A novel approach to calibrate the hemodynamic model using functional Magnetic Resonance Imaging (fMRI) measurements

Abstract

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Page 1 of 42A c c e p t e d M a n u s c r i p t• An efficient solution methodology for estimating the parameters of the brain response model is proposed.• This method distinguishes itself from existing calibrating techniques by employing intelligently (a) the Newton algorithm, (b) a Tikhonov regularization approach, and (c) a Kalman filtering procedure.• Both synthetic and real fMRI measurements were used to assess the performance of this method.• The fast convergence, the accuracy, and the robustness to the noise effect of this method are clearly demonstrated by the reported numerical results.• This methods outperforms existing methods. Highlights (for review)Page 2 of 42 A c c e p t e d M a n u s c r i p t Abstract BackgroundThe calibration of the hemodynamic model that describes changes in blood flow and blood oxygenation during brain activation is a crucial step for successfully monitoring and possibly predicting brain activity. This in turn has the potential to provide diagnosis and treatment of brain diseases in early stages. New MethodWe propose an efficient numerical procedure for calibrating the hemodynamic model using some fMRI measurements. The proposed solution methodology is a regularized iterative method equipped with a Kalman filtering-type procedure. The Newton component of the proposed method addresses the nonlinear aspect of the problem. The regularization feature is used to ensure the stability of the algorithm. The Kalman filter procedure is incorporated here to address the noise in the data. Results Numerical results obtained with synthetic data as well as with real fMRI measurements are presented to illustrate the accuracy, robustness to the noise, and the cost-effectiveness of the proposed method.

Joint state and parameter estimation of the hemodynamic model by particle smoother expectation maximization method

“…However, both Hu et al and Riera et al utilized these algorithms in the filtering sense. One other interesting method which works in the filtering sense is Tikhonov-regularized Newton-cubature Kalman filter (TNF-CKF) proposed recently by Khoram et al [21,22]. This method is reported to be robust under high measurement noise conditions.…”