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Shannon L. Risacher
Researcher at Indiana University
Publications - 288
Citations - 12478
Shannon L. Risacher is an academic researcher from Indiana University. The author has contributed to research in topics: Cognition & Medicine. The author has an hindex of 51, co-authored 246 publications receiving 9808 citations. Previous affiliations of Shannon L. Risacher include Indiana University – Purdue University Indianapolis & University of Texas at Arlington.
Papers
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Book ChapterDOI
Multimodal neuroimaging predictors for cognitive performance using structured sparse learning
Jingwen Yan,Shannon L. Risacher,Sungeun Kim,Jacqueline C. Simon,Taiyong Li,Jing Wan,Hua Wang,Heng Huang,Andrew J. Saykin,Li Shen +9 more
TL;DR: Compared with traditional linear and ridge regression methods, these new models not only demonstrate superior and more stable predictive performances, but also identify a small set of imaging markers that are biologically meaningful.
Book ChapterDOI
Structural brain network constrained neuroimaging marker identification for predicting cognitive functions
TL;DR: A novel network constrained feature selection (NCFS) model is proposed to identify the neuroim imaging markers guided by the structural brain network, which is constructed by the sparse representation method such that the interrelations between neuroimaging features are encoded into probabilities.
Journal ArticleDOI
Genome-wide association analysis of hippocampal volume identifies enrichment of neurogenesis-related pathways
Emrin Horgusluoglu,Kwangsik Nho,Shannon L. Risacher,Paul K. Crane,Derrek P. Hibar,Paul M. Thompson,Andrew J. Saykin +6 more
Telomere Shortening in the Alzheimer’s Disease Neuroimaging Initiative Cohort
Kelly N.H. Nudelman,Jue Lin,Kathleen A. Lane,Kwangsik Nho,Sungeun Kim,Kelley Faber,Shannon L. Risacher,Tatiana Foroud,Sujuan Gao,Justin W. Davis,Michael W. Weiner,Andrew J. Saykin +11 more
TL;DR: The results do not support telomere shortening as a robust biomarker of AD progression, and further investigation in larger samples and for subgroups of participants may be informative.
Posted Content
Cognitive Biomarker Prioritization in Alzheimer's Disease using Brain Morphometric Data
TL;DR: A machine learning paradigm enabling personalized cognitive assessments prioritization achieves superior performance on prioritizing cognitive biomarkers that have great potentials to facilitate personalized diagnosis, disease subtyping, and ultimately precision medicine in AD.