1. Transfer Entropy and Information Flow Patterns in Functional Brain Networks during Cognitive Activity Presented By Md. Hedayetul Islam Shovon Cognitive Neuroengineering Laboratory University of South Australia 5 th February, 2015

2. 2 Presentation Outline Introduction Research Background Research Objectives Methodology Results and Discussion Conclusion

3. 3 Mental Health Issues Mental health is a serious issue world wide 25% of world population suffer from mental disorders (WHO, 2013) Mental disorders rank first among illnesses that result in disability – US, Canada and Western Europe Global cost of mental illness ~$2.5T in 2010 and projected to increase to $6T by 2030 (NIMH 2013) Someone commits suicide every 40 seconds Introduction - Motivation Mental health refers to our cognitive, and/or emotional wellbeing - it is all about how we think, feel and behave.

4. 4 Introduction (Cont.) Brain Network Electroencephalography (EEG) Recording of brain electrical activity by placing multiple electrodes on the scalp High temporal resolution and cheaper costs

5. 5 General Approach 5 Statistical Measures Linear: Cross Correlation Pearson’s Correlation Coefficient Coherence Partial Directed Coherence Granger Causality Nonlinear: Mutual Information Partial Mutual Information Transfer Entropy (TE) Functional brain network EEG signals EEG data to functional brain networks

6. 6 Research Background Transfer Entropy (TE) An information theoretical measure which determines the direction and quantifies the information transfer between two processes TE estimates the amount of activity of a system which is not dependent on its own past activity but on the past activity of another system (Schreiber, 2000)Schreiber, 2000 6

7. 7 Transfer Entropy (TE) (Cont.)

8. 8 Normalized Transfer Entropy (NTE)

9. 9 Advantages of TE - Direction of information transfer - Model free - Nonlinear ( Vicente et al., 2011 ) Vicente et al., 2011 Recent Applications of TE: - identifying information transfer between auditory cortical neurons (Gourévitch & Eggermont, 2007)Gourévitch & Eggermont, 2007 - investigating the influence of heart rate on breath rate and vice versa (Schreiber, 2000)Schreiber, 2000 - localization of the epileptic focus of epileptic patients (Sabesan et al., 2007)Sabesan et al., 2007 But, TE has not been applied to the construction functional brain network extensively (Bullmore & Sporns, 2009)Bullmore & Sporns, 2009 Why TE?

10. 10 Research Objectives The research objectives are three fold: Explore the application of information theoretic NTE measure to construct EEG based directed functional brain network To quantify the topological features of the constructed directed functional brain network during different cognitive states; and To determine information flow patterns during different cognitive states

11. 11 Methodology Study participants Six healthy right handed adults (4 males, 2 females) Age range 19-59 Recruited from the staff, student and academic populations of the University of South Australia (Mawson Lakes) Have no known psychological, neurological or psychiatric disorder All normal or corrected-to-normal vision

12. 12 Methodology (Cont.) EEG Data Acquisition Sampling rate 1000 Hz, 30 EEG channels, 2 reference channel 1 Hz – 70 Hz band pass filter with 50 Hz notch filter Eye blinks are detected using template matching methods of Neuroscan Curry software and removed using Principal Component Analysis (PCA) EEG Recording Criteria and Pre-processing Eyes Open (EOP) Driving (Drive)Driving with Audio Distraction (DriveAdo)

13. 13 Transfer Entropy Analysis Framework (TEAF) Raw EEG Data EEG Data Acquisition Participant Pre-processing EEG Signal Pre-processing Raw EEG Data Pre-processed EEG Data - Transfer Entropy Matrix Noise/ Bias Matrix Normalized Transfer Entropy matrix Pre-processed EEG Data Normalization Graph Database Construction (Subtraction) Results/ Visualization / Graph Theoretical Analysis /Statistical Analysis Graph Database Weighted/Binary/ Binary (Influential edge) Baseline – Eyes Open Cognitive load – Driving, Driving Audio Filtering (Band Pass, Notch) Eye Blink Removal (PCA)

14. 14 Results and Discussion 14 NTE matrices (each are 30 by 30 in size) during EOP, Drive and DriveAdo was calculated by computing NTE for each pairs of electrodes of EEG data Increased information flow during cognitive load is demonstrated by the appearance of more cluttered brighter pixels EOPDriveDriveAdo 0.1 0.0

15. 15 Functional Brain Network Metrics - Connectivity Density Connectivity density represents the actual number of edges as a proportion to the total number of possible edges More connections are established during cognitive load to facilitate more active information flow than baseline condition (EOP)

16. 16 Functional Brain Network Metrics - Clustering Coefficient 16 Clustering coefficient for node i represents the ratio between all directed triangles actually formed by i and the number of all possible triangles that i could form directed triangles Clustering coefficient value also increases during cognition than baseline condition (EOP) As a consequence, information transfer among the neighbour nodes increases during cognitive load Electrodes Clustering Coefficient

17. 17 Directed Functional Brain Network (Weighted) NTE matrices without applying any threshold were used to construct weighted directed functional brain network Node strength represents the total of all incoming and outgoing link weights Most of the nodes (underpinning neuronal populations) send and receive more information during cognitive load Node position - Electrodes Node Strength

18. 18 Statistical Analysis P1 P2 P3 Y Axis 1: EOP 2: Drive 3: DriveAdo States P 1P 2P 3 Mean Diff95% CIMean Diff95% CIMean Diff95% CI DriveEOP0.0244[0.0244,0.0651]0.0218[-0.0033,0.0469]0.0155 * [ 0.0053, 0.0258] DriveAdoDrive-0.0108[-0.0329,0.0113]0.0239[-0.0071,0.0550]0.0235 * [ 0.0110, 0.0360] DriveAdoEOP0.0339 * [0.0191,0.0487]0.0457 * [0.0200,0.0715]0.039 * [0.0285, 0.0495] * Mean difference is significant at p<.05 level. Statistical Validation using t-test and one way ANOVA Mean information flow is significantly different in EOP and DriveAdo experiments for all the participants Mean of Total Information Flow Mean of Total Information Flow Cognitive States Mean of Total Information Flow In each cognitive state, the total information flow from each electrode to all other electrodes was calculated

19. 19 Conclusion Directed functional brain network constructed using NTE is sensitive to cognitive load This sensitivity of NTE based functional brain network has the potential to assist in the development of quantitative metrics to measure cognition This NTE approach may be applied in the clinical diagnosis of cognitive impairments

20. 20 Future Work Increase the sample size Remove the spurious links from the directed graph which are formed due to cascade or common driver effects Apply various graph mining algorithms on the constructed directed functional brain networks to detect and track possible information flow direction patterns during cognition

21. 21 References Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059- 1069 (2010) Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10, 186-198 (2009) Nandagopal, N.D., Vijayalakshmi, R., Cocks, B., Dahal, N., Dasari, N., Thilaga, M., Dharwez, S.: Computational Techniques for Characterizing Cognition Using EEG Data – New Approaches. Procedia Computer Science 22, 699-708 (2013) Vicente, R., Wibral, M., Lindner, M., Pipa, G.: Transfer entropy—a model-free measure of effective connectivity for the neurosciences. Journal of computational neuroscience 30, 45-67 (2011) Schreiber, T.: Measuring information transfer. Physical review letters 85, 461 (2000) Chávez, M., Martinerie, J., Le Van Quyen, M.: Statistical assessment of nonlinear causality: application to epileptic EEG signals. Journal of Neuroscience Methods 124, 113-128 (2003) Gourévitch, B., Eggermont, J.J.: Evaluating information transfer between auditory cortical neurons. Journal of Neurophysiology 97, 2533-2543 (2007) Sabesan, S., Narayanan, K., Prasad, A., Iasemidis, L., Spanias, A., Tsakalis, K.: Information flow in coupled nonlinear systems: Application to the epileptic human brain. Data Mining in Biomedicine, pp. 483-503. Springer (2007) Neymotin, S.A., Jacobs, K.M., Fenton, A.A., Lytton, W.W.: Synaptic information transfer in computer models of neocortical columns. Journal of computational neuroscience 30, 69-84 (2011) Fagiolo, G.: Clustering in complex directed networks. Physical Review E 76, 026107 (2007) Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’networks. Nature 393, 440-442 (1998)

22. 22 Questions and Comments 22

23. 23 Directed Functional Brain Network during Driving