Neural networks in the brain
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This document discusses neural networks in the brain and various methods for analyzing functional connectivity between brain areas using electrophysiology data. It introduces several measures of neural synchronization such as coherence, phase locking value, pairwise phase consistency, phase slope index, and Granger causality. It also discusses issues like the common reference problem, common input problem, and volume conduction that need to be addressed when applying these methods. The document provides references for further reading on graph analysis techniques and interpreting results.
Neural networks in the brain
- 1. Neural networks in the brain Ilya Zakharov Research associate Developmental behavioral genetics lab Psychological Institute Russian Academy of Education
- 4. Networks on different levels. Spikes
- 5. Networks on different levels. Brain areas
- 6. Networks on different levels. Electrophysiology
- 8. Measures
- 10. Perfect synchronization Synchronization with time lag No synchronization
- 11. Coherence coefficient Coherence - the frequency domain equivalent to the time domain cross- correlation function. *Phase Locking Value Coherence for normalized Fourier- transformed signals
- 12. Phase lag index The PLI is a metric that evaluates the distribution of phase differences across observations. Stam et al., 2007 the PPC is computed from the distribution of all pairwise differences (between pairs of observations) of the relative phases. Pairwise phase consistency Vinck et al., 2010 Non-directed
- 13. Phase slope index Granger causality Computed from the complex-valued coherency, and quantifies the consistency of the direction of the change in the phase difference across. The sign of the PSI informs about which signal is temporally leading the other Nolte et al., 2008 Directed Represents the result of a model comparison. It is rooted in the autoregressive modeling framework, where future values of time series are modeled as a weighted combination of past values of time series. Specifically, the quality of an AR-model can be quantified by the variance of the model’s residuals, and Granger causality is defined as the natural logarithm of a ratio of residual variances, obtained from two different AR-models. Ding et al., 2006
- 14. Common reference problem Bipolar referencing?
- 15. Common input problem Partial out third input? Granger causality?
- 16. Common source problem/ Volume conduction/field spread Current source density? Imanigary coherence? Source localization?
- 17. Back to graph analysis
- 18. Back to graph analysis Your suggestions?
- 19. References • Bastos, André M., and Jan-Mathijs Schoffelen. “A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls.” Frontiers in Systems Neuroscience, 2016, 175. doi:10.3389/fnsys.2015.00175. • Durand, Dominique M., Eun-Hyoung Park, and Alicia L. Jensen. “Potassium Diffusive Coupling in Neural Networks.” Philosophical Transactions of the Royal Society of London B: Biological Sciences 365, no. 1551 (August 12, 2010): 2347–62. doi:10.1098/rstb.2010.0050. • Finn, Emily S., Xilin Shen, Dustin Scheinost, Monica D. Rosenberg, Jessica Huang, Marvin M. Chun, Xenophon Papademetris, and R. Todd Constable. “Functional Connectome Fingerprinting: Identifying Individuals Using Patterns of Brain Connectivity.” Nature Neuroscience 18, no. 11 (November 2015): 1664–71. doi:10.1038/nn.4135. • Fraga González, G., M. J. W. Van der Molen, G. Žarić, M. Bonte, J. Tijms, L. Blomert, C. J. Stam, and M. W. Van der Molen. “Graph Analysis of EEG Resting State Functional Networks in Dyslexic Readers.” Clinical Neurophysiology. Accessed July 15, 2016. doi:10.1016/j.clinph.2016.06.023. • Hardmeier, Martin, Florian Hatz, Habib Bousleiman, Christian Schindler, Cornelis Jan Stam, and Peter Fuhr. “Reproducibility of Functional Connectivity and Graph Measures Based on the Phase Lag Index (PLI) and Weighted Phase Lag Index (wPLI) Derived from High Resolution EEG.” PloS One 9, no. 10 (2014): e108648. doi:10.1371/journal.pone.0108648. • Lachaux, J. P., E. Rodriguez, J. Martinerie, and F. J. Varela. “Measuring Phase Synchrony in Brain Signals.” Human Brain Mapping 8, no. 4 (1999): 194–208. • Neubauer, Aljoscha C., and Andreas Fink. “Intelligence and Neural Efficiency: Measures of Brain Activation versus Measures of Functional Connectivity in the Brain.” Intelligence, Intelligence and the Brain, 37, no. 2 (March 2009): 223–29. doi:10.1016/j.intell.2008.10.008. • Nolte, Guido, Andreas Ziehe, Vadim V. Nikulin, Alois Schlögl, Nicole Krämer, Tom Brismar, and Klaus-Robert Müller. “Robustly Estimating the Flow Direction of Information in Complex Physical Systems.” Physical Review Letters 100, no. 23 (June 10, 2008): 234101. doi:10.1103/PhysRevLett.100.234101. • Rashid, Barnaly, Mohammad R. Arbabshirani, Eswar Damaraju, Mustafa S. Cetin, Robyn Miller, Godfrey D. Pearlson, and Vince D. Calhoun. “Classification of Schizophrenia and Bipolar Patients Using Static and Dynamic Resting-State fMRI Brain Connectivity.” NeuroImage 134 (July 1, 2016): 645–57. doi: 10.1016/j.neuroimage.2016.04.051. • Stam, Cornelis J., Guido Nolte, and Andreas Daffertshofer. “Phase Lag Index: Assessment of Functional Connectivity from Multi Channel EEG and MEG with Diminished Bias from Common Sources.” Human Brain Mapping 28, no. 11 (November 1, 2007): 1178–93. doi:10.1002/hbm.20346. • Termenon, M., A. Jaillard, C. Delon-Martin, and S. Achard. “Reliability of Graph Analysis of Resting State fMRI Using Test-Retest Dataset from the Human Connectome Project.” NeuroImage. Accessed June 18, 2016. doi:10.1016/j.neuroimage.2016.05.062. • Trongnetrpunya, Amy, Bijurika Nandi, Daesung Kang, Bernat Kocsis, Charles E. Schroeder, and Mingzhou Ding. “Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations.” Frontiers in Systems Neuroscience, 2016, 189. doi: 10.3389/fnsys.2015.00189. • Vinck, Martin, Marijn van Wingerden, Thilo Womelsdorf, Pascal Fries, and Cyriel M. A. Pennartz. “The Pairwise Phase Consistency: A Bias-Free Measure of Rhythmic Neuronal Synchronization.” NeuroImage 51, no. 1 (May 15, 2010): 112–22. doi:10.1016/j.neuroimage.2010.01.073. • Wang, Xiangpeng, Ting Wang, Zhencai Chen, Glenn Hitchman, Yijun Liu, and Antao Chen. “Functional Connectivity Patterns Reflect Individual Differences in Conflict Adaptation.” Neuropsychologia 70 (April 2015): 177–84. doi:10.1016/j.neuropsychologia.2015.02.031. • Ding, M., Chen, Y., & Bressler, S. L. (2006). 17 Granger causality: basic theory and application to neuroscience. Handbook of time series analysis: recent theoretical developments and applications, 437.
- 20. Thanks for your attention! We are always ready to cooperate! iliazaharov@gmail.com