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One of the most important issues in blood-oxygen-level-dependent (BOLD)-based brain functional magnetic resonance imaging is the understanding of the vascular structures that are responsible for the signal changes observed. The T2*-related signal changes observed during variations in susceptibility-induced magnetic field gradients are a function both of non-refocusable mechanisms, such as diffusion, and of refocusable effects such as field inhomogeneities. Conversely, T2-related signal changes are only a function of non-refocusable effects. It has been suggested that T2-weighted images could be less sensitive to blood susceptibility changes in a macrovascular environment than T2*-weighted images and could thus be more accurate in identifying the "activation" of the parenchyma rather than "draining vein" effects. In this study we use hypoxia and hypercapnia challenges in cats to provide a change in blood deoxyhemoglobin concentration (as a model for classic BOLD changes and not as a model for neuronal activation). A combined gradient echo and spin echo echo-planar-imaging (EPI) pulse sequence was used to map DeltaR2 (i.e., Delta(1/T2)) and DeltaR2* (i.e., Delta(1/T2*)) changes during the challenges. Our experiments demonstrate that: (i) the acquisition of T2-weighted EPI data does not in itself differentiate signal changes in the parenchyma from those occurring in regions around larger vessels, but that (ii) the simultaneous acquisition of T2- and T2*-weighted images could be useful in identifying microvascular regions in gray matter by analyzing the ratio DeltaR2/DeltaR2*. This value seems independent of the degree of deoxyhemoglobin concentration change, but is related to properties of the vascular environment. We suggest a possible application of the results to the study of brain function in humans.

Original publication

DOI

10.1006/nimg.1997.0288

Type

Journal article

Journal

Neuroimage

Publication Date

10/1997

Volume

6

Pages

191 - 200

Keywords

Animals, Brain, Brain Mapping, Cats, Hemoglobins, Hypercapnia, Hypoxia, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Neurological, Oxygen Consumption