Probabilistic Brain Extraction in MR Images via Conditional Generative Adversarial Networks.

Brain extraction, or the task of segmenting the brain in MR images, forms an essential step for many neuroimaging applications. These include quantifying brain tissue volumes, monitoring neurological diseases, and estimating brain atrophy. Several algorithms have been proposed for brain extraction, including image-to-image deep learning methods that have demonstrated significant gains in accuracy. However, none of them account for the inherent uncertainty in brain extraction. Motivated by this, we propose a novel, probabilistic deep learning algorithm for brain extraction that recasts this task as a Bayesian inference problem and utilizes a conditional generative adversarial network (cGAN) to solve it. The input to the cGAN's generator is an MR image of the head, and the output is a collection of likely brain images drawn from a probability density conditioned on the input. These images are used to generate a pixel-wise mean image, serving as the estimate for the extracted brain, and a standard deviation image, which quantifies the uncertainty in the prediction. We test our algorithm on head MR images from five datasets: NFBS, CC359, LPBA, IBSR, and their combination. Our datasets are heterogeneous regarding multiple factors, including subjects (with and without symptoms), magnetic field strengths, and manufacturers. Our experiments demonstrate that the proposed approach is more accurate and robust than a widely used brain extraction tool and at least as accurate as the other deep learning methods. They also highlight the utility of quantifying uncertainty in downstream applications. Additional information and codes for our method are available at: https://github.com/bmri/bmri.

Dimethyl fumarate preserves brainstem and cervical spinal cord integrity in radiologically isolated syndrome.

BACKGROUND AND PURPOSE: The first randomized placebo-controlled therapeutic trial in radiologically isolated syndrome (RIS), ARISE, demonstrated that treatment with dimethyl fumarate (DMF) delayed the onset of a first clinical event related to CNS demyelination and was associated with a significant reduction in new and/or newly enlarging T2-weighted hyperintense lesions. The purpose of this study was to explore the effect of DMF on volumetric measures, including whole brain, thalamic, and subcortical gray matter volumes, brainstem and upper cervical spine three-dimensional (3D) volumes, and brainstem and upper cervical spine surface characteristics. METHODS: Standardized 3T MRIs including 3D isotropic T1-weighted gradient echo images were acquired at baseline and end-of-study according to the ARISE study protocol. The acquired data were analyzed using Structural Image Evaluation Using Normalization of Atrophy (SIENA), FreeSurfer v7.3, and an in-house pipeline for 3D conformational metrics. Multivariate mixed models for repeated measures were used to analyze rates of change in whole brain, thalamic, subcortical gray matter, as well as change in the 3D surface curvature of the dorsal pons and dorsal medulla and 3D volume change at the medulla-upper cervical spinal cord. RESULTS: The study population consisted of 64 RIS subjects (DMF:30, placebo:34). No significant difference was seen in whole brain, thalamic, or subcortical gray matter volumes in treated vs. untreated RIS patients. A significant difference was observed in dorsal pons curvature with the DMF group having a lower least squares mean change of - 4.46 (standard estimate (SE): 3.77) when compared to placebo [6.94 (3.71)] (p = 0.036). In individuals that experienced a first clinical event, a greater reduction in medulla-upper cervical spinal cord volume (p = 0.044) and a decrease in surface curvature was observed at the dorsal medulla (p = 0.009) but not at the dorsal pons (p = 0.443). CONCLUSIONS: The benefit of disease-modifying therapy in RIS may extend to CNS structures impacted by neurodegeneration that is below the resolution of conventional volumetric measures.

Toward Precision Medicine Using a "Digital Twin" Approach: Modeling the Onset of Disease-Specific Brain Atrophy in Individuals with Multiple Sclerosis.

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.

Toward precision medicine using a "digital twin" approach: modeling the onset of disease-specific brain atrophy in individuals with multiple sclerosis.

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 tenfold 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 versus simultaneous onset of brain atrophy.