Working-memory load and dimensional complexity of the EEG

Sensorimotor Cortical Activity during Respiratory Arousals in Obstructive Sleep Apnea

Intensity of respiratory cortical arousals (RCA) is a pathophysiologic trait in obstructive sleep apnea (OSA) patients. We investigated the brain oscillatory features related to respiratory arousals in moderate and severe OSA. Raw electroencephalography (EEG) data recorded during polysomnography (PSG) of 102 OSA patients (32 females, mean age 51.6 ± 12 years) were retrospectively analyzed. Among all patients, 47 had moderate (respiratory distress index, RDI = 15−30/h) and 55 had severe (RDI > 30/h) OSA. Twenty RCA per sleep stage in each patient were randomly selected and a total of 10131 RCAs were analyzed. EEG signals obtained during, five seconds before and after the occurrence of each arousal were analyzed. The entropy (approximate (ApEn) and spectral (SpEn)) during each sleep stage (N1, N2 and REM) and area under the curve (AUC) of the EEG signal during the RCA was computed. Severe OSA compared to moderate OSA patients showed a significant decrease (p < 0.0001) in the AUC of the EEG signal during the RCA. Similarly, a significant decrease in spectral entropy, both before and after the RCA was observed, was observed in severe OSA patients when compared to moderate OSA patients. Contrarily, the approximate entropy showed an inverse pattern. The highest increase in approximate entropy was found in sleep stage N1. In conclusion, the dynamic range of sensorimotor cortical activity during respiratory arousals is sleep-stage specific, dependent on the frequency of respiratory events and uncoupled from autonomic activation. These findings could be useful for differential diagnosis of severe OSA from moderate OSA.

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components coordinate to perform specific functions while also exhibiting the potential to re-structure and then perform other functions as task demands change. Consistent with this notion, within cognitive neuroscience it is increasingly accepted that the brain flexibly coordinates the networks needed to cope with changing task demands. However, evaluation of various indices of soft-assembly has so far been absent from neurophysiological research. To begin addressing this gap, we investigated task-related changes in two distinct indices of soft-assembly using the established phenomenon of EEG repetition suppression. In a repetition priming task, we assessed evidence for changes in the correlation dimension and fractal scaling exponents during stimulus-locked event-related potentials, as a function of stimulus onset and familiarity, and relative to spontaneous non-task-related activity. Consistent with predictions derived from soft-assembly, results indicated decreases in dimensionality and increases in fractal scaling exponents from resting to pre-stimulus states and following stimulus onset. However, contrary to predictions, familiarity tended to increase dimensionality estimates. Overall, the findings support the view from soft-assembly that neural dynamics should become increasingly ordered as external task demands increase, and support the broader application of soft-assembly logic in understanding human behavior and electrophysiology.

Memory load effect in auditory-verbal short-term memory task: EEG fractal and spectral analysis

The objective of this preliminary study was to quantify changes in complexity of EEG using fractal dimension (FD) alongside linear methods of spectral power, event-related spectral perturbations, coherence, and source localization of EEG generators for theta (4-7 Hz), alpha (8-12 Hz), and beta (13-23 Hz) frequency bands due to a memory load effect in an auditory-verbal short-term memory (AVSTM) task for words. We examined 20 healthy individuals using the Sternberg's paradigm with increasing memory load (three, five, and seven words). The stimuli were four-letter words. Artifact-free 5-s EEG segments during retention period were analyzed. The most significant finding was the increase in FD with the increase in memory load in temporal regions T3 and T4, and in parietal region Pz, while decrease in FD with increase in memory load was registered in frontal midline region Fz. Results point to increase in frontal midline (Fz) theta spectral power, decrease in alpha spectral power in parietal region-Pz, and increase in beta spectral power in T3 and T4 region with increase in memory load. Decrease in theta coherence within right hemisphere due to memory load was obtained. Alpha coherence increased in posterior regions with anterior decrease. Beta coherence increased in fronto-temporal regions. Source localization delineated theta activity increase in frontal midline region, alpha decrease in superior parietal region, and beta increase in superior temporal gyrus with increase in memory load. In conclusion, FD as a nonlinear measure may serve as a sensitive index for quantifying dynamical changes in EEG signals during AVSTM tasks.

Psychophysiology of dissociated consciousness

Recent study of consciousness provides an evidence that there is a limit of consciousness, which presents a barrier between conscious and unconscious processes. This barrier likely is specifically manifested as a disturbance of neural mechanisms of consciousness that through distributed brain processing, attentional mechanisms and memory processes enable to constitute integrative conscious experience. According to recent findings a level of conscious integration may change during certain conditions related to experimental cognitive manipulations, hypnosis, or stressful experiences that can lead to dissociation of consciousness. In psychopathological research the term dissociation was proposed by Pierre Janet for explanation of processes related to splitting of consciousness due to traumatic events or during hypnosis. According to several recent findings dissociation of consciousness likely is related to deficits in global distribution of information and may lead to heightened levels of "neural complexity" that reflects brain integration or differentiation based on numbers of independent neural processes in the brain that may be specifically related to various mental disorders.

Investigation of changes in EEG complexity during memory retrieval: the effect of midazolam

The aim of this study is applying nonlinear methods to assess changes in brain dynamics in a placebo-controlled study of midazolam-induced amnesia. Subjects injected with saline and midazolam during study, performed old/new recognition memory tests with EEG recording. Based on previous studies, as midazolam causes anterograde amnesia, we expected that midazolam would affect the EEG's degree of complexity. Recurrence quantification analysis, and approximate entropy were used in this assessment. These methods compare with other nonlinear techniques such as computation of the correlation dimension, are suitable for non-stationary EEG signals. Our findings suggest that EEG's complexity decreases during memory retrieval. Although this trend is observed in nonlinear curves related to the midazolam condition, the overall complexity were greater than in the saline condition. This result implies that impaired memory function caused by midazolam is associated with greater EEG's complexity compared to normal memory retrieval in saline injection.

Schizophrenia, dissociation, and consciousness

Current thinking suggests that dissociation could be a significant comorbid diagnosis in a proportion of schizophrenic patients with a history of trauma. This potentially may explain the term "schizophrenia" in its original definition by Bleuler, as influenced by his clinical experience and personal view. Additionally, recent findings suggest a partial overlap between dissociative symptoms and the positive symptoms of schizophrenia, which could be explained by inhibitory deficits. In this context, the process of dissociation could serve as an important conceptual framework for understanding schizophrenia, which is supported by current neuroimaging studies and research of corollary discharges. These data indicate that the original conception of "split mind" may be relevant in an updated context. Finally, recent data suggest that the phenomenal aspects of dissociation and conscious disintegration could be related to underlying disruptions of connectivity patterns and neural integration.

The application of graph theoretical analysis to complex networks in the brain

Considering the brain as a complex network of interacting dynamical systems offers new insights into higher level brain processes such as memory, planning, and abstract reasoning as well as various types of brain pathophysiology. This viewpoint provides the opportunity to apply new insights in network sciences, such as the discovery of small world and scale free networks, to data on anatomical and functional connectivity in the brain. In this review we start with some background knowledge on the history and recent advances in network theories in general. We emphasize the correlation between the structural properties of networks and the dynamics of these networks. We subsequently demonstrate through evidence from computational studies, in vivo experiments, and functional MRI, EEG and MEG studies in humans, that both the functional and anatomical connectivity of the healthy brain have many features of a small world network, but only to a limited extent of a scale free network. The small world structure of neural networks is hypothesized to reflect an optimal configuration associated with rapid synchronization and information transfer, minimal wiring costs, resilience to certain types of damage, as well as a balance between local processing and global integration. Eventually, we review the current knowledge on the effects of focal and diffuse brain disease on neural network characteristics, and demonstrate increasing evidence that both cognitive and psychiatric disturbances, as well as risk of epileptic seizures, are correlated with (changes in) functional network architectural features.

Dimensional complexity of neuromagnetic activity reduced during finger movement of greater difficulty

OBJECTIVE

We investigated the variation in dimensionality (D2) of neuromagnetic activity over the primary sensorimotor cortex (SM1) in healthy adults performing motor tasks of different difficulty.

METHODS

Magnetoencephalography (MEG) was used to record neuromagnetic activity during self-paced, brisk unimanual finger extension at a rate of 1 and 2 Hz using the index finger of the dominant and non-dominant hands in 16 healthy subjects. Motor task difficulty was rated by the relative difference in time measurement between 1 and 2 Hz finger movements of both hands. The relative difference in dimensionality of SM1 activity was calculated by subtracting the D2 value in 2 Hz movement from that in 1 Hz one within subjects.

RESULTS

Simple regression analyses show a significantly negative relationship between the relative dimensional complexity and the relative motor task difficulty in the contralateral SM1 for the left- (p<0.05), but not the right- (p=0.447) hand movement.

CONCLUSIONS

The present data suggest that a motor task of greater difficulty may engender a reduction of simultaneously active quasi-independent neuronal generators in the contralateral SM1 underpinned by stronger neuronal connectivity of a relatively low dimensionality.

SIGNIFICANCE

The decrease in dimensional complexity of MEG activity associated with a motor task of greater difficulty gives new insights to motor control strategy.

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.

Brain dynamics in theta and alpha frequency bands and working memory performance in humans

In this study non-linear and linear global electroencephalogram (EEG) changes during a visual working memory task were studied using a separate analysis of theta, lower alpha and upper alpha band filtered data. EEGs were recorded in 21 healthy subjects (62.5 year; SD 2.1; 12 females, nine males) during an eyes-closed no-task condition and a working memory condition. Coarse-grained dimension was estimated for both conditions from spatially embedded EEG data filtered in the theta band and both alpha bands. Linear measures of coupling and mean amplitude were also computed. During the working memory condition lower alpha band dimension increased. Linear analysis showed alpha1 band desynchronization. Female subjects had a higher dimension in the theta band as well as more desynchronization in the theta and alpha1 band. Working memory capacity correlated with a lower theta band dimension during the no-task condition in female subjects. The increase in alpha1 band complexity can be interpreted as increased desynchronization corresponding with attentional processes. Higher complexity/desynchronization in females seems to be a more structural phenomenon and may be more intimately related to task performance.

Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy--an overview

Deterministic chaos offers a striking explanation for apparently irregular behavior, a characteristic feature of brain electrical activity. The framework of the theory of non-linear dynamics provides new concepts and powerful algorithms to analyze such time series. However, different influencing factors render the use of non-linear measures in a strict sense problematic. Nevertheless, if interpreted with care, particularly the correlation dimension or the Lyapunov-exponents provide a means to reliably characterize different states of normal and pathological brain function. This overview summarizes recent findings applying this concept in the field of epileptology that promise to be important for clinical practice. Non-linear measures extracted from the intra-cranially recorded EEG allow (a) localization of epileptogenic areas in different cerebral regions even during seizure-free intervals, (b) investigation of the influence of anticonvulsive drugs and (c) detection of features predictive of imminent seizure activity. Moreover, particularly the dimensional complexity proves a valuable parameter reflecting spatially distributed neuronal activity during verbal learning and memory processes. Specific changes in time of this non-linear measure allow the prediction of memory performance and, in addition, represent an estimate of the recruitment potency in the anterior mesial temporal lobes. Thus, the application of non-linear time series analysis to brain electrical activity offers new information about the dynamics of the underlying neuronal networks.

Event-related dimensional reductions in the primary auditory cortex of the conscious cat are revealed by new techniques for enhancing the non-linear dimensional algorithms

The analytic algorithms derived from non-linear deterministic models may be more sensitive to differences in physiological data than those based on linear stochastic models. Among the non-linear algorithms the time-dependent dimensional ones appear to be the most sensitive discriminators. In the present study dimensional responses were examined in both electronically and mathematically generated data and in high-resolution physiological data. The latter were event-related potentials (ERPs) recorded from the primary auditory cortex of cats during classical conditioning. Two techniques were found to lengthen and stabilize the linear scaling region in the correlation integral of the dimensional algorithms: (1) linking trials to increase data length; and (2) gain reduction to lower integer-values of noise, combined with algorithmic setting of slopes < 0.5 to zero. Of the three dimensional algorithms examined, only the time-dependent Point Correlation Dimension (PD2i) showed low error rates when tracking the dimensional shifts in non-stationary generated data. This algorithm also uniquely distinguished between the conditioned and unconditioned physiological responses. The ERPs had corresponding PD2i's that were significantly different from each other as well as from their own randomized-phase surrogates. The brief dimensional reduction that follows a conditioned stimulus is interpreted to be related to 'cooperativity' among the underlying cortical neurons that contribute to its electrogenesis.

Working memory load and EEG-dynamics as revealed by point correlation dimension analysis

The results of the present study showed a decreased dimensional complexity during working-memory load, which was induced by the Corsi-Bock-Tapping task as compared to a baseline task. This baseline task was similar to the Corsi-Block-Tapping with respect to both the required processes of perception and the motor performance. A load-specific change of brain dynamics was observed, which was most pronounced over frontal cortical areas. This finding is in agreement with the literature and encourages further research on the ongoing EEG, especially with clinical tests, where little is known about their psychophysiological correlates. From a methodological point of view, the point correlation dimension, which deals with non-stationary data, was applied to EEG-time series that were a concatenation of several EEG-subepochs of the same experimental conditions. From the results it is proposed that this procedure might offer an approach for EEG analysis, where the EEG-epochs are of short duration and non-stationary data are to be expected.

Hypothesized neural dynamics of working memory: several chunks might be marked simultaneously by harmonic frequencies within an octave band of brain waves

The capacity of working memory (WM) for up to about seven simple items holds true both for humans and other species, and may depend upon a common characteristic of mammalian brains. This paper develops the conjecture that each WM item is represented by a different brain wave frequency. The binding-by-synchrony hypothesis, now being widely investigated, holds that the attributes of a single cognitive element cohere because electroencephalogram (EEG) synchrony temporarily unifies their substrates, which are distributed among different brain regions. However, thought requires keeping active more than one cognitive element, or WM "chunk," at a time. If there is indeed a brain wave frequency code for cognitive item-representations that are copresent within the same volume of neural tissue, the simple mathematical relationships of harmonies could provide a basis for maintaining distinctness and for orderly changes. Thus, a basic aspect of music may provide a model for an essential characteristic of WM. Music is a communicative phenomenon of "intermediate complexity," more highly organized than the firing patterns of individual neurons but simpler than language. If there is a distinct level of neural processing within which the microscopic physiological activity of neurons self-organizes into the macroscopic psychology of the organism, it might require such moderate complexity. Some of the obvious properties of music--orderly mixing and transitions among limited numbers of signal lines-are suggestive of properties that a dynamic neural process might need in order to organize and reorganize WM markers, but there are a number of additional, nonobvious advantageous properties of summating sinusoids in music-like relationships. In particular, harmonies register a stable periodic signal in the briefest possible time. Thus, the regularity of summating sinusoids whose frequencies bear harmony ratios suggests a particular kind of tradeoff between parallel and serial processing. When there are few copresent waves, at EEG frequencies, this sort of parallel coding retains behaviorally meaningful brief periods. A necessary companion hypothesis is that the brain wave frequencies underlying WM are confined to a single octave; that is, the upper and lower bounds of the band are in the ratio of 2:1. This hypothesized restriction, suggested by an empirical property of EEG bands that has been widely reported but rarely commented upon, has the important property of precluding spurious difference rhythms. A restriction to an octave, of "harmonious" frequency-markers for WM items, also seems consistent with a great deal of behavioral data suggesting that WM comprises a rapidly fading trace process in which only up to three or four item-representations are strongly activated simultaneously. There is also an additional, sequential renewal-or-revision process, within which up to another three or four items are being actively refreshed by rehearsal or replaced. Such serial processing may involve a less stringent octave band crowding problem.

The dimensional complexity of the P3 event-related potential: area-specific and task-dependent features

Appropriate methods are needed for the study of the non-linear properties of the nervous system. The correlation dimension, as expressed by the effective point correlation dimension (PD2eff), was used in this study for the analysis of the dimensional complexity of event-related potentials. The ERPs were recorded at Fz, Cz and Pz in an auditory oddball paradigm in which 'easy' and 'difficult' sensory discrimination tasks were applied. The PD2eff was calculated from each EEG epoch used in the process of ERP averaging. The PD2eff significantly decreased during the ERPs in which the P3-component appeared, and this reduction showed area-specific and task-specific patterns. The area-specific PD2eff changes indicated distinctive functional features of the frontal areas in this task. The magnitude of the PD2eff change was found to depend on the degree of task difficulty. Implications of the findings related to theories on the functional significance of the P3-wave in information processing are discussed.

Non-linear dynamical coupling between different brain areas during evoked emotions: an EEG investigation

This study examined changes in the non-linear dynamical coupling between different brain areas in response to emotionally valenced (i.e. neutral, affective positive and negative) film stimuli in 76 healthy volunteers. EEG was recorded from 18 sites (10-20 system). A new dynamical measure, mutual dimension (Dm) was applied to the EEG. The Dm was calculated between each electrode paired to all other electrodes. The results were compared to coherence measures. Differential patterns of significant changes of Dm estimates were found in the two emotional conditions. When compared to the neutral viewing condition, the emotionally negative experience provoked significant decreases of short- and long-distance ipsi- and contralateral Dm estimates that were more left-sided (with a 'center of gravity' of Dm decrease at the F7 site), whereas the emotionally positive experience showed a pattern of symmetrically distributed ipsi- and contralateral increases of Dm estimates over central and posterior cortical regions (with 'main processing nodes' of Dm increases at T5 and T6 sites). It is concluded that estimates of the mutual dimension are sensitive to subtle aspects of emotional processing, not accessible by linear (i.e. coherence) analyses.

Nonlinear dynamical analysis of periodic lateralized epileptiform discharges

Nonlinear time series analysis can be used to investigate the dynamics underlying the generation of EEG signal. In the present study we used this approach to study the pathophysiology of PLEDs. We calculated the correlation dimension D2 of an EEG with typical PLEDs, and compared the results with those obtained for surrogate data. These surrogate data have the same power spectrum and amplitude distribution as the original EEG data, but are otherwise random. By construction, such surrogate data can be described by a linear model. Our results showed that D2 estimations for PLEDs were low, on the order of one, and that the results for EEG and the surrogate data were clearly different, indicating that the EEG with PLEDs reflects nonlinear dynamics of the underlying neural networks.

Non-linear dynamic complexity of the human EEG during evoked emotions

The present study examined EEG dimensional complexity (estimated correlation dimension) in 76 healthy volunteers in response to emotionally valenced (i.e. neutral, positive and negative) film clip stimulation. EEG was recorded from 18 sites (10-20 system). We estimated the dimensional complexity by the Grassberger and Procaccia and Skinner's point-wise dimension (PD2i) methods. The results were compared to spectral measures of the EEG. Only the PD2i algorithm (i.e. the one that did not require data stationarity) discriminated among all the three film categories. The main results showed that both negative and positive emotions occurred with higher values (at some posterior locations) of EEG DCx estimates compared to the neutral viewing condition. The topographical differences (frontal vs. posterior temporal) between positive and negative evoked emotions were obtained. There were also some significant direct relationships between dynamic complexity estimates and intensities of subjective emotional feelings. It is concluded that dimensional complexity estimates turned out to be sensitive to subtle aspects of emotional processing not accessible by linear EEG analyses.