{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dive-a-spatiotemporal-progression-model-of","title":"DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders","arxiv_id":"1901.03553","date":"2019-01-11","proceeding":null,"authors":["Razvan V. Marinescu","Arman Eshaghi","Marco Lorenzi","Alexandra L. Young","Neil P. Oxtoby","Sara Garbarino","Sebastian J. Crutch","Daniel C. Alexander"],"abstract":"Here we present DIVE: Data-driven Inference of Vertexwise Evolution. DIVE is\nan image-based disease progression model with single-vertex resolution,\ndesigned to reconstruct long-term patterns of brain pathology from short-term\nlongitudinal data sets. DIVE clusters vertex-wise biomarker measurements on the\ncortical surface that have similar temporal dynamics across a patient\npopulation, and concurrently estimates an average trajectory of vertex\nmeasurements in each cluster. DIVE uniquely outputs a parcellation of the\ncortex into areas with common progression patterns, leading to a new signature\nfor individual diseases. DIVE further estimates the disease stage and\nprogression speed for every visit of every subject, potentially enhancing\nstratification for clinical trials or management. On simulated data, DIVE can\nrecover ground truth clusters and their underlying trajectory, provided the\naverage trajectories are sufficiently different between clusters. We\ndemonstrate DIVE on data from two cohorts: the Alzheimer's Disease Neuroimaging\nInitiative (ADNI) and the Dementia Research Centre (DRC), UK, containing\npatients with Posterior Cortical Atrophy (PCA) as well as typical Alzheimer's\ndisease (tAD). DIVE finds similar spatial patterns of atrophy for tAD subjects\nin the two independent datasets (ADNI and DRC), and further reveals distinct\npatterns of pathology in different diseases (tAD vs PCA) and for distinct types\nof biomarker data: cortical thickness from Magnetic Resonance Imaging (MRI) vs\namyloid load from Positron Emission Tomography (PET). Finally, DIVE can be used\nto estimate a fine-grained spatial distribution of pathology in the brain using\nany kind of voxelwise or vertexwise measures including Jacobian compression\nmaps, fractional anisotropy (FA) maps from diffusion imaging or other PET\nmeasures. DIVE source code is available online:\nhttps://github.com/mrazvan22/dive","url_abs":"http://arxiv.org/abs/1901.03553v1","url_pdf":"http://arxiv.org/pdf/1901.03553v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dive-a-spatiotemporal-progression-model-of","repo_url":"https://github.com/mrazvan22/dive","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.03553","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}