Abstract
The change of BOLD signal relies heavily upon the resting blood volume fraction () associated with regional vasculature. However, existing hemodynamic data assimilation studies pretermit such concern. They simply assign the value in a physiologically plausible range to get over ill-conditioning of the assimilation problem and fail to explore actual . Such performance might lead to unreliable model estimation. In this work, we present the first exploration of the influence of on fMRI data assimilation, where actual within a given cortical area was calibrated by an MR angiography experiment and then was augmented into the assimilation scheme. We have investigated the impact of on single-region data assimilation and multi-region data assimilation (dynamic cause modeling, DCM) in a classical flashing checkerboard experiment. Results show that the employment of an assumed in fMRI data assimilation is only suitable for fMRI signal reconstruction and activation detection grounded on this signal, and not suitable for estimation of unobserved states and effective connectivity study. We thereby argue that introducing physically realistic in the assimilation process may provide more reliable estimation of physiological information, which contributes to a better understanding of the underlying hemodynamic processes. Such an effort is valuable and should be well appreciated.
Highlights
In 1998, Buxton and his colleagues introduced their celebrated hemodynamic model, Balloon model [1]
We found that two different V0 values produced very similar blood oxygen level dependent (BOLD) estimates (Figure 4, left), only tiny discrepancy in post stimulus undershoot stage could be found
Since statistical inference essentially is grounded on the amplitude of BOLD response, this area may surely be considered active in statistic analysis of the BOLD signal change, though it is absent at the response efficacy elicited by neuronal activity
Summary
In 1998, Buxton and his colleagues introduced their celebrated hemodynamic model, Balloon model [1]. The comprehensive biophysical model of hemodynamic modulation describes the coupling dynamics from neural activity to observed blood oxygen level dependent (BOLD) signal [1,2] It comprises the coupling mechanism of manifold physiological variables, blood flow (f ), blood volume (v), and deoxyhemoglobin content (q), during brain activation. There is a growing interest in assimilating such a model with given sets of fMRI measurements in order to infer physiological parameters and associated states [4,5,6,7,8,9], constrain the activation detection process with classic statistics techniques [10,11], and extrapolate to similar systems and/or different driving conditions [12,13,14,15,16] These works greatly enhance our understanding of the neural systems that mediate specific cognitive processes, they are still kind of problematic in offering reliable inference on the hemodynamic system behaviors
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