannabis non-users. Reduced N20 source strengths were related to higher positive symptom load. These results suggest that the risk for schizophrenia, in contrast to bipolar disorders, may involve an impairment of early cerebral somatosensory processing. Neurophysiologic alterations in schizophrenia precede the onset of initial psychotic episode and may serve as indicator of vulnerability for developing schizophrenia.

Recording human cortical population spikes non-invasively--An EEG tutorial

BACKGROUND

Non-invasively recorded somatosensory high-frequency oscillations (sHFOs) evoked by electric nerve stimulation are markers of human cortical population spikes. Previously, their analysis was based on massive averaging of EEG responses. Advanced neurotechnology and optimized off-line analysis can enhance the signal-to-noise ratio of sHFOs, eventually enabling single-trial analysis.

METHODS

The rationale for developing dedicated low-noise EEG technology for sHFOs is unfolded. Detailed recording procedures and tailored analysis principles are explained step-by-step. Source codes in Matlab and Python are provided as supplementary material online.

RESULTS

Combining synergistic hardware and analysis improvements, evoked sHFOs at around 600 Hz ('σ-bursts') can be studied in single-trials. Additionally, optimized spatial filters increase the signal-to-noise ratio of components at about 1 kHz ('κ-bursts') enabling their detection in non-invasive surface EEG.

CONCLUSIONS

sHFOs offer a unique possibility to record evoked human cortical population spikes non-invasively. The experimental approaches and algorithms presented here enable also non-specialized EEG laboratories to combine measurements of conventional low-frequency EEG with the analysis of concomitant cortical population spike responses.

The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models

During the past two decades growing evidence indicates that brain oscillations in the alpha band (~10 Hz) not only reflect an "idle" state of cortical activity, but also take a more active role in the generation of complex cognitive functions. A recent study shows that more than 60% of the observed inter-subject variability in perceptual learning can be ascribed to ongoing alpha activity. This evidence indicates a significant role of alpha oscillations for perceptual learning and hence motivates to explore the potential underlying mechanisms. Hence, it is the purpose of this review to highlight existent evidence that ascribes intrinsic alpha oscillations a role in shaping our ability to learn. In the review, we disentangle the alpha rhythm into different neural signatures that control information processing within individual functional building blocks of perceptual learning. We further highlight computational studies that shed light on potential mechanisms regarding how alpha oscillations may modulate information transfer and connectivity changes relevant for learning. To enable testing of those model based hypotheses, we emphasize the need for multidisciplinary approaches combining assessment of behavior and multi-scale neuronal activity, active modulation of ongoing brain states and computational modeling to reveal the mathematical principles of the complex neuronal interactions. In particular we highlight the relevance of multi-scale modeling frameworks such as the one currently being developed by "The Virtual Brain" project.

Relation between functional magnetic resonance imaging (fMRI) and single neuron, local field potential (LFP) and electrocorticography (ECoG) activity in human cortex

The relation between changes in the blood oxygen dependent metabolic changes imaged by functional magnetic resonance imaging (fMRI) and neural events directly recorded from human cortex from single neurons, local field potentials (LFPs) and electrocorticogram (ECoG) is critically reviewed, based on the published literature including findings from the authors' laboratories. All these data are from special populations, usually patients with medically refractory epilepsy, as this provides the major opportunity for direct cortical neuronal recording in humans. For LFP and ECoG changes are often sought in different frequency bands, for single neurons in frequency of action potentials. Most fMRI studies address issues of functional localization. The relation of those findings to localized changes in neuronal recordings in humans has been established in several ways. Only a few studies have directly compared changes in activity from the same sites in the same individual, using the same behavioral measure. More often the comparison has been between fMRI and electrophysiologic changes in populations recorded from the same functional anatomic system as defined by lesion effects; in a few studies those systems have been defined by fMRI changes such as the "default" network. The fMRI-electrophysiologic relationships have been evaluated empirically by colocalization of significant changes, and by quantitative analyses, often multiple linear regression. There is some evidence that the fMRI-electrophysiology relationships differ in different cortical areas, particularly primary motor and sensory cortices compared to association cortex, but also within areas of association cortex. Although crucial for interpretation of fMRI changes as reflecting neural activity in human cortex, controversy remains as to these relationships. Supported by: Dutch Technology Foundation and University of Utrecht Grant UGT7685, ERC-Advanced grant 320708 (NR) and NIH grant NS065186 (JO).

The virtual brain integrates computational modeling and multimodal neuroimaging

Brain function is thought to emerge from the interactions among neuronal populations. Apart from traditional efforts to reproduce brain dynamics from the micro- to macroscopic scales, complementary approaches develop phenomenological models of lower complexity. Such macroscopic models typically generate only a few selected-ideally functionally relevant-aspects of the brain dynamics. Importantly, they often allow an understanding of the underlying mechanisms beyond computational reproduction. Adding detail to these models will widen their ability to reproduce a broader range of dynamic features of the brain. For instance, such models allow for the exploration of consequences of focal and distributed pathological changes in the system, enabling us to identify and develop approaches to counteract those unfavorable processes. Toward this end, The Virtual Brain (TVB) ( www.thevirtualbrain.org ), a neuroinformatics platform with a brain simulator that incorporates a range of neuronal models and dynamics at its core, has been developed. This integrated framework allows the model-based simulation, analysis, and inference of neurophysiological mechanisms over several brain scales that underlie the generation of macroscopic neuroimaging signals. In this article, we describe how TVB works, and we present the first proof of concept.

Repetitive tactile stimulation changes resting-state functional connectivity-implications for treatment of sensorimotor decline

Neurological disorders and physiological aging can lead to a decline of perceptual abilities. In contrast to the conventional therapeutic approach that comprises intensive training and practicing, passive repetitive sensory stimulation (RSS) has recently gained increasing attention as an alternative to countervail the sensory decline by improving perceptual abilities without the need of active participation. A particularly effective type of high-frequency RSS, utilizing Hebbian learning principles, improves perceptual acuity as well as sensorimotor functions and has been successfully applied to treat chronic stroke patients and elderly subjects. High-frequency RSS has been shown to induce plastic changes of somatosensory cortex such as representational map reorganization, but its impact on the brain's ongoing network activity and resting-state functional connectivity has not been investigated so far. Here, we applied high-frequency RSS in healthy human subjects and analyzed resting state Electroencephalography (EEG) functional connectivity patterns before and after RSS by means of imaginary coherency (ImCoh), a frequency-specific connectivity measure which is known to reduce over-estimation biases due to volume conduction and common reference. Thirty minutes of passive high-frequency RSS lead to significant ImCoh-changes of the resting state mu-rhythm in the individual upper alpha frequency band within distributed sensory and motor cortical areas. These stimulation induced distributed functional connectivity changes likely underlie the previously observed improvement in sensorimotor integration.

The intravascular susceptibility effect and the underlying physiology of fMRI

In this article, I will first give a brief account of my work at MGH on characterizing the intravascular susceptibility effect. Then I will describe studies into the underlying physiology of BOLD-fMRI which has become of interest to my group in the following decade. I will touch issues such as signal source of BOLD fMRI, capillary recruitment, the elusive initial dip and others.

High-frequency EEG covaries with spike burst patterns detected in cortical neurons

Invasive microelectrode recordings measure neuronal spikes, which are commonly considered inaccessible through standard surface electroencephalogram (EEG). Yet high-frequency EEG potentials (hf-EEG, f > 400 Hz) found in somatosensory evoked potentials of primates may reflect the mean population spike responses of coactivated cortical neurons. Since cortical responses to electrical nerve stimulation vary strongly from trial to trial, we investigated whether the hf-EEG signal can also echo single-trial variability observed at the single-unit level. We recorded extracellular single-unit activity in the primary somatosensory cortex of behaving macaque monkeys and identified variable spike burst responses following peripheral stimulation. Each of these responses was classified according to the timing of its spike constituents, conforming to one of a discrete set of spike patterns. We here show that these spike patterns are accompanied by variations in the concomitant epidural hf-EEG. These variations cannot be explained by fluctuating stimulus efficacy, suggesting that they were generated within the thalamocortical network. As high-frequency EEG signals can also be reliably recorded from the scalp of human subjects, they may provide a noninvasive window on fluctuating cortical spike activity in humans.

Time series fMRI measures detect changes in pontine raphé following acute tryptophan depletion

Serotonin is synthesized from its precursor, tryptophan, by brainstem raphé neurons and their synaptic terminals in limbic regions. The omission of tryptophan from an Acute Tryptophan Depletion (ATD) diet transiently diminishes serotonin synthesis, alters raphé activity, and mimics symptoms of depression. Raphé functional magnetic resonance imaging (fMRI) poses challenges using signal-averaging analyses. Time-series properties of fMRI blood oxygenation level dependent (BOLD) signals may hold promise, so we analyzed raphé signals for changes with the ATD diet. Eleven remitted (previously depressed) patients were awake with eyes-closed during seven-minute resting scans with 0.5s(-1) sampling. BOLD signal time-series data were frequency-filtered using wavelet transforms, yielding three octave-width frequency bands from 0.25 to 0.03s(-1) and an unbounded band below 0.03s(-1). Spectral power, reflecting signal information, increased in pontine raphé at high frequencies (0.25 to 0.125s(-1)) during ATD (compared to control, balanced, diet, P<0.004) but was unchanged at other frequencies. Functional connectivity, the correlation between time-series data from pairs of regions, weakened between pontine raphé and anterior thalamus at low frequencies during ATD (P<0.05). This preliminarily supports using fMRI time-series features to assess pontine raphé function. Whether, and how, high frequency activity oscillations interfere with low frequency signaling requires further study.

Exploiting the potential of three dimensional spatial wavelet analysis to explore nesting of temporal oscillations and spatial variance in simultaneous EEG-fMRI data

Synchronization of the activity in neural networks is a fundamental mechanism of brain function, putatively serving the integration of computations on multiple spatial and temporal scales. Time scales are thought to be nested within distinct spatial scales, so that whereas fast oscillations may integrate local networks, slow oscillations might integrate computations across distributed brain areas. We here describe a newly developed approach that provides potential for the further substantiation of this hypothesis in future studies. We demonstrate the feasibility and important caveats of a novel wavelet-based means of relating time series of three-dimensional spatial variance (energy) of fMRI data to time series of temporal variance of EEG. The spatial variance of fMRI data was determined by employing the three-dimensional dual-tree complex wavelet transform. The temporal variance of EEG data was estimated by using traditional continuous complex wavelets. We tested our algorithm on artificial signals with known signal-to-noise ratios and on empirical resting state EEG-fMRI data obtained from four healthy human subjects. By employing the human posterior alpha rhythm as an exemplar, we demonstrated face validity of the approach. We believe that the proposed method can serve as a suitable tool for future research on the spatiotemporal properties of brain dynamics, hence moving beyond analyses based exclusively in one domain or the other.

Current themes in neuroimaging of epilepsy: brain networks, dynamic phenomena, and clinical relevance

Brain scanning methods were first applied in patients with epilepsy more than 30years ago. A very substantial literature now exists in this field, which is exponentially increasing. Contemporary neuroimaging studies in epilepsy reflect new concepts in the epilepsies, as well as current methodological developments. In particular, this area is emphasising the role of networks in epileptogenicity, the existence of dynamic phenomena which can be captured by imaging, and is beginning to validate the implementation of neuroimaging in the clinic. Here, recent studies of the last 5years are reviewed, covering the full range of neuroimaging methods with SPECT, PET and MRI in epilepsy.

Neuronal correlates of functional magnetic resonance imaging in human temporal cortex

The relationship between changes in functional magnetic resonance imaging and neuronal activity remains controversial. Data collected during awake neurosurgical procedures for the treatment of epilepsy provided a rare opportunity to examine this relationship in human temporal association cortex. We obtained functional magnetic resonance imaging blood oxygen dependent signals, single neuronal activity and local field potentials from 8 to 300 Hz at 13 temporal cortical sites, from nine subjects, during paired associate learning and control measures. The relation between the functional magnetic resonance imaging signal and the electrophysiologic parameters was assessed in two ways: colocalization between significant changes in these signals on the same paired associate-control comparisons and multiple linear regressions of the electrophysiologic measures on the functional magnetic resonance imaging signal, across all tasks. Significant colocalization was present between increased functional magnetic resonance imaging signals and increased local field potentials power in the 50-250 Hz range. Local field potentials power greater than 100 Hz was also a significant regressor for the functional magnetic resonance imaging signal, establishing this local field potentials frequency range as a neuronal correlate of the functional magnetic resonance imaging signal. There was a trend for a relation between power in some low frequency local field potentials frequencies and the functional magnetic resonance imaging signal, for 8-15 Hz increases in the colocalization analysis and 16-23 Hz in the multiple linear regression analysis. Neither analysis provided evidence for an independent relation to frequency of single neuron activity.

Ultrahigh-frequency EEG during fMRI: pushing the limits of imaging-artifact correction

Although solutions for imaging-artifact correction in simultaneous EEG-fMRI are improving, residual artifacts after correction still considerably affect the EEG spectrum in the ultrafast frequency band above 100 Hz. Yet this band contains subtle but valuable physiological signatures such as fast gamma oscillations or evoked high-frequency (600 Hz) bursts related to spiking of thalamocortical and cortical neurons. Here we introduce a simultaneous EEG-fMRI approach that integrates hard and software modifications for continuous acquisition of ultrafast EEG oscillations during fMRI. Our approach is based upon and extends the established method of averaged artifact subtraction (AAS). Particularly for recovery of ultrahigh-frequency EEG signatures, AAS requires invariantly sampled and constant imaging-artifact waveforms to achieve optimal imaging-artifact correction. Consequently, we adjusted our acquisition setup such that both physiological ultrahigh-frequency EEG and invariantly sampled imaging artifacts were captured. In addition, we extended the AAS algorithm to cope with other, non-sampling related sources of imaging-artifact variations such as subject movements. A cascaded principal component analysis finally removed remaining imaging-artifact residuals. We provide a detailed evaluation of averaged ultrahigh-frequency signals and unaveraged broadband EEG spectra up to 1 kHz. Evoked nanovolt-sized high-frequency bursts were successfully recovered during periods of MR data acquisition afflicted by imaging artifacts in the millivolt range. Compared to periods without imaging artifacts they exhibited the same mean amplitudes, latencies and waveforms and a signal-to-noise ratio of 72%. Furthermore we identified consistent dipole sources. In conclusion, ultrafast EEG oscillations can be continuously monitored during fMRI using the proposed approach.

Background and evoked activity and their interaction in the human brain

Most functional neuroimaging studies have investigated brain activity evoked by certain types of stimulation or tasks. In recent years, resting brain activity and its influence on evoked activity has become accessible for investigation. However, despite numerous studies on background and evoked activities, either observed with vascular (functional magnetic resonance imaging, positron emission tomography, optical) or electrophysiological (electroencephalography, magnetoencephalography) or a combination of both methods, so far, there is no generally accepted view concerning both the precise meaning of background activity and its relationship to evoked activity. In this article, we give an overview of the current knowledge on this issue and we review recent studies examining the influence of ongoing activity on behavioral responses and the relationship between ongoing and evoked activity.