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Proceedings Paper

Automatic classification of cortical thickness patterns in Alzheimer’s disease patients using the Louvain modularity clustering method
Author(s): Fabian W. Corlier; Daniel Moyer; Meredith N. Braskie; Paul M. Thompson; Guillaume Dorothee; Marie Claude Potier; Marie Sarazin; Michel Bottlaender; Julien Lagarde
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Paper Abstract

Alzheimer’s disease is heterogeneous and despite some consistent neuropathological hallmarks, different clinical forms have been identified, including non-amnestic presentations. Even in amnestic forms, the presentation of the disease can differ across individuals, in terms of age of onset, dynamics of progression and specific impairment profiles. Different distributions of neurofibrillary tangles exist in AD, and these are linked with structural differences detectable on ante-mortem MRI , but these are hard to identify in the earlier stages of disease. In the present work, we validate and test a previously proposed method for identifying subtypes of cortical atrophy in AD, based on MRI data from an independent case/control study of individuals defined by pathophysiological biomarkers. We implemented a clustering method based on the Louvain modularity method, and tested it across a range of pre-processing parameters. Our cohort of participants was comprised of 111 participants (mean age: 67.7 year; range: 51-91), including 37 cognitively normal controls, 43 prodromal AD, and 31 demented AD patients. We identified 4 patient clusters with distinct atrophy patterns either predominantly in the temporal lobes (groups 0 and 1), in the parietal and temporal lobes (group 2), or in the frontal and temporal lobes (group 3). Further evaluation of neuro-psychological characteristics of each patient cluster will be carried out in the future. In conclusion, the modularity-based clustering method may help to identify specific subtypes of atrophy in neurological diseases such as AD.

Paper Details

Date Published: 21 December 2018
PDF: 12 pages
Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750S (21 December 2018); doi: 10.1117/12.2511573
Show Author Affiliations
Fabian W. Corlier, The Univ. of Southern California (United States)
Daniel Moyer, The Univ. of Southern California (United States)
Meredith N. Braskie, The Univ. of Southern California (United States)
Paul M. Thompson, The Univ. of Southern California (United States)
Guillaume Dorothee, Sorbonne Univ., Institut National de la Santé et de la Recherche Médicale (France)
Hôpital Saint-Antoine (France)
Marie Claude Potier, Institut du Cerveau et de la Moelle Épinière, CNRS, INSERM, Hopital de la Pitié-Salpêtrière (France)
Marie Sarazin, Univ. Paris Descartes, Sorbonne Paris Cité, Ctr. Hospitalier Sainte Anne (France)
Michel Bottlaender, IMIV, Service Hospitalier Frédéric Joliot, CEA, INSERM, Univ. Paris-Sud, CNRS, Univ. Paris-Saclay (France)
UNIACT, NeuroSpin, CEA (France)
Julien Lagarde, Univ. Paris Descartes, Sorbonne Paris Cité, Ctr. Hospitalier Sainte Anne (France)


Published in SPIE Proceedings Vol. 10975:
14th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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