On optimal spatial filtering for the detection of phase coupling in multivariate neural recordings.

INTRODUCTION: Neuronal oscillations synchronize processing in the brain over large spatiotemporal scales and thereby facilitate integration of individual functional modules. Up to now, the relation between the phases of neuronal oscillations and behavior or perception has mainly been analyzed in sensor space of multivariate EEG/MEG recordings. However, sensor-space analysis distorts the topographies of the underlying neuronal sources and suffers from low signal-to-noise ratio. Instead, we propose an optimized source reconstruction approach (Phase Coupling Optimization, PCO). METHODS: PCO maximizes the 'mean vector length', calculated from the phases of recovered neuronal sources and a target variable of interest (e.g., experimental performance). As pre-processing, the signal-to-noise ratio in the search-space is maximized by spatio-spectral decomposition. PCO was benchmarked against several competing algorithms and sensor-space analysis using realistic forward model simulations. As a practical example, thirteen 96-channel EEG measurements during a simple reaction time task were analyzed. After time-frequency decomposition, PCO was applied to the EEG to examine the relation between the phases of pre-stimulus EEG activity and reaction times. RESULTS: In simulations, PCO outperformed other spatial optimization approaches and sensor-space analysis. Scalp topographies of the underlying source patterns and the relation between the phases of the source activity and the target variable could be reconstructed accurately even for very low SNRs (-10dB). In a simple reaction time experiment, the phases of pre-stimulus delta waves (<0.1Hz) with widely distributed fronto-parietal source topographies were found predictive of the reaction times. DISCUSSION AND CONCLUSIONS: From multivariate recordings, PCO can reconstruct neuronal sources that are phase-coupled to a target variable using a data-driven optimization approach. Its superiority has been shown in simulations and in the analysis of a simple reaction time experiment. From this data, we hypothesize that the phase entrainment of slow delta waves (<1Hz) facilitates sensorimotor integration in the brain and that this mechanism underlies the faster processing of anticipated stimuli. We further propose that the examined slow delta waves, observed to be phase-coupled to reaction times, correspond to the compound potentials typically observed in paradigms of stimulus anticipation and motor preparation.

Voluntary control of corticomuscular coherence through neurofeedback: a proof-of-principle study in healthy subjects.

Corticomuscular coherence (CMC) relates to synchronization between activity in the motor cortex and the muscle activity. The strength of CMC can be affected by motor behavior. In a proof-of-principle study, we examined whether independent of motor output parameters, healthy subjects are able to voluntarily modulate CMC in a neurofeedback paradigm. Subjects received visual online feedback of their instantaneous CMC strength, which was calculated between an optimized spatial projection of multichannel electroencephalography (EEG) and electromyography (EMG) in an individually defined target frequency range. The neurofeedback training consisted of either increasing or decreasing CMC strength using a self-chosen mental strategy while performing a simple motor task. Evaluation of instantaneous coherence showed that CMC strength was significantly larger when subjects had to increase than when to decrease CMC; this difference between the two task conditions did not depend on motor performance. The exclusion of confounding factors such as motor performance, attention and task complexity in study design provides evidence that subjects were able to voluntarily modify CMC independent of motor output parameters. Additional analysis further strengthened the assumption that the subjects' response was specifically shaped by the neurofeedback. In perspective, we suggest that CMC-based neurofeedback could provide a therapeutic approach in clinical conditions, such as motor stroke, where CMC is altered.

Distinction between added-energy and phase-resetting mechanisms in non-invasively detected somatosensory evoked responses.

Non-invasively recorded averaged event-related potentials (ERP) represent a convenient opportunity to investigate human brain perceptive and cognitive processes. Nevertheless, generative ERP mechanisms are still debated. Two previous approaches have been contested in the past: the added-energy model in which the response raises independently from the ongoing background activity, and the phase-reset model, based on stimulus-driven synchronization of oscillatory ongoing activity. Many criteria for the distinction of these two models have been proposed, but there is no definitive methodology to disentangle them, owing also to the limited information at the single trial level. Here, we propose a new approach combining low-noise EEG technology and multivariate decomposition techniques. We present theoretical analyses based on simulated data and identify in high-frequency somatosensory evoked responses an optimal target for the distinction between the two mechanisms.

Towards non-invasive multi-unit spike recordings: mapping 1kHz EEG signals over human somatosensory cortex.

OBJECTIVE: Scalp-derived human somatosensory evoked potentials (SEPs) contain high-frequency oscillations (600 Hz; 'sigma-burst') reflecting concomitant bursts of spike responses in primary somatosensory cortex that repeat regularly at 600 Hz. Notably, recent human intracranial SEP have revealed also 1 kHz responses ('kappa-burst'), possibly reflecting non-rhythmic spiking summed over multiple cells (MUA: multi-unit activity). However, the non-invasive detection of EEG signals at 1 kHz typical for spikes has always been limited by noise contributions from both, amplifier and body/electrode interface. Accordingly, we developed a low-noise recording set-up optimised to map non-invasively 1 kHz SEP components. METHODS: SEP were recorded upon 4 Hz left median nerve stimulation in 6 healthy human subjects. Scalp potentials were acquired inside an electrically and magnetically shielded room using low-noise custom-made amplifiers. Furthermore, in order to reduce thermal Johnson noise contributions from the sensor/skin interface, electrode impedances were adjusted to ≤ 1 kΩ. Responses averaged after repeated presentation of the stimulus (n=4000 trials) were evaluated by spatio-temporal pattern analyses in complementary spectral bands. RESULTS: Three distinct spectral components were identified: N20 (<100 Hz), sigma-burst (450-750 Hz), and kappa-burst (850-1200 Hz). The two high-frequency bursts (sigma, kappa) exhibited distinct and partially independent spatiotemporal evolutions, indicating subcortical as well as several cortical generators. CONCLUSIONS: Using a dedicated low-noise set-up, human SEP 'kappa-bursts' at 1 kHz can be non-invasively detected and their scalp distribution be mapped. Their topographies indicate a set of subcortical/cortical generators, at least partially distinct from the topography of the 600 Hz sigma-bursts described previously. SIGNIFICANCE: The non-invasive detection and surface mapping of 1 kHz EEG signals presented here provides an essential step towards non-invasive monitoring of multi-unit spike activity.