Very High Frequency Oscillations (VHFO) as a Predictor of Movement Intentions

Gamma band (30-80 Hz) oscillations arising in neuronal ensembles are thought to be a crucial component of the neural code. Recent studies in animals suggest a similar functional role for very high frequency oscillations (VHFO) in the range 80-200Hz. Since some intracerebral studies in humans link VHFO to epileptogenesis, it remains unclear if VHFO appear in the healthy human brain and if so which is their role. This study uses EEG recordings from twelve healthy volunteers, engaged in a visuo-motor reaction time task, to show that VHFO are not necessarily pathological but rather code information about upcoming movements. Oscillations within the range (30-200Hz) occurring in the period between stimuli presentation and the fastest hand responses allow highly accurate (>96%) prediction of the laterality of the responding hand in single trials. Our results suggest that VHFO belong in functional terms to the gamma band that must be considerably enlarged to better understand the role of oscillatory activity in brain functioning. This study has therefore important implications for the recording and analysis of electrophysiological data in normal subjects and patients

Modern Electrophysiological Methods for Brain-Computer Interfaces

Modern electrophysiological studies in animals show that the spectrum of neural oscillations encoding relevant information is broader than previously thought and that many diverse areas are engaged for very simple tasks. However, EEG-based brain-computer interfaces (BCI) still employ as control modality relatively slow brain rhythms or features derived from preselected frequencies and scalp locations. Here, we describe the strategy and the algorithms we have developed for the analysis of electrophysiological data and demonstrate their capacity to lead to faster accurate decisions based on linear classifiers. To illustrate this strategy, we analyzed two typical BCI tasks. (1) Mu-rhythm control of a cursor movement by a paraplegic patient. For this data, we show that although the patient received extensive training in mu-rhythm control, valuable information about movement imagination is present on the untrained high-frequency rhythms. This is the first demonstration of the importance of high-frequency rhythms in imagined limb movements. (2) Self-paced finger tapping task in three healthy subjects including the data set used in the BCI-2003 competition. We show that by selecting electrodes and frequency ranges based on their discriminative power, the classification rates can be systematically improved with respect to results published thus far

Spatiotemporal scales and links between electrical neuroimaging modalities

Recordings of brain electrophysiological activity provide the most direct reflect of neural function. Information contained in these signals varies as a function of the spatial scale at which recordings are done: from single cell recording to large scale macroscopic fields, e.g., scalp EEG. Microscopic and macroscopic measurements and models in Neuroscience are often in conflict. Solving this conflict might require the developments of a sort of bio-statistical physics, a framework for relating the microscopic properties of individual cells to the macroscopic or bulk properties of neural circuits. Such a framework can only emerge in Neuroscience from the systematic analysis and modeling of the diverse recording scales from simultaneous measurements. In this article we briefly review the different measurement scales and models in modern neuroscience to try to identify the sources of conflict that might ultimately help to create a unified theory of brain electromagnetic fields. We argue that seen the different recording scales, from the single cell to the large scale fields measured by the scalp electroencephalogram, as derived from a unique physical magnitude—the electric potential that is measured in all cases—might help to conciliate microscopic and macroscopic models of neural function as well as the animal and human neuroscience literatur

Identification of arm movements using correlation of electrocorticographic spectral components and kinematic recordings

Abstract. The purpose of this study was to explore the possibility of using electrocorticographic (ECoG) recordings from subdural electrodes placed over the motor cortex to identify the upper limb motion performed by a human subject. More specifically, we were trying to identify features in the ECoG signals that could help us determine the type of movement performed by an individual. Two subjects who had subdural electrodes implanted over the motor cortex were asked to perform various motor tasks with the upper limb contralateral to the site of electrode implantation. ECoG signals and upper limb kinematics were recorded while the participants were performing the movements. ECoG frequency components were identified that correlated well with the performed movements measured along 6D coordinates (X, Y, Z, roll, yaw, and pitch). These frequencies were grouped using histograms. The resulting histograms had consistent and unique shapes that were representative of individual upper limb movements performed by the participants. Thus, it was possible to identify which movement was performed by the participant without prior knowledge of the arm and hand kinematics. To confirm these findings a nearest neighbour classifier was applied to identify the specific movement that each participant had performed. The achieved classification accuracy was 89%

Pitfalls in scalp high-frequency oscillation detection from long-term EEG monitoring

Aims: Intracranially recorded high-frequency oscillations (>80 Hz) are considered a candidate epilepsy biomarker. Recent studies claimed their detectability on the scalp surface. We aimed to investigate the applicability of high-frequency oscillation analysis to routine surface EEG obtained at an epilepsy monitoring unit. Methods: We retrospectively analyzed surface EEGs of 18 patients with focal epilepsy and six controls, recorded during sleep under maximal medication withdrawal. As a proof of principle, the occurrence of motor task-related events during wakefulness was analyzed in a subsample of six patients with seizure- or syncope-related motor symptoms. Ripples (80–250 Hz) and fast ripples (>250 Hz) were identified by semi-automatic detection. Using semi-parametric statistics, differences in spontaneous and task-related occurrence rates were examined within subjects and between diagnostic groups considering the factors diagnosis, brain region, ripple type, and task condition. Results: We detected high-frequency oscillations in 17 out of 18 patients and in four out of six controls. Results did not show statistically significant differences in the mean rates of event occurrences, neither regarding the laterality of the epileptic focus, nor with respect to active and inactive task conditions, or the moving hand laterality. Significant differences in general spontaneous incidence [WTS(1) = 9.594; p = 0.005] that indicated higher rates of fast ripples compared to ripples, notably in patients with epilepsy compared to the control group, may be explained by variations in data quality. Conclusion: The current analysis methods are prone to biases. A common agreement on a standard operating procedure is needed to ensure reliable and economic detection of high-frequency oscillations.The presented research was supported by the Austrian Science Fund (FWF): T 798-B27 and KLI657-B31 and by the Research Fund of the Paracelsus Medical University (PMU-FFF): A16/02/021-HÖL and A-18/01/029-HÖL.Peer Reviewe

Influence of spiking activity on cortical local field potentials.

The intra-cortical local field potential (LFP) reflects a variety of electrophysiological processes including synaptic inputs to neurons and their spiking activity. It is still a common assumption that removing high frequencies, often above 300 Hz, is sufficient to exclude spiking activity from LFP activity prior to analysis. Conclusions based on such supposedly spike-free LFPs can result in false interpretations of neurophysiological processes and erroneous correlations between LFPs and behaviour or spiking activity. Such findings might simply arise from spike contamination rather than from genuine changes in synaptic input activity. Although the subject of recent studies, the extent of LFP contamination by spikes is unclear, and the fundamental problem remains. Using spikes recorded in the motor cortex of the awake monkey, we investigated how different factors, including spike amplitude, duration and firing rate, together with the noise statistic, can determine the extent to which spikes contaminate intra-cortical LFPs. We demonstrate that such contamination is realistic for LFPs with a frequency down to ∼10 Hz. For LFP activity below ∼10 Hz, such as movement-related potential, contamination is theoretically possible but unlikely in real situations. Importantly, LFP frequencies up to the (high-) gamma band can remain unaffected. This study shows that spike-LFP crosstalk in intra-cortical recordings should be assessed for each individual dataset to ensure that conclusions based on LFP analysis are valid. To this end, we introduce a method to detect and to visualise spike contamination, and provide a systematic guide to assess spike contamination of intra-cortical LFPs

Spatio-temporal coupling of the electric and hemodynamic brain responses in humans

Our comprehension of human brain functions and their dynamics has been dramatically improved by recent developments in non-invasive imaging techniques. These methods can be divided into two different categories, according to the nature of the measured signal: hemodynamic techniques, such as functional magnetic resonance imaging (fMRI) and positiron emission tomography (PET), and electromagnetic techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG). These two categories are have complementary characteristics: hemodynamic techniques have a good spatial resolution (on a millimeter spatial scale) but have a poor temporal resolution, which is inherently limited by the rate changes in blood flow and oxygenation. Electromagnetic techniques have sub-millisecond temporal resolution but have a poor spatial resolution, since the analysis of intracranial generators requires the solution of an underdetermined inverse problem (i.e. there are infinite solutions that can explain equally well the same scalp-recorded distribution). The complementarity of the characteristics of these two families of methods allowed researchers to suppose that the understanding of spatio-temporal brain dynamics can be drastically improved by their combination (so-called multimodal imaging). Unfortunately some caveats hinder such combination. First, the nature of neurovascular coupling is still poorly understood. Second, analytical methods for multimodal imaging are largely in their infancy. The first part of this thesis focuses on the analysis of the temporal characteristics of the blood oxygenation level dependent (BOLD) signal and on how they are modulated by stimulus conditions. To analyze the BOLD dynamics, a novel method for synchronizing stimulus delivery and volume acquisition was developed. This method allows for estimating the BOLD signal with a high temporal resolution (in this thesis up to 125 ms) and for studying how the temporal characteristics (in this thesis mainly the BOLD peak latency and slope) are modulated by stimulus conditions (with an approach similar to that used in the analysis of the EEG evoked potentials). We applied this novel technique to a simple reaction time task to lateralized visual stimuli (the so-called Poffenberger paradigm) as well as to a multisensory auditory-visual reaction time task. In the first study (the Poffenberger paradigm) the analysis of BOLD dynamics supported the theory of a bilateral visuo-motor pathway even in the case of a visual stimulus ipsilateral to the responding hand. In the second study, (the auditory-visual multisensory reaction-time task), the analysis showed auditory-visual interactions within both primary auditory and visual cortices that could not be otherwise revealed by traditional fMRI analysis methods since it does not involve changes in signal amplitude. The second part of this thesis focuses on the comparison of the statistical results obtained by the analyses of fMRI and of the intracranial local field potentials (LFPs), estimated by the ELECTRA inverse solution. We first developed a new method for the analysis of EEG data. This method is based on the statistical comparison of the spectral characteristics of the estimated intracranial LFPs of the pre- and post- stimulus onset periods. Each single trial is analyzed independently, without including an averaging step, so that the information carried by high frequencies is preserved. We also propose a new metric, called resemblance, to investigate the relationship between fMRI and the estimated intracranial LFPs. Single-trial analysis and the resemblance metric were applied in an experiment involving separate EEG and fMRI acquisitions during the same passive visual stimulation protocol. This experiment revealed that only a limited set of LFP frequencies shows a spatial correlation with fMRI. This set of frequencies changes across brain areas, such that progression from lower to higher cortical levels of visual processing incorporates at each step new frequencies. In conclusion, in this thesis we show that the estimation and the analysis of the BOLD time course can give an important contribution to better understanding brain functions and brain organization. To fully understand the meaning of changes in BOLD dynamics, we need a better knowledge of the neuro-vascular coupling. To do that, we introduced a new method for evaluating the relationship between EEG and fMRI across frequencies and anatomical regions