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THESIS DEFENSE ABSTRACT
Statistical Methods for Inter-Subject Analysis of Neuroscience Data
The
statistical analysis of neuroscience data poses several
challenges due to the data's typically high dimensionality and its complex
spatiotemporal structure. In this work we address statistical issues arising
from two types of neuroscience data: magnetic resonance images (MRI) and
electrocorticographic (ECoG) signals. Permutation tests are commonly applied to
MRIs in the neuroscience literature. A methodology to control for potentially
confounding covariates in permutation tests using a Markov chain Monte Carlo
algorithm is proposed. Temporal relationships in ECoG data are often estimated
using Granger temporality. We propose a methodology to control for potentially
mediating or confounding associations between time series. Finally, an extension
to estimate Granger temporality across multiple subjects is developed using
covariance smoothing. Return to Biostatistics Calendar | Return to Home Page |
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