Diffusion basis spectrum imaging for identifying subtypes of multiple sclerosis
ECTRIMS Online Library. Shirani A. Oct 12, 2018; 232045
Afsaneh Shirani
Afsaneh Shirani
Login now to access Regular content available to all registered users.

You may also access this content "anytime, anywhere" with the Free MULTILEARNING App for iOS and Android
Discussion Forum (0)
Rate & Comment (0)

Abstract: 292

Type: Scientific Session

Abstract Category: Pathology and pathogenesis of MS - Biomarkers

Background: Clinical subtypes of multiple sclerosis (MS) do not capture the known pathological heterogeneity. Imaging biomarkers capable of differentiating and quantifying the underlying heterogeneous pathologies can play an important role in a more accurate classification of MS subtypes. Diffusion basis spectrum imaging (DBSI) models diffusion-weighted MRI signals as a combination of discrete anisotropic diffusion tensors (representing the integrity of axon fibers and myelin), and a spectrum of isotropic diffusion tensors (reflecting the extra-axonal environment associated with inflammation and tissue loss).
Objective: To differentiate pathologically meaningful MS subtypes using DBSI.
Methods: Fifty-five MS patients with pre-identified disease courses of relapsing-remitting MS (RRMS, n=22), secondary-progressive MS (SPMS, n=16), and primary-progressive MS (PPMS, n=17) underwent DBSI. Lesion masks were based on automatic segmentation on FLAIR images using statistical parametric mapping (SPM12). DBSI-derived metrics (radial diffusivity, axial diffusivity, fiber fraction, restricted isotropic fraction, hindered non-restricted isotropic fraction, and free non-restricted isotropic fraction), as well as total lesion volume were included in recursive partitioning analysis to identify the most homogenous subgroups of MS patients based on their clinical subtype.
Results: Four distinct MS subgroups (RRMS, SPMS, and two subgroups of PPMS patients) were identified via recursive partitioning. The most important metrics for identifying MS subgroups were restricted isotropic fraction and fiber fraction. Total lesion volume did not improve the classification. Two distinct subgroups of PPMS patients separated with respect to restricted fraction. RRMS patients had higher fiber fraction and higher restricted isotropic fraction than PPMS. As expected, the free (water-like) unrestricted isotropic fraction played no role in partitioning. DBSI metrics classified 64% of MS patients into their pre-identified clinical subtypes.
Conclusion: DBSI-derived imaging metrics can differentiate subgroups of MS patients. Restricted fraction (reflecting isotropic diffusion that is restricted by cell membranes), and fiber fraction (apparent axonal density) were the most useful DBSI metrics to classify MS patients based on pre-determined clinical subtypes. DBSI might be a promising tool for capturing the pathological heterogeneity that is not optimally reflected in clinical subtypes of MS.
Disclosure: Afsaneh Shirani is funded through a clinician scientist development award from the National Multiple Sclerosis Society (USA), and a clinical research training scholarship from the American Academy of Neurology. Peng Sun: nothing to disclose. Kathryn Trinkaus: nothing to disclose. Ajit George: nothing to disclose. Dana Perantie: nothing to disclose. Robert Naismith has consulted for Alkermes, Acorda, Bayer, Biogen, EMD Serono, Genentech, Genzyme, Novartis, and Teva. Robert Schmidt: nothing to disclose. Sheng-Kwei Song is currently funded by NIH U01EY025500, R01NS047592, P01NS059560, and NMSS RG 5258-A-5, is a co-founder of DxGPS and may financially benefit if the company is successful in marketing its product(s) that is/are related to this research. Anne Cross has been a paid consultant for Abbvie, Bayer, Biogen, EMD Serono, Genentech, Genzyme/Sanofi, Novartis, and Teva. Biogen, EMD-Serono, Genentech, Genzyme, and Novartis. Washington University may receive royalty income based on a technology licensed by Washington University to DxGPS LLC. That technology is evaluated in this research.

Code of conduct/disclaimer available in General Terms & Conditions
Anonymous User Privacy Preferences

Strictly Necessary Cookies (Always Active)

MULTILEARNING platforms and tools hereinafter referred as “MLG SOFTWARE” are provided to you as pure educational platforms/services requiring cookies to operate. In the case of the MLG SOFTWARE, cookies are essential for the Platform to function properly for the provision of education. If these cookies are disabled, a large subset of the functionality provided by the Platform will either be unavailable or cease to work as expected. The MLG SOFTWARE do not capture non-essential activities such as menu items and listings you click on or pages viewed.

Performance Cookies

Performance cookies are used to analyse how visitors use a website in order to provide a better user experience.

Google Analytics is used for user behavior tracking/reporting. Google Analytics works in parallel and independently from MLG’s features. Google Analytics relies on cookies and these cookies can be used by Google to track users across different platforms/services.

Save Settings