1. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease
- Author
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Peter R. Millar, Patrick H. Luckett, Brian A. Gordon, Tammie L.S. Benzinger, Suzanne E. Schindler, Anne M. Fagan, Carlos Cruchaga, Randall J. Bateman, Ricardo Allegri, Mathias Jucker, Jae-Hong Lee, Hiroshi Mori, Stephen P Salloway, Igor Yakushev, John C. Morris, Beau M. Ances, Sarah Adams, MS, Ricardo Allegri, PhD, Aki Araki, Nicolas Barthelemy, PhD, Randall Bateman, MD, Jacob Bechara, BS, Tammie Benzinger, MD, PhD, Sarah Berman, MD, PhD, Courtney Bodge, PhD, Susan Brandon, BS, William (Bill) Brooks, MBBS,MPH, Jared Brosch, MD, PhD, Jill Buck, BSN, Virginia Buckles, PhD, Kathleen Carter, PhD, Lisa Cash, BFA, Charlie Chen, BA, Jasmeer Chhatwal, MD,PhD, Patricio Chrem Mendez, MD, Jasmin Chua, BS, Helena Chui, MD, Laura Courtney, BS, Carlos Cruchaga, PhD, Gregory S Day, MD, Chrismary DeLaCruz, BA, Darcy Denner, PhD, Anna Diffenbacher, MS, Aylin Dincer, BS, Tamara Donahue, MS, Jane Douglas, MPh, Duc Duong, BS, Noelia Egido, BS, Bianca Esposito, BS, Anne Fagan, PhD, Marty Farlow, MD, Becca Feldman, BS,BA, Colleen Fitzpatrick, MS, Shaney Flores, BS, Nick Fox, MD, Erin Franklin, MS, Nelly Joseph-Mathurin, PhD, Hisako Fujii, PhD, Samantha Gardener, PhD, Bernardino Ghetti, MD, Alison Goate, PhD, Sarah Goldberg, MS,LPC,NCC, Jill Goldman, MS,MPhil,CGC, Alyssa Gonzalez, BS, Brian Gordon, PhD, Susanne Gräber-Sultan, PhD, Neill Graff-Radford, MD, Morgan Graham, BA, Julia Gray, MS, Emily Gremminger, BA, Miguel Grilo, MD, Alex Groves, Christian Haass, PhD, Lisa Häsler, MSc, Jason Hassenstab, PhD, Cortaiga Hellm, BA, Elizabeth Herries, BA, Laura Hoechst-Swisher, MS, Anna Hofmann, MD, Anna Hofmann, David Holtzman, MD, Russ Hornbeck, MSCS, MPM, Yakushev Igor, MD, Ryoko Ihara, MD, Takeshi Ikeuchi, MD, Snezana Ikonomovic, MD, Kenji Ishii, MD, Clifford Jack, MD, Gina Jerome, MS, Erik Johnson, MD, PHD, Mathias Jucker, PhD, Celeste Karch, PhD, Stephan Käser, PHD, Kensaku Kasuga, MD, Sarah Keefe, BS, William Klunk, MD, PHD, Robert Koeppe, PHD, Deb Koudelis, MHS,RN, Elke Kuder-Buletta, RN, Christoph Laske, PhD, Allan Levey, MD, PHD, Johannes Levin, MD, Yan Li, PHD, Oscar Lopez, MD, MD, Jacob Marsh, BA, Ralph Martins, PhD, Neal Scott Mason, PhD, Colin Masters, MD, Kwasi Mawuenyega, PhD, Austin McCullough, PhD Candidate, Eric McDade, DO, Arlene Mejia, MD, Estrella Morenas-Rodriguez, MD, PhD, John Morris, MD, James Mountz, MD, Cath Mummery, PhD, N eelesh Nadkarni, MD, PhD, Akemi Nagamatsu, RN, Katie Neimeyer, MS, Yoshiki Niimi, MD, James Noble, MD, Joanne Norton, MSN, RN, PMHCNS-BC, Brigitte Nuscher, Ulricke Obermüller, Antoinette O'Connor, MRCPI, Riddhi Patira, MD, Richard Perrin, MD, PhD, Lingyan Ping, PhD, Oliver Preische, MD, Alan Renton, PhD, John Ringman, MD, Stephen Salloway, MD, Peter Schofield, PhD, Michio Senda, MD, PhD, Nicholas T Seyfried, D.Phil, Kristine Shady, BA, BS, Hiroyuki Shimada, MD, PhD, Wendy Sigurdson, RN, Jennifer Smith, PhD, Lori Smith, PA-C, Beth Snitz, PhD, Hamid Sohrabi, PhD, Sochenda Stephens, BS, CCRP, Kevin Taddei, BS, Sarah Thompson, PA-C, Jonathan Vöglein, MD, Peter Wang, PhD, Qing Wang, PhD, Elise Weamer, MPH, Chengjie Xiong, PhD, Jinbin Xu, PhD, and Xiong Xu, BS, MS
- Subjects
Brain aging ,Alzheimer disease ,Resting-state functional connectivity ,fMRI ,Machine learning ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
“Brain-predicted age” quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18–89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.
- Published
- 2022
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