Functional Connectivity in MRI Is Driven by Spontaneous BOLD Events.
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ABSTRACT:
SUBMITTER: Allan TW
PROVIDER: S-EPMC4429612 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
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Project description:Recent neuroimaging studies have demonstrated that the spontaneous brain activity reflects, to a large extent, the same activation patterns measured in response to cognitive and behavioral tasks. This correspondence between activation and rest has been explored with a large repertoire of computational methods, ranging from analysis of pairwise interactions between areas of the brain to the global brain networks yielded by independent component analysis. In this paper we describe an alternative method based on the averaging of the BOLD signal at a region of interest (target) triggered by spontaneous increments in activity at another brain area (seed). The resting BOLD event triggered averages ("rBeta") can be used to estimate functional connectivity at resting state. Using two simple examples, here we illustrate how the analysis of the average response triggered by spontaneous increases/decreases in the BOLD signal is sufficient to capture the aforementioned correspondence in a variety of circumstances. The computation of the non linear response during rest here described allows for a direct comparison with results obtained during task performance, providing an alternative measure of functional interaction between brain areas.
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Project description:At higher field strengths, spin echo (SE) functional MRI (fMRI) is an attractive alternative to gradient echo (GE) as the increased weighting towards the microvasculature results in intrinsically better localization of the BOLD signal. Images are free of signal voids but the commonly used echo planar imaging (EPI) sampling scheme causes geometric distortions, and T2* effects often contribute considerably to the signal changes measured upon brain activation. Multiply refocused SE sequences such as fast spin echo (FSE) are essentially artifact free but their application to fast fMRI is usually hindered due to high energy deposition, and long sampling times. In the work presented here, a combination of parallel imaging and partial Fourier acquisition is used to shorten FSE acquisition times to near those of conventional SE-EPI, permitting sampling of eight slices (matrix 64 x 64) per second. Signal acquisition is preceded by a preparation experiment that aims at increasing the relative contribution of extravascular dynamic averaging to the BOLD signal. Comparisons are made with conventional SE-EPI using a visual stimulation paradigm. While the observed signal changes are approximately 30% lower, most likely due to the absence of T2* contamination, activation size and t-scores are comparable for both methods, suggesting that HASTE fMRI is a viable alternative, particularly if distortion free images are required. Our data also indicate that the BOLD post-stimulus undershoot is most probably attributable to persistent elevated oxygen metabolism rather than to delayed vascular compliance.
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Project description:Functional connectivity MRI, based on Blood-Oxygenation-Level-Dependent (BOLD) signals, is typically performed while the subject is at rest. On the other hand, BOLD is also widely used in physiological imaging such as cerebrovascular reactivity (CVR) mapping using hypercapnia (HC) as a modulator. We therefore hypothesize that hypercapnia BOLD data can be used to extract FC metrics after factoring out the effects of the physiological modulation, which will allow simultaneous assessment of neural and vascular function and may be particularly important in populations such as aging and cerebrovascular diseases. The present work aims to systematically examine the feasibility of hypercapnia BOLD-based FC mapping using three commonly applied analysis methods, specifically dual-regression Independent Component Analysis (ICA), region-based FC matrix analysis, and graph-theory based network analysis, in a large cohort of 170 healthy subjects ranging from 20 to 88 years old. To validate the hypercapnia BOLD results, we also compared these FC metrics with those obtained from conventional resting-state data. ICA analysis of the hypercapnia BOLD data revealed FC maps that strongly resembled those reported in the literature. FC matrix using region-based analysis showed a correlation of 0.97 on the group-level and 0.54?±?0.10 on the individual-level, when comparing between hypercapnia and resting-state results. Although the correspondence on the individual-level was moderate, this was primarily attributed to variations intrinsic to FC mapping, because a corresponding resting-vs-resting comparison in a sub-cohort (N?=?39) revealed a similar correlation of 0.57?±?0.09. Graph-theory computations were also feasible in hypercapnia BOLD data and indices of global efficiency, clustering coefficient, modularity, and segregation were successfully derived. Hypercapnia FC results revealed age-dependent differences in which within-network connections generally exhibited an age-dependent decrease while between-network connections showed an age-dependent increase.
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