The Blood Oxygen Level Dependent (BOLD) image contrast provides an important mechanism for tissue characterization with Magnetic Resonance Imaging. Among the neuro-functional of applications of BOLD fMRI are fundamental assessments of the processing of motor, visual, auditory, and sensory tasks by the brain, the evaluation of various diseases including neurological disorders, the pre-surgical determination of brain function, and the evaluation of psychiatric diseases. The BOLD effect is also a dominant mechanism for imaging at an ultra-high magnetic field strength and for cardiac imaging.
Currently, the majority of neuro-functional applications use BOLD fMRI in a qualitative fashion and employ statistical analysis to extract the signal changes present in fMRI data. This task is difficult because of the highly spatially and temporally correlated nature of fMRI data and because of the small levels of the signal changes (1-4%).
For a complete understanding of the BOLD effect and its relation to neuronal activation, one must not only understand where signal changes occur but also the physiologic and physical mechanisms causing the signal change. A number of studies have addressed these issues, however many details regarding these physical and physiologic mechanism remain open questions.
The physical modeling of fMRI data involves the description of MRI signal changes due to the diffusion of tissue water molecules in the locally variable magnetic fields produced by paramagnetic deoxyhemoglobin. These spatially variable magnetic fields, on a 10-100 m scale, can be described mathematically; this knowledge can be used to estimate the signal from water proton diffusion in different tissue compartments (intra-, extra-vascular) and in different geometries (of the vascular and micro-vascular network), as well as the amount and distribution of deoxyhemoglobin. Physiological models are needed to explain the altered amount of deoxyhemoglobin during neuronal activation and its dependence on blood oxygenation, cerebral metabolic rate, oxygen extraction fraction, cerebral blood volume, and cerebral blood flow. It needs to account for the interconnectedness of these different factors under normal or pathologically altered physiologic conditions. Using this knowledge, the statistical modeling of BOLD fMRI signal changes can be improved by better descriptions of the spatial and temporal correlations present in such data, and the prior extent of activation for different tasks. This will, in turn, lead to a more accurate understanding of the physiologic and physical mechanisms causing the signal change.
This workshop will bring together researchers from the statistical, imaging, and modeling communities; it seeks to integrate their knowledge to enhance the medical and basic biomedical sciences communities' understanding of the physiologic and physical mechanisms causing BOLD fMRI signal changes.
|Thursday, March 18|
|9:00-9:15am||Welcome and introduction by Avner Friedman and Petra Schmalbrock|
|4:30-5:30pm||Short Talks and Poster Presentations|
|6:00-9:00pm||Dinner at the Holiday Inn|
|Friday, March 19|
|Saturday, March 20|
|11:30-12:00noon||Summary: Petra Schmalbrock and Tom Santner|