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We then created dynamic models with the Nathan Kline Institute\u2010Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Our analyses suggest that diffusion magnetic resonance imaging\u2013based centrality measures can offer a tool for early disease detection before clinical dementia onset.<\/jats:p><\/jats:sec>","DOI":"10.1016\/j.dadm.2018.12.004","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T17:04:10Z","timestamp":1547831050000},"page":"98-107","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Structural connectivity centrality changes mark the path toward Alzheimer's disease"],"prefix":"10.1002","volume":"11","author":[{"given":"Luis R.","family":"Peraza","sequence":"first","affiliation":[{"name":"Institute of Neuroscience, Newcastle University Newcastle upon Tyne United 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