The nonlinear theory of schizophrenia

Cognitive Remediation and Schizophrenia: Effects on Brain Complexity

The objective of this study is to investigate nonlinear neural dynamics of chronic patients with schizophrenia following 3 months of cognitive remediation and to find correlations with neuropsychological measures of cognition. Twenty nine patients were randomized to Cognitive Training (CT) and Treatment as Usual (TAU) group. The system complexity is estimated by Correlation Dimension (D₂) and Largest Lyapunov Exponent (LLE) from the reconstructed attractor of the underlying system. Significant increase in dimensional complexity (D₂) over time is observed in prefrontal and medial frontal-central regions in eyes open and arithmetic condition; and posterior parietal-occipital region under eyes closed after 3 months. Dynamical complexity (LLE) significantly decreased over time in medial left central region under eyes closed and eyes open condition; prefrontal region in eyes open and lateral right temporal region in arithmetic condition. Interaction is significant for medial left central region with TAU group exhibiting greater decrease in LLE compared to CT group. The CT group showed significant correlation of increased D₂ with focused attention. In this study it is found that patients with schizophrenia exhibit higher dimensional and lower dynamical complexity over time indicating improvement in neurodynamics of underlying physiological system.

Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks

The notion that specific cognitive and emotional processes arise from functionally distinct brain regions has lately shifted toward a connectivity-based approach that emphasizes the role of network-mediated integration across regions. The clinical neurosciences have likewise shifted from a predominantly lesion-based approach to a connectomic paradigm-framing disorders as diverse as stroke, schizophrenia (SCZ), and dementia as "dysconnection syndromes". Here we position bipolar disorder (BD) within this paradigm. We first summarise the disruptions in structural, functional and effective connectivity that have been documented in BD. Not surprisingly, these disturbances show a preferential impact on circuits that support emotional processes, cognitive control and executive functions. Those at high risk (HR) for BD also show patterns of connectivity that differ from both matched control populations and those with BD, and which may thus speak to neurobiological markers of both risk and resilience. We highlight research fields that aim to link brain network disturbances to the phenotype of BD, including the study of large-scale brain dynamics, the principles of network stability and control, and the study of interoception (the perception of physiological states). Together, these findings suggest that the affective dysregulation of BD arises from dynamic instabilities in interoceptive circuits which subsequently impact on fear circuitry and cognitive control systems. We describe the resulting disturbance as a "psychosis of interoception".

Sample entropy reveals an age-related reduction in the complexity of dynamic brain

Dynamic reconfiguration of the human brain is characterized by the nature of complexity. The purpose of this study was to measure such complexity and also analyze its association with age. We modeled the dynamic reconfiguration process by dynamic functional connectivity, which was established by resting-state functional magnetic resonance imaging (fMRI) data, and we measured complexity within the dynamic functional connectivity by sample entropy (SampEn). A brainwide map of SampEn in healthy subjects shows larger values in the caudate, the olfactory gyrus, the amygdala, and the hippocampus, and lower values in primary sensorimotor and visual areas. Association analysis in healthy subjects indicated that SampEn of the amygdala-cortical connectivity decreases with advancing age. Such age-related loss of SampEn, however, disappears in patients with schizophrenia. These findings suggest that SampEn of the dynamic functional connectivity is a promising indicator of normal aging.

Analysis of EEG entropy during visual evocation of emotion in schizophrenia

BACKGROUND

In this study, the international affective picture system was used to evoke emotion, and then the corresponding signals were collected. The features from different points of brainwaves, frequency, and entropy were used to identify normal, moderately, and markedly ill schizophrenic patients.

METHODS

The signals were collected and preprocessed. Then, the signals were separated according to three types of emotions and five frequency bands. Finally, the features were calculated using three different methods of entropy. For classification, the features were divided into different sections and classification using support vector machine (principal components analysis on 95%). Finally, simple regression and correlation analysis between the total scores of positive and negative syndrome scale and features were used.

RESULTS

At first, we observed that to classify normal and markedly ill schizophrenic patients, the identification result was as high as 81.5%, and therefore, we further explored moderately and markedly ill schizophrenic patients. Second, the identification rate in both moderately and markedly ill schizophrenic patient was as high as 79.5%, which at the Fz point signal in high valence low arousal fragments was calculated using the ApEn methods. Finally, the total scores of positive and negative syndrome scale were used to analyze the correlation with the features that were the five frequency bands at the Fz point signal. The results show that the p value was less than .001 at the beta wave in the 15-18 Hz frequency range.

Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients

Background

In schizophrenia, executive dysfunction is the most critical cognitive impairment, and is associated with abnormal neural activities, especially in the frontal lobes. Complexity estimation using electroencephalogram (EEG) recording based on nonlinear dynamics and task performance tests have been widely used to estimate executive dysfunction in schizophrenia.

Methods

The present study estimated the cool executive function based on fractal dimension (FD) values of EEG data recorded from first-episode schizophrenia patients and healthy controls during the performance of three cool executive function tasks, namely, the Trail Making Test-A (TMT-A), Trail Making Test-B (TMT-B), and Tower of Hanoi tasks.

Results

The results show that the complexity of the frontal EEG signals that were measured using FD was different in first-episode schizophrenia patients during the manipulation of executive function. However, no differences between patients and controls were found in the FD values of the EEG data that was recorded during the performance of the Tower of Hanoi task.

Conclusions

These results suggest that cool executive function exhibits little impairment in first-episode schizophrenia patients.

Effects of modified electroconvulsive therapy on the electroencephalogram of schizophrenia patients

Background

This study aimed to investigate the modified electroconvulsive therapy (MECT) on the electroencephalogram (EEG) of schizophrenia patients. A total of 26 schizophrenia patients who received MECT were recruited. EEG recording was initiated at 30 min before 1st and 6th MECT and terminated on the 2nd day. Images without artifacts were selected for the analysis of δ, θ, α1, α2 and β bands. The wave energy at each frequency, index of waves at different bands from the same lead, index of waves at the same band from different leads, time of epileptic discharge, time of resting state, and time to the stable EEG were determined and compared.

Results

The energy of slow waves increased. α waves reduced, but θ waves increased in the frontotemporal area. The index of θ waves increased. After resting state, brainwaves first occurred in the frontal area. Significant difference was observed in the time to waves returning to normal (P = 0.05).

Conclusions

After MECT, the θ waves in the same lead increases, and its energy also elevates; α wave in the frontotemporal area reduces; there is transient reduction in cerebral function during MECT. After electric resting state, brainwaves mainly occur in the frontal area, and the time to brainwaves returning to normal reduces over time after MECT.

Real-Time Monitoring of Non-linear Suicidal Dynamics: Methodology and a Demonstrative Case Report

In recent years, a number of different authors have stressed the usefulness of non-linear dynamic systems approach in suicide research and suicide prevention. This approach applies specific methods of time series analysis and, consequently, it requires a continuous and fine-meshed assessment of the processes under consideration. The technical means for this kind of process assessment and process analysis are now available. This paper outlines how suicidal dynamics can be monitored in high-risk patients by an Internet-based application for continuous self-assessment with integrated tools of non-linear time series analysis: the Synergetic Navigation System. This procedure is illustrated by data from a patient who attempted suicide at the end of a 90-day monitoring period. Additionally, future research topics and clinical applications of a non-linear dynamic systems approach in suicidology are discussed.

Fractality of sensations and the brain health: the theory linking neurodegenerative disorder with distortion of spatial and temporal scale-invariance and fractal complexity of the visible world

The theory that ties normal functioning and pathology of the brain and visual system with the spatial-temporal structure of the visual and other sensory stimuli is described for the first time in the present study. The deficit of fractal complexity of environmental influences can lead to the distortion of fractal complexity in the visual pathways of the brain and abnormalities of development or aging. The use of fractal light stimuli and fractal stimuli of other modalities can help to restore the functions of the brain, particularly in the elderly and in patients with neurodegenerative disorders or amblyopia. Non-linear dynamics of these physiological processes have a strong base of evidence, which is seen in the impaired fractal regulation of rhythmic activity in aged and diseased brains. From birth to old age, we live in a non-linear world, in which objects and processes with the properties of fractality and non-linearity surround us. Against this background, the evolution of man took place and all periods of life unfolded. Works of art created by man may also have fractal properties. The positive influence of music on cognitive functions is well-known. Insufficiency of sensory experience is believed to play a crucial role in the pathogenesis of amblyopia and age-dependent diseases. The brain is very plastic in its early development, and the plasticity decreases throughout life. However, several studies showed the possibility to reactivate the adult's neuroplasticity in a variety of ways. We propose that a non-linear structure of sensory information on many spatial and temporal scales is crucial to the brain health and fractal regulation of physiological rhythms. Theoretical substantiation of the author's theory is presented. Possible applications and the future research that can experimentally confirm or refute the theoretical concept are considered.

Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness

Schizophrenia is characterized by heterogeneous pathophysiology. Using multiscale entropy (MSE) analysis, which enables capturing complex dynamics of time series, we characterized MSE patterns of blood-oxygen-level-dependent (BOLD) signals across different time scales and determined whether BOLD activity in patients with schizophrenia exhibits increased complexity (increased entropy in all time scales), decreased complexity toward regularity (decreased entropy in all time scales), or decreased complexity toward uncorrelated randomness (high entropy in short time scales followed by decayed entropy as the time scale increases). We recruited 105 patients with schizophrenia with an age of onset between 18 and 35 years and 210 age- and sex-matched healthy volunteers. Results showed that MSE of BOLD signals in patients with schizophrenia exhibited two routes of decreased BOLD complexity toward either regular or random patterns. Reduced BOLD complexity toward regular patterns was observed in the cerebellum and temporal, middle, and superior frontal regions, and reduced BOLD complexity toward randomness was observed extensively in the inferior frontal, occipital, and postcentral cortices as well as in the insula and middle cingulum. Furthermore, we determined that the two types of complexity change were associated differently with psychopathology; specifically, the regular type of BOLD complexity change was associated with positive symptoms of schizophrenia, whereas the randomness type of BOLD complexity was associated with negative symptoms of the illness. These results collectively suggested that resting-state dynamics in schizophrenia exhibit two routes of pathologic change toward regular or random patterns, which contribute to the differences in syndrome domains of psychosis in patients with schizophrenia.

Joint analysis of band-specific functional connectivity and signal complexity in autism

Examination of resting state brain activity using electrophysiological measures like complexity as well as functional connectivity is of growing interest in the study of autism spectrum disorders (ASD). The present paper jointly examined complexity and connectivity to obtain a more detailed characterization of resting state brain activity in ASD. Multi-scale entropy was computed to quantify the signal complexity, and synchronization likelihood was used to evaluate functional connectivity (FC), with node strength values providing a sensor-level measure of connectivity to facilitate comparisons with complexity. Sensor level analysis of complexity and connectivity was performed at different frequency bands computed from resting state MEG from 26 children with ASD and 22 typically developing controls (TD). Analyses revealed band-specific group differences in each measure that agreed with other functional studies in fMRI and EEG: higher complexity in TD than ASD, in frontal regions in the delta band and occipital-parietal regions in the alpha band, and lower complexity in TD than in ASD in delta (parietal regions), theta (central and temporal regions) and gamma (frontal-central boundary regions); increased short-range connectivity in ASD in the frontal lobe in the delta band and long-range connectivity in the temporal, parietal and occipital lobes in the alpha band. Finally, and perhaps most strikingly, group differences between ASD and TD in complexity and FC appear spatially complementary, such that where FC was elevated in ASD, complexity was reduced (and vice versa). The correlation of regional average complexity and connectivity node strength with symptom severity scores of ASD subjects supported the overall complementarity (with opposing sign) of connectivity and complexity measures, pointing to either diminished connectivity leading to elevated entropy due to poor inhibitory regulation or chaotic signals prohibiting effective measure of connectivity.

Reviewing the ketamine model for schizophrenia

The observation that antagonists of the N-methyl-D-aspartate receptor (NMDAR), such as phencyclidine (PCP) and ketamine, transiently induce symptoms of acute schizophrenia had led to a paradigm shift from dopaminergic to glutamatergic dysfunction in pharmacological models of schizophrenia. The glutamate hypothesis can explain negative and cognitive symptoms of schizophrenia better than the dopamine hypothesis, and has the potential to explain dopamine dysfunction itself. The pharmacological and psychomimetic effects of ketamine, which is safer for human subjects than phencyclidine, are herein reviewed. Ketamine binds to a variety of receptors, but principally acts at the NMDAR, and convergent genetic and molecular evidence point to NMDAR hypofunction in schizophrenia. Furthermore, NMDAR hypofunction can explain connectional and oscillatory abnormalities in schizophrenia in terms of both weakened excitation of inhibitory γ-aminobutyric acidergic (GABAergic) interneurons that synchronize cortical networks and disinhibition of principal cells. Individuals with prenatal NMDAR aberrations might experience the onset of schizophrenia towards the completion of synaptic pruning in adolescence, when network connectivity drops below a critical value. We conclude that ketamine challenge is useful for studying the positive, negative, and cognitive symptoms, dopaminergic and GABAergic dysfunction, age of onset, functional dysconnectivity, and abnormal cortical oscillations observed in acute schizophrenia.

Nonlinear measures and dynamics in psychophysiology of consciousness

According to recent findings nonlinear dynamic processes related to neural chaos and complexity likely play a crucial role in neural synchronization of distributed neural activities that enable information integration and conscious experience. Disturbances in these interactions produce patterns of temporal and spatial disorganization with decreased or increased functional connectivity and complexity that underlie specific changes of perceptual and cognitive states. These perceptual and cognitive changes may be characterized by neural chaos with significantly increased brain sensitivity that may underlie sensitization and kindling, and cognitive hypersensitivity in some mental disorders. Together these findings suggest that processes related to more irregular neural states with higher complexity that may lead to neural chaos, negatively affect information integration and processing in the brain, and may influence disintegrated conscious experience.

Complexity and schizophrenia

Complexity estimators have been broadly utilized in schizophrenia investigation. Early studies reported increased complexity in schizophrenia patients, associated with a higher variability or "irregularity" of their brain signals. However, further investigations showed reduced complexities, thus introducing a clear divergence. Nowadays, both increased and reduced complexity values are reported. The explanation of such divergence is a critical issue to understand the role of complexity measures in schizophrenia research. Considering previous arguments a complementary hypothesis is advanced: if the increased irregularity of schizophrenia patients' neurophysiological activity is assumed, a "natural" tendency to increased complexity in EEG and MEG scans should be expected, probably reflecting an abnormal neuronal firing pattern in some critical regions such as the frontal lobes. This "natural" tendency to increased complexity might be modulated by the interaction of three main factors: medication effects, symptomatology, and age effects. Therefore, young, medication-naïve, and highly symptomatic (positive symptoms) patients are expected to exhibit increased complexities. More importantly, the investigation of these interacting factors by means of complexity estimators might help to elucidate some of the neuropathological processes involved in schizophrenia.

Complexity of spontaneous brain activity in mental disorders

Recent reports of functional and anatomical studies have provided evidence that aberrant neural connectivity lies at the heart of many mental disorders. Information related to neural networks has elucidated the nonlinear dynamical complexity in brain signals over a range of temporal scales. The recent advent of nonlinear analytic methods, which have served for the quantitative description of the brain signal complexity, has provided new insights into aberrant neural connectivity in many mental disorders. Although many studies have underpinned aberrant neural connectivity, findings related to complexity behavior are still inconsistent. This inconsistency might result from (i) heterogeneity in mental disorders, (ii) analytical issues, (iii) interference of typical development and aging. First, most mental disorders are heterogeneous in their clinical feature or intrinsic pathological mechanisms. Second, neurophysiologic output signals from complex brain connectivity might be characterized with multiple time scales or frequencies. Finally, age-related brain complexity changes must be considered when investigating pathological brain because typical brain complexity is not constant across generations. Future systematic studies addressing these issues will greatly expand our knowledge of neural connections and dynamics related to mental disorders.

Effects of memantine on event-related potential, oscillations, and complexity in individuals with and without family histories of alcoholism

OBJECTIVE

Enhanced N-methyl-D-aspartate (NMDA) receptor function associated with a positive family history of alcoholism (FHP) has been hypothesized to contribute to the heritable risk for alcoholism. The objective of this study was to evaluate the relationship of alcoholism family history, NMDA receptor function, and cortical information processing by testing acute effects of the NMDA receptor antagonist memantine on event-related potential (ERP).

METHOD

Twenty-two healthy FHP and 20 healthy family history-negative (FHN; no alcoholic relatives) subjects were administered placebo or 40 mg of memantine under double-blind counterbalanced conditions on two separate occasions. Electroencephalogram data were collected from eight channels with eyes open during an auditory oddball discrimination task. We evaluated P3b amplitude, total theta, alpha activity, and fractal dimension from ERP trials.

RESULTS

FHP and FHN subjects did not differ in P3b amplitude. A significant Group × Drug interaction was observed in theta, alpha activity, and fractal dimension at the parietal and occipital sites. FHP individuals exhibited significantly higher fractal dimension and lower theta and alpha activity after placebo relative to FHN subjects. Following memantine administration, theta activity decreased in both groups but more markedly for FHN individuals. Alpha activity decreased for FHN subjects and increased for FHP individuals, whereas the fractal dimension decreased for FHP subjects and increased for FHN subjects after memantine.

CONCLUSIONS

A plausible interpretation of these results is that FHP individuals may have altered NMDA receptor function compared with FHN individuals. These findings provide additional evidence of differences in the regulation of NMDA receptor function between FHP and FHN individuals.

Linear and non-linear associations of symptom dimensions and cognitive function in first-onset psychosis

BACKGROUND

Associations between symptom dimensions and cognition have been mainly studied in non-affective psychosis. The present study investigated whether previously reported associations between cognition and four symptom dimensions (reality distortion, negative symptoms, disorganisation and depression) in non-affective psychosis generalise to a wider spectrum of psychoses. It also extended the research focus to mania, a less studied symptom dimension.

METHODS

Linear and non-linear (quadratic, curvilinear or inverted-U-shaped) associations between cognition and the above five symptom dimensions were examined in a population-based cohort of 166 patients with first-onset psychosis using regression analyses.

RESULTS

Negative symptoms showed statistically significant linear associations with IQ and processing speed, and a significant curvilinear association with verbal memory/learning. Significant quadratic associations emerged between mania and processing speed and mania and executive function. The contributions of mania and negative symptoms to processing speed were independent of each other. The findings did not differ between affective and non-affective psychoses, and survived correction for multiple testing.

CONCLUSIONS

Mania and negative symptoms are associated with distinct patterns of cerebral dysfunction in first-onset psychosis. A novel finding is that mania relates to cognitive performance by a complex response function (inverted-U-shaped relationship). The associations of negative symptoms with cognition include both linear and quadratic elements, suggesting that this dimension is not a unitary concept. These findings cut across affective and non-affective psychoses, suggesting that different diagnostic entities within the psychosis spectrum lie on a neurobiological continuum.

Nonlinear analysis of electroencephalogram at rest and during cognitive tasks in patients with schizophrenia

BACKGROUND

In spite of the large number of studies on schizophrenia, a full understanding of its core pathology still eludes us. The application of the nonlinear theory of electroencephalography (EEG) analysis provides an interesting tool to differentiate between physiologic conditions (e.g., resting state and mathematical task) and normal and pathologic brain activities. The aim of the present study was to investigate nonlinear EEG activity in patients with schizophrenia.

METHODS

We recorded 19-lead EEGs in patients with stable schizophrenia and healthy controls under 4 different conditions: eyes closed, eyes open, forward counting and backward counting. A nonlinear measure of complexity was calculated by means of correlation dimension (D2).

RESULTS

We included 17 patients and 17 controls in our analysis. Comparing the 2 populations, we observed greater D2 values in the patient group. In controls, increased D2 values were observed during active states (eyes open and the 2 cognitive tasks) compared with baseline conditions. This increase of brain complexity, which can be interpreted as an increase of information processing and integration, was not preserved in the patient population.

LIMITATIONS

Patients with schizophrenia were taking antipsychotic medications, so the presence of medication effects cannot be excluded.

CONCLUSION

Our results suggest that patients with schizophrenia present changes in brain activity compared with healthy controls, and this pathologic alteration can be successfully studied with nonlinear EEG analysis.

Altered resting state complexity in schizophrenia

The complexity of the human brain's activity and connectivity varies over temporal scales and is altered in disease states such as schizophrenia. Using a multi-level analysis of spontaneous low-frequency fMRI data stretching from the activity of individual brain regions to the coordinated connectivity pattern of the whole brain, we investigate the role of brain signal complexity in schizophrenia. Specifically, we quantitatively characterize the univariate wavelet entropy of regional activity, the bivariate pairwise functional connectivity between regions, and the multivariate network organization of connectivity patterns. Our results indicate that univariate measures of complexity are less sensitive to disease state than higher level bivariate and multivariate measures. While wavelet entropy is unaffected by disease state, the magnitude of pairwise functional connectivity is significantly decreased in schizophrenia and the variance is increased. Furthermore, by considering the network structure as a function of correlation strength, we find that network organization specifically of weak connections is strongly correlated with attention, memory, and negative symptom scores and displays potential as a clinical biomarker, providing up to 75% classification accuracy and 85% sensitivity. We also develop a general statistical framework for the testing of group differences in network properties, which is broadly applicable to studies where changes in network organization are crucial to the understanding of brain function.

Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: a multiscale entropy analysis

Multiscale entropy (MSE) analysis is a novel entropy-based approach for measuring dynamical complexity in physiological systems over a range of temporal scales. To evaluate this analytic approach as an aid to elucidating the pathophysiologic mechanisms in schizophrenia, we examined MSE in EEG activity in drug-naive schizophrenia subjects pre- and post-treatment with antipsychotics in comparison with traditional EEG analysis. We recorded eyes-closed resting-state EEG from frontal, temporal, parietal, and occipital regions in drug-naive 22 schizophrenia and 24 age-matched healthy control subjects. Fifteen patients were re-evaluated within 2-8 weeks after the initiation of antipsychotic treatment. For each participant, MSE was calculated on one continuous 60-s epoch for each experimental session. Schizophrenia subjects showed significantly higher complexity at higher time scales (lower frequencies) than did healthy controls in fronto-centro-temporal, but not in parieto-occipital regions. Post-treatment, this higher complexity decreased to healthy control subject levels selectively in fronto-central regions, while the increased complexity in temporal sites remained higher. Comparative power analysis identified spectral slowing in frontal regions in pre-treatment schizophrenia subjects, consistent with previous findings, whereas no antipsychotic treatment effect was observed. In summary, multiscale entropy measures identified abnormal dynamical EEG signal complexity in anterior brain areas in schizophrenia that normalized selectively in fronto-central areas with antipsychotic treatment. These findings show that entropy-based analytic methods may serve as a novel approach for characterizing and understanding abnormal cortical dynamics in schizophrenia and elucidating the therapeutic mechanisms of antipsychotics.

Small-world properties of nonlinear brain activity in schizophrenia

A disturbance in the interactions between distributed cortical regions may underlie the cognitive and perceptual dysfunction associated with schizophrenia. In this article, nonlinear measures of cortical interactions and graph-theoretical metrics of network topography are combined to investigate this schizophrenia "disconnection hypothesis." This is achieved by analyzing the spatiotemporal structure of resting state scalp EEG data previously acquired from 40 young subjects with a recent first episode of schizophrenia and 40 healthy matched controls. In each subject, a method of mapping the topography of nonlinear interactions between cortical regions was applied to a widely distributed array of these data. The resulting nonlinear correlation matrices were converted to weighted graphs. The path length (a measure of large-scale network integration), clustering coefficient (a measure of "cliquishness"), and hub structure of these graphs were used as metrics of the underlying brain network activity. The graphs of both groups exhibited high levels of local clustering combined with comparatively short path lengths--features consistent with a "small-world" topology--as well as the presence of strong, central hubs. The graphs in the schizophrenia group displayed lower clustering and shorter path lengths in comparison to the healthy group. Whilst still "small-world," these effects are consistent with a subtle randomization in the underlying network architecture--likely associated with a greater number of links connecting disparate clusters. This randomization may underlie the cognitive disturbances characteristic of schizophrenia.

Chaos in schizophrenia associations, reality or metaphor?

There is evidence that schizophrenic associations display "chaotic", random-like behavior and decreased predictability. The evidence suggests a hypothesis that the "chaotic" mental disorganization could be explained within the concept of nonlinear dynamics and complexity in the brain that may cause chaotic neural organization. Testing of the hypothesis in the present study was performed using nonlinear analysis of bilateral electrodermal activity (EDA) during resting state and an association test in 56 schizophrenic patients and 44 healthy participants. EDA is a suitable measure of brain and autonomic activity reflecting neurobiological changes in schizophrenia that may indicate changes in nonlinear neural dynamics related to associative process. The results show that quantitative indices of chaotic dynamics (the largest Lyapunov exponents) calculated from EDA signals recorded during rest and the association test are significantly higher in schizophrenia patients than in the control group and increase during the test in comparison to the resting state. The difference was confirmed by statistical methods and using surrogate data testing that rejected an explanation within the linear statistical framework. The results provide supportive evidence that pseudo-randomness of schizophrenic associations and less predictability could be linked to increased complexity of nonlinear neural dynamics, although certain limitations in data interpretation must be taken into account.

Ictal electroencephalographic characteristics during electroconvulsive therapy: a review of determination and clinical relevance

OBJECTIVE

The usefulness of electroencephalogram (EEG) during electroconvulsive therapy (ECT) is not limited to the duration of seizure. The use of studying its characteristics has gained attention among specialized ECT researchers. This review attempts to put the literature in this area of ictal EEG in ECT into a practical perspective.

METHODS

A systematic database search using MEDLINE and EMBASE was performed using the keywords ECT and ictal EEG.

RESULTS

Manual, linear, and nonlinear methods have been used to analyze EEG obtained during ECT. Most studies have used spectral or linear methods of analysis. Studies using nonlinear have been sparse. There are 4 overlapping practical applications of analysis of EEG during ECT. First, EEG can discriminate between electrode positions and more controversially different stimulus doses. Second, EEG can predict treatment response. Postictal suppression is the measure most studied from all vantage points. Nonlinear approaches to determine EEG predictors hold promise. Third, changes in the EEG across ECT sessions may indicate threshold changes and aids more optimum dosimetry. Fourth, EEG characteristics have been incorporated into ECT clinical algorithms, but this still remains controversial, and there is a paucity of studies in this area.

CONCLUSIONS

The use of ictal EEG extends beyond determining the seizure length. The study of ictal EEG has certain practical uses, which could help individualize ECT treatment if applied in routine ECT practice.