Brain complexity increases in mania

An important challenge in measuring whole brain activation is to develop a measure that could distinguish between normal and abnormal mood states. The application of chaos theory and non-linear dynamics to problems in biological sciences has resulted in a growing body of advancements and the notion of brain as a complex, non-linear system has attracted physicists, mathematicians, biologists and psychologists alike. To search for a correlation between alterations in chaotic brain states and mood disorders, we compared the fractal dimension of the electroencephalographic (EEG) signal in patients going through a manic episode of bipolar mood disorder (BMD) type I to a control group of healthy adults and showed that the EEG fractal dimension is significantly augmented in our patients. Thus, for the first time, we draw a clear objective distinction between normal and abnormal mood and associated brain states.

[1]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[2]  J. Bhattacharya.  Reduced degree of long-range phase synchrony in pathological human brain. , 2001, Acta neurobiologiae experimentalis.

[3]  H. Jasper,et al.  The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. , 1999, Electroencephalography and clinical neurophysiology. Supplement.

[4]  W. Catterall,et al.  From Ionic Currents to Molecular Mechanisms The Structure and Function of Voltage-Gated Sodium Channels , 2000, Neuron.

[5]  Giri P Krishnan,et al.  Neural synchronization deficits to auditory stimulation in bipolar disorder , 2004, Neuroreport.

[6]  A. Evins,et al.  Efficacy of newer anticonvulsant medications in bipolar spectrum mood disorders. , 2003, The Journal of clinical psychiatry.

[7]  F. Goodwin,et al.  The de facto US mental and addictive disorders service system. Epidemiologic catchment area prospective 1-year prevalence rates of disorders and services. , 1993, Archives of general psychiatry.

[8]  J. Soares,et al.  The anatomy of mood disorders—review of structural neuroimaging studies , 1997, Biological Psychiatry.

[9]  W. Löscher,et al.  The neurobiology of antiepileptic drugs for the treatment of nonepileptic conditions , 2004, Nature Medicine.

[10]  C. Elger,et al.  CAN EPILEPTIC SEIZURES BE PREDICTED? EVIDENCE FROM NONLINEAR TIME SERIES ANALYSIS OF BRAIN ELECTRICAL ACTIVITY , 1998 .

[11]  A. Harwood,et al.  Search for a common mechanism of mood stabilizers. , 2003, Biochemical pharmacology.

[12]  Werner Lutzenberger,et al.  Fractal dimension of electroencephalographic time series and underlying brain processes , 1995, Biological Cybernetics.

[13]  P. Agostino Accardo,et al.  Use of the fractal dimension for the analysis of electroencephalographic time series , 1997, Biological Cybernetics.

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

[15]  M. Brodie,et al.  Update on the mechanisms of action of antiepileptic drugs. , 2001, Epileptic disorders : international epilepsy journal with videotape.

[16]  Andreas Galka,et al.  Topics in Nonlinear Time Series Analysis, with Implications for Eeg Analysis , 2000 .

[17]  J. Martinerie,et al.  Epileptic seizures can be anticipated by non-linear analysis , 1998, Nature Medicine.

[18]  G. Berns,et al.  The neurobiology of bipolar disorder , 2003, American journal of medical genetics. Part C, Seminars in medical genetics.