A Bayesian Framework for Joint Analysis of Heterogeneous Neuroscience Data

Project Summary

This paper addresses analysis of heterogeneous data, such as ordered, categorical, real and count data. Such data are of interest in our motivating application, cognitive and brain science, in which subjects may answer questionnaires, and also (separately) undergo fMRI interrogation. A contribution of this paper concerns the joint analysis of how people answer questionnaires and how their brain responds to external stimuli (here visual), the latter measured via fMRI.

Themes and Categories

In this paper we ask a novel and practical question, which to our knowledge has not been considered previously: can one predict the fMRI response (here from the amygdala and the ventral striatum) to external stimuli, based upon knowledge of how the subject answers a questionnaire and genetic data?

A new model is developed for joint analysis of ordered, categorical, real and count data. In the motivating application, the ordered and categorical data are answers to questionnaires, the (word) count data correspond to the text questions from the questionnaires, and the real data correspond to fMRI responses for each subject. We also combine the analysis of these data with single-nucleotide polymorphism (SNP) data from each individual. The questionnaires considered here correspond to standard psychological surveys, and the study is motivated by psychology and neuroscience. The proposed Bayesian model infers sparse graphical models (networks) jointly across people, questions, fMRI stimuli and brain activity, integrated within a new matrix factorization based on latent binary features. We demonstrate how the learned model may take fMRI and SNP data from a subject as inputs, and predict (impute) how the individual would answer a psychological questionnaire; going in the other direction, we also use an individual's SNP data and answers from questionnaires to impute unobserved fMRI data. Each of these two imputation settings has practical and theoretical applications for understanding human behavior and mental health, which are discussed.

http://people.ee.duke.edu/~lcarin/Esther_ICML_finalv2.pdf

http://people.ee.duke.edu/~lcarin/Esther_JASA_7.pdf

 

 

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