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Authors: Feng Yang 1 ; Kuang Fu 2 and Ai Zhou 3

Affiliations: 1 Heilongjiang University, China ; 2 The Second Affiliated Hospital of Harbin Medical University, China ; 3 Jilin University, China

Keyword(s): fMRI Time Series, Classical Statistics, Bayesian Inference, Group Analysis.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Medical Image Applications

Abstract: This paper suggests one method to process fMRI time series based on Bayesian inference for group analysis. The method uses multilevel divided by session, subject and group as pair comparison to reinforce posterior probability in group analysis from single subjects as priors. And also it combines classical statistics, i.e., t-test to obtain voxel activation at subject level as prior for Bayesian inference at group level. It effectively solved computation expensive and complexity. And it shows robust on Bayesian inference for group analysis.

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Paper citation in several formats:
Yang, F.; Fu, K. and Zhou, A. (2014). Multilevel Group Analysis on Bayesian in fMRI Time Series. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 3: VISAPP; ISBN 978-989-758-009-3; ISSN 2184-4321, SciTePress, pages 91-97. DOI: 10.5220/0004655000910097

@conference{visapp14,
author={Feng Yang. and Kuang Fu. and Ai Zhou.},
title={Multilevel Group Analysis on Bayesian in fMRI Time Series},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 3: VISAPP},
year={2014},
pages={91-97},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004655000910097},
isbn={978-989-758-009-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 3: VISAPP
TI - Multilevel Group Analysis on Bayesian in fMRI Time Series
SN - 978-989-758-009-3
IS - 2184-4321
AU - Yang, F.
AU - Fu, K.
AU - Zhou, A.
PY - 2014
SP - 91
EP - 97
DO - 10.5220/0004655000910097
PB - SciTePress