Automatic Detection of the EEG Spike-Wave Patterns in Epilepsy: Evaluation of the Effects of Transcranial Current Stimulation Therapy.

This study aims to develop a detection method based on morphological features of spike-wave (SW) patterns in the EEG of epilepsy patients and evaluate the effect of cathodal transcranial direct current stimulation (ctDCS) treatment. The proposed method is based on several simple features describing the shape of SW patterns and their synchronous occurrence on at least two EEG channels. High sensitivity, specificity and selectivity values were achieved for each patient and condition. ctDCS resulted in a significant reduction in the number of detected patterns, a decrease in spike duration and amplitude, and an increased spike mobility. The proposed method allows efficient identification of SW patterns regardless of brain condition, although the recruitment of patterns may be modified by ctDCS. This method can be useful in the clinical evaluation of ctDCS effects.

Clinical Sensitivity of Fractal Neurodynamics.

Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.

Functional Source Separation-Identified Epileptic Network: Analysis Pipeline.

This proof-of-concept (PoC) study presents a pipeline made by two blocks: 1. the identification of the network that generates interictal epileptic activity; and 2. the study of the time course of the electrical activity that it generates, called neurodynamics, and the study of its functional connectivity to the other parts of the brain. Network identification is achieved with the Functional Source Separation (FSS) algorithm applied to electroencephalographic (EEG) recordings, the neurodynamics quantified through signal complexity with the Higuchi Fractal Dimension (HFD), and functional connectivity with the Directed Transfer Function (DTF). This PoC is enhanced by the data collected before and after neuromodulation via transcranial Direct Current Stimulation (tDCS, both Real and Sham) in a single drug-resistant epileptic person. We observed that the signal complexity of the epileptogenic network, reduced in the pre-Real, pre-Sham, and post-Sham, reached the level of the rest of the brain post-Real tDCS. DTF changes post-Real tDCS were maintained after one month. The proposed approach can represent a valuable tool to enhance understanding of the relationship between brain neurodynamics characteristics, the effects of non-invasive brain stimulation, and epileptic symptoms.

Region-specific complexity of the intracranial EEG in the sleeping human brain.

As the brain is a complex system with occurrence of self-similarity at different levels, a dedicated analysis of the complexity of brain signals is of interest to elucidate the functional role of various brain regions across the various stages of vigilance. We exploited intracranial electroencephalogram data from 38 cortical regions using the Higuchi fractal dimension (HFD) as measure to assess brain complexity, on a dataset of 1772 electrode locations. HFD values depended on sleep stage and topography. HFD increased with higher levels of vigilance, being highest during wakefulness in the frontal lobe. HFD did not change from wake to stage N2 in temporo-occipital regions. The transverse temporal gyrus was the only area in which the HFD did not differ between any two vigilance stages. Interestingly, HFD of wakefulness and stage R were different mainly in the precentral gyrus, possibly reflecting motor inhibition in stage R. The fusiform and parahippocampal gyri were the only areas showing no difference between wakefulness and N2. Stages R and N2 were similar only for the postcentral gyrus. Topographical analysis of brain complexity revealed that sleep stages are clearly differentiated in fronto-central brain regions, but that temporo-occipital regions sleep differently.

Statistical Analysis of Graph-Theoretic Indices to Study EEG-TMS Connectivity in Patients With Depression.

AIM: The objective of this work was to demonstrate the usefulness of a novel statistical method to study the impact of transcranial magnetic stimulation (TMS) on brain connectivity in patients with depression using different stimulation protocols, i.e., 1 Hz repetitive TMS over the right dorsolateral prefrontal cortex (DLPFC) (protocol G1), 10 Hz repetitive TMS over the left DLPFC (G2), and intermittent theta burst stimulation (iTBS) consisting of three 50 Hz burst bundle repeated at 5 Hz frequency (G3). METHODS: Electroencephalography (EEG) connectivity analysis was performed using Directed Transfer Function (DTF) and a set of 21 indices based on graph theory. The statistical analysis of graph-theoretic indices consisted of a combination of the k-NN rule, the leave-one-out method, and a statistical test using a 2 × 2 contingency table. RESULTS: Our new statistical approach allowed for selection of the best set of graph-based indices derived from DTF, and for differentiation between conditions (i.e., before and after TMS) and between TMS protocols. The effects of TMS was found to differ based on frequency band. CONCLUSION: A set of four brain asymmetry measures were particularly useful to study protocol- and frequency-dependent effects of TMS on brain connectivity. SIGNIFICANCE: The new approach would allow for better evaluation of the therapeutic effects of TMS and choice of the most appropriate stimulation protocol.

The Impact of Repetitive Transcranial Magnetic Stimulation on Functional Connectivity in Major Depressive Disorder and Bipolar Disorder Evaluated by Directed Transfer Function and Indices Based on Graph Theory.

The objective of this work was to study the impact of repetitive Transcranial Magnetic Stimulation (rTMS) on the EEG connectivity evaluated by indices based on graph theory, derived from Directed Transfer Function (DTF), in patients with major depressive disorder (MDD) or with bipolar disorder (BD). The results showed the importance of beta and gamma rhythms. The indices density, degree and clustering coefficient increased in MDD responders in beta and gamma bands after rTMS. Interestingly, the density and the degree changed in theta band in both groups of nonresponders (decreased in MDD nonresponders but increased in BD nonresponders). Moreover, both indices of integration (the characteristic path length and the global efficiency) as well as the clustering coefficient increased in BD nonresponders for gamma band. In BD responders, the activity increased in the frontal lobe, mainly in the left hemisphere, while in MDD responders in the central posterior part of brain. The fronto-posterior asymmetry decreased in both groups of responders in delta and beta bands. Changes in inter-hemispheric asymmetry were found only in BD nonresponders in all bands, except gamma band. Comparison between groups showed that the degree increased in delta band independently on disease (BD, MDD). These preliminary results showed that the DTF may be a useful marker allowing for evaluation of effectiveness of the rTMS therapy as well for group differentiation between MDD and BD considering separately groups of responders and nonresponders. However, further investigation should be performed over larger groups of patients to confirmed our findings.

Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation.

Aim: The aim of this work was to study the neurophysiological effect of repetitive transcranial magnetic stimulation (rTMS) applied to the left dorsolateral prefrontal cortex (DLPFC) in 8 patients with major depression disorder (MDD) and 10 patients with bipolar disorder (BP), considering separately responders and non-responders to rTMS therapy in each of both groups. Methods: The Higuchi's Fractal Dimension (FD) was analyzed from 64-channels EEG signals in five physiological frequency bands and every channel separately. Changes of FD were analyzed before and after 1st, 10th, and 20th session of rTMS. Results: Some differences in response to the rTMS therapy was found across individual groups. In MDD responders, FD decreased in all bands after longer stimulation (20th session). Whereas, in BP non-responders, FD decreased after 1st session in all bands as well as after 10th session in lower frequencies (delta and theta). In MDD non-responders and BP responders FD increased at the beginning of the therapy (1st and 10th session, respectively), but the final FD value did not changed in comparison to the initial FD value, except the FD decrease for theta band in BP responders. Comparison between groups showed a higher FD in MDD responders than in MDD non-responders in every band before as well as after stimulation. In contrast to MDD patients, FD was lower in BP responders than in BP non-responders in higher frequency bands (alpha, beta, and gamma) in both conditions as well as in lower frequency bands (delta and theta) after stimulation. Comparing both groups of responders, FD was lower in MDD than in BP in every band, except alpha. In case of non-responders, FD was higher in BP than in MDD in all bands in both conditions. Conclusion: The results showed that FD may be useful marker for evaluation of the rTMS effectiveness and the therapy progress as well as for group differentiation between MDD and BP or between responders and non-responders. The changes of FD under the influence of rTMS allow to unambiguously conclude whether the effect of stimulation is positive or negative as well as allow to evaluate an optimal time of rTMS.

EEG Phase Synchronization in Persons With Depression Subjected to Transcranial Magnetic Stimulation.

Aim: The main objective of this work was to study the impact of repetitive Transcranial Magnetic Stimulation (rTMS) treatment on brain activity in 8 patients with major depressive disorder (MDD) and 10 patients with bipolar disorder (BP). Changes due to rTMS stimulation of the left dorsolateral prefrontal cortex (DLPFC) were investigated considering separately responders and non-responders to therapy in each of both groups. The aim of the research is to determine whether non-responders differ from responders suffered from both diseases, as well as if any change occurred due to rTMS across consecutive rTMS sessions. Methods: The graph-theory-based connectivity analysis of non-linearity measure of phase interdependencies-Phase Locking Value (PLV)-was examined from EEG data. The approximately 15-min EEG recordings from each of participants were recorded before and after 1st, 10th, and 20th session, respectively. PLV calculated from data was analyzed using principal graph theory indices (strength and degree) within five physiological frequency bands and in individual channels separately. The impact of rTMS on the EEG connectivity in every group of patients evaluated by PLV was assessed. Results: Each of four groups reacted differently to rTMS treatment. The strength and degree of PLV increased in gamma band in both groups of responders. Moreover, an increase of indices in beta band for BP-responders was observed. While, in MDD-non-responders the indices decreased in gamma band and increased in beta band. Moreover, the index strength was lower in alpha band for BP- non-responders. The rTMS stimulation caused topographically specific changes, i.e., the increase of the activity in the left DLPFC as well as in other brain regions such as right parieto-occipital areas. Conclusions: The analysis of PLV allowed for evaluation of the rTMS impact on the EEG activity in each group of patients. The changes of PLV under stimulation might be a good indicator of response to depression treatment permitting to improve the effectiveness of therapy.

Graph-based analysis of brain connectivity in schizophrenia.

The present study evaluated brain connectivity using electroencephalography (EEG) data from 14 patients with schizophrenia and 14 healthy controls. Phase-Locking Value (PLV), Phase-Lag Index (PLI) and Directed Transfer Function (DTF) were calculated for the original EEG data and following current source density (CSD) transformation, re-referencing using the average reference electrode (AVERAGE) and reference electrode standardization techniques (REST). The statistical analysis of adjacency matrices was carried out using indices based on graph theory. Both CSD and REST reduced the influence of volume conducted currents. The largest group differences in connectivity were observed for the alpha band. Schizophrenic patients showed reduced connectivity strength, as well as a lower clustering coefficient and shorter characteristic path length for both measures of phase synchronization following CSD transformation or REST re-referencing. Reduced synchronization was accompanied by increased directional flow from the occipital region for the alpha band. Following the REST re-referencing, the sources of alpha activity were located at parietal rather than occipital derivations. The results of PLV and DTF demonstrated group differences in fronto-posterior asymmetry following CSD transformation, while for PLI the differences were significant only using REST. The only analysis that identified group differences in inter-hemispheric asymmetry was DTF calculated for REST. Our results suggest that a comparison of different connectivity measures using graph-based indices for each frequency band, separately, may be a useful tool in the study of disconnectivity disorders such as schizophrenia.

The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation.

Electroencephalographic (EEG) patterns were analyzed in a group of ambulatory patients who ranged in age and sex using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity. We tested the impact of window size and choice of reference electrode on the identification of two or more peaks with close frequencies in the spectral power distribution, so called "split alpha." Together with the connectivity analysis, examination of spatiotemporal maps showing the distribution of amplitudes of EEG patterns allowed for better explanation of the mechanisms underlying the generation of split alpha peaks. It was demonstrated that the split alpha spectrum can be generated by two or more independent and interconnected alpha wave generators located in different regions of the cerebral cortex, but not necessarily in the occipital cortex. We also demonstrated the importance of appropriate reference electrode choice during signal recording. In addition, results obtained using the original data were compared with results obtained using re-referenced data, using average reference electrode and reference electrode standardization techniques.

Comparison of connectivity analyses for resting state EEG data.

OBJECTIVE: In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. APPROACH: The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. MAIN RESULTS: The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. SIGNIFICANCE: Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Neuronal electrical ongoing activity as a signature of cortical areas.

Brodmann's pioneering work resulted in the classification of cortical areas based on their cytoarchitecture and topology. Here, we aim at documenting that diverse cortical areas also display different neuronal electric activities. We investigated this notion in the hand-controlling sections of the primary somatosensory (S1) and motor (M1) areas, in both hemispheres. We identified S1 and M1 in 20 healthy volunteers by applying functional source separation (FSS) to their recorded electroencephalograms (EEG). Our results show that S1 and M1 can be clearly differentiated by their neuroelectric activities in both hemispheres and independently of the subject's state (i.e., at rest or performing movements or receiving external stimulations). In particular, S1 displayed higher relative power than M1 in the alpha and low beta frequency ranges (8-25 Hz, p < .003), whereas the opposite occurred in the high gamma band (52-90 Hz, p = .006). In addition, S1's activity had a smaller Higuchi's fractal dimensions (HFD) than M1's (p < .00001) in all subjects, permitting a reliable classification of the two areas. Moreover, HFD of M1's activity resulted correlated with the hand's fine motor control, as expressed by the 9-hole peg test scores. The present work is a first step toward the identification and classification of brain cortical areas based on neuronal dynamics rather than on cytoarchitectural features. We deem this step to be an improvement of our knowledge of the brain's structural-functional unity.

Synchronization of brain activity during induction of propofol anesthesia: Comparison of methods.

Synchrony patterns between the EEG measured from the subthalamic nucleus and scalp are analyzed during induction of propofol anesthesia. Various methods such as Coherence, Phase Locking Value, Partial Directed Coherence, Mutual Information, and Transfer Entropy are applied. The results indicate that, in general, the synchrony patterns seen in the depth-scalp electrode pair are similar to those seen between two scalp electrodes of opposite hemispheres. A clear change of state is revealed at about 10 minutes before burst suppression; this event is revealed by linear (Coherence) as well as nonlinear (Phase Locking Value) and information theoretic (Mutual Information and Transfer Entropy) measures. The results suggest that relatively large depth electrodes such as those used in the treatment of the Parkinson's disease are predominantly sensitive to cortical activity.

Age-Related Changes in Electroencephalographic Signal Complexity.

The study of active and healthy aging is a primary focus for social and neuroscientific communities. Here, we move a step forward in assessing electrophysiological neuronal activity changes in the brain with healthy aging. To this end, electroencephalographic (EEG) resting state activity was acquired in 40 healthy subjects (age 16-85). We evaluated Fractal Dimension (FD) according to the Higuchi algorithm, a measure which quantifies the presence of statistical similarity at different scales in temporal fluctuations of EEG signals. Our results showed that FD increases from age twenty to age fifty and then decreases. The curve that best fits the changes in FD values across age over the whole sample is a parabola, with the vertex located around age fifty. Moreover, FD changes are site specific, with interhemispheric FD asymmetry being pronounced in elderly individuals in the frontal and central regions. The present results indicate that fractal dimension well describes the modulations of brain activity with age. Since fractal dimension has been proposed to be related to the complexity of the signal dynamics, our data demonstrate that the complexity of neuronal electric activity changes across the life span of an individual, with a steady increase during young adulthood and a decrease in the elderly population.

Neuroelectrical correlates of trustworthiness and dominance judgments related to the observation of political candidates.

The present research investigates the neurophysiological activity elicited by fast observations of faces of real candidates during simulated political elections. We used simultaneous recording of electroencephalographic (EEG) signals as well as galvanic skin response (GSR) and heart rate (HR) as measurements of central and autonomic nervous systems. Twenty healthy subjects were asked to give judgments on dominance, trustworthiness, and a preference of vote related to the politicians' faces. We used high-resolution EEG techniques to map statistical differences of power spectral density (PSD) cortical activity onto a realistic head model as well as partial directed coherence (PDC) and graph theory metrics to estimate the functional connectivity networks and investigate the role of cortical regions of interest (ROIs). Behavioral results revealed that judgment of dominance trait is the most predictive of the outcome of the simulated elections. Statistical comparisons related to PSD and PDC values highlighted an asymmetry in the activation of frontal cortical areas associated with the valence of the judged trait as well as to the probability to cast the vote. Overall, our results highlight the existence of cortical EEG features which are correlated with the prediction of vote and with the judgment of trustworthy and dominant faces.

Fractal dimension of EEG activity senses neuronal impairment in acute stroke.

The brain is a self-organizing system which displays self-similarities at different spatial and temporal scales. Thus, the complexity of its dynamics, associated to efficient processing and functional advantages, is expected to be captured by a measure of its scale-free (fractal) properties. Under the hypothesis that the fractal dimension (FD) of the electroencephalographic signal (EEG) is optimally sensitive to the neuronal dysfunction secondary to a brain lesion, we tested the FD's ability in assessing two key processes in acute stroke: the clinical impairment and the recovery prognosis. Resting EEG was collected in 36 patients 4-10 days after a unilateral ischemic stroke in the middle cerebral artery territory and 19 healthy controls. National Health Institute Stroke Scale (NIHss) was collected at T0 and 6 months later. Highuchi FD, its inter-hemispheric asymmetry (FDasy) and spectral band powers were calculated for EEG signals. FD was smaller in patients than in controls (1.447±0.092 vs 1.525±0.105) and its reduction was paired to a worse acute clinical status. FD decrease was associated to alpha increase and beta decrease of oscillatory activity power. Larger FDasy in acute phase was paired to a worse clinical recovery at six months. FD in our patients captured the loss of complexity reflecting the global system dysfunction resulting from the structural damage. This decrease seems to reveal the intimate nature of structure-function unity, where the regional neural multi-scale self-similar activity is impaired by the anatomical lesion. This picture is coherent with neuronal activity complexity decrease paired to a reduced repertoire of functional abilities. FDasy result highlights the functional relevance of the balance between homologous brain structures' activities in stroke recovery.

Classification of EEG bursts in deep sevoflurane, desflurane and isoflurane anesthesia using AR-modeling and entropy measures.

A study relating signal patterns of burst onsets in burst suppression EEG to the anesthetic agent or anesthesia induction protocol is presented. A dataset of 82 recordings of sevoflurane, isoflurane and desflurane anesthesia underlies the study. 3 second segments from the onset of altogether 3214 bursts are described using AR model parameters, spectral entropy and sample entropy as features. The features are clustered using the K-means algorithm. The results indicate that no clear cut distinction can be made between the burst patterns induced by the mentioned anesthetics although bursts of certain properties are more common in certain patient groups. Several directions for further investigations are proposed based on visual inspection of the recordings.

Automatic detection and analysis of the EEG sharp wave-slow wave patterns evoked by fluorinated inhalation anesthetics.

OBJECTIVE: The aim of this study was to develop a method for the automatic detection of sharp wave-slow wave (SWSW) patterns evoked in EEG by volatile anesthetics and to identify the patterns' characteristics. METHODS: The proposed method consisted in the k-NN classification with a reference set obtained using expert knowledge, the morphology of the EEG patterns and the condition for their synchronization. The decision rules were constructed and evaluated using 24h EEG records in ten patients. RESULTS: The sensitivity, specificity and selectivity of the method were 0.88 ± 0.10, 0.81 ± 0.13 and 0.42 ± 0.16, respectively. SWSW patterns' recruitment was strictly dependent on anesthetic concentration. SWSW patterns evoked by different types of anesthetics expressed different characteristics. CONCLUSIONS: Synchronization criterion and adequately selected morphological features of "slow wave" were sufficient to achieve the high sensitivity and specificity of the method. SIGNIFICANCE: The monitoring of SWSW patterns is important in view of possible side effects of volatile anesthetics. The analysis of SWSW patterns' recruitment and morphology could be helpful in the diagnosis of the anesthesia effects on the CNS.

[Fractal dimension--a new EEG-based method of assessing the depth of anaesthesia]. / Próba wykoraystania analizy sygnalów EEG metoda wymiaru fraktalnego do okreslania stopnia zniesienia swiadomosci podczas indukcji znieczulenia ogólnego.

BACKGROUND: It has been suggested that analysis of the EEG signal using the fractal dimension method may be useful for assessment of depth of anaesthesia. METHODS: Thirty ASA I and II patients, scheduled for elective surgery under general anaesthesia were induced with midazolam, fentanyl and propofol and paralyzed with rocuronium or cis-atracurium. Clinical signs of the depth of anaesthesia were classified to one of five OAA/S levels. Standard vital parameters were observed and brain electrical activity was measured using the bispectral index (BIS) and burst suppression ratio (BSR). The EEG signal was recorded and processed postoperatively to calculate Higuchi's fractal dimension (FD). The latter was presented as a derivative: (D(F)-1) x 100. RESULTS: Mean correlation coefficients between OAA/S scale levels, and BIS and (D(F)-1) x 100 values, were respectively: 0.749+/-0.172 and 0.753+/-0.220. In 28 (93.3%) patients, BIS correlated well with FD (r=0.63+/-0.33). In twenty cases, burst suppression occurred and the correlation coefficient between BIS and DF was much lower (r=0.5860+/-3650), when compared to the group of 10 patients in which no burst suppression was detected (r=0.711+/-0.251). Appropriate correction was made using the following formula: D(FK)=D(F)-(D(F) x BSR). The mean correlation coefficient between BIS values and D(FK) in the BS group was r=0.629+/-0.331. In all cases, the mean correlation coefficient between (D(F)-1) x 100 and BIS was r=0.661+/-0.307 (p<0.001). CONCLUSIONS: The fractal dimension method can be regarded as equal to BIS for assessment of depth of anaesthesia.