Synopsis

Keywords: Contrast mechanisms: fMRI, Contrast mechanisms: Susceptibility, Neuro: Brain function

In recent years, there has been growing interest in fMRI techniques that utilize information beyond signal-magnitude variations, such as time-course phase and susceptibility data. This talk aims to investigate the reasons behind the BOLD response in phase and susceptibility maps, which complement magnitude data, and to explore the effects of processing steps on this data. The talk also delves into the dynamics of phase and magnitude brain activity during both rest and task conditions, while considering physiological and behavioral factors.

Objective

This lecture will focus on applying QSM to study hemodynamic response in fMRI experiments (fQSM).Brain activity leads to a local change in Cerebral Metabolic Rate of Oxygen (CMRO2), while the local supply of oxygenated blood is increased. The net effect of these intertwined mechanisms is a transient local decrease in blood deoxyhemoglobin, which is paramagnetic, hence its presence perturbs the surrounding magnetic field. Such decrease in deoxyhemoglobin causes a local transient increase in the blood oxygenation level dependent (BOLD) signal, not only in the location where the change in deoxyhemoglobin actually occurs, but also in neighboring voxels. Despite being a fundamental tool in both neuroscience and clinical applications, the BOLD signal is therefore non-local. Moreover, the percent BOLD signal variation cannot be used as a quantitative measurement, because it depends on several factors, such as: the operating static field; MRI parameter settings; shimming; and a patient’s anatomy, physiological condition and positioning (Rudko et al, 2014). Conventional fMRI uses only the T2* weighted signal magnitude and discards the phase that comprises half of the information embedded in the acquired data. QSM analysis of fMRI phase data has recently been used to study the brain responses to visual, somatosensory and motor tasks, as well as in resting state (Balla et al., 2014; Bianciardi et al., 2014; Chen et al., 2016; Sun et al., 2016; Ozbay et al., 2016). It has the potential to quantitatively map brain activation regions with less blooming artifacts, and to capture variations in the interplay among changes in fractional oxygen saturation, cerebral blood flow and volume. During this course attendees will be guided through the phase processing steps to use susceptibility data for time-series analyses.

Acknowledgements

No acknowledgement found.

References

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