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. 2022 Aug 1:256:119228.
doi: 10.1016/j.neuroimage.2022.119228. Epub 2022 Apr 20.

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

Peter R Millar  1 , Patrick H Luckett  2 , Brian A Gordon  3 , Tammie L S Benzinger  3 , Suzanne E Schindler  2 , Anne M Fagan  2 , Carlos Cruchaga  4 , Randall J Bateman  2 , Ricardo Allegri  5 , Mathias Jucker  6 , Jae-Hong Lee  7 , Hiroshi Mori  8 , Stephen P Salloway  9 , Igor Yakushev  10 , John C Morris  2 , Beau M Ances  11 ; Dominantly Inherited Alzheimer Network
Collaborators, Affiliations

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

Peter R Millar et al. Neuroimage. .

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.

Keywords: Alzheimer disease; Brain aging; Machine learning; Resting-state functional connectivity; fMRI.

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Conflict of interest statement

Declaration of Competing Interest The authors declare no conflicts of interests. JC Morris is funded by NIH grants # P30 AG066444; P01AG003991; P01AG026276; U19 AG032438; and U19 AG024904. Neither Dr. Morris nor his family owns stock or has equity interest (outside of mutual funds or other externally directed accounts) in any pharmaceutical or biotechnology company. Dr. Salloway reports personal fees from EISAI, NOVARTIS, GENENTECH, ROCHE, GEMVAX, AVID, and LILLY. Dr. Bateman is on the scientific advisory board of C2N Diagnostics and reports research support from Abbvie, Avid Radiopharmaceuticals, Biogen, Centene, Eisai, Eli Lilly and Company, Genentech, Hoffman-LaRoche, Janssen, and United Neuroscience.

Figures

Figure 1.
Figure 1.
Performance of the FC-predicted brain age model. Scatterplots show the cross-validated model predictions in the training set (A) and in the held-out test set (B). Age predicted by the model (y axis) is plotted against true age (x axis). Blue lines represent regression lines. Dashed black lines represent perfect prediction. Model performance is evaluated by proportion of variance explained (R2), root-mean-square error (RMSE), and mean absolute error (MAE).
Figure 2.
Figure 2.
Group differences in FC-predicted brain age in the analysis sets. Comparisons are presented between cognitively normal (CDR = 0, blue) vs. symptomatic AD (CDR > 0, red) (A, B); A−T− (blue) vs. A+T− (green) vs. A+T+ (gold) (C, D); and cognitively normal APOE ε4 carriers (blue) vs. non-carriers (green) (E, F). Scatterplots (A, C, E) show predicted vs. true age for each group. Colored lines and shaded areas represent group-specific regression lines and 95% confidence regions. Dashed black lines represent perfect prediction. Violin plots (B, D, F) show residual FC-BAG (controlling for true age) in each group. Group differences are reported from pairwise independent-samples t tests. *** p < .001, ** p < .01, * p < .05, ^ p < .10.
Figure 3.
Figure 3.
Strongest FC predictors of (A) healthy age differences in the cognitively normal, amyloid-negative training set, (B) preclinical AD vs. amyloid-negative controls, and (C) symptomatic AD vs. amyloid-negative controls. Matrices display intra-network (on diagonal) and inter-network (off diagonal) FC features that were identified as strongest predictors via forward sequential feature selection (see methods). Overlapping features (D) are plotted for age, preclinical AD, and symptomatic AD.
See this image and copyright information in PMC

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