Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Alzheimer's Disease Neuroimaging Initiative, Genevera I Allen, Nicola Amoroso, Catalina Anghel, Venkat Balagurusamy, Christopher J Bare, Derek Beaton, Roberto Bellotti, David A Bennett, Kevin L Boehme, Paul C Boutros, Laura Caberlotto, Cristian Caloian, Frederick Campbell, Elias Chaibub Neto, Yu-Chuan Chang, Beibei Chen, Chien-Yu Chen, Ting-Ying Chien, Tim ClarkSudeshna Das, Christos Davatzikos, Jieyao Deng, Donna Dillenberger, Richard J B Dobson, Qilin Dong, Jimit Doshi, Denise Duma, Rosangela Errico, Guray Erus, Evan Everett, David W Fardo, Stephen H Friend, Holger Fröhlich, Jessica Gan, Peter St George-Hyslop, Satrajit S Ghosh, Enrico Glaab, Robert C Green, Yuanfang Guan, Ming-Yi Hong, Chao Huang, Jinseub Hwang, Joseph Ibrahim, Paolo Inglese, Anandhi Iyappan, Qijia Jiang, Yuriko Katsumata, John S K Kauwe, Arno Klein, Yunyun Zhou

Research output: Contribution to journalArticlepeer-review

63 Scopus citations

Abstract

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Original languageEnglish
Pages (from-to)645-653
Number of pages9
JournalAlzheimer's and Dementia
Volume12
Issue number6
DOIs
StatePublished - Jun 1 2016

Keywords

  • Alzheimer Disease/complications
  • Apolipoproteins E/genetics
  • Biomarkers
  • Cognition Disorders/diagnosis
  • Computational Biology
  • Databases, Bibliographic/statistics & numerical data
  • Humans
  • Predictive Value of Tests

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