Modeling Dependence in a Network of Brain Signals

Start: 11/03/2011 - 4:15pm
End  : 11/03/2011 - 4:15pm

Statistics/OR/Math Finance Seminar

Hernando Ombao (Brown)


 In this talk, we shall discuss methods for characterizing dependence between brain regions. My own interest in this area stems from a growing body of evidence suggesting that various neurological disorders, including Alzheimers disease, depression, and Parkinsons disease may be associated with altered brain connectivity.

Dependence may be portrayed in a number of ways. This talk will be focused on measures that depict interactions in oscillations between brain regions. We discuss basic coherence measures and develop more comprehensive measures of coherence that capture complex dependence structures in brain signals. The classical notion of coherence pertains only to contemporaneous single-frequency interactions between signals. To generalize this notion, we introduce the time-lagged dual-frequency coherence which measures, as a specific example, oscillatory interactions between alpha activity on a current time block at one electroencephalogram channel and beta activity on a future time block at another channel. We develop formal methods for statistical inference under the framework of harmonizable processes. This new approach will be applied to analyze an electroencephalographic data set to investigate dependence between the visual, parietal and pre-motor cortices under the context of a visual-motor task.
This is joint work with PhD students Mark Fiecas and Cristina Gorrostieta and neuroscience collaborators at Brown and MGH.
Millikan 213, Pomona College

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