Changes in the MEG background activity in patients with positive symptoms of schizophrenia: spectral analysis and impact of age

The frequency spectrum of the magnetoencephalogram (MEG) background activity was analysed in 15 schizophrenia (SCH) patients with predominant positive symptoms and 17 age-matched healthy control subjects using the following variables: median frequency (MF), spectral entropy (SpecEn) and relative power in delta (RPδ), theta (RPθ), lower alpha (RPα1), upper alpha (RPα2), beta (RPβ) and gamma (RPγ) bands. We found significant differences between the two subject groups in the average level of MF and RPγ in some regions of the scalp. Additionally, the MF, SpecEn, RPβ and RPγ values of SCH patients with positive symptoms had a different dependence on age as compared with the results of control subjects, suggesting that SCH affects the way in which the brain activity evolves with age. Moreover, we also classified the MEG signals by means of a cross-validated feature selection process followed by a logistic regression. The subjects were classified with 71.3% accuracy and an area under the ROC curve of 0.741. Thus, the spectral and classification analysis of the MEG in SCH may provide insights into how this condition affects the brain activity and may help in its early detection.

[1]  Seung-Hwan Lee,et al.  Nonlinear Analysis of Electroencephalogram in Schizophrenia Patients with Persistent Auditory Hallucination , 2008, Psychiatry investigation.

[2]  Reza Boostani,et al.  Entropy and complexity measures for EEG signal classification of schizophrenic and control participants , 2009, Artif. Intell. Medicine.

[3]  J. Kwon,et al.  Gamma Oscillation in Schizophrenia , 2011, Psychiatry investigation.

[4]  S. Tong,et al.  Abnormal EEG complexity in patients with schizophrenia and depression , 2008, Clinical Neurophysiology.

[5]  T M Itil,et al.  Qualitative and quantitative EEG findings in schizophrenia. , 1977, Schizophrenia bulletin.

[6]  Reza Boostani,et al.  An efficient classifier to diagnose of schizophrenia based on the EEG signals , 2009, Expert Syst. Appl..

[7]  Roberto Hornero,et al.  Lempel–Ziv complexity in schizophrenia: A MEG study , 2011, Clinical Neurophysiology.

[8]  Umberto Volpe,et al.  Evidence-Based Medicine and Electrophysiology in Schizophrenia , 2009, Clinical EEG and neuroscience.

[9]  Reza Boostani,et al.  A new approach for EEG signal classification of schizophrenic and control participants , 2011, Expert Syst. Appl..

[10]  Roberto Hornero,et al.  MEG spectral profile in Alzheimer's disease and mild cognitive impairment , 2006, Clinical Neurophysiology.

[11]  W. Iacono,et al.  The status of spectral EEG abnormality as a diagnostic test for schizophrenia , 2008, Schizophrenia Research.

[12]  S. Nagarajan,et al.  Cognitive Impairments in Schizophrenia as Assessed Through Activation and Connectivity Measures of Magnetoencephalography (MEG) Data , 2009, Front. Hum. Neurosci..

[13]  Roberto Hornero,et al.  Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment , 2012, Journal of psychopharmacology.

[14]  V. S. Bagnato,et al.  Photodynamic therapy induced vascular damage: an overview of experimental PDT , 2013 .

[15]  Werner Lutzenberger,et al.  Physical aspects of the EEG in schizophrenics , 1992, Biological Psychiatry.

[16]  S. Rossi,et al.  Clinical neurophysiology of aging brain: From normal aging to neurodegeneration , 2007, Progress in Neurobiology.

[17]  D. Abásolo,et al.  Brain oscillatory complexity across the life span , 2012, Clinical Neurophysiology.

[18]  B. S. Raghavendra,et al.  Complexity analysis of EEG in patients with schizophrenia using fractal dimension , 2009, Physiological measurement.

[19]  W. Singer,et al.  Abnormal neural oscillations and synchrony in schizophrenia , 2010, Nature Reviews Neuroscience.

[20]  T. Ortiz,et al.  The perception of emotion-free faces in schizophrenia: A magneto-encephalography study , 2008, Schizophrenia Research.

[21]  Roberto Hornero,et al.  Complexity and schizophrenia , 2013, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[22]  D A Steyn-Ross,et al.  Cortical entropy changes with general anaesthesia: theory and experiment. , 2004, Physiological measurement.

[23]  Roberto Hornero,et al.  Complexity Analysis of Spontaneous Brain Activity in Attention-Deficit/Hyperactivity Disorder: Diagnostic Implications , 2009, Biological Psychiatry.

[24]  R. Palaniappan,et al.  Classification of biological signals using linear and nonlinear features , 2010, Physiological measurement.

[25]  Roberto Hornero,et al.  Spectral and Nonlinear Analyses of MEG Background Activity in Patients With Alzheimer's Disease , 2008, IEEE Transactions on Biomedical Engineering.