Stochastic Dynamic Causal Modelling of fMRI Data with Multiple-Model Kalman Filters

P. Osório; P. Rosa; C. Silvestre; P. Figueiredo

doi:10.3414/ME13-02-0052

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2015; 54(03): 232-239
DOI: 10.3414/ME13-02-0052

Focus Theme – Original Articles

Schattauer GmbH

Stochastic Dynamic Causal Modelling of fMRI Data with Multiple-Model Kalman Filters

Authors

P. Osório

¹Institute for Systems and Robotics, Lisbon, Portugal

²Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
P. Rosa

¹Institute for Systems and Robotics, Lisbon, Portugal

³Department of Electrical and Computer Engineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

⁴Deimos Engenharia, Lisbon, Portugal
C. Silvestre

⁵Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
P. Figueiredo

¹Institute for Systems and Robotics, Lisbon, Portugal

²Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

Abstract

Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Biosignal Interpretation: Advanced Methods for Neural Signals and Images”.

Background: Dynamic Causal Modelling (DCM) is a generic formalism to study effective brain connectivity based on neuroimaging data, particularly functional Magnetic Resonance Imaging (fMRI). Recently, there have been attempts at modifying this model to allow for stochastic disturbances in the states of the model.

Objectives: This paper proposes the Multiple- Model Kalman Filtering (MMKF) technique as a stochastic identification model discriminating among different hypothetical connectivity structures in the DCM framework; moreover, the performance compared to a similar de terministic identification model is assessed.

Methods: The integration of the stochastic DCM equations is first presented, and a MMKF algorithm is then developed to perform model selection based on these equations. Monte Carlo simulations are performed in order to investigate the ability of MMKF to distinguish between different connectivity structures and to estimate hidden states under both deterministic and stochastic DCM.

Results: The simulations show that the proposed MMKF algorithm was able to successfully select the correct connectivity model structure from a set of pre-specified plausible alternatives. Moreover, the stochastic approach by MMKF was more effective compared to its deterministic counterpart, both in the selection of the correct connectivity structure and in the estimation of the hidden states.

Conclusions: These results demonstrate the applicability of a MMKF approach to the study of effective brain connectivity using DCM, particularly when a stochastic formulation is desirable.

Keywords

Stochastic - DCM - fMRI - Multiple-Model Kalman Filter

Full Text

References

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