Riemannian Geometry Applied to Detection of Respiratory States From EEG Signals: The Basis for a Brain-Ventilator Interface.

GOAL: During mechanical ventilation, patient-ventilator disharmony is frequently observed and may result in increased breathing effort, compromising the patient's comfort and recovery. This circumstance requires clinical intervention and becomes challenging when verbal communication is difficult. In this study, we propose a brain-computer interface (BCI) to automatically and noninvasively detect patient-ventilator disharmony from electroencephalographic (EEG) signals: a brain-ventilator interface (BVI). METHODS: Our framework exploits the cortical activation provoked by the inspiratory compensation when the subject and the ventilator are desynchronized. Use of a one-class approach and Riemannian geometry of EEG covariance matrices allows effective classification of respiratory states. The BVI is validated on nine healthy subjects that performed different respiratory tasks that mimic a patient-ventilator disharmony. RESULTS: Classification performances, in terms of areas under receiver operating characteristic curves, are significantly improved using EEG signals compared to detection based on air flow. Reduction in the number of electrodes that can achieve discrimination can be often desirable (e.g., for portable BCI systems). By using an iterative channel selection technique, the common highest order ranking, we find that a reduced set of electrodes (n = 6) can slightly improve for an intrasubject configuration, and it still provides fairly good performances for a general intersubject setting. CONCLUSION: Results support the discriminant capacity of our approach to identify anomalous respiratory states, by learning from a training set containing only normal respiratory epochs. SIGNIFICANCE: The proposed framework opens the door to BVIs for monitoring patient's breathing comfort and adapting ventilator parameters to patient respiratory needs.

[Seizure prediction: from myth to reality]. / Prédiction des crises d'épilepsie: du mythe à la réalité.

INTRODUCTION: Clinical, metabolic and electrophysiologic studies suggest the existence of a preictal state, a transition between the interictal state and seizure. STATE OF THE ART: Analysis of the intracranial EEG by mathematical methods shows changes of the brain dynamics several minutes before the occurrence of partial seizures. These modifications can be widespread and not restricted to the epileptogenic focus, which would explain why they can also be detected from scalp EEG. Several scenarios could underlie the preictal state: a progressive recruitment of neurons or a facilitating state with a high probability of seizure occurrence. Because of the high rate of false predictions, no satisfactory method for seizure prediction has been currently proposed. PERSPECTIVES: A European multicenter study (Evolving platform for improving living expectation of patients suffering from IctAl events [EPILEPSIAE]) is currently evaluating a combination of 44 methods applied for EEG and ECG analysis on long-term recordings obtained from a large multicenter database (www.epilepsiae.eu). CONCLUSION: Combining analyses of multi-level signals including intracranial EEG and microelectrodes, scalp EEG and in vitro electrophysiological studies of post-operative tissues should help clarify brain dynamics during the pre-ictal state.

Functional modularity of background activities in normal and epileptic brain networks.

We analyze the connectivity structure of weighted brain networks extracted from spontaneous magnetoencephalographic signals of healthy subjects and epileptic patients (suffering from absence seizures) recorded at rest. We find that, for the activities in the 5-14 Hz range, healthy brains exhibit a sparse connectivity, whereas the brain networks of patients display a rich connectivity with a clear modular structure. Our results suggest that modularity plays a key role in the functional organization of brain areas during normal and pathological neural activities at rest.

Complex modular structure of large-scale brain networks.

Modular structure is ubiquitous among real-world networks from related proteins to social groups. Here we analyze the modular organization of brain networks at a large scale (voxel level) extracted from functional magnetic resonance imaging signals. By using a random-walk-based method, we unveil the modularity of brain webs and show modules with a spatial distribution that matches anatomical structures with functional significance. The functional role of each node in the network is studied by analyzing its patterns of inter- and intramodular connections. Results suggest that the modular architecture constitutes the structural basis for the coexistence of functional integration of distant and specialized brain areas during normal brain activities at rest.

Dynamic small-world behavior in functional brain networks unveiled by an event-related networks approach.

There is growing interest in studying the role of connectivity patterns in brain functions. In recent years, functional brain networks were found to exhibit small-world properties during different brain states. In previous studies, time-independent networks were recovered from long time periods of brain activity. In this paper, we propose an approach, the event-related networks, that allows one to characterize the dynamical evolution of functional brain networks in time-frequency space. We illustrate this approach by characterizing connectivity patterns in magnetoencephalographic signals recorded during a visual stimulus paradigm. When compared with equivalent random and regular networks, the results reveal that functional connectivity varies with time and frequency during the processing of the stimulus, while maintaining a small-world structure. This approach may provide insights into the connectivity of other complex and spatially extended nonstationary systems.

Loss of phase synchrony in an animal model of partial status epilepticus.

Interrupting a focal, chronic infusion of GABA to the rat motor cortex initiates the progressive emergence of a sustained spiking electroencephalographic (EEG) activity, associated with myoclonic jerks of the corresponding body territory. This activity is maintained over several hours, has an average frequency of 1.5 Hz, is localized to the infusion site and never generalizes. The GABA withdrawal syndrome (GWS) has therefore features of partial status epilepticus. Changes in EEG signals associated with the GWS were studied in freely moving rats by measuring the phase synchrony between bilateral epidural records from the neocortex. Our results showed (i) epileptic activity was associated with a striking decrease in phase synchrony between all pairs of electrodes including the focus, predominantly in the 1-6 Hz frequency range. There was a mean decrease of 75.34+/-5.26% in phase synchrony levels between the period before GABA interruption and the period after epileptic activity appeared. (ii) This reduction in synchrony contrasted with an increase of power spectral density in the corresponding EEG channels over the same 1-6 Hz frequency range, (iii) neither changes in synchrony nor in nonlinear dynamics were detected before the first EEG spikes, (iv) systemic injection of ketamine, an antagonist of N-methyl-d-aspartic acid (NMDA) receptors, modified transiently both epileptic activity and the synchrony profile. (v) Spiking activity and synchrony changes were suppressed by reperfusion of GABA. Our data suggest that, during a partial status epilepticus, interactions between the epileptic focus and connected neocortical neuronal populations are dramatically decreased in low frequencies.

Towards a proper estimation of phase synchronization from time series.

In experimental synchronization studies a continuous phase variable is commonly estimated from a scalar time series by means of its representation on the complex plane. The aim is to obtain a pair of functions [A(t), phi(t)] defining its instantaneous amplitude and phase, respectively. However, any arbitrary pair of functions cannot be considered as the amplitude and the phase of the real observable. Here, we point out some criteria that the pair [A(t), phi(t)] must observe to unambiguously define the instantaneous amplitude and phase of the observed signal. In this work, we illustrate how the complex representation may fail if the signal possesses a multi-component or a broadband spectra. We also point out a practical procedure to test whether a signal, not displaying a single oscillation at a unique frequency, has a narrow-band behavior. Implications for the study of phase interdependencies are illustrated and discussed. Phase dynamics estimated from electric brain activities recorded from an epileptic patient are also discussed.

Degree mixing and the enhancement of synchronization in complex weighted networks.

Real networks often consist of local units interacting with each other by means of heterogeneous connections. In many cases, furthermore, such networks feature degree mixing properties, i.e., the tendency of nodes with high degree (with low degree) to connect with connectivity peers (with highly connected nodes). Such degree-degree correlations may have an important influence in the spreading of information or infectious agents on a network. We explore the role played by these correlations for the synchronization of networks of coupled dynamical systems. Using a stochastic optimization technique, we find that the value of degree mixing providing optimal conditions for synchronization depends on the weighted coupling scheme. We also show that a minimization of the assortative coefficient may induce a strong destabilization of the synchronous state. We illustrate our findings for weighted networks with scale free and random topologies.

Comparison of Hilbert transform and wavelet methods for the analysis of neuronal synchrony.

The quantification of phase synchrony between neuronal signals is of crucial importance for the study of large-scale interactions in the brain. Two methods have been used to date in neuroscience, based on two distinct approaches which permit a direct estimation of the instantaneous phase of a signal [Phys. Rev. Lett. 81 (1998) 3291; Human Brain Mapping 8 (1999) 194]. The phase is either estimated by using the analytic concept of Hilbert transform or, alternatively, by convolution with a complex wavelet. In both methods the stability of the instantaneous phase over a window of time requires quantification by means of various statistical dependence parameters (standard deviation, Shannon entropy or mutual information). The purpose of this paper is to conduct a direct comparison between these two methods on three signal sets: (1) neural models; (2) intracranial signals from epileptic patients; and (3) scalp EEG recordings. Levels of synchrony that can be considered as reliable are estimated by using the technique of surrogate data. Our results demonstrate that the differences between the methods are minor, and we conclude that they are fundamentally equivalent for the study of neuroelectrical signals. This offers a common language and framework that can be used for future research in the area of synchronization.

Nonlinear analysis of orthostatic posture in patients with vertigo or balance disorders.

The stabilometry signals involve irregular and unpredictable components. In order to identify the hidden dynamics that underlie the multi-link networks consisted of the multiple sensory systems, motor components and central integration, we applied a nonlinear analysis to these signals. We evaluated the postural control differences between eyes open and closed by means of the dynamical closeness between two states, known as similarity index, for the patients with vestibular disorders. We were able to demonstrate that some patients (eight of 21) showed a difference between the conventional and nonlinear measures. Especially, the similarity index tended to reflect the clinical course of the vestibular compensation and the findings in the patients with benign paroxysmal positional vertigo (BPPV) demonstrated that its vestibular function may include various pathologies besides canalithiasis. These results suggest that nonlinear analysis can elucidate the complex postural control networks and this procedure may also be able to provide the new findings of the stabilometry examinations.

Characterizing neurodynamic changes before seizures.

The study of dynamic changes in neural activity preceding epileptic seizure allows the characterization of a preictal state several minutes before seizure onset. This opens up new perspectives for studying the mechanisms of epileptogenesis as well as for possible therapeutic interventions, which represent a major breakthrough. In this review the authors present and discuss the results from their group in this domain using nonlinear analysis of brain signals, as well as the limitations of this topic and current questions.

The brainweb: phase synchronization and large-scale integration.

The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.

Anticipation of epileptic seizures from standard EEG recordings.

BACKGROUND: New methods derived from non-linear analysis of intracranial recordings permit the anticipation of an epileptic seizure several minutes before the seizure. Nevertheless, anticipation of seizures based on standard scalp electroencephalographical (EEG) signals has not been reported yet. The accessibility to preictal changes from standard EEGs is essential for expanding the clinical applicability of these methods. METHODS: We analysed 26 scalp-EEG/video recordings, from 60 min before a seizure, in 23 patients with temporal-lobe epilepsy. For five patients, simultaneous scalp and intracranial EEG recordings were assessed. Long-term changes before seizure onset were identified by a measure of non-linear similarity, which is very robust in spite of large artifacts and runs in real-time. FINDINGS: In 25 of 26 recordings, measurement of non-linear changes in EEG signals allowed the anticipation of a seizure several minutes before it occurred (mean 7 min). These preictal changes in the scalp EEG correspond well with concurrent changes in depth recordings. INTERPRETATION: Scalp-EEG recordings retain sufficient dynamical information which can be used for the analysis of preictal changes leading to seizures. Seizure anticipation strategies in real-time can now be envisaged for diverse clinical applications, such as devices for patient warning, for efficacy of ictal-single photon emission computed tomography procedures, and eventual treatment interventions for preventing seizures.

A quantitative study of gamma-band activity in human intracranial recordings triggered by visual stimuli.

This paper studies gamma-band responses from two implanted epileptic patients during a simple visual discrimination task. Our main aim was to ascertain, in a reliable manner, whether evoked (stimulus-locked) and induced (triggered by, but not locked to, stimuli) responses are present in intracranial recordings. For this purpose, we introduce new methods adapted to detect the presence of gamma responses at this level of recording, intermediary between EEG-scalp and unicellular responses. The analysis relies on a trial-by-trial time-frequency analysis and on the use of surrogate data for statistical testing. We report that visual stimulation reliably elicits evoked and induced responses in human intracranial recordings. Induced intracranial gamma activity is significantly present in short oscillatory bursts (a few cycles) following visual stimulation. These responses are highly variable from trial to trial, beginning after 200 ms and lasting up to 500 ms. In contrast, intracranial-evoked gamma responses concentrate around 100 ms latencies corresponding to evoked responses observed on the scalp. We discuss our results in relation to scalp gamma response in a similar protocol [Tallon-Baudry et al. (1996) J. Neurosci., 16, 4240-4249] and draw some conclusions for bridging the gap between gamma oscillations observed on the scalp surface and their possible cortical sources.

Spatio-temporal characterizations of non-linear changes in intracranial activities prior to human temporal lobe seizures.

Recent studies have shown that non-linear analysis of intracranial activities can detect a 'pre-ictal phase' preceding the epileptic seizure. Nevertheless, the dynamical nature of the underlying neuronal process and the spatial extension of this pre-ictal phase still remain unknown. In this paper, we address these aspects using a new non-linear measure of dynamic similarity between different parts of intracranial recordings of nine patients with medial temporal lobe epilepsy recorded during transitions to seizure. Our results confirm that non-linear changes in neuronal dynamics allow, in most cases (16 out of 17), a seizure anticipation several minutes in advance. Furthermore, we show that the spatial distribution of pre-ictal changes often involves an extended network projecting beyond the limits of the epileptogenic region. Finally, the pre-ictal phase could frequently (13 out of 17) be characterized with a marked shift toward slower frequencies in upper delta or theta frequency range.

[Interactions between the epileptic network and brain function: an approach by nonlinear analysis of intracranial EEG]. / Interactions entre réseau épileptique et fonctionnement cérébral: approache par analyse non linéaire de l'EEG intracrânien.

Recent advances in the non-linear dynamics analysis have made it possible to identify hidden recurrences in EEG signals that could be missed by more traditional linear techniques such as power spectrum or coherence analysis. This is particularly true for epileptic EEG recordings either in animals or in humans as epileptic phenomena are usually concomitant with the emergence a strong non-linear EEG behavior. Non-linear dynamical analysis techniques quantify the relations between EEG signals. The literature concerning the spatio-temporal characteristics of the epileptic processes during seizures and interictal periods is reviewed. Our attention has been mainly focused on the interdependences between brain structures or on the dynamical changes of one particular brain region during intracranial recordings. These data could explain in part the dysfunctioning of the cerebral cortex induced by epileptic activities and provide an insight into the spatio-temporal organization of the epileptic network. Futhermore, by tracking the time variation of non-linear indices, one can anticipate the occurrence of seizures in temporal lobe epilepsies. All this information could contribute to improve definitions of the epileptogenic zone in partial epilepsy and also open the way to preventive interventions.

Why bother to spatially embed EEG? Comments on Pritchard et al., Psychophysiology, 33, 362-368, 1996.

In a recent paper, Pritchard, Krieble, and Duke (Psychophysiology, 33, 362-368, 1996) studied the validity of spatial embedding of electroencephalographic (EEG) data and rejected this method in favor of time-delay embedding. The present paper describes the nonlinear characterization of brain dynamics using either spatial or time-delay embedding. We discuss the arguments published in Pritchard et al. (1996) and demonstrate that the spatial embedding cannot be rejected on this basis. We also point out the limitations of both spatial and time-delay embeddings related to the spatial extension and the high-dimensional dynamics of brain activity.

Anticipating epileptic seizures in real time by a non-linear analysis of similarity between EEG recordings.

In a previous publication we showed that non-linear analysis can extract spatio-temporal changes of brain electrical activity prior to epileptic seizures. Here we describe a new method to analyze this long-term non-stationarity in the EEG by a measure of dynamical similarity between different parts of the time series. We apply this method to the study of a group of patients with temporal lobe epilepsy recorded intracranially during transitions to seizure. We show that the method, which can be implemented on a personal computer, can track in real time spatio-temporal changes in brain dynamics several minutes prior to seizure.

Perception's shadow: long-distance synchronization of human brain activity.

Transient periods of synchronization of oscillating neuronal discharges in the frequency range 30-80 Hz (gamma oscillations) have been proposed to act as an integrative mechanism that may bring a widely distributed set of neurons together into a coherent ensemble that underlies a cognitive act. Results of several experiments in animals provide support for this idea. In humans, gamma oscillations have been described both on the scalp (measured by electroencephalography and magnetoencephalography) and in intracortical recordings, but no direct participation of synchrony in a cognitive task has been demonstrated so far. Here we record electrical brain activity from subjects who are viewing ambiguous visual stimuli (perceived either as faces or as meaningless shapes). We show for the first time, to our knowledge, that only face perception induces a long-distance pattern of synchronization, corresponding to the moment of perception itself and to the ensuing motor response. A period of strong desynchronization marks the transition between the moment of perception and the motor response. We suggest that this desynchronization reflects a process of active uncoupling of the underlying neural ensembles that is necessary to proceed from one cognitive state to another.

Measuring phase synchrony in brain signals.

This article presents, for the first time, a practical method for the direct quantification of frequency-specific synchronization (i.e., transient phase-locking) between two neuroelectric signals. The motivation for its development is to be able to examine the role of neural synchronies as a putative mechanism for long-range neural integration during cognitive tasks. The method, called phase-locking statistics (PLS), measures the significance of the phase covariance between two signals with a reasonable time-resolution (<100 ms). Unlike the more traditional method of spectral coherence, PLS separates the phase and amplitude components and can be directly interpreted in the framework of neural integration. To validate synchrony values against background fluctuations, PLS uses surrogate data and thus makes no a priori assumptions on the nature of the experimental data. We also apply PLS to investigate intracortical recordings from an epileptic patient performing a visual discrimination task. We find large-scale synchronies in the gamma band (45 Hz), e.g., between hippocampus and frontal gyrus, and local synchronies, within a limbic region, a few cm apart. We argue that whereas long-scale effects do reflect cognitive processing, short-scale synchronies are likely to be due to volume conduction. We discuss ways to separate such conduction effects from true signal synchrony.