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| Date & Location | Speakers |
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September 25, 2009 |
No Meeting Society for Neuroeconomics |
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October 2, 2009 3:30 PM–5:00 PM Meyer 815 |
Souheil Inati & Pablo Velasco, NYU Center for Brain Imaging Souheil Inati will present a new method for functional to structural registration based on a multi-echo gradient echo B0 and T2* mapping calibration scan. This 2-3 minute calibration scan be easily added on to existing FMRI protocols. Pablo Velasco will give a brief demonstration the new realtime data monitoring tool. |
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October 16, 2009 |
No Meeting Society for Neuroscience |
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October 30, 2009 |
Damian Stanley from the Phelps Lab, NYU Presentation and open discussion of an experimental design to assess the neural correlates of the relationship between implicit bias & economic decision-making |
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November 6, 2009 3:30 PM–5:00 PM Meyer 815 |
David Amodio, NYU Motivated perception as a mechanism of prejudice control: An ERP approach |
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November 13, 2009 3:30 PM–5:00 PM Meyer 815 |
Katherine Duncan from the Davachi lab, NYU Characterizing the variability of hemodynamic responses in the medial temporal lobe: Functional significance and implications for modeling |
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November 20, 2009 3:30 PM–5:00 PM Meyer 815 |
Edward Vessel from the NYU Center for Brain Imaging Event-related designs with multi-component trials |
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December 4, 2009 3:30 PM–5:00 PM Meyer 815 |
Rahul Garg, IBM Watson Research Center "Modeling Brain as a Dynamical System using the Granger Causality Analysis." Novel techniques to analyze fMRI data has enabled newer experimental paradigms. For example, using functional connectivity analysis it is possible to analyze the interpret data from experiments involving resting state, virtual reality games, watching movies, listening to music etc. Several machine learning approaches have been used to predict behavior using fMRI data. These techniques are able to extract complex patterns of brain activity from fMRI data and provide additional scientific insights. In this talk I will describe our machine-learning based techniques including the Granger causality analysis that may be used to model brain as a dynamic system. These techniques are based on sparse regression and multivariate autoregressive modeling. We show that it is possible to extract additional information about brain function which is not accssible using the standard analysis techniques. |