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Progression along data-driven disease timelines is predictive of Alzheimer's disease in a population-based cohort

Vikram Venkatraghavan, Elisabeth J. Vinke, Esther E. Bron, Wiro J. Niessen, M. Arfan Ikram, Stefan Klein, Meike W. Vernooij
2021 NeuroImage  
Our results suggest that co-init DEBM trained on case-controlled data is generalizable to a population-based cohort setting and that progression along the disease timelines is predictive of the development  ...  if progression along these disease timelines is predictive of AD.  ...  Acknowledgement This study is part of the EuroPOND initiative, which is funded by the European Union 's Horizon 2020 research and innovation programme  ... 
doi:10.1016/j.neuroimage.2021.118233 pmid:34091030 fatcat:mwqpuzmi7ndn5ocawmrhmlcqlu

Cohort discovery and risk stratification for Alzheimer's disease: an electronic health record‐based approach

Donna Tjandra, Raymond Q. Migrino, Bruno Giordani, Jenna Wiens
2020 Alzheimer s & Dementia Translational Research & Clinical Interventions  
Using EHR data from the University of Michigan (UM) hospitals and consensus-based diagnoses from the Michigan Alzheimer's Disease Research Center, we developed and validated a cohort discovery tool for  ...  We sought to leverage data routinely collected in electronic health records (EHRs), with the goal of developing patient risk stratification tools for predicting risk of developing Alzheimer's disease (  ...  Each row represents a timeline for the respective dataset, and encounters are indicated with squares. Shading along the Michigan-ADRC timeline indicates consensus-based diagnoses.  ... 
doi:10.1002/trc2.12035 pmid:32548236 pmcid:PMC7293993 fatcat:mkc2yqw2qzds3bwr2d7isnnu6a

Statistical Disease Progression Modeling in Alzheimer Disease

Lars Lau Raket
2020 Frontiers in Big Data  
Maximum-likelihood estimation in these models induces a data-driven criterion for separating disease progression and baseline cognition.  ...  along the continuous time progression of disease.  ...  In this article, we propose a new approach to disease progression modeling that separates disease stage and deviations from the mean pattern in a fully data-driven manner.  ... 
doi:10.3389/fdata.2020.00024 pmid:33693397 pmcid:PMC7931952 fatcat:jrhvrc6wp5a4jgru5cje7vnunq

A Vertex Clustering Model for Disease Progression: Application to Cortical Thickness Images [chapter]

Răzvan Valentin Marinescu, Arman Eshaghi, Marco Lorenzi, Alexandra L. Young, Neil P. Oxtoby, Sara Garbarino, Timothy J. Shakespeare, Sebastian J. Crutch, Daniel C. Alexander
2017 Lecture Notes in Computer Science  
We present a disease progression model with single vertex resolution that we apply to cortical thickness data.  ...  Moreover, our clustering model finds similar patterns of atrophy for typical Alzheimer's disease (tAD) subjects on two independent datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a  ...  A hypothetical model of disease progression has been proposed by [1] , describing the trajectory of key biomarkers along the progression of Alzheimer's disease.  ... 
doi:10.1007/978-3-319-59050-9_11 fatcat:nkxw7qrlfffrfesdgobwjqcnxa

Modelling the Neuroanatomical Progression of Alzheimer's Disease and Posterior Cortical Atrophy [article]

Razvan V. Marinescu
2020 arXiv   pre-print
In this work I developed novel models of disease progression and applied them to estimate the progression of Alzheimer's disease and Posterior Cortical atrophy, a rare neurodegenerative syndrome causing  ...  In order to find effective treatments for Alzheimer's disease (AD), we need to identify subjects at risk of AD as early as possible.  ...  Secondly, I'd also like to thank Alexandra Young and Neil Oxtoby for teaching me disease progression modelling, especially in the early years of my PhD.  ... 
arXiv:2003.04805v1 fatcat:a5gmy75lvnfnhbtvztqoxikukm

A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients

Angier Allen, Anna Siefkas, Emily Pellegrini, Hoyt Burdick, Gina Barnes, Jacob Calvert, Qingqing Mao, Ritankar Das
2021 Applied Sciences  
Methods: In this study, we apply a digital twin model based on a variational autoencoder to a population of patients who went on to experience an ischemic stroke.  ...  Machine learning methods have been developed to predict the likelihood of a given event or classify patients into two or more diagnostic categories.  ...  Covariance structure similarity is important for ensuring that trends of disease progression found in models generated with real data will result in the same trends of disease progression as those found  ... 
doi:10.3390/app11125576 fatcat:dswr52cl5nhp7nsexmf2mn3nsu

DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders

Răzvan V. Marinescu, Arman Eshaghi, Marco Lorenzi, Alexandra L. Young, Neil P. Oxtoby, Sara Garbarino, Sebastian J. Crutch, Daniel C. Alexander
2019 NeuroImage  
DIVE is an image-based disease progression model with single-vertex resolution, designed to reconstruct long-term patterns of brain pathology from short-term longitudinal data sets.  ...  Here we present DIVE: Data-driven Inference of Vertexwise Evolution.  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number  ... 
doi:10.1016/j.neuroimage.2019.02.053 pmid:30844504 fatcat:gxq53thcfjhm3ohvggrdbaxway

Application of the ATN classification scheme in a population without dementia: Findings from the EPAD cohort

Silvia Ingala, Casper De Boer, Larissa A Masselink, Ilaria Vergari, Luigi Lorenzini, Kaj Blennow, Gaël Chételat, Carol Di Perri, Michael Ewers, Wiesje M van der Flier, Nick C Fox, Juan Domingo Gispert (+18 others)
2021 Alzheimer's & Dementia  
Age was 65 ± 7 years, with 58% females and 38% apolipoprotein E (APOE) ε4 carriers; 57.1% were A-T-N-, 32.5% were in the Alzheimer's disease (AD) continuum, and 10.4% suspected non-Alzheimer's pathology  ...  Paradoxically higher regional gray matter volumes were observed in A+T-N- compared to A-T-N- (P < 0.001). In non-demented individuals along the AD continuum, p-tau drives cognitive dysfunction.  ...  INTRODUCTION Finding disease-modifying therapies for Alzheimer's disease (AD), the most prevalent cause of dementia, is an international priority.  ... 
doi:10.1002/alz.12292 pmid:33811742 pmcid:PMC8359976 fatcat:kxc42eyqmnaq5dnhmy64klpcbe

Differences in topological progression profile among neurodegenerative diseases from imaging data

Sara Garbarino, Marco Lorenzi, Neil P Oxtoby, Elisabeth J Vinke, Razvan V Marinescu, Arman Eshaghi, M Arfan Ikram, Wiro J Niessen, Olga Ciccarelli, Frederik Barkhof, Jonathan M Schott, Meike W Vernooij (+1 others)
2019 eLife  
We introduce the notion of a topological profile — a characteristic combination of topological descriptors that best describes the propagation of pathology in a particular disease.  ...  By drawing on recent advances in disease progression modeling, we estimate topological profiles from the full course of pathology accumulation, at both cohort and individual levels.  ...  model of mechanistic brain Atrophy Propagation in Dementia'.  ... 
doi:10.7554/elife.49298 pmid:31793876 pmcid:PMC6922631 fatcat:yyornpu2jnhcnm53t5eavctnje

A Precision Medicine Initiative for Alzheimer's disease: the road ahead to biomarker-guided integrative disease modeling

H. Hampel, S. E. O'Bryant, S. Durrleman, E. Younesi, K. Rojkova, V. Escott-Price, J-C. Corvol, K. Broich, B. Dubois, S. Lista
2017 Climacteric  
patient in a different way; (V) turning descriptive scenarios of disease progression into predictive systems.  ...  The integration of spatio-temporal measurements into a digital model of disease progression is often based on the idea of regressing measurements against an estimated time to disease onset 80, 81 .  ... 
doi:10.1080/13697137.2017.1287866 pmid:28286989 fatcat:5bdwpvg4jbej7ba3tkmrqw3keq

Quantifying Neurodegenerative Progression With DeepSymNet, an End-to-End Data-Driven Approach

Danilo Pena, Arko Barman, Jessika Suescun, Xiaoqian Jiang, Mya C. Schiess, Luca Giancardo, the Alzheimer's Disease Neuroimaging Initiative
2019 Frontiers in Neuroscience  
Alzheimer's disease (AD) is the most common neurodegenerative disorder worldwide and is one of the leading sources of morbidity and mortality in the aging population.  ...  Here we propose a data-driven method based on an extension of a deep learning architecture, DeepSymNet, that identifies longitudinal changes without relying on prior brain regions of interest, an atlas  ...  We found that the INTRODUCTION Alzheimer's disease (AD) is the leading cause of dementia globally (50-75%) and is distinguished by a progressive cognitive decline (Lane et al., 2018) .  ... 
doi:10.3389/fnins.2019.01053 pmid:31636533 pmcid:PMC6788344 fatcat:fow6zw4egne5pc23xdzzwezxbm

Proteomics Landscape of Alzheimer's Disease

Ankit P. Jain, Gajanan Sathe
2021 Proteomes  
Alzheimer's disease (AD) is the most prevalent form of dementia, and the numbers of AD patients are expected to increase as human life expectancy improves.  ...  Deposition of β-amyloid protein (Aβ) in the extracellular matrix and intracellular neurofibrillary tangles are molecular hallmarks of the disease.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/proteomes9010013 pmid:33801961 fatcat:n4xy432lljdo5dx64oy6ealuhi

The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease

K. Antila, J. Lotjonen, L. Thurfjell, J. Laine, M. Massimini, D. Rueckert, R. A. Zubarev, M. Oresic, M. van Gils, J. Mattila, A. Hviid Simonsen, G. Waldemar (+1 others)
2013 Interface Focus  
One contribution of 25 to a Theme Issue 'The virtual physiological human: integrative approaches to computational biomedicine'.  ...  The project provided several novel tools for biomarker discovery and a novel data-driven and evidence-based disease profiling.  ...  Introduction Alzheimer's disease (AD) is the most common cause of dementia.  ... 
doi:10.1098/rsfs.2012.0072 pmid:24427524 pmcid:PMC3638476 fatcat:v5qefbq7wbgmxpjlz5njxhqky4

Deep representation learning of electronic health records to unlock patient stratification at scale

Isotta Landi, Benjamin S. Glicksberg, Hao-Chih Lee, Sarah Cherng, Giulia Landi, Matteo Danieletto, Joel T. Dudley, Cesare Furlanello, Riccardo Miotto
2020 npj Digital Medicine  
However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis.  ...  We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising a total of 57,464 clinical concepts.  ...  ACKNOWLEDGEMENTS R.M. would like to thank the support from the Hasso Plattner Foundation, the Alzheimer's Drug Discovery Foundation and a courtesy GPU donation from Nvidia.  ... 
doi:10.1038/s41746-020-0301-z pmid:32699826 pmcid:PMC7367859 fatcat:ddt7xa36jvbzzdkpirhdslxnty

DPVis: Visual Exploration of Disease Progression Pathways [article]

Bum Chul Kwon, Vibha Anand, Kristen A Severson, Soumya Ghosh, Zhaonan Sun, Brigitte I Frohnert, Markus Lundgren, Kenney Ng
2019 arXiv   pre-print
In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and designing  ...  One approach for disease progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures.  ...  ACKNOWLEDGMENTS We wish to thank the T1DI Study Group for their help in this work.  ... 
arXiv:1904.11652v1 fatcat:am3ei3niabbinkobr7v5yqbhoe
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