One of the significant challenges facing treatment of people with multiple sclerosis (MS) is determining their individual likelihood of progression, as this information would significantly influence the type of therapy selected. Thus, developing specific tools to monitor and predict progression is critical to better manage patient care and to understand mechanisms of disease. We have been developing a multi-faceted approach to more readily monitor (through imaging) and predict (through both imaging and genetic analysis) disease progression in a real-time fashion. In the past cycle of this grant we demonstrated the utility of high resolution spectral domain optical coherence tomography (SD-OCT) and magnetic resonance imaging (3T MRI) in estimating disease burden in different CNS compartments. We showed that retinal degeneration occurs throughout the disease course and mirrors grey matter compartment atrophy in the cerebrum. A critical finding validating the clinical utility of this approach was that in a multicenter analysis of pooled data, a single OCT at baseline predicted risk of disability progression at 5 years of follow up. As MS is thought to have a strong genetic component, we sought to investigate whether there was an underlying genetic predisposition towards progression, which was made possible by the ability to utilize OCT in a real-time fashion to monitor degeneration and correlate with clinical outcome. We thus expanded the imaging study to include a genetic component in which we conducted a gene array to evaluate genetic variation among people with heterogeneous courses of MS and have preliminarily found that several gene variants in network pathways appear to be associated with more rapid rates of retinal neurodegeneration. The large data set that will be generated from these studies will also allow a corollary analysis in which we can begin to develop a risk profile model in which other population characteristics known to be associated with disease such as sex and ethnicity can be incorporated. The central hypotheses of the proposed studies are; that retinal ganglion layer thickness, thalamic and GM volumes predict 10 year disability across MS subtypes, that patients with high genetic load for gene variants in specific network pathways undergo faster neurodegeneration, and that combinations of OCT, MRI and genetic load measures may be used to develop clinically meaningful individual predictive scores for precision medicine.
Aim 1 : To determine whether baseline retinal ganglion layer thickness and thalamic and GM volumes predict 10 year disease outcomes.
Aim 2 : To determine whether genetic variation, sex and ethnicity influence rates of GCIP, thalamic, GM atrophy, and disability accumulation.
Aim 3 : To develop an algorithm disease progression model to predict disease outcome.

Public Health Relevance

This proposal aims to address the important problem that we are currently unable to predict accurately the course of multiple sclerosis (MS) in individual patients. In ongoing efforts to improve care, we have been developing a multi-faceted approach to readily monitor (through state of the art high-resolution retinal and brain imaging) and predict (through imaging and patient specific factors including genetic profiles) disease severity and progression in order to guide individualized therapeutic decision-making among patients with MS. The clinical need in MS for the predictive tools being developed in this study are an utmost priority given the expanding number of increasingly more effective and potent treatments that may be associated with serious and potentially life-threatening complications.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS082347-08
Application #
9867757
Study Section
Clinical Neuroimmunology and Brain Tumors Study Section (CNBT)
Program Officer
Utz, Ursula
Project Start
2013-04-01
Project End
2023-01-31
Budget Start
2020-02-01
Budget End
2021-01-31
Support Year
8
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Neurology
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
Filippatou, Angeliki; Shoemaker, Thomas; Esch, Megan et al. (2018) Spinal cord and infratentorial lesions in radiologically isolated syndrome are associated with decreased retinal ganglion cell/inner plexiform layer thickness. Mult Scler :1352458518815597
Nguyen, James; Rothman, Alissa; Fitzgerald, Kathryn et al. (2018) Visual Pathway Measures are Associated with Neuropsychological Function in Multiple Sclerosis. Curr Eye Res 43:941-948
Valcarcel, Alessandra M; Linn, Kristin A; Vandekar, Simon N et al. (2018) MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions. J Neuroimaging 28:389-398
Wang, Liang; Kwakyi, Ohemaa; Nguyen, James et al. (2018) Microvascular blood flow velocities measured with a retinal function imager: inter-eye correlations in healthy controls and an exploration in multiple sclerosis. Eye Vis (Lond) 5:29
Fleishman, Greg M; Valcarcel, Alessandra; Pham, Dzung L et al. (2018) Joint Intensity Fusion Image Synthesis Applied to Multiple Sclerosis Lesion Segmentation. Brainlesion (2017) 10670:43-54
Oguz, Ipek; Carass, Aaron; Pham, Dzung L et al. (2018) Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation. Brainlesion (2017) 10670:3-14
Gonzalez Caldito, Natalia; Antony, Bhavna; He, Yufan et al. (2018) Analysis of Agreement of Retinal-Layer Thickness Measures Derived from the Segmentation of Horizontal and Vertical Spectralis OCT Macular Scans. Curr Eye Res 43:415-423
Dong, Mengjin; Oguz, Ipek; Subbana, Nagesh et al. (2017) Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion. Patch Based Tech Med Imaging (2017) 10530:138-145
Glaister, Jeffrey; Carass, Aaron; NessAiver, Tziona et al. (2017) Thalamus segmentation using multi-modal feature classification: Validation and pilot study of an age-matched cohort. Neuroimage 158:430-440
Al-Louzi, Omar; Button, Julia; Newsome, Scott D et al. (2017) Retrograde trans-synaptic visual pathway degeneration in multiple sclerosis: A case series. Mult Scler 23:1035-1039

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