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Updated: Jun 6, 2026

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

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Multiple-Model Set-Valued Observers: a new tool for HRF model selection in fMRI.

C Silvestre¹, P Figueiredo, P Rosa

¹Institute for Systems and Robotics - Instituto Superior Tecnico, Av. Rovisco Pais, 1, 1049-001 Lisboa, Portugal. cjs@isr.ist.utl.pt

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

This study introduces Multiple Model Set-Valued Observers (MMSVOs) to select accurate biophysical models for the haemodynamic response function (HRF) in BOLD-fMRI data. The method successfully identifies correct HRF models from plausible alternatives using falsification techniques.

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Area of Science:

Neuroimaging
Biophysics
Systems Biology

Background:

Blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) relies on accurate modeling of the haemodynamic response function (HRF).
Selecting the correct biophysical HRF model from various plausible options is challenging due to inherent system uncertainties and measurement noise.

Purpose of the Study:

To propose and validate a novel model falsification approach for selecting biophysical HRF models in BOLD-fMRI.
To introduce the Multiple Model Set-Valued Observers (MMSVOs) methodology for HRF model selection.

Main Methods:

Development of Multiple Model Set-Valued Observers (MMSVOs) incorporating set-valued initial states, uncertain linear time-varying dynamics, and bounded output noise.
Application of model falsification principles to distinguish between different HRF models.
Theoretical analysis and simulation studies to evaluate the observer performance.

Main Results:

The MMSVOs method successfully distinguished the correct HRF model from a set of physiologically plausible alternatives.
The technique demonstrated feasibility through successful application to an empirical BOLD-fMRI dataset.
The proposed methodology proved effective in identifying the appropriate HRF model under realistic conditions.

Conclusions:

The developed MMSVOs methodology offers a robust approach for biophysical HRF model selection in BOLD-fMRI.
This technique holds significant potential for improving the accuracy and reliability of BOLD-fMRI data modeling.
The findings suggest that MMSVOs can enhance the interpretation of neuroimaging studies by providing better HRF model identification.