Cognitive Load Driven Directed Information Flow in Functional Brain Networks
The human brain connectome analysis describes the patterns of structural and functional brain networks and has become one of the most studied topics in computational neuroscience in recent years. Detailed investigation of functional brain networks based on the direction of information flow has subsequently gained significance. This study identifies changes in information flow direction between different brain regions during cognitive activity compared to baseline state using Normalized Transfer Entropy (NTE) estimated from electroencephalogram (EEG) signals. An algorithm is proposed for finding the cognitive state specific information flow direction patterns (IFDP) among various regions (lobes) of the brain. Results clearly demonstrate that IFDP based analysis is able to detect the changing information flow directional patterns during cognitive activity among four different brain regions: Frontal, Central, Parietal and Occipital. During cognitive activity, noticeable long range interconnections are established in the directed functional brain network from frontal to central, parietal and occipital lobes, and as well as from the central to occipital lobe. This suggests that the IFDP approach may have potential applications in the detection of cognitive impairments as well as in the clinical research e.g., for finding seizure foci in epilepsy.
KeywordsTransfer entropy Information flow Directed functional brain network EEG Cognitive activity
The authors wish to acknowledge partial support provided by the Defence Science and Technology Organisation (DSTO), Australia. The assistance and technical support provided by fellow researchers Mr Nabaraj Dahal and Mr Naga Dasari are greatly appreciated.
- 3.Shovon, M.I., Nandagopal, D., Vijayalakshmi, R., Du, J.T., Cocks, B.: Transfer entropy and information flow patterns in functional brain networks during cognitive activity. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds.) ICONIP 2014, Part I. LNCS, vol. 8834, pp. 1–10. Springer, Heidelberg (2014)Google Scholar
- 10.Sabesan, S., Narayanan, K., Prasad, A., Iasemidis, L., Spanias, A., Tsakalis, K.: Information flow in coupled nonlinear systems: Application to the epileptic human brain. In: Pardalos, P.M., Boginski, V.L., Vazacopoulos, A. (eds.) Data Mining in Biomedicine, pp. 483–503. Springer, New York (2007)CrossRefGoogle Scholar
- 15.CURRY 7 EEG Acquisition and Analysis Software. Compumedics Neuroscan USA LtdGoogle Scholar