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Modeling and Estimating Granger Causality between Signals

Modeling and Estimating Granger Causality between Signals. Syed Ashrafulla October 11, 2013. Granger Causality …. models for testing. restricted:. unrestricted:. Hypothesis:. rejection implies Granger causality. assume Gaussianity  the signals are defined by their mean & variance.

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Modeling and Estimating Granger Causality between Signals

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  1. Modeling and Estimating Granger Causality between Signals Syed Ashrafulla October 11, 2013

  2. Granger Causality … models for testing restricted: unrestricted: Hypothesis: rejection implies Granger causality assume Gaussianity the signals are defined by their mean & variance

  3. Granger Causality … Ebert-Uphoff2012, Geophys Research Lett Michalareas2012, HBM Geweke1982, J Amer Stat Assoc GNP deflator vs M1 geopotential height economics climatology genetics neuroscience ecology expression profile of HeLa kniematics & music Fujita2007, BMC Systems Biology D’Ausillo2012, PLoS ONE

  4. … in Mean: Canonical Granger Causality Granger causality Ashrafulla2013, NeuroImage

  5. … in Mean: Canonical Granger Causality how big is this number? Ashrafulla2012, Proc IEEE ISBI  most causal signals between two sets

  6. … in Mean: Canonical Granger Causality application: visuomotor processing Bressler1993, Nature Ashrafulla2013, NeuroImage

  7. … in Variance: ARCH & ADMM signal-dependent variation Papiez2013, Energy Econ

  8. … in Variance: ARCH & ADMM conditional heteroscedasticity: how big are these numbers? model:

  9. … in Variance: ARCH & ADMM maximum likelihood: penalized ML fixed-point iteration least-squares dual ascent ADMM: Ashrafulla2013, Asilmoar SSC

  10. … in Variance: ARCH & ADMM univariate model ADMM is faster than current methods to estimate ARCH

  11. … in Variance: ARCH & ADMM bivariate model ADMM is faster than current methods to estimate ARCH

  12. Conclusions causality in mean: a new model causality in variance: a faster method ADMM: Granger causality Ashrafulla2012, Proc IEEE ISBI Ashrafulla2013, NeuroImage Ashrafulla2013, Asilmoar SSC

  13. Journal Papers • Ashrafulla, S., Haldar, J. P., Joshi, A. A. & Leahy, R. M. (2013). Canonical Granger causality between regions of interest.NeuroImage, 83, 189–199. • Ashrafulla, S., Pantazis, D., Mosher, J., Hämäläinen, M. & Leahy, R. M. (2013). Multicenter consistency of localization of MEG. (in preparation) • Niv, S., Ashrafulla, S., Tuvblad, C., Joshi, A.A., Raine, A., Leahy, R. M. & Baker, L.A. (2013). Childhood EEG frontal alpha power as a predictor of adolescent antisocial behavior: a twin heritability study. (submitted) • Niv, S., Ashrafulla, S., Tuvblad, C., Joshi, A.A., Raine, A., Leahy, R. M. & Baker, L.A. (2013). Relationships of alpha, beta, and theta EEG spectral properties with aggressive and nonaggressive ASB in children and adolescents. (submitted) • Joshi, A.A., Ashrafulla, S., Damasio, H., Shattuck, D. W. & Leahy, R. M. (2013). Automated generation, representation and analysis of sulcal curves on human cortex. (submitted) • Conference Submissions • Ashrafulla, S., Mosher, J. C. & Leahy, R. M. (2013). Causality in variance in electrophysiological data Using the GARCH model, Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA. • Ashrafulla, S., Haldar, J. P., Joshi, A. A. & Leahy, R. M. (2012). Canonical Granger causality. IntConfBiomag. Paris, France. • Ashrafulla, S., Haldar, J. P., Joshi, A. A. & Leahy, R. M. (2012). Canonical Granger causality applied to functional brain data. Proc IEEE ISBI (pp. 1751–1754). Barcelona, Spain: IEEE. • Ashrafulla, S., Pantazis, D., Mosher, J. C., Hämäläinen, M., Liu, B. J. & Leahy, R. M. (2011). Viability of sharing MEG data using minimum-norm imaging. (W. W. Boonn & B. J. Liu, Eds.), 7967, 79670F–79670F–8. • Ashrafulla, S., Tadel, F., Baillet, S., Liu, B. J., Mosher, J. C., Khan, S., Lefevre, J., Huang, H. K. & Leahy, RM (2010). An imaging informatics tool for visualization of cortical flow in epilepsy via EEG. RSNA. • Ashrafulla, S., Pantazis, D. & Leahy, R. M. (2010). Evaluation of cross-site consistency of MEG data. IntConfBiomag, 4, 90089. • Aydore, S., Ashrafulla, S., Joshi, A. A. & Leahy, R. M. (2013). A measure of connectivity in the presence of crosstalk, Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA. • Joshi, A. A., Ashrafulla, S., Shattuck, D. W., Damasio, H. & Leahy, R. M. (2013). Cortical shape analysis using the anisotropic Global Point Signature, MICCAI 16, MFCA workshop, Nagoya, Japan. • Dery, S., Aydore, S., Ashrafulla, S., Florin, E., Bock, E., Leahy, R. M., Baillet, S. & Tadel, F. (2013). Functional connectivity using BrainStorm, OHBM 19, Seattle, WA, USA. • Joshi, A. A., Ashrafulla, S., Shattuck, D. W., Damasio, H. & Leahy, R. M. (2013). Automated analysis of the shape of sulcal curves using the anisotropic Helmholtz equation. OHBM 19, Seattle, WA, USA. • Joshi, A. A., Ashrafulla, S., Shattuck, D. W., Damasio, H. & Leahy, R. M. (2012). An invariant shape representation using the anisotropic Helmholtz equation. MICCAI, 15(3), 607–14. This work was funded under the NIBIB T32EB00438 Bioinformatics Training Program and NIH grants R01EB009048 and R01 5R01EB000473. http://ashraful.lasyedashrafulla@gmail.com

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