Spatial extension of brain activity fools the single-channel reconstruction of EEG dynamics

Using centrality measures to extract core pattern of brain dynamics during the resting state

The patterns of brain dynamics were studied during resting state on a macroscopic scale for control subjects and multiple sclerosis patients. Macroscopic brain dynamics is defined after successive coarse-grainings and selection of significant patterns and transitions based on Markov representation of brain activity. The resulting networks show that control dynamics is merely organized according to a single principal pattern whereas patients dynamics depict more variable patterns. Centrality measures are used to extract core dynamical pattern in brain dynamics and classification technique allow to define MS dynamics with relevant error rate.

Decoding the Attentional Demands of Gait through EEG Gamma Band Features

Rehabilitation techniques are evolving focused on improving their performance in terms of duration and level of recovery. Current studies encourage the patient’s involvement in their rehabilitation. Brain-Computer Interfaces are capable of decoding the cognitive state of users to provide feedback to an external device. On this paper, cortical information obtained from the scalp is acquired with the goal of studying the cognitive mechanisms related to the users’ attention to the gait. Data from 10 healthy users and 3 incomplete Spinal Cord Injury patients are acquired during treadmill walking. During gait, users are asked to perform 4 attentional tasks. Data obtained are treated to reduce movement artifacts. Features from δ(1 − 4Hz), θ(4 − 8Hz), α(8 − 12Hz), β(12 − 30Hz), γ_low(30 − 50Hz), γ_high(50 − 90Hz) frequency bands are extracted and analyzed to find which ones provide more information related to attention. The selected bands are tested with 5 classifiers to distinguish between tasks. Classification results are also compared with chance levels to evaluate performance. Results show success rates of ∼67% for healthy users and ∼59% for patients. These values are obtained using features from γ band suggesting that the attention mechanisms are related to selective attention mechanisms, meaning that, while the attention on gait decreases the level of attention on the environment and external visual information increases. Linear Discriminant Analysis, K-Nearest Neighbors and Support Vector Machine classifiers provide the best results for all users. Results from patients are slightly lower, but significantly different, than those obtained from healthy users supporting the idea that the patients pay more attention to gait during non-attentional tasks due to the inherent difficulties they have during normal gait. This study provides evidence of the existence of classifiable cortical information related to the attention level on the gait. This fact could allow the development of a real-time system that obtains the attention level during lower limb rehabilitation. This information could be used as feedback to adapt the rehabilitation strategy.

Dynamical nonstationarity of resting EEGs in patients with attention-deficit/hyperactivity disorder (AD/HD)

This study applied dynamical nonstationarity analysis (DNA) to the resting EEGs of patients with attention-deficit/hyperactivity disorder (AD/HD). We aimed to assess and characterize AD/HD using features based on the local and global duration of dynamical microstate. We hypothesized that AD/HD patients would have difficulties in maintaining stable cognitive states (e.g., attention deficit and impulsivity) and that they would thus exhibit EEGs with temporal dynamics distinct from normal controls, i.e., rapidly and frequently changing dynamics. To test this hypothesis, we recorded EEGs from 12 adolescent subjects with AD/HD and 11 age-matched healthy subjects in the resting state with eyes closed and eyes open. We found that AD/HD patients exhibited significantly faster changes in dynamics than controls in the right temporal region during the eyes closed condition, but slower changes in dynamics in the frontal region during the eyes open condition. AD/HD patients exhibited a disruption in the rate of change of dynamics in the frontotemporal region at rest, probably due to executive and attention processes. We suggest that the DNA using complementary local and global features based on the duration of dynamical microstates could be a useful tool for the clinical diagnosis of subjects with AD/HD.

Nonlinear dynamical analysis of EEG and MEG: review of an emerging field

Many complex and interesting phenomena in nature are due to nonlinear phenomena. The theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a stage, where it becomes possible to study self-organization and pattern formation in the complex neuronal networks of the brain. One approach to nonlinear time series analysis consists of reconstructing, from time series of EEG or MEG, an attractor of the underlying dynamical system, and characterizing it in terms of its dimension (an estimate of the degrees of freedom of the system), or its Lyapunov exponents and entropy (reflecting unpredictability of the dynamics due to the sensitive dependence on initial conditions). More recently developed nonlinear measures characterize other features of local brain dynamics (forecasting, time asymmetry, determinism) or the nonlinear synchronization between recordings from different brain regions. Nonlinear time series has been applied to EEG and MEG of healthy subjects during no-task resting states, perceptual processing, performance of cognitive tasks and different sleep stages. Many pathologic states have been examined as well, ranging from toxic states, seizures, and psychiatric disorders to Alzheimer's, Parkinson's and Cre1utzfeldt-Jakob's disease. Interpretation of these results in terms of 'functional sources' and 'functional networks' allows the identification of three basic patterns of brain dynamics: (i) normal, ongoing dynamics during a no-task, resting state in healthy subjects; this state is characterized by a high dimensional complexity and a relatively low and fluctuating level of synchronization of the neuronal networks; (ii) hypersynchronous, highly nonlinear dynamics of epileptic seizures; (iii) dynamics of degenerative encephalopathies with an abnormally low level of between area synchronization. Only intermediate levels of rapidly fluctuating synchronization, possibly due to critical dynamics near a phase transition, are associated with normal information processing, whereas both hyper-as well as hyposynchronous states result in impaired information processing and disturbed consciousness.

Electroencephalography/magnetoencephalography study of cortical activities preceding prosaccades and antisaccades

The temporal and spatial characteristics of brain activity preceding prosaccades and antisaccades were investigated using source reconstructions of 64-channel electroencephalography and 148-channel magnetoencephalography data. Stimulus-locked data showed early cuneus activity was stronger during antisaccades, and later occipital gyrus activity was stronger preceding prosaccades, which suggests a top-down influence on early visual processing. Response-locked data showed that supplementary eye field, prefrontal cortex, and medial frontal eye field activity was greater for antisaccades than for prosaccades prior to saccade generation. Lateral frontal eye field activity appeared to be inhibited prior to antisaccade response generation. The spatial and temporal resolution of combined electroencephalography/magnetoencephalography data allows the evaluation of specific cortical activities preceding saccades and for demonstration of how activities differ as a function of response contingencies.

Exploring the nonlinear dynamics of the brain

The growing need for a better understanding of large-scale brain dynamics has stimulated in the last decade the development of new and more advanced data analysis techniques. Progress in this domain has greatly benefited from developments in nonlinear time series analysis. This review gives a short overview of some of the nonlinear properties one may wish to infer from brain recordings and presents some examples and recent applications.

Measurements of brain activity complexity for varying mental loads

Using functional magnetic resonance imaging, we investigate the variation in dynamical complexity of human brain activity for different mental loads. Our experiments measured the activity of ten subjects under three experimental conditions: a rest condition, a periodic task of finger opposition, and a task of finger opposition alternated with mathematical serial calculation. We used the correlation dimension to gauge the spatiotemporal complexity of brain activity. The experiments show a direct relationship between this complexity and the difficulty of the task. A natural interpretation is that higher levels of mental load recruit a larger number of independent neural processes that contribute to complex brain dynamics. These results suggest the possibility that the relative change in correlation dimension can be a useful global measure of brain dynamics, e.g., in determining the levels of mental activity, even if little is known about the underlying neurological processes.

Comparison of data transformation procedures to enhance topographical accuracy in time-series analysis of the human EEG

We describe a methodology to apply current source density (CSD) and minimum norm (MN) estimation as pre-processing tools for time-series analysis of single trial EEG data. The performance of these methods is compared for the case of wavelet time-frequency analysis of simulated gamma-band activity. A reasonable comparison of CSD and MN on the single trial level requires regularization such that the corresponding transformed data sets have similar signal-to-noise ratios (SNRs). For region-of-interest approaches, it should be possible to optimize the SNR for single estimates rather than for the whole distributed solution. An effective implementation of the MN method is described. Simulated data sets were created by modulating the strengths of a radial and a tangential test dipole with wavelets in the frequency range of the gamma band, superimposed with simulated spatially uncorrelated noise. The MN and CSD transformed data sets as well as the average reference (AR) representation were subjected to wavelet frequency-domain analysis, and power spectra were mapped for relevant frequency bands. For both CSD and MN, the influence of noise can be sufficiently suppressed by regularization to yield meaningful information, but only MN represents both radial and tangential dipole sources appropriately as single peaks. Therefore, when relating wavelet power spectrum topographies to their neuronal generators, MN should be preferred.

Early seizure detection

For patients with medically intractable epilepsy, there have been few effective alternatives to resective surgery, a destructive, irreversible treatment. A strategy receiving increased attention is using interictal spike patterns and continuous EEG measurements from epileptic patients to predict and ultimately control seizure activity via chemical or electrical control systems. This work compares results of seven linear and nonlinear methods (analysis of power spectra, cross-correlation, principal components, phase, wavelets, correlation integral, and mutual prediction) in detecting the earliest dynamical changes preceding 12 intracranially-recorded seizures from 4 patients. A method of counting standard deviations was used to compare across methods, and the earliest departures from thresholds determined from non-seizure EEG were compared to a neurologist's judgement. For these data, the nonlinear methods offered no predictive advantage over the linear methods. All the methods described here were successful in detecting changes leading to a seizure between one and two minutes before the first changes noted by the neurologist, although analysis of phase correlation proved the most robust. The success of phase analysis may be due in part to its complete insensitivity to amplitude, which may provide a significant source of error.

Investigation of non-linear properties of multichannel EEG in the early stages of Parkinson's disease

OBJECTIVES

Modifications of brain activity in the early stages of Parkinson's disease (PD) are difficult to detect using electroencephalography (EEG) signals and are often biased by L-DOPA treatment. We compare here the performances of both linear and non-linear methods in differentiating EEG of L-DOPA naive PD patients from that of control subjects.

METHODS

Resting multichannel EEG (20 electrodes, 30 s epochs) of 9 patients with PD in Hoehn and Yahr stages 1-2 (4 women, 5 men, mean age 54.3 years, range 48-63 years) were compared with those of 9 control subjects (7 women, two men, mean age 51.3 years, range 43-61 years). The following measurements were computed: theta-, alpha- and beta-band relative powers constituted the linear indices; localized entropy, slope asymmetry and number of non-linear EEG segments constituted the non-linear indices.

RESULTS

In the case of linear quantification, only a decrease in the beta-band was observed for patients. Significant non-linear structures were observed in our EEG data. Non-linear quantifiers demonstrate an increase in entropy and in the number of non-linear EEG segments for the patients.

CONCLUSIONS

Changes in EEG dynamics observed here in L-DOPA naive PD patients may represent early signs of cortical dysfunction produced by subcortical dopamine depletion.

Linear inverse filtering improves spatial separation of nonlinear brain dynamics: a simulation study

We examined topographic variations in nonlinear measures based on scalp voltages, which were generated by two simulated current dipoles each placed in a different hemisphere of a spherical volume conductor (three-shell model). Dipole dynamics were that of a three-torus and the x-component of the Lorenz-system and scalp voltage were calculated for a configuration of 29 electrode positions. Although estimates for correlation dimension D2 and Lyapunov exponent L1 were close to the theoretical values for the original time series, the simulated scalp voltage data showed almost no topographic resolution of dipole positions. In order to enhance topographic differentiation, we constructed linear inverse filters, to focus on brain activity from a specified brain region. It turned out that the nonlinear measures for the inversely filtered time series were much closer to the expected values (with respect to the location of the dipoles used in the simulation) than when using unfiltered data. Our preliminary results indicate that inverse filtering can improve the topographic resolution of nonlinear scalp EEG estimates.

Towards a quantitative characterisation of functional states of the brain: from the non-linear methodology to the global linear description

The paper traces the development of a global approach to the electric activity of the brain, from its roots in non-linear dynamical approach to the current state of art. The rationale of a three-dimensional system of global multichannel EEG descriptors (sigma, phi and omega) is provided and results obtained by means of the global descriptors in various application areas are summarised. Finally, arguments in favour of a global, 'holistic' assessment of brain functional states are presented. Definitions and properties of the global EEG descriptors are summarised in the Appendix.

Spatiotemporal EEG analysis and distributed source estimation in presurgical epilepsy evaluation

In the attempts to localize electric sources in the brain on the basis of multichannel EEG and/or MEG measurements, distributed source estimation procedures have become of increasing interest. Several commercial software packages offer such localization programs and results using these methods are seen more and more frequently in the literature. It is crucial that the users understand the similarities and differences of these methods and that they become aware of the advantages and limitations that are inherent to each approach. This review provides this information from a theoretical as well as from a practical point of view. The theoretical part gives the algorithmic basis of the electromagnetic inverse problem and shows how the different a priori assumptions are formally integrated in these equations. The authors restrict this formalism to the linear inverse solutions i.e., those solutions in which the inversion procedure can be represented as a matrix applied to the data. It will be shown that their properties can be best characterized by their resolution kernels and that methods with optimal resolution matrices can be designed. The authors also discuss the important problem of regularization strategies that are used to minimize the influence of noise. Finally, a new kind of inverse solution, termed ELECTRA (for ELECTRical Analysis), is presented that is based on constraining the source model on the basis of the currents that can actually be measured by the scalp recorded EEG. The practical part of the review illustrates the localization procedures with different clinical data sets. Three aspects become important when working with real data: 1) Clinical data is usually far from ideal (limited number of electrodes, noise, etc.). The behavior of inverse procedures in such unfortunate situations has to be evaluated. 2) The selection of the time points or time periods of interest is crucial, especially in the analysis of spontaneous EEG. 3) Additional information coming from other modalities is usually available and can be incorporated. The authors are illustrating these important points in the case of interictal and ictal epileptiform activity. Spike averaging, frequency domain source localization, and temporal segmentation based on electric field topographies will be discussed. Finally, the technique of EEG-triggered functional magnetic resonance imaging (fMRI) will be illustrated, where EEG is recorded in the magnet and is used to synchronize fMRI acquisition with interictal events. The analysis of both functional data, i.e. the EEG in terms of three-dimensional source localization and the EEG-triggered fMRI, combines the advantages of the two techniques: the temporal resolution of the EEG and the spatial resolution of the fMRI.

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.

Epileptic seizures can be anticipated by non-linear analysis

Epileptic seizures are a principal brain dysfunction with important public health implications, as they affect 0.8% of humans. Many of these patients (20%) are resistant to treatment with drugs. The ability to anticipate the onset of seizures in such cases would permit clinical interventions. The view of chronic focal epilepsy now is that abnormally discharging neurons act as pacemakers to recruit and entrain other normal neurons by loss of inhibition and synchronization into a critical mass. Thus, preictal changes should be detectable during the stages of recruitment. Traditional signal analyses, such as the count of focal spike density, the frequency coherence or spectral analyses are not reliable predictors. Non-linear indicators may undergo consistent changes around seizure onset. Our objective was to follow the transition into seizure by reconstructing intracranial recordings in implanted patients as trajectories in a phase space and then introduce non-linear indicators to characterize them. These indicators take into account the extended spatio-temporal nature of the epileptic recruitment processes and the corresponding physiological events governed by short-term causalities in the time series. We demonstrate that in most cases (17 of 19), seizure onset could be anticipated well in advance (between 2-6 minutes beforehand), and that all subjects seemed to share a similar 'route' towards seizure.

Entropy maps characterize drug effects on brain dynamics in Alzheimer's disease

Non-linear quantifiers of brain electrical dynamics (entropy maps computed from the degradation of temporal forecasting of EEG signals) were studied in relation to drug treatment of Alzheimer's disease. A placebo condition was compared to three drug doses (50, 100 and 200 mg). A significant general effect of the drug was found when compared to placebo and specific contrasts between placebo and each of the three drug doses only reveal a significant entropy increase for the highest dose. These effects were localized bilaterally in fronto-temporal areas and support changes in the dynamics of the cerebral structures involved in memory processes.