Since its introduction over a decade ago, functional magnetic resonance imaging (fMRI) has revolutionized studies of the working human brain. While most fMRI studies measure the blood oxygenation level dependent (BOLD) response to a functional task, there has been growing interest in the use of resting-state BOLD fMRI, in which the connectivity between different brain regions is measured while the subject is at rest (e.g. not actively performing a task). Resting-state BOLD approaches may be particularly effective for clinical usage because they do not require the patient to perform a task and can be obtained in a short amount of time. A number of studies applying resting-state connectivity measures to the assessment of disease have reported significant disease-related changes in connectivity. However, the interpretation of these changes in connectivity is not straightforward, because the mechanisms underlying resting-state BOLD connectivity are not well understood. The BOLD signal represents the hemodynamic response to neural activity, and is a complicated function of changes in blood flow, blood volume, and oxygen metabolism. As a result, changes in resting-state connectivity can reflect a complex combination of neural, vascular, and metabolic factors. A better understanding of the primary factors that modulate resting-state connectivity is therefore critical for the accurate interpretation of resting-state measures. The goal of this proposal is to identify measures of neural power fluctuations and neurovascular coupling that can best account for changes in resting-state BOLD connectivity.
The specific aims of this project are to identify the measures that most effectively explain changes in resting-state connectivity due to (a) caffeine usage and (b) inter-subject differences in physiology.
(Relevance to Public Health) The proposed project will provide a detailed understanding of the neurovascular factors that can modulate measures of resting-state BOLD connectivity. Accomplishment of the aims of this project is also expected to result in improved methods for analyzing resting-state measures. These advances will significantly improve the application of resting-state measures for the assessment of disease.
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