Diffusion-MRI in neurodegenerative disorders. Magn Reson Imaging 2015 Sep;33(7):853-76
Date
04/29/2015Pubmed ID
25917917DOI
10.1016/j.mri.2015.04.006Scopus ID
2-s2.0-84930537081 (requires institutional sign-in at Scopus site) 61 CitationsAbstract
The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.
Author List
Goveas J, O'Dwyer L, Mascalchi M, Cosottini M, Diciotti S, De Santis S, Passamonti L, Tessa C, Toschi N, Giannelli MAuthor
Joseph S. Goveas MD Professor in the Psychiatry and Behavioral Medicine department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsBrain
Contrast Media
Diffusion Magnetic Resonance Imaging
Humans
Image Enhancement
Image Interpretation, Computer-Assisted
Neurodegenerative Diseases
Reproducibility of Results
Sensitivity and Specificity