Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

1

Click here to load reader

Transcript of Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

Page 1: Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

OHBM

S169

15th Annual Meeting ■ June 18–23, 2009 ■ San Francisco, CA, USA

Schedule of Poster Presentations

398 SU-PM Power Spectrum Density and Shannon Entropy Methods Quantify Limbic Dysregulation using fMRIand NIRS, D Tolkunov, D Rubin, J Carlson, T Greenberg, LR Mujica-Parodi, State University of New Yorkat Stony Brook, Stony Brook, NY, USA

400 SU-PM Aging Effects on Cortical Network Connectivity during Speech Perception in Noise: A Graph TheoreticalAnalysis, JP Sheppard, JP Wang, PCM Wong, Roxelyn & Richard Pepper Department of CommunicationSciences and Disorders, Northwestern University Interdepartmental Neuroscience Program, Hugh KnowlesCenter, Department of Otolaryngology - Head and Neck Surgery, Northwestern University, Evanston, IL, USA

402 SU-PM Towards a functional hierarchy of resting-state networks, SM Smith , KL Miller, CE Mackay, N Filippini,CF Beckmann, FMRIB Centre, Dept. of Clinical Neurology, University of Oxford, Oxford, United Kingdom

404 SU-PM Temporal Dynamics of Functional Network Connectivity at Rest: A Comparison of Schizophrenia Patients and Healthy Controls, U Sakoglu, VD Calhoun, The Mind Research Network, Albuquerque, NM, USA

406 SU-PM Dynamic Granger-Geweke causality modeling with application to interictal spike propagation, F-H Lin,K Hara, V Solo, M Vangel, J Belliveau, S Stufflebeam, M Hamalainen, A. A. Martinos Center, MassachusettsGeneral Hospital, Charlestown, MA, USA

408 SU-PM Withdrawn

410 SU-PM Small-world attributes to characterize the relationship between functional and structural connectivityusing fMRI and DTI, A. Messé, G. Marrelec, V. Perlbarg, M. Pélégrini-Issac, J. Doyon, H. Benali, Inserm,UPMC Univ Paris 06, UMR_S 678, Laboratoire d'Imagerie Fonctionnelle, Paris, France

412 SU-PM Considering Afferent Pathways on Interneurons in the Jansen and Rit Neural Mass Model, A. Spiegler,T.R. Knösche, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

414 SU-PM Robust methods for estimating functional connectivity, S.S. Nagarajan, J.P. Owen, L.B. Hinkley, H.T. Attias, K. Sekihara, University of California, San Francisco, CA, USA

416 SU-PM Whole Brain Network Analysis Based on Morphological and Diffusion Tensor Imaging Reveals Connectivity Deficits in Autistics, H Li, Z Xue, L Guo, JV Hunter, STC Wong, The Center for Biotechnologyand Informatics, The Methodist Hospital Research Institute and Department of Radiology, The MethodistHospital, Weill Cornell Medical College, Houston, TX, USA

418 SU-PM Connectivity between resting state networks and fractional anisotropy reveled by joint independentcomponent analysis, A.R. Franco, J. Ling, A. Caprihan, V.D. Calhoun , R. Jung, G.L. Heileman, A.R. Mayer,Mind Research Network, Albuquerque, NM, USA

420 SU-PM Functional Connectivity Using Weighted Voxel Coexpression Network Analysis, JA Mumford, RA Poldrack, MC Oldham, DH Geshwind, P Langfelder, S Horvath, Department of Psychology, University ofCalifornia, Los Angeles, Los Angeles, CA, USA

422 SU-PM Imaging the Effective Connectivity behind Frontal Control Processes in a Simon Task using TransferEntropy, M Wibral, R Vicente, G Pipa, Meg Unit, Brain Imaging Center, JW Goethe University, Frankfurt,Germany

424 SU-PM Data-driven parcellation of the insular cortex using resting-state fMRI, S.J. Peltier, M.C Hsu, R.C. Welsh, R Bhavsar, R.E. Harris, D.J. Clauw, L. Symonds, L. Yang, D.A. Williams, Functional MRILaboratory, University of Michigan, Ann Arbor, MI, USA

426 SU-PM* Brain connectivity toolbox: a collection of complex network measurements and brain connectivity (O-F2) datasets, M Rubinov, R Kötter, P Hagmann, O Sporns, University of New South Wales, Sydney, Australia

428 SU-PM Morphological modeling of synaptic arborisations and the neural field model to mimic electromagneticbrain activities, M. Nguyen Trong, A. Spiegler, T.R. Knösche, Max Planck Institute for Human Cognitive andBrain Sciences, Leipzig, Germany

Sunday, June 21, 2009