Intelligence and eeg measures of information flow: efficiency and homeostatic neuroplasticity

The purpose of this study was to explore the relationship between the magnitude of EEG information flow and intelligence. The electroencephalogram (EEG) was recorded from 19 scalp locations from 371 subjects ranging in age from 5 years to 17.6 years. The Wechler Intelligence Scale for Children (WISC-R) was administered for individuals between 5 years of age and 16 years and the Weschler Adult Intelligence Scale revised (WAIS-R) was administered to subjects older than 16 years to estimate I.Q. The phase slope index estimated the magnitude of information flow between all electrode combinations for difference frequency bands. Discriminant analyses were performed between high I.Q. (>120) and low I.Q. groups (<90). The magnitude of information flow was inversely related to I.Q. especially in the alpha and beta frequency bands. Long distance inter-electrode distances exhibited greater information flow than short inter-electrode distances. Frontal-parietal correlations were the most significant. It is concluded that higher I.Q. is related to increased efficiency of local information processing and reduced long distance compensatory dynamics that supports a small-world model of intelligence.

[1]  R. Kahn,et al.  Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis , 2010, The Journal of Neuroscience.

[2]  R. Thatcher,et al.  Quantitative EEG Normative Databases: Validation and Clinical Correlation , 2003 .

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

[4]  Jonathan D. Cohen,et al.  An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. , 2005, Annual review of neuroscience.

[5]  William H. Press,et al.  Numerical recipes in C , 2002 .

[6]  Alan C. Evans,et al.  Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans , 2009, PloS one.

[7]  Robert W Thatcher,et al.  Development of cortical connections as measured by EEG coherence and phase delays , 2008, Human brain mapping.

[8]  L. Jäncke,et al.  The effects of working memory training on functional brain network efficiency , 2013, Cortex.

[9]  A. Pérez-Villalba Rhythms of the Brain, G. Buzsáki. Oxford University Press, Madison Avenue, New York (2006), Price: GB £42.00, p. 448, ISBN: 0-19-530106-4 , 2008 .

[10]  N. Jausovec,et al.  Spatiotemporal brain activity related to intelligence: a low resolution brain electromagnetic tomography study. , 2003, Brain research. Cognitive brain research.

[11]  O. Sporns,et al.  Motifs in Brain Networks , 2004, PLoS biology.

[12]  Lorena R. R. Gianotti,et al.  Functional brain network efficiency predicts intelligence , 2012, Human brain mapping.

[13]  Ana-Maria Cebolla,et al.  Gravity Influences Top-Down Signals in Visual Processing , 2014, PloS one.

[14]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[15]  H. L. Gray,et al.  Applied time series analysis , 2011 .

[16]  R. W. Thatcher,et al.  Intelligence and EEG phase reset: A two compartmental model of phase shift and lock , 2008, NeuroImage.

[17]  Biyu J. He,et al.  Electrophysiological correlates of the brain's intrinsic large-scale functional architecture , 2008, Proceedings of the National Academy of Sciences.

[18]  Werner Lutzenberger,et al.  Dimensional analysis of the human EEG and intelligence , 1992, Neuroscience Letters.

[19]  Guido Nolte,et al.  Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study , 2013, Biomedizinische Technik. Biomedical engineering.

[20]  Jens F. Beckmann,et al.  Intelligence and individual differences in becoming neurally efficient. , 2004, Acta psychologica.

[21]  A. Hofman,et al.  The Generation R Study. , 2013 .

[22]  M. Buchsbaum,et al.  Intelligence and changes in regional cerebral glucose metabolic rate following learning , 1992 .

[23]  S. Rossi,et al.  Efficiency of weak brain connections support general cognitive functioning , 2014, Human brain mapping.

[24]  Claudia Clopath,et al.  Local inhibitory plasticity tunes macroscopic brain dynamics and allows the emergence of functional brain networks , 2016, NeuroImage.

[25]  R. Thatcher,et al.  Human cerebral hemispheres develop at different rates and ages. , 1987, Science.

[26]  R. Thatcher,et al.  EEG and intelligence: Relations between EEG coherence, EEG phase delay and power , 2005, Clinical Neurophysiology.

[27]  M. Posner,et al.  The attention system of the human brain: 20 years after. , 2012, Annual review of neuroscience.

[28]  R. Thatcher,et al.  Self‐organized criticality and the development of EEG phase reset , 2009, Human brain mapping.

[29]  R. Thatcher,et al.  Intelligence and EEG current density using low‐resolution electromagnetic tomography (LORETA) , 2007, Human brain mapping.

[30]  Emi Tanaka,et al.  Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity , 2016, Front. Hum. Neurosci..

[31]  Alexander Aue,et al.  Applied Time Series Analysis , 2010 .

[32]  Ellen Perecman,et al.  Cognitive processing in the right hemisphere , 1983 .

[33]  M. Posner,et al.  The attention system of the human brain. , 1990, Annual review of neuroscience.

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

[35]  David A. Kaiser,et al.  Automatic Artifact Detection, Overlapping Windows, and State Transitions , 2000 .

[36]  K. Müller,et al.  Robustly estimating the flow direction of information in complex physical systems. , 2007, Physical review letters.

[37]  J. Changeux,et al.  Experimental and Theoretical Approaches to Conscious Processing , 2011, Neuron.

[38]  Albert Hofman,et al.  Functional connectivity between parietal and frontal brain regions and intelligence in young children: The Generation R study , 2013, Human brain mapping.

[39]  Explore Configuring,et al.  A Simulation Study to , 2004 .

[40]  N. Birbaumer,et al.  Spatiotemporal organization of brain dynamics and intelligence: an EEG study in adolescents. , 1999, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[41]  A. Iacobucci Spectral Analysis for Economic Time Series , 2005 .

[42]  S. Marinakis,et al.  Experimental and theoretical approaches , 2015 .

[43]  Andreas Ziehe,et al.  Comparison of Granger Causality and Phase Slope Index , 2008, NIPS Causality: Objectives and Assessment.