MS is a disease with profound heterogeneity pathologically, clinically, and radiologically. Clinically-defined subtypes of MS do not capture the known pathological heterogeneity. Imaging biomarkers capable of differentiating and quantifying the underlying heterogeneous pathologies can provide important insights into the disease spectrum in MS. 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).

To differentiate pathologically meaningful subtypes of multiple sclerosis (MS) using a novel diffusion imaging method called diffusion basis spectrum imaging (DBSI).

Fifty-five MS patients with pre-identified clinically-defined disease subtype of relapsing-remitting MS (RRMS, n=22), secondary-progressive MS (SPMS, n=16), and primary-progressive MS (PPMS, n=17) underwent brain DBSI. Two sets of regions of interests (ROIs) were defined for each patient including white matter lesions, and normal appearing corpus callosum (excluding lesions). DBSI-derived metrics (radial diffusivity, axial diffusivity, fiber fraction, restricted isotropic fraction, hindered non-restricted isotropic fraction, and free non-restricted isotropic fraction) were included in recursive partitioning analysis to identify the most homogenous subgroups based on their clinically-defined subtype.

For the white matter brain lesions, and for normal appearing corpus callosum, the most important metrics for identifying MS subgroups were restricted isotropic fraction and fiber fraction. Total lesion volume did not improve classification. DBSI metrics classified 60-64% of MS patients into their clinically-defined subtypes using a single scan and without including any clinical or demographic characteristics.

DBSI-derived restricted fraction (reflecting isotropic diffusion within cells), and fiber fraction (apparent axonal content) capture some of the differences between patients having different pre-determined clinically-defined MS subtypes. DBSI might be a promising tool for noninvasively capturing the pathological heterogeneity that is characteristic of people with MS.