Hara Tsekou

Effect of Schizotypy on Cognitive Performance and Its Tuning by COMT val158 Met Genotype Variations in a Large Population of Young Men

BACKGROUND Mirroring schizophrenia, specific dimensions of schizotypy are related to cognitive dysfunction. The relation of schizotypy and state psychopathology to cognitive performance and its link to catechol-O-methyltransferase (COMT) val(158) met genotype variations was studied in a large sample of young men. METHODS State psychopathology and schizotypy were assessed with self-rated questionnaires. Cognitive performance was assessed with tests of reasoning ability, sustained attention, and verbal and spatial working memory. Subjects were genotyped for the val(158) met polymorphism of the gene for COMT (low enzymatic activity met/met, intermediate met/val, and high val/val). RESULTS The val/val group had higher scores in measures of state psychopathology as well as negative and disorganized schizotypy dimensions, whereas there was no effect of COMT genotype on cognitive performance measures. Structural equation modeling showed that cognitive performance accuracy but not speed decreased with increasing negative schizotypy, increased with increasing paranoid schizotypy, and was not affected by state psychopathology. Increasing val loading resulted in a dose-dependent increase in the factor loading for the relation between negative schizotypy and cognitive performance accuracy. CONCLUSIONS Different schizotypal phenotypes had opposing relations to cognitive performance in the population. COMT genotype modulated the relation between the negative schizotypal phenotype and cognitive performance.

Time-frequency analysis methods to quantify the time-varying microstructure of sleep EEG spindles: Possibility for dementia biomarkers?

The time-varying microstructure of sleep EEG spindles may have clinical significance in dementia studies and can be quantified with a number of techniques. In this paper, real and simulated sleep spindles were regarded as AM/FM signals modeled by six parameters that define the instantaneous envelope (IE) and instantaneous frequency (IF) waveforms for a sleep spindle. These parameters were estimated using four different methods, namely the Hilbert transform (HT), complex demodulation (CD), matching pursuit (MP) and wavelet transform (WT). The average error in estimating these parameters was lowest for HT, higher but still less than 10% for CD and MP, and highest (greater than 10%) for WT. The signal distortion induced by the use of a given method was greatest in the case of HT and MP. These two techniques would necessitate the removal of about 0.4s from the spindle data, which is an important limitation for the case of spindles with duration less than 1s. Although the CD method may lead to a higher error than HT and MP, it requires a removal of only about 0.23s of data. An application of this sleep spindle parameterization via the CD method is proposed, in search of efficient EEG-based biomarkers in dementia. Preliminary results indicate that the proposed parameterization may be promising, since it can quantify specific differences in IE and IF characteristics between sleep spindles from dementia subjects and those from aged controls.

Independent component analysis for source localization of EEG sleep spindle components

Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11–16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle “components” (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles.

Potential dementia biomarkers based on the time-varying microstructure of sleep EEG spindles

The time-varying microstructure of sleep EEG spindles may have clinical significance in dementia studies. In this work, the sleep spindle is modeled as an AM-FM signal and parameterized in terms of six parameters, three quantifying the instantaneous envelope (IE) and three quantifying the instantaneous frequency (IF) of the spindle model. The IE and IF waveforms of sleep spindles from patients with dementia and normal controls were estimated using the time-frequency technique of complex demodulation (CD). Sinusoidal curve-fitting using a matching pursuit (MP) approach was applied to the IE and IF waveforms for the estimation of the six model parameters. Specific differences were found in sleep spindle instantaneous frequency dynamics between spindles from dementia subjects and spindles from controls.

Differences in EEG delta frequency characteristics and patterns in slow-wave sleep between dementia patients and controls: a pilot study.

Purpose To evaluate the modifications of EEG activity during slow-wave sleep in patients with dementia compared with healthy elderly subjects, using spectral analysis and period-amplitude analysis. Methods Five patients with dementia and 5 elderly control subjects underwent night polysomnographic recordings. For each of the first three nonrapid eye movement–rapid eye movement sleep cycles, a well-defined slow-wave sleep portion was chosen. The delta frequency band (0.4–3.6 Hz) in these portions was analyzed with both spectral analysis and period-amplitude analysis. Results Spectral analysis showed an increase in the delta band power in the dementia group, with a decrease across the night observed only in the control group. For the dementia group, period-amplitude analysis showed a decrease in well-defined delta waves of frequency lower than 1.6 Hz and an increase in such waves of frequency higher than 2 Hz, in incidence and amplitude. Conclusions Our study showed (1) a loss of the dynamics of delta band power across the night sleep, in dementia, and (2) a different distribution of delta waves during slow-wave sleep in dementia compared with control subjects. This kind of computer-based analysis can highlight the presence of a pathologic delta activity during slow-wave sleep in dementia and may support the hypothesis of a dynamic interaction between sleep alteration and cognitive decline.

Performance evaluation of an Artificial Neural Network automatic spindle detection system

Sleep spindles are transient waveforms found in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. Sleep spindles are used for the classification of sleep stages and have been studied in the context of various psychiatric and neurological disorders, such as Alzheimers disease (AD) and the so-called Mild Cognitive Impairment (MCI), which is considered to be a transitional stage between normal aging and dementia. The visual processing of wholenight sleep EEG recordings is tedious. Therefore, various techniques have been proposed for automatically detecting sleep spindles. In the present work an automatic sleep spindle detection system, that has been previously proposed, using a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), is evaluated in detecting spindles of both healthy controls, as well as MCI and AD patients. An investigation is carried also concerning the visual detection process, taking into consideration the feedback information provided by the automatic detection system. Results indicate that the sensitivity of the detector was 81.4%, 62.2%, and 83.3% and the false positive rate was 34%, 11.5%, and 33.3%, for the control, MCI, and AD groups, respectively. The visual detection process had a sensitivity rate ranging from 46.5% to 60% and a false positive rate ranging from 4.8% to 19.2%.

Sleep EEG and spindle characteristics after combination treatment with clozapine in drug-resistant schizophrenia: a pilot study.

Purpose: Clozapine is an atypical neuroleptic agent, effective in treating drug-resistant schizophrenia. The aim of this work was to investigate overall sleep architecture and sleep spindle morphology characteristics, before and after combination treatment with clozapine, in patients with drug-resistant schizophrenia who underwent polysomnography. Methods: Standard polysomnographic techniques were used. To quantify the sleep spindle morphology, a modeling technique was used that quantifies time-varying patterns in both the spindle envelope and the intraspindle frequency. Results: After combination treatment with clozapine, the patients showed clinical improvement. In addition, their overall sleep architecture and, more importantly, parameters that quantify the time-varying sleep spindle morphology were affected. Specifically, the results showed increased stage 2 sleep, reduced slow-wave sleep, increased rapid eye movement sleep, increased total sleep time, decreased wake time after sleep onset, as well as effects on spindle amplitude and intraspindle frequency parameters. However, the above changes in overall sleep architecture were statistically nonsignificant trends. Conclusions: The findings concerning statistically significant effects on spindle amplitude and intraspindle frequency parameters may imply changes in cortical sleep EEG generation mechanisms, as well as changes in thalamic pacing mechanisms or in thalamo–cortical network dynamics involved in sleep EEG generation, as a result of combination treatment with clozapine. Significance: Sleep spindle parameters may serve as metrics for the eventual development of effective EEG biomarkers to investigate treatment effects and pathophysiological mechanisms in schizophrenia.

EEG-based investigation of brain connectivity changes in psychotic patients undergoing the primitive expression form of dance therapy: a methodological pilot study

Primitive expression (PE) is a form of dance therapy (DT) that involves an interaction of ethologically and socially based forms which are supplied for re-enactment. There exist very few studies of DT applications including in their protocol the measurement of neurophysiological parameters. The present pilot study investigates the use of the correlation coefficient (ρ) and mutual information (MI), and of novel measures extracted from ρ and MI, on electroencephalographic (EEG) data recorded in patients with schizophrenia while they undergo PE DT, in order to expand the set of neurophysiology-based approaches for quantifying possible DT effects, using parameters that might provide insights about any potential brain connectivity changes in these patients during the PE DT process. Indication is provided for an acute potentiation effect, apparent at late-stage PE DT, on the inter-hemispheric connectivity in frontal areas, as well as for attenuation of the inter-hemispheric connectivity of left frontal and right central areas and for potentiation of the intra-hemispheric connectivity of frontal and central areas, bilaterally, in the transition from early to late-stage PE DT. This pilot study indicates that by using EEG connectivity measures based on ρ and MI, the set of useful neurophysiology-based approaches for quantifying possible DT effects is expanded. In the framework of the present study, the causes of the observed connectivity changes cannot be attributed with certainty to PE DT, but indications are provided that these measures may contribute to a detailed assessment of neurophysiological mechanisms possibly being affected by this therapeutic process.

Comparison of fractal dimension estimation algorithms for epileptic seizure onset detection

The fractal dimension (FD) is a natural measure of the irregularity of a curve. In this study the performances of two FD-based methodologies are compared in terms of their ability to detect the onset of epileptic seizures in scalp EEG. The FD algorithms used is Katzpsilas, which has been broadly utilized in the EEG analysis literature, and the k-nearest neighbor (k-NN), which is applied in this study in a time series sense for the first time. 244.9 hours of EEG recordings, including 16 seizures in 3 patients, were analyzed. Both approaches achieved 100% sensitivity with a false positive rate of 0.85 FP/h for the k-NN algorithm and 1 FP/h for Katzpsilas algorithm. The corresponding detection delays were 6.5 s and 10.5 s on the average, respectively. The k-NN algorithm seems to outperform Katzpsilas algorithm. Results are satisfactory in comparison to other methodologies applied on scalp EEG and proposed in the literature.

Dynamics of regional brain activity in epilepsy: a cross-disciplinary study on both intracranial and scalp-recorded epileptic seizures

OBJECTIVE Recent cross-disciplinary literature suggests a dynamical analogy between earthquakes and epileptic seizures. This study extends the focus of inquiry for the applicability of models for earthquake dynamics to examine both scalp-recorded and intracranial electroencephalogram recordings related to epileptic seizures. APPROACH First, we provide an updated definition of the electric event in terms of magnitude and we focus on the applicability of (i) a model for earthquake dynamics, rooted in a nonextensive Tsallis framework, (ii) the traditional Gutenberg and Richter law and (iii) an alternative method for the magnitude-frequency relation for earthquakes. Second, we apply spatiotemporal analysis in terms of nonextensive statistical physics and we further examine the behavior of the parameters included in the nonextensive formula for both types of electroencephalogram recordings under study. MAIN RESULTS We confirm the previously observed power-law distribution, showing that the nonextensive formula can adequately describe the sequences of electric events included in both types of electroencephalogram recordings. We also show the intermittent behavior of the epileptic seizure cycle which is analogous to the earthquake cycles and we provide evidence of self-affinity of the regional electroencephalogram epileptic seizure activity. SIGNIFICANCE This study may provide a framework for the analysis and interpretation of epileptic brain activity and other biological phenomena with similar underlying dynamical mechanisms.