EEG network connectivity changes in mild cognitive impairment - Preliminary results.

Resting state EEGs were compared between patients with amnestic subtype of mild cognitive impairment (aMCI) and matched elderly controls at two times over a one year period. The study aimed at investigating the role of functional connectivity between and within different brain regions in relation to the progression of cognitive deficit in MCI. The EEG was recorded in two sessions during eyes closed and eyes open resting conditions. Functional brain connectivity was investigated based on the measurement of phase synchronization in different frequency bands. Delta and theta synchronization characteristics indicated decreased level of local and large-scale connectivity in the patients within the frontal, between the frontal and temporal, and frontal and parietal brain areas which was more pronounced 1year later. As a consequence of opening the eyes connectivity in the alpha1 band within the parietal lobe decreased compared to the eyes closed condition but only in the control group. The lack of alpha1 band reactivity following eye opening could reliably differentiate patients from controls. Our preliminary results support the notion that the functional disconnection between distant brain areas is a characteristic feature of MCI, and may prove to be predictive in terms of the progression of this condition.

[1]  Jie Tian,et al.  Altered topological patterns of brain networks in mild cognitive impairment and Alzheimer's disease: A resting-state fMRI study , 2012, Psychiatry Research: Neuroimaging.

[2]  M. Borga,et al.  Small baseline volume of left hippocampus is associated with subsequent conversion of MCI into dementia: The Göteborg MCI study , 2008, Journal of the Neurological Sciences.

[3]  G. Knyazev,et al.  Neuroscience and Biobehavioral Reviews , 2012 .

[4]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[5]  T. Arendt,et al.  The cholinergic system in aging and neuronal degeneration , 2011, Behavioural Brain Research.

[6]  P. Gregorio,et al.  Actividad espontánea electroencefalográfica y magnetoencefalográfica como marcador de la enfermedad de Alzheimer y el deterioro cognitivo leve , 2012 .

[7]  Andreas Kleinschmidt,et al.  EEG Alpha Power Modulation of fMRI Resting-State Connectivity , 2012, Brain Connect..

[8]  C. J. Stam,et al.  EEG synchronization likelihood in mild cognitive impairment and Alzheimer's disease during a working memory task , 2004, Clinical Neurophysiology.

[9]  R. Petersen.  Mild cognitive impairment as a diagnostic entity , 2004, Journal of internal medicine.

[10]  Francesco Rundo,et al.  Fronto-parietal coupling of brain rhythms in mild cognitive impairment: A multicentric EEG study , 2006, Brain Research Bulletin.

[11]  E. John,et al.  Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment , 2005, Neurobiology of Aging.

[12]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[13]  Massimo Buscema,et al.  Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy? , 2008, Clinical Neurophysiology.

[14]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[15]  S. Debener,et al.  Default-mode brain dysfunction in mental disorders: A systematic review , 2009, Neuroscience & Biobehavioral Reviews.

[16]  V. Ibáñez,et al.  Abnormal-induced theta activity supports early directed-attention network deficits in progressive MCI , 2009, Neurobiology of Aging.

[17]  Paolo Massimo Buscema,et al.  The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy , 2007, Artif. Intell. Medicine.

[18]  M. Greicius.  Resting-state functional connectivity in neuropsychiatric disorders , 2008, Current opinion in neurology.

[19]  J. Morris,et al.  Current concepts in mild cognitive impairment. , 2001, Archives of neurology.

[20]  Cornelis J Stam,et al.  Efficacy of Souvenaid in mild Alzheimer's disease: results from a randomized, controlled trial. , 2012, Journal of Alzheimer's disease : JAD.

[21]  C. Stam.  Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders , 2010, Journal of the Neurological Sciences.

[22]  Young-Min Park,et al.  Decreased EEG synchronization and its correlation with symptom severity in Alzheimer's disease , 2008, Neuroscience Research.

[23]  Panteleimon Giannakopoulos,et al.  Electrophysiological markers of rapid cognitive decline in mild cognitive impairment. , 2009, Frontiers of neurology and neuroscience.

[24]  P. Giannakopoulos,et al.  Working memory load–related electroencephalographic parameters can differentiate progressive from stable mild cognitive impairment , 2007, Neuroscience.

[25]  Charles DeCarli,et al.  Mild cognitive impairment: prevalence, prognosis, aetiology, and treatment , 2003, The Lancet Neurology.

[26]  A. Bokde,et al.  Assessing neuronal networks: Understanding Alzheimer's disease , 2009, Progress in Neurobiology.

[27]  Cornelis J Stam,et al.  Graph theoretical analysis of complex networks in the brain , 2007, Nonlinear biomedical physics.

[28]  Richard S. Frackowiak,et al.  Evolution of source EEG synchronization in early Alzheimer's disease , 2013, Neurobiology of Aging.

[29]  J. Gore,et al.  Mutual information analysis of the EEG in patients with Alzheimer's disease , 2001, Clinical Neurophysiology.

[30]  Li Yao,et al.  Impairment and compensation coexist in amnestic MCI default mode network , 2010, NeuroImage.

[31]  P. Rossini,et al.  Cortical sources of resting EEG rhythms in mild cognitive impairment and subjective memory complaint , 2010, Neurobiology of Aging.

[32]  M. Molnár,et al.  Age-dependent features of EEG-reactivity—Spectral, complexity, and network characteristics , 2010, Neuroscience Letters.

[33]  T. Cummins,et al.  Theta oscillations are affected by amnestic mild cognitive impairment and cognitive load. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[34]  F. Varela,et al.  Perception's shadow: long-distance synchronization of human brain activity , 1999, Nature.

[35]  Claudio Babiloni,et al.  Resting EEG sources correlate with attentional span in mild cognitive impairment and Alzheimer's disease , 2007, The European journal of neuroscience.

[36]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[37]  Leilei Zheng,et al.  Inter-and intra-hemispheric EEG coherence in patients with mild cognitive impairment at rest and during working memory task , 2006, Journal of Zhejiang University SCIENCE B.

[38]  E. Basar,et al.  Increased frontal phase-locking of event-related theta oscillations in Alzheimer patients treated with cholinesterase inhibitors. , 2007, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[39]  Cornelis J. Stam,et al.  Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease , 2012, NeuroImage.