title: A computational method for computing an Alzheimer's disease progression score; experiments and validation with the ADNI data set.
abstract: Understanding the time-dependent changes of biomarkers related to Alzheimer's disease (AD) is a key to assessing disease progression and measuring the outcomes of disease-modifying therapies. In this article, we validate an AD progression score model which uses multiple biomarkers to quantify the AD progression of subjects following 3 assumptions: (1) there is a unique disease progression for all subjects; (2) each subject has a different age of onset and rate of progression; and (3) each biomarker is sigmoidal as a function of disease progression. Fitting the parameters of this model is a challenging problem which we approach using an alternating least squares optimization algorithm. To validate this optimization scheme under realistic conditions, we use the Alzheimer's Disease Neuroimaging Initiative cohort. With the help of Monte Carlo simulations, we show that most of the global parameters of the model are tightly estimated, thus enabling an ordering of the biomarkers that fit the model well, ordered as: the Rey auditory verbal learning test with 30 minutes delay, the sum of the 2 lateral hippocampal volumes divided by the intracranial volume, followed (by the clinical dementia rating sum of boxes score and the mini-mental state examination score) in no particular order and at last the AD assessment scale-cognitive subscale.
authors.affiliation: Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA; Laboratoire de Mathématiques Paul Painlevé, Université des Sciences et Technologies de Lille, Villeneuve d'Ascq, France. Electronic address: bruno.jedynak@jhu.edu.
authors.forename: Bruno M
authors.initials: BM
authors.lastname: Jedynak
authors.affiliation: Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Liu
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Lang
authors.affiliation: Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
authors.forename: Yulia
authors.initials: Y
authors.lastname: Gel
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology / Q000523:psychology; D015415:Biomarkers; D003071:Cognition; D015331:Cohort Studies; D003205:Computing Methodologies; D003943:Diagnostic Techniques, Neurological; D018450:Disease Progression; D006624:Hippocampus / Q000473:pathology; D006801:Humans; D007361:Intelligence Tests; D009010:Monte Carlo Method; D059906:Neuroimaging; D011581:Psychological Tests; D013997:Time Factors; D014706:Verbal Learning
chemical_list: D015415:Biomarkers
keywords: Alzheimer's disease; Biomarkers; Progression score; Sampling from the residuals