Exploring the mind: Integrating questionnaires and fMRI

Esther Salazar; Ryan Bogdan; Adam Gorka; Ahmad R. Hariri; Lawrence Carin

Exploring the mind: Integrating questionnaires and fMRI

Esther Salazar, Ryan Bogdan, Adam Gorka, Ahmad R. Hariri, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. Copyright 2013 by the author(s).

Original language	English (US)
Title of host publication	30th International Conference on Machine Learning, ICML 2013
Publisher	International Machine Learning Society (IMLS)[email protected]
Pages	921-929
Number of pages	9
State	Published - Jan 1 2013
Externally published	Yes

Cite this

@inproceedings{5415b3b8e627478a9db9660bf7d27b64,

title = "Exploring the mind: Integrating questionnaires and fMRI",

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. Copyright 2013 by the author(s).",

author = "Esther Salazar and Ryan Bogdan and Adam Gorka and Hariri, {Ahmad R.} and Lawrence Carin",

note = "Generated from Scopus record by KAUST IRTS on 2021-02-09",

year = "2013",

month = jan,

day = "1",

language = "English (US)",

pages = "921--929",

booktitle = "30th International Conference on Machine Learning, ICML 2013",

publisher = "International Machine Learning Society (IMLS)[email protected]",

}

TY - GEN

T1 - Exploring the mind: Integrating questionnaires and fMRI

AU - Salazar, Esther

AU - Bogdan, Ryan

AU - Gorka, Adam

AU - Hariri, Ahmad R.

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2013/1/1

Y1 - 2013/1/1

N2 - 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. Copyright 2013 by the author(s).

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. Copyright 2013 by the author(s).

UR - http://www.scopus.com/inward/record.url?scp=84897488107&partnerID=8YFLogxK

M3 - Conference contribution

SP - 921

EP - 929

BT - 30th International Conference on Machine Learning, ICML 2013

PB - International Machine Learning Society (IMLS)[email protected]

ER -

Exploring the mind: Integrating questionnaires and fMRI

Abstract

Other files and links

Fingerprint

Cite this