Decreased nonlinear complexity and chaos during sleep in first episode schizophrenia: a preliminary report

NREM sleep oscillations and their relations with sleep-dependent memory consolidation in early course psychosis and first-degree relatives

Sleep spindles are believed to mediate sleep-dependent memory consolidation, particularly when coupled to neocortical slow oscillations. Schizophrenia is characterized by a deficit in sleep spindles that correlates with reduced overnight memory consolidation. Here, we examined sleep spindle activity, slow oscillation-spindle coupling, and both motor procedural and verbal declarative memory consolidation in early course, minimally medicated psychosis patients and non-psychotic first-degree relatives. Using a four-night experimental procedure, we observed significant deficits in spindle density and amplitude in patients relative to controls that were driven by individuals with schizophrenia. Schizophrenia patients also showed reduced sleep-dependent consolidation of motor procedural memory, which correlated with spindle density. Contrary to expectations, there were no group differences in the consolidation of declarative memory on a word pairs task. Nor did the relatives of patients differ in spindle activity or memory consolidation compared with controls, however increased consistency in the timing of SO-spindle coupling were seen in both patient and relatives. Our results extend prior work by demonstrating correlated deficits in sleep spindles and sleep-dependent motor procedural memory consolidation in early course, minimally medicated patients with schizophrenia, but not in first-degree relatives. This is consistent with other work in suggesting that impaired sleep-dependent memory consolidation has some specificity for schizophrenia and is a core feature rather than reflecting the effects of medication or chronicity.

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.

Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review

Cognitive dysfunctions represent a core feature of schizophrenia-spectrum disorders due to their presence throughout different illness stages and their impact on functioning. Abnormalities in electrophysiology (EEG) measures are highly related to these impairments, but the use of EEG indices in clinical practice is still limited. A systematic review of articles using Pubmed, Scopus and PsychINFO was undertaken in November 2021 to provide an overview of the relationships between EEG indices and cognitive impairment in schizophrenia-spectrum disorders. Out of 2433 screened records, 135 studies were included in a qualitative review. Although the results were heterogeneous, some significant correlations were identified. In particular, abnormalities in alpha, theta and gamma activity, as well as in MMN and P300, were associated with impairments in cognitive domains such as attention, working memory, visual and verbal learning and executive functioning during at-risk mental states, early and chronic stages of schizophrenia-spectrum disorders. The review suggests that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.

Alteration of power law scaling of spontaneous brain activity in schizophrenia

Nonlinear dynamical analysis has been used to quantify the complexity of brain signal at temporal scales. Power law scaling is a well-validated method in physics that has been used to describe the dynamics of a system in the frequency domain, ranging from noisy oscillation to complex fluctuations. In this research, we investigated the power-law characteristics in a large-scale resting-state fMRI data of schizophrenia and healthy participants derived from Taiwan Aging and Mental Illness cohort. We extracted the power spectral density (PSD) of resting signal by Fourier transform. Power law scaling of PSD was estimated by determining the slope of the regression line fitting to the logarithm of PSD. t-Test was used to assess the statistical difference in power law scaling between schizophrenia and healthy participants. The significant differences in power law scaling were found in six brain regions. Schizophrenia patients have significantly more positive power law scaling (i.e., more homogenous frequency components) at four brain regions: left precuneus, left medial dorsal nucleus, right inferior frontal gyrus, and right middle temporal gyrus and less positive power law scaling (i.e., more dominant at lower frequency range) in bilateral putamen compared with healthy participants. Moreover, significant correlations of power law scaling with the severity of psychosis were found. These findings suggest that schizophrenia has abnormal brain signal complexity linked to psychotic symptoms. The power law scaling represents the dynamical properties of resting-state fMRI signal may serve as a novel functional brain imaging marker for evaluating patients with mental illness.

Schizophrenia EEG Signal Classification Based on Swarm Intelligence Computing

One of the serious mental disorders where people interpret reality in an abnormal state is schizophrenia. A combination of extremely disordered thinking, delusion, and hallucination is caused due to schizophrenia, and the daily functions of a person are severely disturbed because of this disorder. A wide range of problems are caused due to schizophrenia such as disturbed thinking and behaviour. In the field of human neuroscience, the analysis of brain activity is quite an important research area. For general cognitive activity analysis, electroencephalography (EEG) signals are widely used as a low-resolution diagnosis tool. The EEG signals are a great boon to understand the abnormality of the brain disorders, especially schizophrenia. In this work, schizophrenia EEG signal classification is performed wherein, initially, features such as Detrend Fluctuation Analysis (DFA), Hurst Exponent, Recurrence Quantification Analysis (RQA), Sample Entropy, Fractal Dimension (FD), Kolmogorov Complexity, Hjorth exponent, Lempel Ziv Complexity (LZC), and Largest Lyapunov Exponent (LLE) are extracted initially. The extracted features are, then, optimized for selecting the best features through four types of optimization algorithms here such as Artificial Flora (AF) optimization, Glowworm Search (GS) optimization, Black Hole (BH) optimization, and Monkey Search (MS) optimization, and finally, it is classified through certain classifiers. The best results show that, for normal cases, a classification accuracy of 87.54% is obtained when BH optimization is utilized with Support Vector Machine-Radial Basis Function (SVM-RBF) kernel, and for schizophrenia cases, a classification accuracy of 92.17% is obtained when BH optimization is utilized with SVM-RBF kernel.

The interaction between subclinical psychotic experiences, insomnia and objective measures of sleep

Investigations into schizophrenia have revealed a high incidence of comorbidity with disturbed sleep and circadian timing. Acknowledging this comorbidity on a dimensional level, we tested prospectively whether subclinical psychotic symptoms are more prevalent in individuals with insomnia. An insomnia group (n=21) and controls (n=22) were recruited on their subjective sleep quality, recorded actigraphically for 3weeks and assessed for psychotic-like experiences with The Prodromal Questionnaire-16. Using multivariate Poisson regression analyses, we found that objective and subjective sleep measures interact to predict the highest risk for psychotic experiences. Objective measures of sleep and statistical modelling are rarely used in either clinical trials or practice for schizophrenia, yet this study highlights their value in these areas.

Childhood trauma, depression, and sleep quality and their association with psychotic symptoms and suicidality in schizophrenia

This study involved the examination of the relationship between childhood trauma and both psychotic symptoms and suicidality in patients with schizophrenia after controlling for the possible confounding factors, such as clinical features, depression, and sleep quality. The Childhood Trauma Questionnaire-Short Form, Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia (CDSS), Pittsburgh Sleep Quality Index (PSQI), and the suicidality subscale of Mini-International Neuropsychiatric Interview (MINI) were administered to 199 patients with schizophrenia. We used sequential multiple stepwise regression analyses in which positive symptoms, negative symptoms, overall psychopathology, total symptoms of schizophrenia, and suicidality were dependent variables. Depressive symptomatology and childhood physical abuse significantly contributed to positive, negative, general psychopathology, and global schizophrenia symptomatology. Interestingly, general psychopathology scores were negatively associated with childhood physical neglect. Also, subjective sleep quality significantly contributed to positive schizophrenia symptoms. Although prior suicide attempts and depression were significant antecedents of suicidal ideation, no association between suicidality and both childhood trauma and sleep was found. Childhood physical abuse could have an impact on psychopathology in schizophrenia. In addition to childhood trauma, depression, sleep disturbances, and clinical features should be considered and inquired about in the course of clinical care of schizophrenia patients.

Comparing EEG Nonlinearity in Deficit and Nondeficit Schizophrenia Patients: Preliminary Data

Electroencephalogram (EEG) contains valuable information obtained noninvasively that can be used for assessment of brain's processing capacity of patients with psychiatric disorders. The purpose of the present work was to evaluate possible differences in EEG complexity between deficit (DS) and nondeficit (NDS) subtypes of schizophrenia as a reflection of the cognitive processing capacities in these groups. A particular nonlinear metric known as Lempel-Ziv complexity (LZC) was used as a computational tool in order to determine the randomness in EEG alpha band time series from 3 groups (deficit schizophrenia [n = 9], nondeficit schizophrenia [n = 10], and healthy controls [n = 10]) according to time series randomness. There was a significant difference in frontal EEG complexity between the DS and NDS subgroups ( p = .013), with DS group showing less complexity. A significant positive correlation was found between LZC values and Positive and Negative Syndrome Scale (PANSS) general psychopathology scores (ie, larger frontal EEG complexity correlated with more severe psychopathology), explained partially by the emotional component subscore of the PANSS. These findings suggest that cognitive processing occurring in the frontal networks in DS is less complex compared to NDS patients as reflected by EEG complexity measures. The data also suggest that there may be a relationship between the degree of emotionality and the complexity of the frontal EEG signal.

Neural complexity as a potential translational biomarker for psychosis

BACKGROUND

The adaptability of the human brain to the constantly changing environment is reduced in patients with psychotic disorders, leading to impaired cognitive functions. Brain signal complexity, which may reflect adaptability, can be readily quantified via resting-state functional magnetic resonance imaging (fMRI) signals. We hypothesized that resting-state brain signal complexity is altered in psychotic disorders, and is correlated with cognitive impairment.

METHODS

We assessed 156 healthy controls (HC) and 330 probands, including 125 patients with psychotic bipolar disorder (BP), 107 patients with schizophrenia (SZ), 98 patients with schizoaffective disorder (SAD) and 230 of their unaffected first-degree relatives (76 BPR, 79 SADR, and 75 SZR) from four sites of the Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) consortium. Using multi-scale entropy analysis, we determined whether patients and/or relatives had pathologic differences in complexity of resting-state fMRI signals toward regularity (reduced entropy in all time scales), or toward uncorrelated randomness (increased entropy in fine time scales that decays as the time scale increases) and how these complexity differences might be associated with cognitive impairment.

RESULTS

Compared to HC subjects, proband groups showed either decreased complexity toward regularity or toward randomness. SZ probands showed decreased complexity toward regular signal in hypothalamus, and BP probands in left inferior occipital, right precentral and left superior parietal regions, whereas no brain region with decreased complexity toward regularity was found in SAD probands. All proband groups showed significantly increased brain signal randomness in dorsal and ventral prefrontal cortex (PFC), and unaffected relatives showed no complexity differences in PFC regions. SZ had the largest area of involvement in both dorsal and ventral PFC. BP and SAD probands shared increased brain signal randomness in ventral medial PFC, BP and SZ probands shared increased brain signal randomness in ventral lateral PFC, whereas SAD and SZ probands shared increased brain signal randomness in dorsal medial PFC. Only SZ showed increased brain signal randomness in dorsal lateral PFC. The increased brain signal randomness in dorsal or ventral PFC was weakly associated with reduced cognitive performance in psychotic probands.

CONCLUSION

These observations support the loss of brain complexity hypothesis in psychotic probands. Furthermore, we found significant differences as well as overlaps of pathologic brain signal complexity between psychotic probands by DSM diagnoses, thus suggesting a biological approach to categorizing psychosis based on functional neuroimaging data.

Consensus paper of the WFSBP Task Force on Biological Markers: Criteria for biomarkers and endophenotypes of schizophrenia part I: Neurophysiology

The neurophysiological components that have been proposed as biomarkers or as endophenotypes for schizophrenia can be measured through electroencephalography (EEG) and magnetoencephalography (MEG), transcranial magnetic stimulation (TMS), polysomnography (PSG), registration of event-related potentials (ERPs), assessment of smooth pursuit eye movements (SPEM) and antisaccade paradigms. Most of them demonstrate deficits in schizophrenia, show at least moderate stability over time and do not depend on clinical status, which means that they fulfil the criteria as valid endophenotypes for genetic studies. Deficits in cortical inhibition and plasticity measured using non-invasive brain stimulation techniques seem promising markers of outcome and prognosis. However the utility of these markers as biomarkers for predicting conversion to psychosis, response to treatments, or for tracking disease progression needs to be further studied.

A systematic review of the nature and correlates of sleep disturbance in early psychosis

Sleep disturbances are common in people with a diagnosis of schizophrenia and have been associated with increased symptom severity, neurocognitive deficits and reduced quality of life. Despite a significant body of literature in this field, there has been limited investigation of sleep disturbance in the early course of the illness. This systematic review aims to synthesise and evaluate the available data exploring sleep in early psychosis, with two key research questions: 1) What is the nature of sleep disturbance in early psychosis? and 2) What are the correlates of sleep disturbance in early psychosis? From an initial search, 16,675 papers were identified, of which 21 met inclusion/exclusion criteria. The preliminary evidence suggests that self-reported sleep disturbances are prevalent in early psychosis and may be associated with symptom severity, as well as elevated rates of both help-seeking and suicidality. Abnormalities in sleep architecture and sleep spindles are also commonly observed and may correlate with symptom severity and neurocognitive deficits. However, due to significant methodological limitations and considerable heterogeneity across studies, evidence to support the reliability of these associations is limited. We outline a research agenda, emphasising the prospective use of gold-standard sleep measurement to investigate the prevalence and nature of sleep disturbances in early psychosis, as well as how these may be related to the onset and persistence of psychotic symptoms.

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.

Electrophysiological aberrations associated with negative symptoms in schizophrenia

Clinical heterogeneity is a confound common to all of schizophrenia research. Deficit schizophrenia has been proposed as a homogeneous disease entity within the schizophrenia syndrome. The use of the Schedule for the Deficit Syndrome (SDS) has allowed the definition of a subgroup dominated by persistent and primary negative symptoms. While a number of studies have appeared over the years examining the electrophysiological correlates of the cluster of negative symptoms in schizophrenia, only a few studies have actually focused on the Deficit Syndrome (DS). In this chapter, electrophysiological investigations utilizing EEG, Evoked Potentials (EPs), polysomnography (PSG), or magnetoencephalography (MEG) to probe "negative symptoms," or "Deficit Syndrome" are reviewed. While this line of research is evidently in its infancy, two significant trends emerge. First, spectral EEG studies link increased slow wave activity during wakefulness to the prevalence of negative symptoms. Second, sleep studies point to an association between decrease in slow wave sleep and prevalence of negative symptoms. Several studies also indicate a relationship of negative symptoms with reduced alpha activity. A host of other abnormalities including sensory gating and P300 attenuation are less consistently reported. Three studies specifically addressed electrophysiology of the DS. Two of the three studies provided evidence suggesting that the DS may be a separate disease entity and not simply a severe form of 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.

Computational non-linear dynamical psychiatry: a new methodological paradigm for diagnosis and course of illness

The goal of this article is to highlight the significant potential benefits of applying computational mathematical models to the field of psychiatry, specifically in relation to diagnostic conceptualization. The purpose of these models is to augment the current diagnostic categories that utilize a "snapshot" approach to describing mental states. We hope to convey to researchers and clinicians that non-linear dynamics can provide an additional useful longitudinal framework to understand mental illness. Psychiatric phenomena are complex processes that evolve in time, similar to many other processes in nature that have been successfully described and understood within deterministic chaos and non-linear dynamic computational models. Dynamical models describe mental processes and phenomena that change over time, more like a movie than a photograph, with multiple variables interacting over time. The use of these models may help us understand why and how current diagnostic categories are insufficient. They may also provide a new, more descriptive and ultimately more predictive approach leading to better understanding of the interrelationship between psychological, neurobiological, and genetic underpinnings of mental illness.

Decreased cortical complexity in methamphetamine abusers

This study aimed to investigate if methamphetamine (MA) abusers exhibit alterations in complexity of the electroencephalogram (EEG) and to determine if these possible alterations are associated with their abuse patterns. EEGs were recorded from 48 former MA-dependent males and 20 age- and sex-matched healthy subjects. Approximate Entropy (ApEn), an information-theoretical measure of irregularity, of the EEGs was estimated to quantify the degree of cortical complexity. The ApEn values in MA abusers were significantly lower than those of healthy subjects in most of the cortical regions, indicating decreased cortical complexity of MA abusers, which may be associated with impairment in specialization and integration of cortical activities owing to MA abuse. Moreover, ApEn values exhibited significant correlations with the clinical factors including abuse patterns, symptoms of psychoses, and their concurrent drinking and smoking habits. These findings provide insights into abnormal information processing in MA abusers and suggest that ApEn of EEG recordings may be used as a potential supplementary tool for quantitative diagnosis of MA abuse. This is the first investigation to assess the "severity-dependent dynamical complexity" of EEG patterns in former MA abusers and their associations with the subjects' abuse patterns and other clinical measures.

Bioelectromagnetics, complex behaviour and psychotherapeutic potential

The brain is a complex non-linear dynamical system that is associated with a wide repertoire of behaviours. There is an ongoing debate as to whether low-intensity radio frequency (RF) bioelectromagnetic interactions induce a biological response. If they do, it is reasonable to expect that the interaction is non-linear. Contradictory reports are found in the literature and attempts to reproduce the subtle effects have often proved difficult. Researchers have already speculated that low-intensity RF radiation may offer therapeutic potential and millimetre-wave therapy is established in the countries of the former Soviet Union. A recent study using transgenic mice that exhibit Alzheimer's-like cognitive impairment shows that microwave radiation may possibly have therapeutic application. By using a highly dynamic stimulus and feedback it may be possible to augment the small effects that have been reported using static parameters. If a firm connection between low-intensity RF radiation and biological effects is established then the possibility arises for its psychotherapeutic application. Low intensity millimetre-wave and peripheral nervous system interactions also merit further investigation. Controlled RF exposure could be associated with quite novel characteristics and dynamics when compared to those associated with pharmacotherapy.

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.

Linear versus non-linear measures of temporal variability in finger tapping and their relation to performance on open- versus closed-loop motor tasks: comparing standard deviations to Lyapunov exponents

The nature of temporal variability during speeded finger tapping was examined using linear (standard deviation) and non-linear (Lyapunov exponent) measures. Experiment 1 found that right hand tapping was characterised by lower amounts of both linear and non-linear measures of variability than left hand tapping, and that linear and non-linear measures of variability were often negatively correlated with one another. Experiment 2 found that increased non-linear variability was associated with relatively enhanced performance on a closed-loop motor task (mirror tracing) and relatively impaired performance on an open-loop motor task (pointing in a dark room), especially for left hand performance. The potential uses and significance of measures of non-linear variability are discussed.

Abnormal EEG complexity in patients with schizophrenia and depression

OBJECTIVE

Schizophrenics are usually unable to perform well on cognitive tasks due to disturbances in cortical information processing that are observable as abnormalities in electroencephalogram (EEG) signals. However, whether such cortical disturbances can be assessed by quantitative EEG analysis remains unclear. The purpose of this study was to characterize EEG disturbances, using the Lempel-Ziv complexity (LZC), in the subjects with schizophrenia at rest or while performing mental arithmetic tasks. The results were compared to those from the subjects with depression and with healthy controls.

METHODS

The subjects included 62 schizophrenia patients, 48 depression patients and 26 age-matched healthy controls. EEG was recorded under two conditions: (i) resting with eyes closed, and (ii) a mentally active condition wherein the subjects were asked to subtract 7 from 100 iteratively with their eyes closed. EEG signals were analyzed by LZC and conventional spectral methods.

RESULTS

In all the groups, LZC of EEG decreased during the mental arithmetic compared with those under the resting conditions. Both the schizophrenia and the depression groups had a higher LZC (p<0.05) than the controls. Also, the schizophrenia group had a lower LZC (p<0.05) than the depression group during the mental arithmetic task as well as during the resting state. Significant differences in LZC, at some symmetrically located loci (FP1/FP2, F7/F8), between the two hemispheres were found in all the patient groups only during the arithmetic task.

CONCLUSIONS

Compared with conventional spectral analysis, LZC was more sensitive to both the power spectrum and the temporal amplitude distribution. LZC was associated with the ability to attend to the task and adapt the information processing system to the cognitive challenge. Thus, it would be useful in studying the disturbances in the cortical information processing patients with depression or schizophrenia.

SIGNIFICANCE

LZC of EEG is associated with mental activity. Thus, LZC analysis can be an important tool in understanding the pathophysiology of schizophrenia and depression in future studies.

Differentiation of MEG activity in multiple sclerosis patients with the use of nonlinear analysis

The aim of this study is to investigate if there is any nonlinearity in the magnetoencephalographic recordings of patients with multiple sclerosis in comparison with controls in order to find out the differences in the mechanisms underlying their brain waves. Five multiple sclerosis patients and five controls were included in this study. Chaotic activity of multiple sclerosis patients is lower than in the normal brain. Nonlinear analysis may offer fertile perspectives for understanding the features of patients with multiple sclerosis.

Novel features for brain-computer interfaces

While conventional approaches of BCI feature extraction are based on the power spectrum, we have tried using nonlinear features for classifying BCI data. In this paper, we report our test results and findings, which indicate that the proposed method is a potentially useful addition to current feature extraction techniques.

A novel technique to evaluate fluctuations of mood: implications for evaluating course and treatment effects in bipolar/affective disorders

OBJECTIVES

Several psychiatric conditions are associated with frequent fluctuations of affect. In this study, we propose a new technique to uniformly score depression and mania objectively and use a new mathematical technique to model the frequent fluctuations in mood using simulated data. Our main aim is to examine the usefulness of this measure for evaluating treatment effects or course of illness, especially in bipolar or unipolar affective illness to quantify mood fluctuations.

METHODS

We use a prototypical model, which takes into account the mean, the standard deviation (SD) and the coefficient of variation (CV = SD*100/mean) of the mood scores of the subjects over a user-defined period. We utilize simulated data of subjects for euthymia, minor depression, minor mania, severe depression, severe mania and cyclic bipolar illness (manic depression, MDP). We propose an objective method to quantify the mood of the subjects at weekly intervals (the interval can be user-defined) using a scale of 1-9 (1-4 = degrees of depression, 5 = euthymia, 6-9 = degrees of mania). These scores can be sampled according to the convenience and feasibility of the measurements, which can be derived from various clinical scales or by observation of the subjects in hospitals or other environments. We derive a new mathematical technique to arrive at a normalized measure for each of these conditions of simulated data in addition to the mean, SD and approximate entropy (ApEn).

RESULTS

We utilize three sets of data, one to train the model to classify the condition of the subjects and the other two to test the reliability of the technique. We are able to successfully classify the condition of the subjects over a 52-timepoint period (length can be days or weeks depending upon the sampling rate). The New Index (NI) correlates significantly only with the mean (r(2) = 0.78), but not with the SD or ApEn score.

CONCLUSIONS

These results indicate that it may be beneficial to reduce data according to the techniques we propose so that there is greater uniformity within which to compare future studies to evaluate treatment effects, not only in rapid-cycling MDP but also in other affective disorders. This method may be suitable for the meta-analysis of several studies, although different scales have been used in each of those studies. Our measure derived from simulated data has shown sufficient deviation of all the abnormal states from the euthymic state. The advantages and pitfalls of these techniques are further discussed to evaluate affect in various disorders. However, future prospective studies must address the importance of this measure in comparison with mean, SD and ApEn scores or other nonlinear measures of these time series. We are evaluating other nonlinear dynamic models, which may provide a continuous measure with which to identify different degrees of fluctuation of mood.

Chaos analysis of electro encephalography and control of seizure attack of epilepsy patients

In order to evaluate the EEG of patients with epilepsy, chaos analysis was performed for the subdural EEG time series data. The chaos attractor was reconstructed in the phase space and the correlation dimension. KS entropy calculated from the Lyapunov exponents was evaluated. Before the seizure attack, the KS entropy showed a lower value when compared with the time series data recorded during healthy condition. The results of our study suggest that it is possible to predict the seizure attack by the chaos analysis of the EEG signal. Further, we aim at developing an automatic control system for predicting a seizure attack by the use of local cooling of the focus with Peltier elements.