• Dinesh K Sivakolundu, Madison R Hansen, Kathryn L West, Yeqi Wang, Thomas Stanley, Andrew Wilson, Morgan McCreary, Monroe P Turner, Marco C Pinho, Braeden D Newton, Xiaohu Guo, Bart Rypma, and Darin T Okuda.
• NeuroPsychometric Research Laboratory, Center for BrainHealth, University of Texas at Dallas, Dallas, TX.
• J Neuroimaging. 2019 Sep 1; 29 (5): 605-614.

Background And PurposeMultiple sclerosis (MS) clinical management is based upon lesion characterization from 2-dimensional (2D) magnetic resonance imaging (MRI) views. Such views fail to convey the lesion-phenotype (ie, shape and surface texture) complexity, underlying metabolic alterations, and remyelination potential. We utilized a 3-dimensional (3D) lesion phenotyping approach coupled with imaging to study physiologic profiles within and around MS lesions and their impacts on lesion phenotypes.MethodsLesions were identified in 3T T2 -FLAIR images and segmented using geodesic active contouring. A calibrated fMRI sequence permitted measurement of cerebral blood flow (CBF), blood-oxygen-level-dependent signal (BOLD), and cerebral metabolic rate of oxygen (CMRO2 ). These metrics were measured within lesions and surrounding tissue in concentric layers exact to the 3D-lesion shape. BOLD slope was calculated as BOLD changes from a lesion to its surrounding perimeters. White matter integrity was measured using diffusion kurtosis imaging. Associations between these metrics and 3D-lesion phenotypes were studied.ResultsOne hundred nine lesions from 23 MS patients were analyzed. We identified a noninvasive biomarker, BOLD slope, to metabolically characterize lesions. Positive BOLD slope lesions were metabolically active with higher CMRO2 and CBF compared to negative BOLD slope or inactive lesions. Metabolically active lesions with more intact white matter integrity had more symmetrical shapes and more complex surface textures compared to inactive lesions with less intact white matter integrity.ConclusionThe association of lesion phenotypes with their metabolic signatures suggests the prospect for translation of such data to clinical management by providing information related to metabolic activity, lesion age, and risk for disease reactivation and self-repair. Our findings also provide a platform for disease surveillance and outcome quantification involving myelin repair therapeutics.© 2019 by the American Society of Neuroimaging.

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