Single-trial multiwavelet coherence in application to neurophysiological time series

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user's mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.

Quantitative feature analysis of continuous analytic wavelet transforms of electrocardiography and electromyography

Theoretical and practical advances in time-frequency analysis, in general, and the continuous wavelet transform (CWT), in particular, have increased over the last two decades. Although the Morlet wavelet has been the default choice for wavelet analysis, a new family of analytic wavelets, known as generalized Morse wavelets, which subsume several other analytic wavelet families, have been increasingly employed due to their time and frequency localization benefits and their utility in isolating and extracting quantifiable features in the time-frequency domain. The current paper describes two practical applications of analysing the features obtained from the generalized Morse CWT: (i) electromyography, for isolating important features in muscle bursts during skating, and (ii) electrocardiography, for assessing heart rate variability, which is represented as the ridge of the main transform frequency band. These features are subsequently quantified to facilitate exploration of the underlying physiological processes from which the signals were generated.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

Characterization of network structure in stereoEEG data using consensus-based partial coherence

Coherence is a widely used measure to determine the frequency-resolved functional connectivity between pairs of recording sites, but this measure is confounded by shared inputs to the pair. To remove shared inputs, the 'partial coherence' can be computed by conditioning the spectral matrices of the pair on all other recorded channels, which involves the calculation of a matrix (pseudo-) inverse. It has so far remained a challenge to use the time-resolved partial coherence to analyze intracranial recordings with a large number of recording sites. For instance, calculating the partial coherence using a pseudoinverse method produces a high number of false positives when it is applied to a large number of channels. To address this challenge, we developed a new method that randomly aggregated channels into a smaller number of effective channels on which the calculation of partial coherence was based. We obtained a 'consensus' partial coherence (cPCOH) by repeating this approach for several random aggregations of channels (permutations) and only accepting those activations in time and frequency with a high enough consensus. Using model data we show that the cPCOH method effectively filters out the effect of shared inputs and performs substantially better than the pseudo-inverse. We successfully applied the cPCOH procedure to human stereotactic EEG data and demonstrated three key advantages of this method relative to alternative procedures. First, it reduces the number of false positives relative to the pseudo-inverse method. Second, it allows for titration of the amount of false positives relative to the false negatives by adjusting the consensus threshold, thus allowing the data-analyst to prioritize one over the other to meet specific analysis demands. Third, it substantially reduced the number of identified interactions compared to coherence, providing a sparser network of connections from which clear spatial patterns emerged. These patterns can serve as a starting point of further analyses that provide insight into network dynamics during cognitive processes. These advantages likely generalize to other modalities in which shared inputs introduce confounds, such as electroencephalography (EEG) and magneto-encephalography (MEG).

Phenomenology of neurophysiologic changes during surgical treatment of carotid stenosis using signal analysis

Objective

To describe the changes in the shape and topology of the somatosensory evoked potential (SSEP) during carotid endarterectomy, with particular reference to the time of clamping.

Methods

Routine intraoperative monitoring was performed on 30 patients undergoing carotid endarterectomy (15) or undergoing stenting (15) using median nerve SSEPs. Post-operatively the first and second derivatives of the potential were examined. Separate analysis of the SSEP using wavelets was also performed.

Results

In no instances did changes in the SSEP reach clinical significance. The first derivative showed significant changes that were temporally related to the clamp period. After clamping the 'velocity' was higher than baseline. There were changes in the wavelets related to the clamp period with more marked spectral edges at the conclusion of the procedure than baseline. In all instances the patient had a good clinical outcome.

Conclusions

Wavelet and derivative analysis of evoked potentials show changes that are not apparent with measures of amplitude and latency. The clinical relevance of these changes remains uncertain and await larger studies.

Significance

Increased velocity and spectral edges may be markers of increased cerebral blood flow, at least in the setting of pre-existing carotid stenosis.

Adaptive spectral tracking for coherence estimation: the z-tracker

OBJECTIVE

A major challenge in non-stationary signal analysis is reliable estimation of correlation. Neurophysiological recordings can be many minutes in duration with data that exhibits correlation which changes over different time scales. Local smoothing can be used to estimate time-dependency, however, an effective framework needs to adjust levels of smoothing in response to changes in correlation.

APPROACH

Here we present a novel data-adaptive algorithm, the z-tracker, for estimating local correlation in segmented data. The algorithm constructs single segment coherence estimates using multi-taper windows. These are subject to adaptive Kalman filtering/smoothing in the z-domain to construct a local coherence estimate for each segment. The error residual for each segment determines the levels of process noise, allowing the filter to adapt rapidly to sudden changes in correlation while applying greater smoothing to data where the correlation is consistent across segments. The method is compared to wavelet coherence, calculated using orthogonal Morse wavelets.

MAIN RESULTS

The performance of the z-tracker is quantified against Morse wavelet coherence using a mean square deviation (MSD) metric. The z-tracker has significantly lower MSD than the wavelet estimate for time-varying coherence over long time scales (∼10-20 s), whereas the wavelet has lower MSD for coherence varying over short time scales (∼1-2 s). The z-tracker also has a lower MSD for slowly varying coherence with occasional step changes. The method is applied to detect changes in coherence in paired LFP recordings from rat prefrontal cortex and amygdala in response to a pharmacological challenge.

SIGNIFICANCE

The z-tracker provides an effective and efficient method to estimate time varying correlation in multivariate data, leading to better characterisation of neurophysiology signals where correlation is subject to slow modulation over time. A number of suggestions are included for future refinements.

A novel cross-frequency coupling detection method using the generalized Morse wavelets

BACKGROUND

Cross-frequency coupling (CFC) occurs when non-identical frequency components entrain one another. A ubiquitous example from neuroscience is low frequency phase to high frequency amplitude coupling in electrophysiological signals. Seminal work by Canolty revealed CFC in human ECoG data. Established methods band-pass the data into component frequencies then convert the band-passed signals into the analytic representation, from which we infer the instantaneous amplitude and phase of each component. Though powerful, such methods resolve signals with respect to time and frequency without addressing the multiresolution problem.

NEW METHOD

We build upon the ground-breaking work of Canolty and others and derive a wavelet-based CFC detection algorithm that efficiently searches a range of frequencies using a sequence of filters with optimal trade-off between time and frequency resolution. We validate our method using simulated data and analyze CFC within and between the primary motor cortex and dorsal striatum of rats under ketamine-xylazine anesthesia.

RESULTS

Our method detects the correct CFC in simulated data and reveals CFC between frequency bands that were previously shown to participate in corticostriatal effective connectivity.

COMPARISON WITH EXISTING METHODS

Other CFC detection methods address the need to increase bandwidth when analyzing high frequency components but none to date permit rigorous bandwidth selection with no a priori knowledge of underlying CFC. Our method is thus particularly useful for exploratory studies.

CONCLUSIONS

The method developed here permits rigorous and efficient exploration of a hypothesis space and is particularly useful when the frequencies participating in CFC are unknown.

Fairness influences early signatures of reward-related neural processing

Many humans exhibit a strong preference for fairness during decision-making. Although there is evidence that social factors influence reward-related and affective neural processing, it is unclear if this effect is mediated by compulsory outcome evaluation processes or results from slower deliberate cognition. Here we show that the feedback-related negativity (FRN) and late positive potential (LPP), two signatures of early hedonic processing, are modulated by the fairness of rewards during a passive rating task. We find that unfair payouts elicit larger FRNs than fair payouts, whereas fair payouts elicit larger LPPs than unfair payouts. This is true both in the time-domain, where the FRN and LPP are related, and in the time-frequency domain, where the two signals are largely independent. Ultimately, this work demonstrates that fairness affects the early stages of reward and affective processing, suggesting a common biological mechanism for social and personal reward evaluation.

Behavior modulates effective connectivity between cortex and striatum

It has been notoriously difficult to understand interactions in the basal ganglia because of multiple recurrent loops. Another complication is that activity there is strongly dependent on behavior, suggesting that directional interactions, or effective connections, can dynamically change. A simplifying approach would be to examine just the direct, monosynaptic projections from cortex to striatum and contrast this with the polysynaptic feedback connections from striatum to cortex. Previous work by others on effective connectivity in this pathway indicated that activity in cortex could be used to predict activity in striatum, but that striatal activity could not predict cortical activity. However, this work was conducted in anesthetized or seizing animals, making it impossible to know how free behavior might influence effective connectivity. To address this issue, we applied Granger causality to local field potential signals from cortex and striatum in freely behaving rats. Consistent with previous results, we found that effective connectivity was largely unidirectional, from cortex to striatum, during anesthetized and resting states. Interestingly, we found that effective connectivity became bidirectional during free behaviors. These results are the first to our knowledge to show that striatal influence on cortex can be as strong as cortical influence on striatum. In addition, these findings highlight how behavioral states can affect basal ganglia interactions. Finally, we suggest that this approach may be useful for studies of Parkinson's or Huntington's diseases, in which effective connectivity may change during movement.

A role for the subthalamic nucleus in response inhibition during conflict

The subthalamic nucleus (STN) is a key node in the network that supports response inhibition. It is suggested that the STN rapidly inhibits basal ganglia activity, to pause motor output during conflict until an appropriate motor plan is ready. Here, we recorded neural activity during a Stroop task from deep brain stimulation electrodes implanted in the human STN. We intended to determine whether cognitive psychological phenomena such as the Stroop effect can be explained via mechanisms of response inhibition involving the STN, or whether higher cognitive centers are alone responsible. We show stimulus-driven desychronization in the beta band (15-35 Hz) that lasts throughout the verbal response, in keeping with the idea that beta-band synchrony decreases to allow motor output to occur. During incongruent trials--in which response times were elongated due to the Stroop effect--a resynchronization was seen in the beta band before response. Crucially, in the incongruent trials during which the participant was unable to withhold the prepotent response, this resynchronization occurred after response onset. We suggest that this beta-band resynchronization pauses the motor system until conflict can be resolved.

Oscillatory activity in the subthalamic nucleus during arm reaching in Parkinson's disease

Oscillatory activities in the brain within the beta (15-30 Hz) and gamma (70-90 Hz) ranges have been implicated in the generation of voluntary movement. However, their roles remain unclear. Here, we record local field potential activity from the region of the subthalamic nucleus during movement of the contralateral limb in 11 patients with Parkinson's disease. Patients were on their normal dopaminergic medication and were cued to perform arm-reaching movements after a delay period at three different speeds: 'slow', 'normal', and 'fast'. Beta activity desynchronized earlier in response to the cue indicating an upcoming fast reach than to the cues for slow or normal speed movement. There was no difference in the degree of beta desynchronization between reaching speeds and beta desynchronization was established prior to movement onset in all cases. In contrast, synchronization in the gamma range developed during the reaching movement, and was especially pronounced during fast reaching. Thus the timing of suppression in the beta band depended on task demands, whereas the degree of increase in gamma oscillations depended on movement speed. These findings point to functionally segregated roles for different oscillatory frequencies in motor preparation and performance.

Cortical magnetoencephalography of deep brain stimulation for the treatment of postural tremor

The effects of deep brain stimulation (DBS) on motor cortex circuitry in Essential tremor (ET) and Parkinson's disease (PD) patients are not well understood, in part, because most imaging modalities have difficulty capturing and localizing motor cortex dynamics on the same temporal scale as motor symptom expression. Here, we report on the use of magnetoencephalography (MEG) to characterize sources of postural tremor activity within the brain of an ET/PD patient and the effects of bilateral subthalamic nucleus DBS on these sources. Recordings were performed during unilateral and bilateral DBS at stimulation amplitudes of 0 V, 1 V, and 3 V corresponding to no therapy, subtherapeutic, and therapeutic configurations, respectively. Dipole source localization in reference to the postural tremor frequency recorded with electromyography (EMG) showed prominent sources in both right and left motor cortices when no therapy was provided. These sources dissipated as the amplitude of stimulation increased to a therapeutic level (P = 0.0062). Coherence peaks between the EMG and MEG recordings were seen at both 4 Hz, postural tremor frequency, and at 8 Hz, twice the tremor frequency, with no therapy. Both peaks were reduced with therapeutic DBS. These results demonstrate the capabilities of MEG to record cortical dynamics of tremor during deep brain stimulation and suggest that MEG could be used to examine DBS in the context of motor symptoms of PD and of ET.

Balanced multifilter banks for multiple description coding

The parametrization for one kind of multifilter banks generating balanced multiwavelets is presented in this paper, in which two lowpass filters are flipping filters, and two highpass filters have linear phase. Based on these parametric expressions, some balanced multiwavelets and analysis-ready multiwavelets are constructed, which are symmetric, or antisymmetric. Moreover, on the basis of balanced multiwavelet transform, a new method of multiple description coding is given, and experiments show that this method works well. Compared with the traditional multiple description coding method, this method has low redundancy.

Local Field Potentials Reveal a Distinctive Neural Signature of Cluster Headache in the Hypothalamus

Cluster headache (CH) is a debilitating neurovascular condition characterized by severe unilateral periorbital head pain. Deep brain stimulation of the posterior hypothalamus has shown potential in alleviating CH in its most severe, chronic form. During surgical implantation of stimulating macroelectrodes for cluster head pain, one of our patients suffered a CH attack. During the attack local field potentials displayed a significant increase in power of approximately 20 Hz. To the authors' knowledge, this is the first recorded account of neuronal activity observed during a cluster attack. Our results both support and extend the current literature, which has long implicated hypothalamic activation as key to CH generation, predominantly through indirect haemodynamic neuroimaging techniques. Our findings reveal a potential locus in CH neurogenesis and a potential rationale for efficacious stimulator titration.

Estimating Granger causality from fourier and wavelet transforms of time series data

Experiments in many fields of science and engineering yield data in the form of time series. The Fourier and wavelet transform-based nonparametric methods are used widely to study the spectral characteristics of these time series data. Here, we extend the framework of nonparametric spectral methods to include the estimation of Granger causality spectra for assessing directional influences. We illustrate the utility of the proposed methods using synthetic data from network models consisting of interacting dynamical systems.