Abnormal EEG complexity in patients with schizophrenia and depression

Li, Yingjie; Tong, Shanbao; Liú, Dan; Gai, Yi; Wang, Xiuyuan; Wang, Jijun; Qiu, Yihong; Zhu, Yan

doi:10.1016/j.clinph.2008.01.104

Cited by 182 publications

(132 citation statements)

References 49 publications

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“…A logistic model was able to predict group assignment (diagnosis) on the basis of individual LZC scores and ages. Our findings partially support prior reports (Elbert et al, 1992;Na et al, 2002;Irisawa et al, 2006;Li et al, 2008) of increased complexity values in SCH, however unlike previous investigators we also investigated the effects of age.…”

Section: Logistic Regression Analysessupporting

confidence: 91%

“…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%

“…However, many studies revealed a pattern of increased complexity values, especially in frontal areas, in SCH patients (Elbert et al, 1992;Saito et al, 1998;Na et al, 2002;Irisawa et al, 2006;Li et al, 2008). This may be related to the increased "irregularity" of SCH patients' behaviour (Koukkou et al, 1993).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Logistic Regression Analysessupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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Lempel–Ziv complexity in schizophrenia: A MEG study

Fernández

López‐Ibor

Turrero

et al. 2011

Clinical Neurophysiology

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“…LZC has been used to analyse EEG and MEG signals in patients with Alzheimer's disease Fernández et al, 2010;Gómez et al, 2006), attention deficit-hyperactivity disorder (ADHD) (Fernández et al, 2009), depression and schizophrenia (Li et al, 2008;Fernández et al, 2011a;Méndez et al, 2011;) as well as to measure the depth of anaesthesia (Zhang et al, 2001), or to study epileptic seizures (Radhakrishnan and Gangadhar, 1998). The increasing clinical use of LZC and other estimates of oscillatory complexity is the main reason to carry out a normative study where the 'normal' behaviour of complexity values is defined according to age and gender influences in a large population.…”

mentioning

confidence: 99%

Brain oscillatory complexity across the life span

Fernández

Zuluaga

Abásolo

et al. 2012

Clinical Neurophysiology

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Objective: Considering the increasing use of complexity estimates in neuropsychiatric populations, a normative study is critical to define the 'normal' behaviour of brain oscillatory complexity across the life span. Method:This study examines changes in resting-state magnetoencephalogram (MEG) complexity -quantified with the Lempel-Ziv complexity (LZC) algorithm -due to age and gender in a large sample of 222 (100 males/122 females) healthy participants with ages ranging from 7 to 84 years.Results: A significant quadratic (curvilinear) relationship (p < 0.05) between age and complexity was found, with LZC maxima being reached by the sixth decade of life.Once that peak was crossed, complexity values slowly decreased until late senescence.Females exhibited higher LZC values than males, with significant differences in the anterior, central and posterior regions (p < 0.05). Conclusions:These results suggest that the evolution of brain oscillatory complexity across the life span might be considered a new illustration of a 'normal' physiological rhythm.Significance: Previous and forthcoming clinical studies using complexity estimates might be interpreted from a more complete and dynamical perspective. Pathologies not 3 only cause an 'abnormal' increase or decrease of complexity values but they actually 'break' the 'normal' pattern of oscillatory complexity evolution as a function of age.Keywords: Life Span, Ageing, Complexity, Brain Development, White Matter Development Highlights 1. A significant quadratic (curvilinear) relationship between age and oscillatory complexity exists, with complexity maxima reached by the sixth decade of life.2. As in previous studies, females exhibit higher complexity values than males, at least in some brain regions.3. The evolution of oscillatory complexity across the life span is interpreted as a physiological rhythm which is altered by several brain pathologies.

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“…It is a nonlinear dynamic measure indicating the rate of appearance of the new patterns in a time series. A larger LZC implies a greater chance of the occurrence of new sequence patterns and thus a more complex dynamical behavior (Li et al , 2008). LZC can be viewed as independent of number of samples when n is large (Zhang, Roy, 2001).…”

Section: Lempel-ziv Complexity (Lzc)mentioning

confidence: 99%