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

Altered Microstate Dynamics and Spatial Complexity in Late-Life Schizophrenia

BACKGROUND

Resting-state EEG microstate and omega complexity analyses have been widely used to explore deviant brain function in various neuropsychiatric disorders. This study aimed to investigate the features of microstate dynamics and spatial complexity in patients with late-life schizophrenia (LLS).

METHOD

Microstate and omega complexity analyses were performed on resting-state EEG data from 39 in patients with LLS and compared with 40 elderly normal controls (NCs).

RESULT

The duration of microstate classes A and D were significantly higher in patients with LLS compared with NCs. The occurrence of microstate classes A, B, and C was significantly lower in patients with LLS compared with NCs. LLS patients have a lower time coverage of microstate class A and a higher time coverage of class D than NCs. Transition probabilities from microstate class A to B and from class A to C were significantly lower in patients with LLS compared with NCs. Transition probabilities between microstate class B and D were significantly higher in patients with LLS compared with NCs. Global omega complexity and anterior omega complexity were significantly higher in patients with LLS compared with NCs.

CONCLUSION

This study revealed an altered pattern of microstate dynamics and omega complexity in patients with LLS. This may reflect the disturbed neural basis underlying LLS and enhance the understanding of the pathophysiology of LLS.

Fundamentally altered global- and microstate EEG characteristics in Huntington's disease

OBJECTIVE

Huntington's disease (HD) is characterized by psychiatric, cognitive, and motor disturbances. The study aimed to determine electroencephalography (EEG) global state and microstate changes in HD and their relationship with cognitive and behavioral impairments.

METHODS

EEGs from 20 unmedicated HD patients and 20 controls were compared using global state properties (connectivity and dimensionality) and microstate properties (EEG microstate analysis). For four microstate classes (A, B, C, D), three parameters were computed: duration, occurrence, coverage. Global- and microstate properties were compared between groups and correlated with cognitive test scores for patients.

RESULTS

Global state analysis showed reduced connectivity in HD and an increasing dimensionality with increasing HD severity. Microstate analysis revealed parameter increases for classes A and B (coverage), decreases for C (occurrence) and D (coverage and occurrence). Disease severity and poorer test performances correlated with parameter increases for class A (coverage and occurrence), decreases for C (coverage and duration) and a dimensionality increase.

CONCLUSIONS

Global state changes may reflect higher functional dissociation between brain areas and the complex microstate changes possibly the widespread neuronal death and corresponding functional deficits in brain regions associated with HD symptomatology.

SIGNIFICANCE

Combining global- and microstate analyses can be useful for a better understanding of progressive brain deterioration in HD.

Intrinsic Synchronization Analysis of Brain Activity in Obsessive-compulsive Disorders

Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.

Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

Correlation of EEG spectra, connectivity, and information theoretical biomarkers with psychological states in the epilepsy monitoring unit - A pilot study

At the level of individual experience, the relation between electroencephalographic (EEG) phenomena and subjective ratings of psychological states is poorly examined. This study investigated the correlation of quantitative EEG markers with systematic high-frequency monitoring of psychological states in patients admitted to the epilepsy monitoring unit (EMU). We used a digital questionnaire, including eight standardized items about stress, energy level, mood, ward atmosphere, seizure likelihood, hopefulness/frustration, boredom, and self-efficacy. Self-assessments were collected four times per day, in total 15 times during the stay in the EMU. We extracted brainrate, Hjorth parameters, Hurst exponent, Wackermann parameters, and power spectral density from the EEG. We performed correlation between these quantitative EEG measures and responses to the 8 items and evaluated their significance on single subject and on group level. Twenty-one consecutive patients (12 women/9 men, median age: 29 years, range: 18-74 years) were recruited. On group level, no significant correlations were found whereas on single-subject level, we found significant correlations for 6 out of 21 patients. Most significant correlations were found between Hjorth parameters and items that reflect changes in mood or stress. This study supports the feasibility of correlating quantitative EEG measures with psychological states in routine EMU settings and emphasizes the need for single-subject statistics when assessing aspects with high interindividual variance. Future studies should select samples with high within-subject variability of psychological states and examine a subsample with patients encountering a critical number of seizures needed in order to relate the psychological states to the ultimate question: Are psychological states potential indicators for seizure likelihood?

Decreased global field synchronization of multichannel frontal EEG measurements in obsessive-compulsive disorders

Global field synchronization (GFS) quantifies the synchronization level of brain oscillations. The GFS method has been introduced to measure functional synchronization of EEG data in the frequency domain. GFS also detects phase interactions between EEG signals acquired from all of the electrodes. If a considerable amount of local brain neurons has the same phase, these neurons appear to interact with each other. EEG data were received from 17 obsessive-compulsive disorder (OCD) patients and 17 healthy controls (HC). OCD effects on local and large-scale brain circuits were studied. Analysis of the GFS results showed significantly decreased values in the delta and full frequency bands. This research suggests that OCD causes synchronization disconnection in both the frontal and large-scale regions. This may be related to motivational, emotional and cognitive dysfunctions.

Atypical Bilateral Brain Synchronization in the Early Stage of Human Voice Auditory Processing in Young Children with Autism

Autism spectrum disorder (ASD) has been postulated to involve impaired neuronal cooperation in large-scale neural networks, including cortico-cortical interhemispheric circuitry. In the context of ASD, alterations in both peripheral and central auditory processes have also attracted a great deal of interest because these changes appear to represent pathophysiological processes; therefore, many prior studies have focused on atypical auditory responses in ASD. The auditory evoked field (AEF), recorded by magnetoencephalography, and the synchronization of these processes between right and left hemispheres was recently suggested to reflect various cognitive abilities in children. However, to date, no previous study has focused on AEF synchronization in ASD subjects. To assess global coordination across spatially distributed brain regions, the analysis of Omega complexity from multichannel neurophysiological data was proposed. Using Omega complexity analysis, we investigated the global coordination of AEFs in 3–8-year-old typically developing (TD) children (n = 50) and children with ASD (n = 50) in 50-ms time-windows. Children with ASD displayed significantly higher Omega complexities compared with TD children in the time-window of 0–50 ms, suggesting lower whole brain synchronization in the early stage of the P1m component. When we analyzed the left and right hemispheres separately, no significant differences in any time-windows were observed. These results suggest lower right-left hemispheric synchronization in children with ASD compared with TD children. Our study provides new evidence of aberrant neural synchronization in young children with ASD by investigating auditory evoked neural responses to the human voice.

Modalities of Thinking: State and Trait Effects on Cross-Frequency Functional Independent Brain Networks

Functional states of the brain are constituted by the temporally attuned activity of spatially distributed neural networks. Such networks can be identified by independent component analysis (ICA) applied to frequency-dependent source-localized EEG data. This methodology allows the identification of networks at high temporal resolution in frequency bands of established location-specific physiological functions. EEG measurements are sensitive to neural activity changes in cortical areas of modality-specific processing. We tested effects of modality-specific processing on functional brain networks. Phasic modality-specific processing was induced via tasks (state effects) and tonic processing was assessed via modality-specific person parameters (trait effects). Modality-specific person parameters and 64-channel EEG were obtained from 70 male, right-handed students. Person parameters were obtained using cognitive style questionnaires, cognitive tests, and thinking modality self-reports. EEG was recorded during four conditions: spatial visualization, object visualization, verbalization, and resting. Twelve cross-frequency networks were extracted from source-localized EEG across six frequency bands using ICA. RMANOVAs, Pearson correlations, and path modelling examined effects of tasks and person parameters on networks. Results identified distinct state- and trait-dependent functional networks. State-dependent networks were characterized by decreased, trait-dependent networks by increased alpha activity in sub-regions of modality-specific pathways. Pathways of competing modalities showed opposing alpha changes. State- and trait-dependent alpha were associated with inhibitory and automated processing, respectively. Antagonistic alpha modulations in areas of competing modalities likely prevent intruding effects of modality-irrelevant processing. Considerable research suggested alpha modulations related to modality-specific states and traits. This study identified the distinct electrophysiological cortical frequency-dependent networks within which they operate.

Similar Spectral Power Densities Within the Schumann Resonance and a Large Population of Quantitative Electroencephalographic Profiles: Supportive Evidence for Koenig and Pobachenko

In 1954 and 1960 Koenig and his colleagues described the remarkable similarities of spectral power density profiles and patterns between the earth-ionosphere resonance and human brain activity which also share magnitudes for both electric field (mV/m) and magnetic field (pT) components. In 2006 Pobachenko and colleagues reported real time coherence between variations in the Schumann and brain activity spectra within the 6-16 Hz band for a small sample. We examined the ratios of the average potential differences (~3 μV) obtained by whole brain quantitative electroencephalography (QEEG) between rostral-caudal and left-right (hemispheric) comparisons of 238 measurements from 184 individuals over a 3.5 year period. Spectral densities for the rostral-caudal axis revealed a powerful peak at 10.25 Hz while the left-right peak was 1.95 Hz with beat-differences of ~7.5 to 8 Hz. When global cerebral measures were employed, the first (7-8 Hz), second (13-14 Hz) and third (19-20 Hz) harmonics of the Schumann resonances were discernable in averaged QEEG profiles in some but not all participants. The intensity of the endogenous Schumann resonance was related to the 'best-of-fitness' of the traditional 4-class microstate model. Additional measurements demonstrated real-time coherence for durations approximating microstates in spectral power density variations between Schumann frequencies measured in Sudbury, Canada and Cumiana, Italy with the QEEGs of local subjects. Our results confirm the measurements reported by earlier researchers that demonstrated unexpected similarities in the spectral patterns and strengths of electromagnetic fields generated by the human brain and the earth-ionospheric cavity.

A study on validity of cortical alpha connectivity for schizophrenia

Abnormalities in schizophrenia are thought to be associated with functional disconnections between different brain regions. Most previous studies on schizophrenia have considered high-band connectivity in preference to the Alpha band, as there has been some uncertainty correlating the latter to the condition. In this paper we attempt to clarify this correlation using an Electroencephalogram (EEG) analysis of the Alpha band from schizophrenic patients. Global, regional Omega and dimensional complexity and local Omega complexity differentials (LCD) of single channel are calculated using 16 channels of resting EEG data from 31 adult patients with schizophrenia and 31 age/sex matched control subjects. It was found that, compared to the controls, anterior alpha Omega and dimensional complexity are higher in schizophrenia patients (p<0.05) with the single channel LCD also increasing at FP1, FP2, F7 and F8 electrodes. Furthermore, higher left hemisphere dimensional complexity and LCD at T3 point was also found. The results suggest there is lower connectivity in the pre-frontal and left temporal regions with respect to the alpha band in schizophrenia patients.

Contextual emergence of mental states

The concept of contextual emergence has been proposed as a non-reductive, yet well-defined relation between different levels of description of physical and other systems. It yields a formally sound and empirically applicable procedure to translate between descriptive levels in an overall consistent fashion. This will be discussed for the contextual emergence of mental states from a neural level of description.

The consciousness state space (CSS)-a unifying model for consciousness and self

Every experience, those we are aware of and those we are not, is embedded in a subjective timeline, is tinged with emotion, and inevitably evokes a certain sense of self. Here, we present a phenomenological model for consciousness and selfhood which relates time, awareness, and emotion within one framework. The consciousness state space (CSS) model is a theoretical one. It relies on a broad range of literature, hence has high explanatory and integrative strength, and helps in visualizing the relationship between different aspects of experience. Briefly, it is suggested that all phenomenological states fall into two categories of consciousness, core and extended (CC and EC, respectively). CC supports minimal selfhood that is short of temporal extension, its scope being the here and now. EC supports narrative selfhood, which involves personal identity and continuity across time, as well as memory, imagination and conceptual thought. The CSS is a phenomenological space, created by three dimensions: time, awareness and emotion. Each of the three dimensions is shown to have a dual phenomenological composition, falling within CC and EC. The neural spaces supporting each of these dimensions, as well as CC and EC, are laid out based on the neuroscientific literature. The CSS dynamics include two simultaneous trajectories, one in CC and one in EC, typically antagonistic in normal experiences. However, this characteristic behavior is altered in states in which a person experiences an altered sense of self. Two examples are laid out, flow and meditation. The CSS model creates a broad theoretical framework with explanatory and unificatory power. It constructs a detailed map of the consciousness and selfhood phenomenology, which offers constraints for the science of consciousness. We conclude by outlining several testable predictions raised by the CSS model.

Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness

Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.

Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13–30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.

Potential production of Hughlings Jackson's "parasitic consciousness" by physiologically-patterned weak transcerebral magnetic fields: QEEG and source localization

Exotic experiences such as the sensing of another consciousness or the detachment of consciousness from the body are occasionally reported by individuals with partial seizures from a temporal lobe focus. The experiences display the characteristics of Hughlings Jackson's "parasitic consciousness". We have hypothesized that these experiences are encouraged by slight discrepancies in hemispheric activity that can be simulated by application of weak, physiologically-patterned magnetic fields across the cerebral hemispheres. Electroencephalographic and Low Resolution Electromagnetic Tomography (sLORETA) data revealed altered activity bands within specific regions within the cerebral cortices during these experiences. The clear changes in power of brain activity were discerned after consistent durations of exposure to specifically patterned weak magnetic fields. Millisecond range point durations were required. The technology may be useful to explore the subjective components associated with complex partial seizures.

Multichannel linear descriptors analysis for sustained attention-related electroencephalography

This study investigated the differences in brain functional state between sustained attention and ignoring task conditions using the electroencephalography in association with sustained attention to response task (SART) performance. Multichannel electroencephalography data were obtained from 10 male healthy volunteers while performing the SART. Three multichannel linear descriptors, that is spatial complexity (Ω), field strength (Σ), and frequency of field changes (Φ), were applied to analyze three frequency bands (θ, α, and β) for sustained attention and ignoring task conditions. The experimental results showed that participants had a significantly lower Ω value in the θ and α band in the SART state. The Σ value was significantly higher in each frequency band of interest in almost all region of interest areas during SART performance. In addition, the Φ value was significantly lower in the θ band and significantly higher in the β band during the sustained attention condition. The results indicated that multichannel linear descriptors could show the differences in brain functions between sustained attention and ignoring task conditions, and might be used to evaluate disorders with an attentional dysfunction.

Content-specific coordination of listeners' to speakers' EEG during communication

Cognitive neuroscience has recently begun to extend its focus from the isolated individual mind to two or more individuals coordinating with each other. In this study we uncover a coordination of neural activity between the ongoing electroencephalogram (EEG) of two people—a person speaking and a person listening. The EEG of one set of twelve participants (“speakers”) was recorded while they were narrating short stories. The EEG of another set of twelve participants (“listeners”) was recorded while watching audiovisual recordings of these stories. Specifically, listeners watched the superimposed videos of two speakers simultaneously and were instructed to attend either to one or the other speaker. This allowed us to isolate neural coordination due to processing the communicated content from the effects of sensory input. We find several neural signatures of communication: First, the EEG is more similar among listeners attending to the same speaker than among listeners attending to different speakers, indicating that listeners' EEG reflects content-specific information. Secondly, listeners' EEG activity correlates with the attended speakers' EEG, peaking at a time delay of about 12.5 s. This correlation takes place not only between homologous, but also between non-homologous brain areas in speakers and listeners. A semantic analysis of the stories suggests that listeners coordinate with speakers at the level of complex semantic representations, so-called “situation models”. With this study we link a coordination of neural activity between individuals directly to verbally communicated information.

Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography

Brain functional states are established by functional connectivities between brain regions. In experienced meditators (13 Tibetan Buddhists, 15 QiGong, 14 Sahaja Yoga, 14 Ananda Marga Yoga, 15 Zen), 19-channel EEG was recorded before, during and after that meditation exercise which their respective tradition regards as route to the most desirable meditative state. The head surface EEG data were recomputed (sLORETA) into 19 cortical regional source model time series. All 171 functional connectivities between regions were computed as 'lagged coherence' for the eight EEG frequency bands (delta through gamma). This analysis removes ambiguities of localization, volume conduction-induced inflation of coherence, and reference-dependence. All significant differences (corrected for multiple testing) between meditation compared to no-task rest before and after meditation showed lower coherence during meditation, in all five traditions and eight (inhibitory as well as excitatory) frequency bands. Conventional coherence between the original head surface EEG time series very predominantly also showed reduced coherence during meditation. The topography of the functional connectivities was examined via PCA-based computation of principal connectivities. When going into and out of meditation, significantly different connectivities revealed clearly different topographies in the delta frequency band and minor differences in the beta-2 band. The globally reduced functional interdependence between brain regions in meditation suggests that interaction between the self process functions is minimized, and that constraints on the self process by other processes are minimized, thereby leading to the subjective experience of non-involvement, detachment and letting go, as well as of all-oneness and dissolution of ego borders during meditation.

Frontal areas contribute to reduced global coordination of resting-state gamma activities in drug-naïve patients with schizophrenia

Schizophrenia has been postulated to involve impaired neuronal cooperation in large-scale neural networks, including cortico-cortical circuitry. Alterations in gamma band oscillations have attracted a great deal of interest as they appear to represent a pathophysiological process of cortical dysfunction in schizophrenia. Gamma band oscillations reflect local cortical activities, and the synchronization of these activities among spatially distributed cortical areas has been suggested to play a central role in the formation of networks. To assess global coordination across spatially distributed brain regions, Omega complexity (OC) in multichannel EEG was proposed. Using OC, we investigated global coordination of resting-state EEG activities in both gamma (30-50 Hz) and below-gamma (1.5-30 Hz) bands in drug-naïve patients with schizophrenia and investigated the effects of neuroleptic treatment. We found that gamma band OC was significantly higher in drug-naïve patients with schizophrenia compared to control subjects and that a right frontal electrode (F3) contributed significantly to the higher OC. After neuroleptic treatment, reductions in the contribution of frontal electrodes to global OC in both bands correlated with the improvement of schizophrenia symptomatology. The present study suggests that frontal brain processes in schizophrenia were less coordinated with activity in the remaining brain. In addition, beneficial effects of neuroleptic treatment were accompanied by improvement of brain coordination predominantly due to changes in frontal regions. Our study provides new evidence of improper intrinsic brain integration in schizophrenia by investigating the resting-state gamma band activity.

Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

Age-dependent features of EEG-reactivity--spectral, complexity, and network characteristics

Our goal was to measure indices characterizing EEG-reactivity in young and elderly subjects. It was hypothesized that EEG-reactivity as reflected by different measures would be lower in the elderly. In two age groups (young: N= 23, mean age = 21.5 +/- 2.2 years; old: N= 25, mean age = 66.9 +/- 3.6 years) absolute frequency spectra, Omega-complexity, synchronization likelihood and network properties (clustering coefficient and characteristic path length) of the EEG were analyzed in the delta, theta, alpha1, alpha2, beta1 and beta2 frequency bands occurring as a result of eyes opening. Absolute spectral power was higher in the young in the delta, alpha1 and alpha2 bands in the posterior area. The alpha1 peak frequency decreased following eyes opening in the young, while no change was observed in the elderly. Omega-complexity was higher in the elderly especially in the frontal area and increased following eyes opening. Values of the clustering coefficient, path length and that of the "small-world index" decreased as a result of eyes opening, the latter in the fast frequency range. The results suggest reduced reactivity in the elderly as shown by frequency spectra and decreased level of integrative activity particularly in the frontal area probably as a result of reduced interneuronal processing capacity. Indices of network characteristics reveal a shift towards more random topology especially in the beta frequencies caused by eyes opening.

Ordinal pattern based similarity analysis for EEG recordings

OBJECTIVE

Ordinal patterns analysis such as permutation entropy of the EEG series has been found to usefully track brain dynamics and has been applied to detect changes in the dynamics of EEG data. In order to further investigate hidden nonlinear dynamical characteristics in EEG data for differentiating brain states, this paper proposes a novel dissimilarity measure based on the ordinal pattern distributions of EEG series.

METHODS

Given a segment of EEG series, we first map this series into a phase space, then calculate the ordinal sequences and the distribution of these ordinal patterns. Finally, the dissimilarity between two EEG series can be qualified via a simple distance measure. A neural mass model was proposed to simulate EEG data and test the performance of the dissimilarity measure based on the ordinal patterns distribution. Furthermore, this measure was then applied to analyze EEG data from 24 Genetic Absence Epilepsy Rats from Strasbourg (GAERS), with the aim of distinguishing between interictal, preictal and ictal states.

RESULTS

The dissimilarity measure of a pair of EEG signals within the same group and across different groups was calculated, respectively. As expected, the dissimilarity measures during different brain states were higher than internal dissimilarity measures. When applied to the preictal detection of absence seizures, the proposed dissimilarity measure successfully detected the preictal state prior to their onset in 109 out of 168 seizures (64.9%).

CONCLUSIONS

Our results showed that dissimilarity measures between EEG segments during the same brain state were significant smaller that those during different states. This suggested that the dissimilarity measure, based on the ordinal patterns in the time series, could be used to detect changes in the dynamics of EEG data. Moreover, our results suggested that ordinal patterns in the EEG might be a potential characteristic of brain dynamics.

SIGNIFICANCE

This dissimilarity measure is a promising method to reveal dynamic changes in EEG, for example as occur in the transition of epileptic seizures. This method is simple and fast, so might be applied in designing an automated closed-loop seizure prevention system for absence epilepsy.

Global synchronization index as a biological correlate of cognitive decline in Alzheimer's disease

OBJECTIVE

The recently developed global synchronization index (GSI) quantifies synchronization between neuronal signals at multiple sites. This study explored the clinical significance of the GSI in Alzheimer's disease (AD) patients.

METHODS

Electroencephalograms were recorded from 25 AD patients and 22 age-matched healthy normal controls (NC). GSI values were computed both across the entire frequency band and separately in the delta, theta, alpha, beta1, beta2, beta3, and gamma bands. The Mini-Mental Status Examination (MMSE) and Clinical Dementia Rating scale (CDR) were used to assess the symptom severity.

RESULTS

GSI values in the beta1, beta2, beta3, and gamma bands were significantly lower in AD patients than in NC. GSI values in the beta and gamma bands were positively correlated with the MMSE scores in all participants (AD and NC). In AD patients, GSI values were negatively correlated with MMSE scores in the delta bands, but positively correlated in the beta1 and gamma band. Also, GSI values were positively correlated with CDR scores in the delta bands, but negatively correlated in the gamma band.

CONCLUSIONS

GSI values of mainly high-frequency bands were significantly lower in AD patients than in NC, they were significantly correlated with scores on symptom severity scales.

SIGNIFICANCE

Our results suggest that GSI values are a useful biological correlate of cognitive decline in AD patients.

Computer based synchronization analysis on sleep EEG in insomnia

Inter-hemispheric sleep EEG coherence is studied in 10 subjects with psycho physiological insomnia, in 10 with paradoxical insomnia, and in 10 matched controls through different states of the sleep/wakefulness cycle. Inter hemispheric EEG coherence between central electrode pairs are compared to each other within these groups. A linear measure called as Coherence Function (CF) and a nonlinear measure called as Mutual Information (MI) are performed by using the Information Theory Toolbox in the present sleep EEG synchronization study. Regarding as tests, for all-night EEG recordings of participants, both measures indicate higher degree of EEG coherence for insomnia than for controls. The results further validate inter-hemispheric CF as a sign of activity in insomnia where the EEG series from stage2, REM sleep and the eyes closed waking state. In particular, the CF is found to be more useful tool than the MI for detection of insomnia when the power spectral density estimations of sleep stages are provided by the Burg Method. In conclusion, the CF provides insights into functional connectivity of brain regions during sleep. Since the CF has a characteristic shape for sleep states, it can be proposed to identify the degree of EEG complexity depending on sleep disorders.

Comparison of adaptive features with linear discriminant classifier for Brain computer Interfaces

Many Brain-computer Interfaces (BCI) use band-power estimates with more or less subject-specific optimization of the frequency bands. However, a number of alternative EEG features do not need to select the frequency bands; estimators for these features have been modified for an adaptive use. The popular band power estimates were compared with Adaptive AutoRegressive parameters, Hjorth, Barlow, Wackermann, Brain-Rate and a new feature type called Time Domain Parameter. The results from 21 subjects show that several features provide an equally good or even better performance, while no subject-specific optimization is needed, and they are also preferable to band power when the most discriminating frequency band of a subject is not known.

Viewing brain processes as Critical State Transitions across levels of organization: Neural events in Cognition and Consciousness, and general principles

In this theoretical and speculative essay, I propose that insights into certain aspects of neural system functions can be gained from viewing brain function in terms of the branch of Statistical Mechanics currently referred to as "Modern Critical Theory" [Stanley, H.E., 1987. Introduction to Phase Transitions and Critical Phenomena. Oxford University Press; Marro, J., Dickman, R., 1999. Nonequilibrium Phase Transitions in Lattice Models. Cambridge University Press, Cambridge, UK]. The application of this framework is here explored in two stages: in the first place, its principles are applied to state transitions in global brain dynamics, with benchmarks of Cognitive Neuroscience providing the relevant empirical reference points. The second stage generalizes to suggest in more detail how the same principles could also apply to the relation between other levels of the structural-functional hierarchy of the nervous system and between neural assemblies. In this view, state transitions resulting from the processing at one level are the input to the next, in the image of a 'bucket brigade', with the content of each bucket being passed on along the chain, after having undergone a state transition. The unique features of a process of this kind will be discussed and illustrated.

Two-stage biometric authentication method using thought activity brain waves

Brain waves are proposed as a biometric for verification of the identities of individuals in a small group. The approach is based on a novel two-stage biometric authentication method that minimizes both false accept error (FAE) and false reject error (FRE). These brain waves (or electroencephalogram (EEG) signals) are recorded while the user performs either one or several thought activities. As different individuals have different thought processes, this idea would be appropriate for individual authentication. In this study, autoregressive coefficients, channel spectral powers, inter-hemispheric channel spectral power differences, inter-hemispheric channel linear complexity and non-linear complexity (approximate entropy) values were used as EEG features by the two-stage authentication method with a modified four fold cross validation procedure. The results indicated that perfect accuracy was obtained, i.e. the FRE and FAE were both zero when the proposed method was tested on five subjects using certain thought activities. This initial study has shown that the combination of the two-stage authentication method with EEG features from thought activities has good potential as a biometric as it is highly resistant to fraud. However, this is only a pilot type of study and further extensive research with more subjects would be necessary to establish the suitability of the proposed method for biometric applications.

Consciousness related neural events viewed as brain state space transitions

This theoretical and speculative essay addresses a categorical distinction between neural events of sensory-motor cognition and those presumably associated with consciousness. It proposes to view this distinction in the framework of the branch of Statistical Physics currently referred to as Modern Critical Theory (Stanley, Introduction to phase transitions and critical phenomena, 1987; Marro and Dickman, Nonequilibrium phase transitions in lattice, 1999). Based on established landmarks of brain dynamics, network configurations and their role for conveying oscillatory activity of certain frequencies bands, the question is examined: what kind of state space transitions can systems with these properties undergo, and could the relation between neural processes of sensory-motor cognition and those of events in consciousness be of the same category as is characterized by state transitions in non-equilibrium physical systems? Approaches for empirical validation of this view by suitably designed brain imaging studies, and for computational simulations of the proposed principle are discussed.

On the meaning and interpretation of global descriptors of brain electrical activity. Including a reply to X. Pei et al

Global descriptors of the brain's electrical activity, Sigma, Phi, and Omega, provide a comprehensive characterisation of brain functional states. Recently, Pei et al. [Pei, X., Zheng, C., Zhang, A., Duan, F., Bin, G., 2005. Discussion on "Towards a quantitative characterisation of functional states of the brain: from the nonlinear methodology to the global linear description" by J. Wackermann. Int. J. Psychophysiol. 56, 201-207] discussed the effects of signal power on the global measure of spatial complexity, Omega, and suggested a modification consisting in epoch-wise and channel-wise normalisation of input data to unit power. In the present paper, the basic principles of the global approach are reviewed, and the issues of Pei et al.'s approach are assessed. The original and the modified measures of spatial complexity are compared in two case studies. Numerical simulation shows that both methods veridically estimate small numbers of signal sources, but systematically underestimate as the number increases; the modified method yields a minor relative improvement. A study on real EEG data shows that the two measures sensibly differ only where artefactual inhomogeneities in channel variances affect the data; a combined procedure, consisting in record-wise equalisation of channel variances before Omega calculations, is suggested as the optimal strategy. Differences between the original objectives of the global methodology and the proposed modifications are pointed out and critically discussed.

Increased omega complexity and decreased microstate duration in nonmedicated schizophrenic patients

To explore brain functions in schizophrenic patients, the global analytic strategy of multichannel EEG was performed that combines measures of global complexity (Omega), total power (Sigma) and generalized frequency (Phi), and EEG microstate analysis was applied to multichannel EEG data for 24 nonmedicated patients and 24 healthy subjects. The patients had higher Omega and Sigma values, and lower Phi values compared with healthy subjects. Three topographical classes were obtained from all EEG data by EEG microstate analysis. The mean duration of one topographical class in the patients was shortened compared to healthy subjects. These results indicated looser cooperativity, or decreased connectivity of the active brain process and deviant brain information processing in schizophrenic patients.

Rationality, universality, and individuality in a functional conception of theory

In the present paper we reflect on some critically important issues in theory construction from the point of view of a practicing scientist. The starting point is to suggest the need for a minimal base of common agreement on the role of successfully working theories. It is proposed that scientific knowledge is not composed of singular facts but rather of relational structures connecting facts. Useful theories are both receptive and productive. Theories provide models, i.e., idealised representations of reality, expressed, in their most developed phases, in a mathematically formalised language. We further focus on the notions of rationality and universality, and show that these are mutually related and actually inseparable. Universality means description of observable phenomena in terms of universally valid laws that are essentially of a rational character, i.e., stated in terms of relational invariants preserved in variant, contingent conditions. Law-like components of a theory are universal by definition, not given by circumstances, and rational by their form, not by their content. Facts, on the other hand, are irrational elements unless they can be derived from law-like relations of another theory. Relational definition of rationality is self-consistent and independent from vaguely defined notions like 'reason'. Pertinent to studies of human nature, including psychophysiology, is the problem of individuality. To reconcile the claim of universality with an adequate account of unique individuality, we advocate a 'distributed nomothesis', distinguishing first-order laws ruling in an individual 'idioversum', from the higher-order, universal laws. Idioversal laws play the role of 'facts' in construction of universal theories.

Spectral and complexity features of the EEG changed by visual input in a case of subcortical stroke compared to healthy controls

OBJECTIVE

To compare spectral and complexity characteristics of the EEG in a unique case of subcortical infarct to those seen in healthy controls.

METHODS

Absolute and relative frequency spectra, theta/beta ratio, the brain symmetry index (BSI), Omega-complexity and synchronization likelihood were calculated of the EEG recorded in eyes closed and eyes open conditions.

RESULTS

Increased absolute delta, theta, and Omega-complexity in these frequency bands, higher theta/beta ratios, and decreased relative beta activity were found in the side of the infarct. The BSI localized the excess of slow, and decrease of fast frequency activity to the area of ischemia. Following eyes opening the increase of fast and decrease of slow frequencies, the increase of Omega-complexity in the alpha and beta bands, and the decrease of synchronization likelihood for the fast frequency bands were reduced in the side of the infarct.

CONCLUSIONS

The subcortical infarct caused ipsilaterally increased slow, and decreased fast frequency activity accompanied by decreased synchronization of slow, increased synchronization of fast frequencies. Reduced reactivity in the ischemic side was particularly apparent for complexity measures.

SIGNIFICANCE

Complexity indices of the EEG are sensitive complementary measures of electrophysiological changes caused by local lesions such as subcortical stroke.

The relationship of theory and methodology in EEG studies of mental activity

Due to the multidisciplinary character of psychophysiology, the problem of comparability of psychological and physiological phenomena of different natures and levels of organization has always been raised. This requires the interaction of theory and methodology to appropriately address the specifics of the psychophysiological paradigm, all the while maintaining their grounding in the actual psychological and physiological concepts. The history of EEG studies of mental activity shows that a weak theoretical basis at certain stages can result not only in methodological crises but can also affect empirical data collection and interpretation. An adequate theory can lend strong support to the methodology with "brain-oriented" structuring of psychological tasks and such a theory improves the neurophysiological informative value of the EEG parameters referring to the psychological characteristics of mental processes etc. On the other hand, the great importance of the EEG recording and processing techniques can result in overrating technological progress, hence frequently holding back meaningful interpretation and construction of a comprehensive psychophysiological conceptual framework. This in turn causes demands for higher material and intellectual outlays, due to overspecialization in research, and results in work duplication as well as the creation of a fragmentary knowledge structure. This article illustrates how the multidisciplinary interaction of theory and methodology, when focused on theoretical problems, can yield a series of concepts with escalating levels of integration, bringing together such different branches of psychophysiology as the study of functional states and of individual differences. As a result, this extends the theoretical model based on normal material to encompass borderline constitutional psychopathology.

Multi-channel linear descriptors for event-related EEG collected in brain computer interface

By three multi-channel linear descriptors, i.e. spatial complexity (omega), field power (sigma) and frequency of field changes (phi), event-related EEG data within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the event-related EEG data indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the event-related paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing event-related EEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of EEG patterns in the application of brain computer interfaces.

Distribution of Spatial Complexity of EEG in Idiopathic Generalized Epilepsy and Its Change After Chronic Valproate Therapy

The objective of this study was to investigate the global and regional spatial synchrony of the EEG background activity, and to assess the effect of chronic valproate therapy on spatial synchrony. 15 idiopathic generalized epilepsy (IGE) patients were examined and compared to 16 normal controls. Resting EEG with 19 channels was investigated before and during chronic administration of valproate (VPA). Omega, a single-valued measure of spatial covariance complexity, was calculated to assess the degree of spatial synchrony of EEG. Furthermore, a new parameter was defined to characterize the distribution of spatial synchrony (Antero-Posterior Complexity Ratio, APCR). Global Omega complexity was significantly lower in IGE compared to controls, while regional complexity showed significant differences only in the anterior region: the IGE group showed lower complexity. APCR was significantly lower in IGE. VPA therapy (1) lowered the global complexity, (2) increased regional complexity in the anterior region, but decreased it in the posterior region, and (3) increased APCR. In IGE lower complexity, i.e. enhanced spatial synchrony, was found, especially in the anterior cortical area. VPA modified the distribution of spatial synchrony in IGE patients towards that of normal controls, although the effect is not identical with full normalization of cortical bioelectric activity. Whether the observed change of spatial synchrony distribution may reflect the normalizing effect of valproate on the brain state is worth further investigation.