Cortical network topology during successful memory encoding in a lifelike experiment

In the present work, we estimated the functional networks in the frequency domain from a set of high-resolution EEG data in a group of healthy subjects during the showing of commercial spots within a neutral documentary. Then, we evaluated the differences in the cortical network associated with later remembered and not-remembered commercials by calculating the global- Eg and local-efficiency El indexes. During the visualization of the video-clips that will be forgotten (FRG), the cortical network exhibited high values of global- and local-efficiency, reflecting a small-world configuration. During the visualization of the video-clips that will be remembered (RMB), the same indexes appeared significantly lower. Such a difference seems not depending on the spectral content of the cortical activity. This result shows how the network communication efficiency would be affected by the presence of attentional and semantic processes that are behind a successful memory encoding in a lifelike situation.

[1]  Olaf Sporns,et al.  Graph Theory Methods for the Analysis of Neural Connectivity Patterns , 2003 .

[2]  W. Klimesch Memory processes, brain oscillations and EEG synchronization. , 1996, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[3]  Massimo Marchiori,et al.  Economic small-world behavior in weighted networks , 2003 .

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

[5]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[6]  Matthias M. Müller,et al.  Induced gamma band responses: an early marker of memory encoding and retrieval , 2004, Neuroreport.

[7]  J. Born,et al.  EEG theta synchronization conjoined with alpha desynchronization indicate intentional encoding , 2002, The European journal of neuroscience.

[8]  Koichi Sameshima,et al.  Using partial directed coherence to describe neuronal ensemble interactions , 1999, Journal of Neuroscience Methods.

[9]  James Theiler,et al.  Testing for nonlinearity in time series: the method of surrogate data , 1992 .

[10]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[11]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[12]  E. Bullmore,et al.  Neurophysiological architecture of functional magnetic resonance images of human brain. , 2005, Cerebral cortex.

[13]  Marc W Howard,et al.  Theta and Gamma Oscillations during Encoding Predict Subsequent Recall , 2003, The Journal of Neuroscience.

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

[15]  M. Grigorov Global properties of biological networks. , 2005, Drug discovery today.

[16]  Barry Horwitz,et al.  The elusive concept of brain connectivity , 2003, NeuroImage.

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

[18]  S. Strogatz Exploring complex networks , 2001, Nature.

[19]  G. A. Miller,et al.  Comparison of different cortical connectivity estimators for high‐resolution EEG recordings , 2007, Human brain mapping.

[20]  F. Babiloni,et al.  Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function , 2005, NeuroImage.

[21]  Erol Başar,et al.  Memory and Brain Dynamics: Oscillations Integrating Attention, Perception, Learning, and Memory , 2004 .