Lempel–Ziv complexity in schizophrenia: A MEG study

Abstract

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.

Neural complexity as a potential translational biomarker for psychosis

“…Measures of complexity (including those developed in our research) have provided a useful tool for differentiating people of different age groups (McIntosh et al, 2008; McIntosh et al, 2014; Smith et al, 2014; Takahashi et al, 2009; Vakorin et al, 2011; Yang et al, 2013a) and with different clinical disorders including Alzheimer’s disease (Escudero et al, 2006; Mizuno et al, 2010; Yang et al, 2013b), autism (Bosl et al, 2011; Catarino et al, 2011; Ghanbari et al, 2015), attention deficit hyperactivity disorder (Gomez et al, 2013; Sokunbi et al, 2013), depression (Mendez et al, 2012; Niemiec and Lithgow, 2005; Saletu et al, 2010), SZ (Fernandez et al, 2011; Sokunbi et al, 2014; Takahashi et al, 2010; Yang et al, 2015), and traumatic brain injury (Lu et al, 2012; Raja Beharelle et al, 2012). Of note, the interpretation of complexity is often by the simple up vs. down approach in a single time scale along the continuum of regularity toward randomness, leading to the confusing conclusion that randomness is complex.…”

“…Brain complexity in SZ has been examined using electroencephalography (Keshavan et al, 2004; Takahashi et al, 2010), magnetoencephalography (Fernandez et al, 2011), and gyral folding using structural MRI (Narr et al, 2004). A valuable approach to investigating the complexity of brain activity in SZ is resting-state fMRI.…”

Multiscale Lempel–Ziv complexity for EEG measures

“…It has been shown that schizophrenics and patients with depressive disorders exhibit a high LZC (Li et al, 2008). The level of complexity changes with patients' age (Fernández et al, 2011(Fernández et al, , 2012. Alzheimer patients show an abnormally low LZC (e.g., Hornero et al, 2009;Dauwels et al, 2011).…”

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

“…In addition, EEG dimensional complexity in schizophrenia was lower than that in healthy controls [9]. In EEG studies, abnormal brain activity in schizophrenia patients can be detected using nonlinear analysis algorithms such as dimensional complexity [10], correlation dimension (D2) [11], Lempel_Ziv complexity (LZC) [12], approximate entropy (ApEn) [13], mutual information (MI) [14], and fractal dimension (FD) [15]. Among these algorithms, FD is applicable to nonlinear analysis of non-stationary and transient time series data like EEG signals [16], but FD has several limitations in stationary, noise-free, and long time series data.…”