Marinescu, RV;
Eshaghi, A;
Lorenzi, M;
Young, AL;
Oxtoby, NP;
Garbarino, S;
Shakespeare, TJ;
... Alexander, DC; + view all
(2017)
A vertex clustering model for disease progression: Application to cortical thickness images.
In: Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Rangan, C. Pandu and Steffen, Bernhard and Terzopoulos, Demetri and Tygar, Doug and Weikum, Gerhard, (eds.)
Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science.
(pp. pp. 134-145).
Springer: Cham, Switzerland.
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Abstract
We present a disease progression model with single vertex resolution that we apply to cortical thickness data. Our model works by clustering together vertices on the cortex that have similar temporal dynamics and building a common trajectory for vertices in the same cluster. The model estimates optimal stages and progression speeds for every subject. Simulated data show that it is able to accurately recover the vertex clusters and the underlying parameters. 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 cohort from the Dementia Research Centre (DRC), UK. Using a separate set of subjects with Posterior Cortical Atrophy (PCA) from the DRC dataset, we also show that the model finds different patterns of atrophy in PCA compared to tAD. Finally, our model provides a novel way to parcellate the brain based on disease dynamics.
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