Interindividual reaction time variability is related to resting-state network topology: an electroencephalogram study

Both anatomical and functional brain network studies have drawn great attention recently. Previous studies have suggested the significant impacts of brain network topology on cognitive function. However, the relationship between non-task-related resting-state functional brain network topology and overall efficiency of sensorimotor processing has not been well identified. In the present study, we investigated the relationship between non-task-related resting-state functional brain network topology and reaction time (RT) in a Go/Nogo task using an electroencephalogram (EEG). After estimating the functional connectivity between each pair of electrodes, graph analysis was applied to characterize the network topology. Two fundamental measures, clustering coefficient (functional segregation) and characteristic path length (functional integration), as well as "small-world-ness" (the ratio between the clustering coefficient and characteristic path length) were calculated in five frequency bands. Then, the correlations between the network measures and RT were evaluated in each band separately. The present results showed that increased overall functional connectivity in alpha and gamma frequency bands was correlated with a longer RT. Furthermore, shorter RT was correlated with a shorter characteristic path length in the gamma band. This result suggested that human RTs were likely to be related to the efficiency of the brain integrating information across distributed brain regions. The results also showed that a longer RT was related to an increased gamma clustering coefficient and decreased small-world-ness. These results provided further evidence of the association between the resting-state functional brain network and cognitive function.

[1]  C. Stam,et al.  Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets , 2002 .

[2]  J. Palva,et al.  New vistas for α-frequency band oscillations , 2007, Trends in Neurosciences.

[3]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[4]  L. M. Ward,et al.  From local inhibition to long-range integration: A functional dissociation of alpha-band synchronization across cortical scales in visuospatial attention , 2009, Brain Research.

[5]  C. J. Stam,et al.  Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? , 2004, Neuroscience Letters.

[6]  E. Bullmore,et al.  Human brain networks in health and disease , 2009, Current opinion in neurology.

[7]  S. Bressler,et al.  Large-scale brain networks in cognition: emerging methods and principles , 2010, Trends in Cognitive Sciences.

[8]  Theodore P. Zanto,et al.  Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory , 2011, Nature Neuroscience.

[9]  H. Berendse,et al.  The application of graph theoretical analysis to complex networks in the brain , 2007, Clinical Neurophysiology.

[10]  H. Petsche,et al.  Synchronization between prefrontal and posterior association cortex during human working memory. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Jürgen Kayser,et al.  Principal components analysis of Laplacian waveforms as a generic method for identifying ERP generator patterns: I. Evaluation with auditory oddball tasks , 2006, Clinical Neurophysiology.

[12]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[13]  C. Tallon-Baudry,et al.  Attention and awareness in synchrony , 2004, Trends in Cognitive Sciences.

[14]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[15]  K. Kaski,et al.  Intensity and coherence of motifs in weighted complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Edward M. Bowden,et al.  The origins of insight in resting-state brain activity , 2008, Neuropsychologia.

[17]  R. Barry,et al.  EEG differences between eyes-closed and eyes-open resting conditions , 2007, Clinical Neurophysiology.

[18]  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.

[19]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[20]  Martin Suter,et al.  Small World , 2002 .

[21]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[22]  Daniel Gembris,et al.  Top-down attentional processing enhances auditory evoked gamma band activity , 2003, Neuroreport.

[23]  O. Sporns,et al.  Organization, development and function of complex brain networks , 2004, Trends in Cognitive Sciences.

[24]  Simon Hanslmayr,et al.  Prestimulus oscillations predict visual perception performance between and within subjects , 2007, NeuroImage.

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

[26]  G. Sandini,et al.  Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. , 2009, Brain : a journal of neurology.

[27]  K. Gurney,et al.  Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence , 2008, PloS one.

[28]  Barry Gordon,et al.  The basis for choice reaction time slowing in Alzheimer's disease , 1990, Brain and Cognition.

[29]  E. Miller,et al.  Response to Comment on "Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices" , 2007, Science.

[30]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[31]  H. Bergman,et al.  Pathological synchronization in Parkinson's disease: networks, models and treatments , 2007, Trends in Neurosciences.

[32]  Anticipatory activity in the human thalamus is predictive of reaction times , 2008, Neuroscience.

[33]  Steven G Potkin,et al.  ALPHA EEG PREDICTS VISUAL REACTION TIME , 2006, The International journal of neuroscience.

[34]  Peter Brown,et al.  The relationship between oscillatory activity and motor reaction time in the parkinsonian subthalamic nucleus , 2005, The European journal of neuroscience.

[35]  L F Lago-Fernández,et al.  Fast response and temporal coherent oscillations in small-world networks. , 1999, Physical review letters.

[36]  Manuel Schabus,et al.  A shift of visual spatial attention is selectively associated with human EEG alpha activity , 2005, The European journal of neuroscience.

[37]  J. Palva,et al.  New vistas for alpha-frequency band oscillations. , 2007, Trends in neurosciences.

[38]  J. Ford,et al.  Relationships between pre-stimulus γ power and subsequent P300 and reaction time breakdown in schizophrenia. , 2011, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[39]  K. Reinikainen,et al.  Selective attention enhances the auditory 40-Hz transient response in humans , 1993, Nature.

[40]  Jun Li,et al.  Brain Anatomical Network and Intelligence , 2009, NeuroImage.

[41]  Abraham Z. Snyder,et al.  A default mode of brain function: A brief history of an evolving idea , 2007, NeuroImage.

[42]  W. Klimesch,et al.  EEG alpha oscillations: The inhibition–timing hypothesis , 2007, Brain Research Reviews.

[43]  M. Corbetta,et al.  Electrophysiological signatures of resting state networks in the human brain , 2007, Proceedings of the National Academy of Sciences.

[44]  M. Raichle,et al.  Resting states affect spontaneous BOLD oscillations in sensory and paralimbic cortex. , 2008, Journal of neurophysiology.

[45]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[46]  Julieta Ramos-Loyo,et al.  Relationship between resting alpha activity and the ERPs obtained during a highly demanding selective attention task. , 2004, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[47]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[48]  Simon Hanslmayr,et al.  Alpha phase reset contributes to the generation of ERPs. , 2006, Cerebral cortex.

[49]  Yanling Yin,et al.  EEG default mode network in the human brain: Spectral regional field powers , 2008, NeuroImage.

[50]  Adam Gazzaley,et al.  Top-down modulation of visual feature processing: The role of the inferior frontal junction , 2010, NeuroImage.

[51]  W. Klimesch,et al.  Alpha frequency, reaction time, and the speed of processing information. , 1996, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[52]  W. Singer Synchronization of cortical activity and its putative role in information processing and learning. , 1993, Annual review of physiology.

[53]  J. Kaiser,et al.  Induced Gamma-Band Activity and Human Brain Function , 2003, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[54]  A. Engel,et al.  Neuronal Synchronization along the Dorsal Visual Pathway Reflects the Focus of Spatial Attention , 2008, Neuron.

[55]  Alan C. Evans,et al.  Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. , 2007, Cerebral cortex.

[56]  R. Dolan,et al.  Neuroimaging of Cognition: Past, Present, and Future , 2008, Neuron.

[57]  Christopher R. Brown,et al.  EEG differences in children between eyes-closed and eyes-open resting conditions , 2009, Clinical Neurophysiology.

[58]  Gregor Thut,et al.  Prediction of response speed by anticipatory high‐frequency (gamma band) oscillations in the human brain , 2005, Human brain mapping.