State Estimation of Hemodynamic Model for fMRI Under Confounds: SSM Method

Through hemodynamic models, the change of neuronal state can be estimated from functional magnetic resonance imaging (fMRI) signals. Usually, there are confounds in the fMRI signal, which will degrade the performance of the estimation for the neuronal state change. For the reason, this paper introdu...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics Vol. 24; no. 3; pp. 804 - 814
Main Authors: Wu, Haifeng, Lu, Mingzhi, Zeng, Yu
Format: Journal Article
Language:English
Published: United States IEEE 01.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Biological system modeling
Brain - diagnostic imaging
Brain modeling
Computer Simulation
confounds
Covariance
Error reduction
Estimation
fMRI
Functional magnetic resonance imaging
hemodynamic model
Hemodynamics
Hemodynamics - physiology
Heuristic algorithms
Humans
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Mathematical model
Models, Biological
Performance degradation
Performance evaluation
Signal Processing, Computer-Assisted
State estimation
State space models
ISSN:2168-2194, 2168-2208, 2168-2208
Online Access:Get full text
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Summary:Through hemodynamic models, the change of neuronal state can be estimated from functional magnetic resonance imaging (fMRI) signals. Usually, there are confounds in the fMRI signal, which will degrade the performance of the estimation for the neuronal state change. For the reason, this paper introduces a state-space model with confounds, from a conventional hemodynamic model. In this model, a successive state estimation method requires a state value vector, an error covariance, an innovation covariance, and a cross covariance to be re-derived. Thus, a confounds square-root cubature Kalman smoothing (CSCKS) algorithm is proposed in this paper. We use a Balloon-Windkessel model to generate simulation data and add confounds signals to evaluate the performance of the proposed algorithm. The experiment results show that when the signal-to-interference ratio is less than 21 dB, the CSCKS proposed in this paper reduced estimation error to 16%, whereas the traditional algorithm reduced it to only 73%.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2019.2917093