Characterization of the sleep EEG in acutely depressed men using detrended fluctuation analysis

OBJECTIVE The aim of the present paper is to study the fluctuations of the sleep EEG over various time scales during a specific pathological condition: major depressive episode. Focus is made on scaling behaviour, which is the signature of the absence of characteristic time scale, and the presence of long-range correlations associated to physiological constancy preservation, variability reduction and mostly adaptability. METHODS Whole night sleep electroencephalogram signals were recorded in 24 men: 10 untreated patients with a major depressive episode (41.70+/-8.11 years) and 14 healthy subjects (42.43+/-5.67 years). Scaling in these time series was investigated with detrended fluctuation analysis (time range: 0.16-2.00s). Scaling exponents (alpha) were determined in stage 2, slow wave sleep (stages 3 and 4) and during REM sleep. Forty-five epochs of 20s were chosen randomly in each of these stages. RESULTS The median values of alpha were lower in patients during stage 2 and SWS. CONCLUSIONS Major depressive episodes are characterized by a modification in the correlation structure of the sleep EEG time series. The finding which shows decreasing rate of the temporal correlations being different within the two groups in stage 2 and SWS provides an electrophysiologic argument that the underlying neuronal dynamics are modified during acute depression. SIGNIFICANCE The observed modifications in scaling behaviour in acutely depressed patients could be an explanation of the sleep fragmentation and instability found during major depressive episode.

[1]  B. Carroll,et al.  EEG studies of sleep in the diagnosis of depression. , 1982, Biological psychiatry.

[2]  P. F. Meier,et al.  Dimensional complexity and spectral properties of the human sleep EEG , 2003, Clinical Neurophysiology.

[3]  M. Riley,et al.  IN FRACTAL PHYSIOLOGY , 2022 .

[4]  Glenda M MacQueen,et al.  Course of illness, hippocampal function, and hippocampal volume in major depression , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[5]  R. Mantegna,et al.  Long-range correlation properties of coding and noncoding DNA sequences: GenBank analysis. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[6]  Michael Marriott,et al.  Lower hippocampal volume in patients suffering from depression: a meta-analysis. , 2004, The American journal of psychiatry.

[7]  H. Mayberg Modulating dysfunctional limbic-cortical circuits in depression: towards development of brain-based algorithms for diagnosis and optimised treatment. , 2003, British medical bulletin.

[8]  D. Kupfer,et al.  Interval between onset of sleep and rapid-eye-movement sleep as an indicator of depression. , 1972, Lancet.

[9]  I. Jánosi,et al.  Detrended fluctuation analysis of daily temperature records: Geographic dependence over Australia , 2004, physics/0403120.

[10]  Bruce J. West,et al.  Fractal physiology , 1994, IEEE Engineering in Medicine and Biology Magazine.

[11]  Blanco,et al.  Time-frequency analysis of electroencephalogram series. II. Gabor and wavelet transforms. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[12]  Dc Washington Diagnostic and Statistical Manual of Mental Disorders, 4th Ed. , 1994 .

[13]  Daniel J Buysse,et al.  The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research , 1989, Psychiatry Research.

[14]  E. Niedermeyer,et al.  The Clinical Relevance of EEG Interpretation , 2003, Clinical EEG.

[15]  Mathias Berger,et al.  Sleep and depression — results from psychobiological studies: an overview , 2001, Biological Psychology.

[16]  P. Linkowski,et al.  Relationship between the Newcastle scale and sleep polysomnographic variables in major depression: a controlled study , 1995, European Neuropsychopharmacology.

[17]  G. Goodwin,et al.  Cognitive deficits in depression: Possible implications for functional neuropathology , 2001, British Journal of Psychiatry.

[18]  E. Wolpert A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[19]  T. Åkerstedt,et al.  Microarousals During Sleep Are Associated With Increased Levels of Lipids, Cortisol, and Blood Pressure , 2004, Psychosomatic medicine.

[20]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[21]  Francesco Rundo,et al.  Dynamics of the EEG slow-wave synchronization during sleep , 2005, Clinical Neurophysiology.

[22]  C. Stam,et al.  Scale‐free dynamics of global functional connectivity in the human brain , 2004, Human brain mapping.

[23]  T. Ferrée,et al.  Fluctuation Analysis of Human Electroencephalogram , 2001, physics/0105029.

[24]  Paul A. Watters,et al.  Time-invariant long-range correlations in electroencephalogram dynamics , 2000, Int. J. Syst. Sci..

[25]  D. Kupfer Sleep research in depressive illness: Clinical implications —a tasting menu , 1995, Biological Psychiatry.

[26]  J. Rabe-Jabłońska,et al.  [Affective disorders in the fourth edition of the classification of mental disorders prepared by the American Psychiatric Association -- diagnostic and statistical manual of mental disorders]. , 1993, Psychiatria polska.

[27]  Julius S. Bendat,et al.  Random Data - Analysis and Measurement Procedures - Second Edition (revised and expanded) , 1986 .

[28]  K. Linkenkaer-Hansen,et al.  Long-Range Temporal Correlations and Scaling Behavior in Human Brain Oscillations , 2001, The Journal of Neuroscience.

[29]  Dae-Jin Kim,et al.  Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data , 2002, Comput. Biol. Medicine.

[30]  A. Goldberger Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside , 1996, The Lancet.

[31]  A. Rechtschaffen A manual of standardized terminology, techniques and scoring system for sleep of human subjects , 1968 .

[32]  Klaus Linkenkaer-Hansen,et al.  Breakdown of Long-Range Temporal Correlations in Theta Oscillations in Patients with Major Depressive Disorder , 2005, The Journal of Neuroscience.

[33]  W. B. Webb,et al.  The first night effect: an EEG study of sleep. , 1966, Psychophysiology.

[34]  D J Kupfer,et al.  REM latency: a psychobiologic marker for primary depressive disease. , 1976, Biological psychiatry.

[35]  In-Young Kim,et al.  Nonlinear-analysis of human sleep EEG using detrended fluctuation analysis. , 2004, Medical engineering & physics.

[36]  George Sugihara,et al.  Fractals in science , 1995 .

[37]  Shlomo Havlin,et al.  Fractals in Science , 1995 .

[38]  a.R.V.,et al.  Clinical neurophysiology , 1961, Neurology.

[39]  Ivanov PCh,et al.  Application of statistical physics to heartbeat diagnosis. , 1999, Physica A.

[40]  Blanco,et al.  Time-frequency analysis of electroencephalogram series. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[41]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[42]  M. Hamilton,et al.  Development of a rating scale for primary depressive illness. , 1967, The British journal of social and clinical psychology.

[43]  L. Amaral,et al.  Multifractality in human heartbeat dynamics , 1998, Nature.

[44]  H. Saunders Literature Review : RANDOM DATA: ANALYSIS AND MEASUREMENT PROCEDURES J. S. Bendat and A.G. Piersol Wiley-Interscience, New York, N. Y. (1971) , 1974 .

[45]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[46]  P Linkowski,et al.  Twenty-four-hour patterns of sleep in depression. , 1991, Sleep.

[47]  Marcel Ausloos,et al.  Coherent and random sequences in financial fluctuations , 1997 .

[48]  C. Peng,et al.  Mosaic organization of DNA nucleotides. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[49]  Daniel J Buysse,et al.  Microstructure of sleep in depressed patients according to the cyclic alternating pattern. , 2003, Journal of affective disorders.

[50]  Edith V. Sullivan,et al.  Differential effect of HIV infection and alcoholism on conflict processing, attentional allocation, and perceptual load: Evidence from a stroop match-to-sample task , 2005, Biological Psychiatry.

[51]  H. Stanley,et al.  Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. , 1995, Chaos.

[52]  T. Brismar,et al.  Long-range temporal correlations in electroencephalographic oscillations: Relation to topography, frequency band, age and gender , 2005, Neuroscience.

[53]  B. Litt,et al.  Long-range temporal correlations in epileptogenic and non-epileptogenic human hippocampus , 2004, Neuroscience.

[54]  C. Peng,et al.  Long-range correlations in nucleotide sequences , 1992, Nature.

[55]  P. Verbanck,et al.  Does sleep EEG data distinguish between UP, BPI or BPII major depressions? An age and gender controlled study. , 1998, Journal of affective disorders.

[56]  E. Castrén,et al.  Is mood chemistry? , 2005, Nature Reviews Neuroscience.

[57]  T. Roth,et al.  Experimental sleep fragmentation. , 1994, Sleep.

[58]  A Värri,et al.  A simple format for exchange of digitized polygraphic recordings. , 1992, Electroencephalography and clinical neurophysiology.

[59]  W. Drevets Neuroimaging studies of mood disorders , 2000, Biological Psychiatry.