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Objective:Cognitive training may benefit older adults with mild cognitive impairment (MCI), but the prognostic factors are not well-established. Methods:This study analyzed data from a 78-week trial with 107 participants with MCI, comparing computerized cognitive training (CCT) and computerized crossword puzzle training (CPT). Outcomes were changes in cognitive and functional measures from baseline. Linear mixed-effect models were used to identify prognostic factors for each intervention. Results:Baseline neuropsychological composite z-score was positively associated with cognitive and functional improvements for both interventions in univariable models, retaining significance in the final multivariable model for functional outcome in CPT (P< 0.001). Apolipoprotein E e4 carriers had worse cognitive (P= 0.023) and functional (P= 0.001) outcomes than noncarriers for CPT but not CCT. African Americans showed greater functional improvements than non-African Americans in both CPT (P= 0.001) and CCT (P= 0.010). Better baseline odor identification was correlated with cognitive improvements in CPT (P= 0.006) and functional improvements in CCT (P< 0.001). Conclusion:Baseline cognitive test performance, African American background, and odor identification ability are potential prognostic factors for improved outcomes with cognitive interventions in older adults with MCI. Apolipoprotein E e4 is associated with poor outcomes. Replication of these findings may improve the selection of cognitive interventions for individuals with MCI. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.