Nonlinear analysis of EEG in major depression with fractal dimensions

Major depressive disorder (MDD) is a psychiatric mood disorder characterized by cognitive and functional impairments in attention, concentration, learning and memory. In order to investigate and understand its underlying neural activities and pathophysiology, EEG methodologies can be used. In this study, we estimated the nonlinearity features of EEG in MDD patients to assess the dynamical properties underlying the frontal and parietal brain activity. EEG data were obtained from 16 patients and 15 matched healthy controls. A wavelet-chaos methodology was used for data analysis. First, EEGs of subjects were decomposed into 5 EEG sub-bands by discrete wavelet transform. Then, both the Katz's and Higuchi's fractal dimensions (KFD and HFD) were calculated as complexity measures for full-band and sub-bands EEGs. Last, two-way analyses of variances were used to test EEG complexity differences on each fractality measures. As a result, a significantly increased complexity was found in both parietal and frontal regions of MDD patients. This significantly increased complexity was observed not only in full-band activity but also in beta and gamma sub-bands of EEG. The findings of the present study indicate the possibility of using the wavelet-chaos methodology to discriminate the EEGs of MDD patients from healthy controls.

[1]  Ronald C. Kessler,et al.  The National Comorbidity Survey , 2003 .

[2]  Judith Jaeger,et al.  Neurocognitive deficits and disability in major depressive disorder , 2006, Psychiatry Research.

[3]  Alexei Sourin,et al.  Human electroencephalograms seen as fractal time series: Mathematical analysis and visualization , 2006, Comput. Biol. Medicine.

[4]  M. J. Katz,et al.  Fractals and the analysis of waveforms. , 1988, Computers in biology and medicine.

[5]  R Jouvent,et al.  Decrease of complexity in EEG as a symptom of depression. , 1994, Neuroreport.

[6]  Lori L. Altshuler,et al.  fMRI abnormalities in dorsolateral prefrontal cortex during a working memory task in manic, euthymic and depressed bipolar subjects , 2010, Psychiatry Research: Neuroimaging.

[7]  Soo-Yong Kim,et al.  Nonlinear Dynamic Analysis of the EEG in Patients with Alzheimer’s Disease and Vascular Dementia , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[8]  R Quian Quiroga,et al.  Wavelet Transform in the analysis of the frequency composition of evoked potentials. , 2001, Brain research. Brain research protocols.

[9]  J. Allen,et al.  Resting frontal electroencephalographic asymmetry in depression: inconsistencies suggest the need to identify mediating factors. , 1998, Psychophysiology.

[10]  Marc Ansseau,et al.  Objective: remission of depression in primary care The Oreon Study , 2009, European Neuropsychopharmacology.

[11]  R. Schandry,et al.  Enhanced cardiac perception is associated with benefits in decision-making. , 2009, Psychophysiology.

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

[13]  Tomas Novak,et al.  Changes in QEEG prefrontal cordance as a predictor of response to antidepressants in patients with treatment resistant depressive disorder: a pilot study. , 2007, Journal of psychiatric research.

[14]  N Pradhan,et al.  Use of running fractal dimension for the analysis of changing patterns in electroencephalograms. , 1993, Computers in biology and medicine.

[15]  Hojjat Adeli,et al.  Wavelet-Chaos Methodology for Analysis of EEGs and EEG Sub-Bands , 2010 .

[16]  D. Narayana Dutt,et al.  Computing Fractal Dimension of Signals using Multiresolution Box-counting Method , 2010 .

[17]  R. Hornero,et al.  Estimating complexity from EEG background activity of epileptic patients. , 1999, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[18]  Fatma Latifoglu,et al.  Investigation of the noise effect on fractal dimension of EEG in schizophrenia patients using wavelet and SSA-based approaches , 2015, Biomed. Signal Process. Control..

[19]  H. Adeli,et al.  Fractality analysis of frontal brain in major depressive disorder. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[20]  Eduardo Aubert,et al.  EEG sources in a group of patients with major depressive disorders. , 2009, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[21]  Arthur W Toga,et al.  Anterior cingulate, gyrus rectus, and orbitofrontal abnormalities in elderly depressed patients: an MRI-based parcellation of the prefrontal cortex. , 2004, The American journal of psychiatry.

[22]  Heather L. Urry,et al.  The stability of resting frontal electroencephalographic asymmetry in depression. , 2004, Psychophysiology.

[23]  Wiro J Niessen,et al.  Vascular Brain Disease and Depression in the Elderly , 2010, Epidemiology.

[24]  Tianzi Jiang,et al.  White matter abnormalities in first-episode, treatment-naive young adults with major depressive disorder. , 2007, The American journal of psychiatry.

[25]  Matthias W. Riepe,et al.  Cognitive estimation in aged patients with major depressive disorder , 2010, Psychiatry Research.

[26]  Matcheri S. Keshavan,et al.  Correlates of insight in first episode psychosis , 2004, Schizophrenia Research.

[27]  Seung-Hwan Lee,et al.  Gamma-Band Auditory Steady-State Responses and Psychopathology , 2011 .

[28]  Å. Hammar,et al.  Cognitive Functioning in Major Depression – A Summary , 2009, Front. Hum. Neurosci..

[29]  T. Higuchi.  Approach to an irregular time series on the basis of the fractal theory , 1988 .