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COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE.
Aganj, Iman; Frau-Pascual, Aina; Iglesias, Juan E; Yendiki, Anastasia; Augustinack, Jean C; Salat, David H; Fischl, Bruce.
Affiliation
  • Aganj I; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School.
  • Frau-Pascual A; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology.
  • Iglesias JE; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School.
  • Yendiki A; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School.
  • Augustinack JC; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology.
  • Salat DH; Center for Medical Image Computing (CMIC), University College London, London, UK.
  • Fischl B; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School.
Proc IEEE Int Symp Biomed Imaging ; 2020: 283-287, 2020 Apr.
Article in En | MEDLINE | ID: mdl-32587665
ABSTRACT
Identification of the specific brain networks that are vulnerable or resilient in neurodegenerative diseases can help to better understand the disease effects and derive new connectomic imaging biomarkers. In this work, we use brain connectivity to find pairs of structural connections that are negatively correlated with each other across Alzheimer's disease (AD) and healthy populations. Such anti-correlated brain connections can be informative for identification of compensatory neuronal pathways and the mechanism of brain networks' resilience to AD. We find significantly anti-correlated connections in a public diffusion-MRI database, and then validate the results on other databases.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Proc IEEE Int Symp Biomed Imaging Year: 2020 Document type: Article
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Proc IEEE Int Symp Biomed Imaging Year: 2020 Document type: Article