Exploring the Mind: Integrating Questionnaires and fMRI

Esther Salazar, Ryan Bogdan, Adam Gorka, Ahmad Hariri, Lawrence Carin
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(2):262-270, 2013.

Abstract

A new model is developed for joint analysis of ordered, categorical, real and count data. 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. The Bayesian model employs the von Mises distribution in a novel manner to infer sparse graphical models jointly across people, questions, fMRI stimuli and brain region, with this integrated within a new matrix factorization based on latent binary features. The model is compared with simpler alternatives on two real datasets. We also demonstrate the ability to predict the response of the brain to visual stimuli (as measured by fMRI), based on knowledge of how the associated person answered classical questionnaires.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-salazar13, title = {Exploring the Mind: Integrating Questionnaires and fMRI}, author = {Salazar, Esther and Bogdan, Ryan and Gorka, Adam and Hariri, Ahmad and Carin, Lawrence}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {262--270}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/salazar13.pdf}, url = {https://proceedings.mlr.press/v28/salazar13.html}, abstract = {A new model is developed for joint analysis of ordered, categorical, real and count data. 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. The Bayesian model employs the von Mises distribution in a novel manner to infer sparse graphical models jointly across people, questions, fMRI stimuli and brain region, with this integrated within a new matrix factorization based on latent binary features. The model is compared with simpler alternatives on two real datasets. We also demonstrate the ability to predict the response of the brain to visual stimuli (as measured by fMRI), based on knowledge of how the associated person answered classical questionnaires.} }
Endnote
%0 Conference Paper %T Exploring the Mind: Integrating Questionnaires and fMRI %A Esther Salazar %A Ryan Bogdan %A Adam Gorka %A Ahmad Hariri %A Lawrence Carin %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-salazar13 %I PMLR %P 262--270 %U https://proceedings.mlr.press/v28/salazar13.html %V 28 %N 2 %X A new model is developed for joint analysis of ordered, categorical, real and count data. 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. The Bayesian model employs the von Mises distribution in a novel manner to infer sparse graphical models jointly across people, questions, fMRI stimuli and brain region, with this integrated within a new matrix factorization based on latent binary features. The model is compared with simpler alternatives on two real datasets. We also demonstrate the ability to predict the response of the brain to visual stimuli (as measured by fMRI), based on knowledge of how the associated person answered classical questionnaires.
RIS
TY - CPAPER TI - Exploring the Mind: Integrating Questionnaires and fMRI AU - Esther Salazar AU - Ryan Bogdan AU - Adam Gorka AU - Ahmad Hariri AU - Lawrence Carin BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-salazar13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 2 SP - 262 EP - 270 L1 - http://proceedings.mlr.press/v28/salazar13.pdf UR - https://proceedings.mlr.press/v28/salazar13.html AB - A new model is developed for joint analysis of ordered, categorical, real and count data. 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. The Bayesian model employs the von Mises distribution in a novel manner to infer sparse graphical models jointly across people, questions, fMRI stimuli and brain region, with this integrated within a new matrix factorization based on latent binary features. The model is compared with simpler alternatives on two real datasets. We also demonstrate the ability to predict the response of the brain to visual stimuli (as measured by fMRI), based on knowledge of how the associated person answered classical questionnaires. ER -
APA
Salazar, E., Bogdan, R., Gorka, A., Hariri, A. & Carin, L.. (2013). Exploring the Mind: Integrating Questionnaires and fMRI. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(2):262-270 Available from https://proceedings.mlr.press/v28/salazar13.html.

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