Bayesian Modeling of
Brain Imaging Data

Idioma: Inglés

Más información

Septiembre 25y 26, 2017

Universidad de California,
Irvine, Estados Unidos

Statistical methods play a crucial role in understanding and analyzing brain-imaging data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable feature of Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data. We will provide a review of some common data types (e.g., fMRI, EEG, PET/MRI, DTI) and the most relevant Bayesian modeling approaches developed in recent years. We will divide methods according to the objective of the analysis. In particular, we will discuss spatio-temporal models for fMRI data that detect task-related activation patterns. We will also address the very important problem of estimating brain connectivity and touch upon methods that focus on making predictions of an individual's brain activity or a clinical or behavioral response. We will conclude with a discussion of recent integrative models that aim at combining fMRI data with other imaging modalities, such as EEG/MEG and DTI data, measured on the same subjects. We also briefly discuss the emerging field of imaging genetics.

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