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Longitudinal imaging biomarkers are invaluable for understanding the course of neurodegeneration, promising the ability to track disease progression and to detect disease earlier than cross-sectional biomarkers. To properly realize their potential, biomarker trajectory models must be robust to both under-sampling and measurement errors and should be able to integrate multi-modal information to improve trajectory inference and prediction. Here we present a parametric Bayesian multi-task learning based approach to modeling univariate trajectories across subjects that addresses these criteria. Our approach learns multiple subjects' trajectories within a single model that allows for different types of information sharing, that is, coupling, across subjects. It optimizes a combination of uncoupled, fully coupled and kernel coupled models. Kernel-based coupling allows linking subjects' trajectories based on one or more biomarker measures. We demonstrate this using Alzheimer's Disease Neuroimaging Initiative (ADNI) data, where we model longitudinal trajectories of MRI-derived cortical volumes in neurodegeneration, with coupling based on APOE genotype, cerebrospinal fluid (CSF) and amyloid PET-based biomarkers. In addition to detecting established disease effects, we detect disease related changes within the insula that have not received much attention within the literature. Due to its sensitivity in detecting disease effects, its competitive predictive performance and its ability to learn the optimal parameter covariance from data rather than choosing a specific set of random and fixed effects a priori, we propose that our model can be used in place of or in addition to linear mixed effects models when modeling biomarker trajectories. A software implementation of the method is publicly available.
Pubmed ID: 31168892
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Open source software suite for processing and analyzing human brain MRI images. Used for reconstruction of brain cortical surface from structural MRI data, and overlay of functional MRI data onto reconstructed surface. Contains automatic structural imaging stream for processing cross sectional and longitudinal data. Provides anatomical analysis tools, including: representation of cortical surface between white and gray matter, representation of the pial surface, segmentation of white matter from rest of brain, skull stripping, B1 bias field correction, nonlinear registration of cortical surface of individual with stereotaxic atlas, labeling of regions of cortical surface, statistical analysis of group morphometry differences, and labeling of subcortical brain structures.Operating System: Linux, macOS.
View all literature mentionsDatabase of the results of the ADNI study. ADNI is an initiative to develop biomarker-based methods to detect and track the progression of Alzheimer's disease (AD) that provides access to qualified scientists to their database of imaging, clinical, genomic, and biomarker data.
View all literature mentionsA public charity whose mission is to support the NIH in its mission to improve health, by forming and facilitating public-private partnerships for biomedical research and training. Its vision is Building Partnerships for Discovery and Innovation to Improve Health. The FNIH draws together the world''s foremost researchers and resources, pressing the frontier to advance critical discoveries. They are recognized as the number-one medical research charity in the countryleveraging support, and convening high level partnerships, for the greatest impact on the most urgent medical challenges we face today. Grants are awarded as part of a public-private partnership with the National Heart, Lung, and Blood Institute (NHLBI) on behalf of The Heart Truth in support of women''s heart health education and research. Funding for the Community Action Program is provided by the FNIH through donations from individuals and corporations including The Heart Truth partners Belk Department Stores, Diet Coke, and Swarovski. Successful biomedical research relies upon the knowledge, training and dedication of those who conduct it. Bringing multiple disciplines to bear on health challenges requires innovation and collaboration on the part of scientists. Foundation for NIH partnerships operate in a variety of ways and formats to recruit, train, empower and retain their next generation of researchers. From lectures and multi-week courses, to scholarships and awards through fellowships and residential training programs, their programs respond to the needs of scientists at every level and stage in their careers.
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