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• W4367671137 abstract "Digital Twin (DT) is a novel concept that may bring a paradigm shift for precision medicine. In this study we demonstrate a DT application for estimating the age of onset of disease-specific brain atrophy in individuals with multiple sclerosis (MS) using brain MRI. We first augmented longitudinal data from a well-fitted spline model derived from a large cross-sectional normal aging data. Then we compared different mixed spline models through both simulated and real-life data and identified the mixed spline model with the best fit. Using the appropriate covariate structure selected from 52 different candidate structures, we augmented the thalamic atrophy trajectory over the lifespan for each individual MS patient and a corresponding hypothetical twin with normal aging. Theoretically, the age at which the brain atrophy trajectory of an MS patient deviates from the trajectory of their hypothetical healthy twin can be considered as the onset of progressive brain tissue loss. With a 10-fold cross validation procedure through 1000 bootstrapping samples, we found the onset age of progressive brain tissue loss was, on average, 5-6 years prior to clinical symptom onset. Our novel approach also discovered two clear patterns of patient clusters: earlier onset vs. simultaneous onset of brain atrophy." @default.
• W4367671137 created "2023-05-03" @default.
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• W4367671137 date "2023-05-02" @default.
• W4367671137 modified "2023-10-14" @default.
• W4367671137 title "Toward Precision Medicine Using a “Digital Twin” Approach: Modeling the Onset of Disease-Specific Brain Atrophy in Individuals with Multiple Sclerosis" @default.
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• W4367671137 doi "https://doi.org/10.21203/rs.3.rs-2833532/v1" @default.
• W4367671137 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37205476" @default.
• W4367671137 hasPublicationYear "2023" @default.
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