Multi-modal brain imaging for investigating and modeling neurological diseases
演題番号 : S1-1-1-4
Gary F. Egan:1
1:Centre for Neuroscience, University of Melbourne
Neuroimaging is an indispensible research tool in the neurosciences with major advances in our understanding of the human brain resulting from imaging studies over the past 20 years. The continued development of novel Magnetic Resonance (MR) imaging techniques continues to provide new insights into brain function in neurological and psychiatric disease processes. MRI techniques including blood oxygenation level dependent (BOLD) functional MR, cerebral blood flow or perfusion, diffusion weighted, susceptibility weighted, contrast enhanced, and spectroscopy techniques provide unique in vivo measures of brain function and microstructure. Current developments in ultra high field strength magnets for brain research have great potential to further revolutionise our understanding of the brain. Multi-modal MRI for the investigation and modeling of neurological diseases including Huntingtons disease (HD) (1-3), Multiple Sclerosis (MS) (4-5) and Freidreichs ataxia will be presented. Ultrahigh field 7 Tesla MR in human studies can reveal higher resolution pathological changes in vivo, and whilst there remain many challenges to routine imaging at ultra-high magnetic field strengths, the development of new imaging techniques such as phase contrast imaging (6) provide new clinical research opportunities. References1. I. Bohanna, et al. MRI identification of neuropathological biomarkers for HD. Brain Research Reviews, 58 (2008) 209-2252. D. Thiruvady, et al. Functional connectivity in HD. J. Neurology Neurosurgery & Psychiatry 78 (2007) 127-133.3. A. Sritharan, et al. A longitudinal DTI study in HD. J. Neurology Neurosurgery & Psychiatry 81 (2010) 257-62.4. S.C. Kolbe, et al. Optic nerve volume and diffusivity predict visual dysfunction. Neuroimage, 45 (2009) 679-86.5. A. Van der Walt, et al. Neuroprotection in MS. Pharmacology Therapeutics 126 (2010) 82-93.6. Z. Chen, et al. An optimised framework for MR phase images. Neuroimage 49 (2010) 1289-300.