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Stochastic dynamic causal modelling of fMRI data with multiple-model kalman filters
Osorio P.4; Rosa P.4; Silvestre C.3; Figueiredo P.4
2015
Source PublicationMethods of Information in Medicine
ISSN2511705X 00261270
Volume54Issue:3Pages:232-239
Abstract

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 neuroimag-ing 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 stochas -tic 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.

KeywordDcm Fmri Multiple-model Kal-man Filter Stochastic
DOI10.3414/ME13-02-0052
URLView the original
Language英语
WOS Research AreaComputer Science ; Health Care Sciences & Services ; Medical Informatics
WOS SubjectComputer Science, Information Systems ; Health Care Sciences & Services ; Medical Informatics
WOS IDWOS:000355057800007
PublisherSCHATTAUER GMBH-VERLAG MEDIZIN NATURWISSENSCHAFTEN, HOLDERLINSTRASSE 3, D-70174 STUTTGART, GERMANY
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Affiliation1.Instituto Superior Técnico
2.DEIMOS Engenharia SA
3.Universidade de Macau
4.Instituto de Sistemas e Robótica
Recommended Citation
GB/T 7714
Osorio P.,Rosa P.,Silvestre C.,et al. Stochastic dynamic causal modelling of fMRI data with multiple-model kalman filters[J]. Methods of Information in Medicine,2015,54(3):232-239.
APA Osorio P.,Rosa P.,Silvestre C.,&Figueiredo P..(2015).Stochastic dynamic causal modelling of fMRI data with multiple-model kalman filters.Methods of Information in Medicine,54(3),232-239.
MLA Osorio P.,et al."Stochastic dynamic causal modelling of fMRI data with multiple-model kalman filters".Methods of Information in Medicine 54.3(2015):232-239.
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