Details

Autor(en) / Beteiligte

• Millar, Peter R.
• Gordon, Brian A.
• Benzinger, Tammie L.S.
• Schindler, Suzanne E.
• Cruchaga, Carlos
• Allegri, Ricardo
• Jucker, Mathias
• Mori, Hiroshi
• Salloway, Stephen P
• Morris, John C.
• Ances, Beau M.
• Araki, Aki
• Bateman, Randall
• Benzinger, Tammie
• Berman, Sarah
• Brosch, Jared
• Buckles, Virginia
• Carter, Kathleen
• Cash, Lisa
• Chhatwal, Jasmeer
• Mendez, Patricio Chrem
• Courtney, Laura
• DeLaCruz, Chrismary
• Dincer, Aylin
• Donahue, Tamara
• Douglas, Jane
• Duong, Duc
• Egido, Noelia
• Esposito, Bianca
• Feldman, Becca
• Fitzpatrick, Colleen
• Flores, Shaney
• Franklin, Erin
• Fujii, Hisako
• Gardener, Samantha
• Ghetti, Bernardino
• Goate, Alison
• Goldman, Jill
• Gordon, Brian
• Gräber-Sultan, Susanne
• Graff-Radford, Neill
• Graham, Morgan
• Gray, Julia
• Grilo, Miguel
• Groves, Alex
• Häsler, Lisa
• Hellm, Cortaiga
• Herries, Elizabeth
• Hoechst-Swisher, Laura
• Hofmann, Anna
• Holtzman, David
• Ihara, Ryoko
• Ikonomovic, Snezana
• Ishii, Kenji
• Jack, Clifford
• Jerome, Gina
• Karch, Celeste
• Kasuga, Kensaku
• Keefe, Sarah
• Klunk, William
• Koeppe, Robert
• Kuder-Buletta, Elke
• Levey, Allan
• Levin, Johannes
• Li, Yan
• Lopez, Oscar
• Marsh, Jacob
• Martins, Ralph
• Masters, Colin
• McCullough, Austin
• Mejia, Arlene
• Mountz, James
• Mummery, Cath
• Nadkarni, N eelesh
• Nagamatsu, Akemi
• Neimeyer, Katie
• Niimi, Yoshiki
• Noble, James
• Norton, Joanne
• Nuscher, Brigitte
• Obermüller, Ulricke
• O'Connor, Antoinette
• Patira, Riddhi
• Perrin, Richard
• Renton, Alan
• Salloway, Stephen
• Schofield, Peter
• Seyfried, Nicholas T
• Shimada, Hiroyuki
• Smith, Jennifer
• Smith, Lori
• Snitz, Beth
• Sohrabi, Hamid
• Stephens, Sochenda
• Taddei, Kevin
• Thompson, Sarah
• Wang, Peter

Titel

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

Ist Teil von

• NeuroImage (Orlando, Fla.), 2022-08, Vol.256, p.119228-119228, Article 119228

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• •A machine learning model can predict age from functional connectivity in controls.•Model estimates of age are significantly elevated in symptomatic Alzheimer disease.•Model estimates of age are surprisingly reduced in preclinical Alzheimer disease.•Distinct functional networks were sensitive to age and Alzheimer differences. “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.