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

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 Clark, Sudeshna DasChristos 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, Dehan Kong, Roland Krause, Emilie Lalonde, Mario Lauria, Eunjee Lee, Xihui Lin, Zhandong Liu, Julie Livingstone, Benjamin A. Logsdon, Simon Lovestone, Tsung Wei Ma, Ashutosh Malhotra, Lara M. Mangravite, Taylor J. Maxwell, Emily Merrill, John Nagorski, Aishwarya Namasivayam, Manjari Narayan, Mufassra Naz, Stephen J. Newhouse, Thea C. Norman, Ramil N. Nurtdinov, Yen Jen Oyang, Yudi Pawitan, Shengwen Peng, Mette A. Peters, Stephen R. Piccolo, Paurush Praveen, Corrado Priami, Veronica Y. Sabelnykova, Philipp Senger, Xia Shen, Andrew Simmons, Aristeidis Sotiras, Gustavo Stolovitzky, Sabina Tangaro, Andrea Tateo, Yi An Tung, Nicholas J. Tustison, Erdem Varol, George Vradenburg, Michael W. Weiner, Guanghua Xiao, Lei Xie, Yang Xie, Jia Xu, Hojin Yang, Xiaowei Zhan, Yunyun Zhou, Fan Zhu, Hongtu Zhu, Shanfeng Zhu

Research output: Contribution to journalArticle

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

Keywords

  • Azheimer's disease
  • Big data
  • Bioinformatics
  • Biomarkers
  • Cognitive decline
  • Crowdsource
  • Genetics
  • Imaging

ASJC Scopus subject areas

  • Epidemiology
  • Health Policy
  • Developmental Neuroscience
  • Clinical Neurology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience

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  • Cite this

    Allen, G. I., Amoroso, N., Anghel, C., Balagurusamy, V., Bare, C. J., Beaton, D., Bellotti, R., Bennett, D. A., Boehme, K. L., Boutros, P. C., Caberlotto, L., Caloian, C., Campbell, F., Chaibub Neto, E., Chang, Y. C., Chen, B., Chen, C. Y., Chien, T. Y., Clark, T., ... Zhu, S. (2016). Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease. Alzheimer's and Dementia, 12(6), 645-653. https://doi.org/10.1016/j.jalz.2016.02.006