An estimation of the first positive Lyapunov exponent of the EEG in patients with schizophrenia

Kim, Daijin; Jeong, Jaeseung; Chae, Jeong‐Ho; Park, Seongchong; Kim, Soo Yong; Go, Hyo Jin; Paik, In‐Ho; Kim, Kwangsoo; Choi, Bo-Moon

doi:10.1016/s0925-4927(00)00052-4

Cited by 76 publications

(48 citation statements)

References 52 publications

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“…Alterations in EEG complexity measures have been reported in some neuropsychiatric disorders including schizophrenia [8,15,25]. The present findings of high heritability of PD2 measures suggest that EEG complexity measures may help in identifying putative neurophysiological abnormalities mediating genetic liability to neuropsychiatric disorders.…”

supporting

confidence: 51%

“…This hypothesis has received increasing support by evidence indicating significant abnormalities in regional or global EEG complexity in patients with neuropsychiatric disorders compared to controls. Examples include Alzheimer's disease [12,13], Parkinson's disease [22,31], schizophrenia [8,15,25], post-traumatic stress disorder [7], and mania [4]. The clinical and diagnostic utility of complexity measures in these and related contexts remains to be explored.…”

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confidence: 99%

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Genetic influences on dynamic complexity of brain oscillations

Anokhin

Müller

Lindenberger

et al. 2006

Neuroscience Letters

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Human electroencephalogram (EEG) consists of complex aperiodic oscillations that are assumed to indicate underlying neural dynamics such as the number and degree of independence of oscillating neuronal networks. EEG complexity can be estimated using measures derived from nonlinear dynamic systems theory. Variations in such measures have been shown to be associated with normal individual differences in cognition and some neuropsychiatric disorders. Despite the increasing use of EEG complexity measures for the study of normal and abnormal brain functioning, little is known about genetic and environmental influences on these measures. Using the pointwise dimension (PD2) algorithm, this study assessed heritability of EEG complexity at rest in a sample of 214 young female twins consisting of 51 monozygotic (MZ) and 56 dizygotic (DZ) pairs. In MZ twins, intrapair correlations were high and statistically significant; in DZ twins, correlations were substantially smaller. Genetic analyses using linear structural equation modeling revealed high and significant heritability of EEG complexity: 62-68% in the eyes-closed condition, and 46-60% in the eyes-open condition. Results suggest that individual differences in the complexity of resting electrocortical dynamics are largely determined by genetic factors. Neurophysiological mechanisms mediating genetic variation in EEG complexity may include the degree of structural connectivity and functional differentiation among cortical neuronal assemblies. © 2005 Elsevier Ireland Ltd. All rights reserved.Keywords: Electroencephalogram; Complexity; Nonlinear dynamics; Twins; Heritability Neural activity underlying cognition and behavior is characterized by a high degree of functional differentiation and, at the same time, functional integration achieved through rapid binding of spatially distributed and functionally specialized neuronal groups [32]. To some extent, these fundamental properties of large-scale brain dynamics are reflected in the electroencephalogram (EEG) recorded from the scalp. The EEG results from the summation of postsynaptic activity of a large number of spatially distributed but functionally connected and interacting cortical neurons and neuronal assemblies. Accordingly, the EEG time series has a complex structure reflecting the complexity of the underlying neural generators [16,26]. A greater number of independent processes contributing to the EEG results in a greater complexity of EEG time series [18]. EEG complexity may reflect the number of states of a system resulting from the interaction * Corresponding author. Tel.: +1 314 286 2201; fax: +1 314 286 0092.E-mail address: andrey@matlock.wustl.edu (A.P. Anokhin).among its elements, with higher complexity reflecting a larger number of separable oscillatory networks [32].In recent years, cortical oscillations have been increasingly viewed from the perspective of nonlinear dynamic systems theory, which investigates complex, aperiodic systems capable of self-organization [9,26]. The behavior of complex syste...

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supporting

confidence: 51%

mentioning

confidence: 99%

Genetic influences on dynamic complexity of brain oscillations

Anokhin

Müller

Lindenberger

et al. 2006

Neuroscience Letters

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“…Although there was no significant correlation between LZC and medication dosageequivalents, drug effects cannot be ruled out. Previously, Jeong et al (1998) and Kim et al (2000) likewise found no correlation between antipsychotic dose and complexity values. Nevertheless, authors were cautious when interpreting this absence of significant correlations.…”

Section: Significancementioning

confidence: 83%

“…Numerous complexity measures have been proposed (see Tononi et al, 1998) including: dimensional complexity, algorithmic complexity, neural complexity, fractal dimension, first Lyapunov component, correlation dimension, Lempel-Ziv Complexity (LZC), etc. (Elbert et al, 1992;Koukkou et al, 1993;Jeong et al, 1998;Kim et al, 2000;Kotini and Anninos, 2002;Li et al, 2008). For example, the dimensional complexity refers to the classical method of the so-called correlation dimension.…”

Section: Introductionmentioning

confidence: 99%

Lempel–Ziv complexity in schizophrenia: A MEG study

Fernández

López‐Ibor

Turrero

et al. 2011

Clinical Neurophysiology

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SchizophreniaLempel-Ziv complexity Magnetoencephalography Neurodevelopmental Neurodegenerative• Schizophrenic patients show higher complexity values as compared to controls.• Schizophrenic patients showed a tendency to reduced complexity values as a function of age while controls showed the opposite tendency.• The tendency observed in schizophrenic patients parallels the tendency observed in Alzheimer disease patients. ABSTRACTObjective: The neurodevelopmental-neurodegenerative debate is a basic issue in the field of the neuropathological basis of schizophrenia (SCH). Neurophysiological techniques have been scarcely involved in such debate, but nonlinear analysis methods may contribute to it. Methods: Fifteen patients (age range 23-42 years) matching DSM IV-TR criteria for SCH, and 15 sex-and age-matched control subjects (age range 23-42 years) underwent a resting-state magnetoencephalographic evaluation and Lempel-Ziv complexity (LZC) scores were calculated. Results: Regression analyses indicated that LZC values were strongly dependent on age. Complexity scores increased as a function of age in controls, while SCH patients exhibited a progressive reduction of LZC values. A logistic model including LZC scores, age and the interaction of both variables allowed the classification of patients and controls with high sensitivity and specificity. Conclusions: Results demonstrated that SCH patients failed to follow the "normal" process of complexity increase as a function of age. In addition, SCH patients exhibited a significant reduction of complexity scores as a function of age, thus paralleling the pattern observed in neurodegenerative diseases. Significance: Our results support the notion of a progressive defect in SCH, which does not contradict the existence of a basic neurodevelopmental alteration.

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“…According to the diagnostic criteria (DSM-IV) of the American Psychiatric Association (1994); International Statistical Classification of Diseases and Health Related Problems (2005), schizophrenic patients show some characteristic symptoms including delusions, hallucinations or disorganized speech. Recently, much attention has been paid to analysis of EEG signals of schizophrenic patients (Barnett & Kirk, 2005;Dierks, Strik, & Maurer, 1995;Elliot Hong et al, 2004;Jin, Ham, & Kim, 2005;Keshavan, David Cashmere, Miewald, & Yeragani, 2004;Kikuchi et al, 2007;Kim et al, 2000;Kirsch, Besthorn, Klein, Rindfleisch, & Olbrich, 2000;Koukkou, Lehmann, Wackermann, Dvorak, & Henggeler, 1993;Light et al, 2006;Boutros et al, 2007;Tong, Kong, & Xu, 1996;Yeragani, Cashmere, Miewald, Tancer, & Keshavan, 2006). In some research Roschke, Fell, & Beckmann, 1995) nonlinear methods have been applied to EEG signals of the two groups of schizophrenic patients and control participants.…”

Section: Introductionmentioning

confidence: 99%