EEG Multiscale Complexity in Schizophrenia During Picture Naming

Abstract

Introduction: Patients with schizophrenia show cognitive deficits that are evident both behaviourally and with EEG recordings. Recent studies have suggested that non-linear analyses of EEG might more adequately reflect the complex, irregular, non-stationary behavior of neural processes than more traditional ERP measures. Non-linear analyses have been mainly applied to EEGs from patients at rest, whereas differences in complexity might be more evident during task performance.Objective: We aimed to investigate changes in non-linear brain dynamics of patients with schizophrenia during cognitive processing.Method: 18 patients and 17 matched healthy controls were asked to name pictures. EEG data were collected at rest and while they were performing a naming task. EEGs were analyzed with the classical Lempel-Ziv Complexity (LZC) and with the Multiscale LZC. Electrodes were grouped in seven regions of interest (ROI).Results: As expected, controls had fewer naming errors than patients. Regarding EEG complexity, the interaction between Group, Task and ROI indicated that patients showed higher complexity values in right frontal regions only at rest, where no differences in complexity between patients and controls were found during the naming task. EEG complexity increased from rest to task in controls in left temporal-parietal regions, while no changes from rest to task were observed in patients. Finally, differences in complexity between patients and controls depended on the frequency bands: higher values of complexity in patients at rest were only observed in fast bands, indicating greater heterogeneity in patients in local dynamics of neuronal assemblies.Conclusion: Consistent with previous studies, schizophrenic patients showed higher complexity than controls in frontal regions at rest. Interestingly, we found different modulations of brain complexity during a simple cognitive task between patients and controls. These data can be interpreted as indicating schizophrenia-related failures to adapt brain functioning to the task, which is reflected in poorer behavioral performance.Highlights: - We measured classical and multiscale Lempel-Ziv Complexity (LZCN and MLZC) of the EEG signal of patients with schizophrenia and controls at rest and while performing a cognitive task. - We found that patients and controls showed a different pattern of brain complexity depending on their cognitive state (at rest or under cognitive challenge). - Our results illustrate the value of the MLZC in the characterization of the pattern of brain complexity in schizophrenia on function of frequency bands. - Nonlinear methodologies of EEG analysis can help to characterize brain dysfunction in schizophrenia.

Introspective and Neurophysiological Measures of Mind Wandering in Schizophrenia

“…However, we found a group modulation by electrode sites in which the EEG complexity was lower in patients than in controls in the frontal and temporal areas. This pattern is consistent with previous research that employed HFD as a measure of complexity 56 , whereas higher values of complexity in patients are usually found when Lempel-Ziv complexity is calculated 53,57 .…”

Alteration of Power Law Scaling of Spontaneous Brain Activity in Schizophrenia

“…Yang et al (2015) applied entropy analysis to resting-state fMRI data from patients with schizophrenia and documented that the decreased resting-state brain activity complexity can be characterized by increased regularity in the temporal lobe and increased randomness pattern in the frontal cortex, which explains the patients' withdrawal behavior and auditory hallucinations. Nonlinear characteristics of brain signals were also reported in studies with various biomedical signal recording techniques, such as electroencephalogram (EEG) (Akar et al, 2016;Carlino et al, 2012;Cerquera et al, 2017;Di Lorenzo et al, 2015;Fernandez et al, 2013;Ibanez-Molina et al, 2018;Keshavan et al, 2004;Kim et al, 2000;Lee et al, 2008;Li et al, 2008;Raghavendra et al, 2009;Takahashi et al, 2010;Xiang et al, 2019;Yu et al, 2016) or magnetoencephalography (MEG) (Brookes et al, 2015;Fernandez et al, 2013;Fernandez et al, 2011;Kotini and Anninos, 2002;Robinson and Mandell, 2015).…”

Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals

“…Ibanez-Molina et al [22] acquired EEG recordings from participants while they were at rest and engaged in a naming task. The Neuroscan SynAmps 32-channel amplifier was employed for the data acquirement.…”