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| Hauptseite > Publikationsdatenbank > Prediction of Cognitive Functioning across the Lifespan Using Structural and Functional Neuroimaging Data |
| Hauptseite > Publikationsdatenbank > Prediction of Cognitive Functioning across the Lifespan Using Structural and Functional Neuroimaging Data |
| Dissertation / PhD Thesis | FZJ-2025-01299 |
2024
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Please use a persistent id in citations: doi:10.34734/FZJ-2025-01299
Abstract: Healthy aging is associated with structural and functional changes in the brain. These changes areespecially pronounced in complex cognitive tasks, like executive functioning (EF). EF is importantfor decision-making, problem-solving, and adaptive behavior. Advances in neuroimaging techniqueshave enabled a more detailed exploration of the neural substrates of cognitive aging, yet theneural underpinnings of EF, especially in the context of cognitive aging, remain incompletely understood.The global demographic shift towards an older population underscores the importance ofunderstanding cognitive aging, particularly changes in EF.This dissertation aimed to investigate the structural and functional neural substrates and dynamicsof age-related differences in EF through a comprehensive methodological framework combiningmeta-analyses, functional connectivity analyses, and predictive modeling.Meta-analyses highlighted left inferior frontal junction and left anterior cuneus/precuneus as regionssignificantly affected by aging, with recruitment patterns varying by task type and age. Subsequently,a meta-analytic synthesis identified a common perceptuo-motor network, comprisingvisual, auditory, and motor-related brain regions. This allows for the investigation of age differencesalready at the in- and output levels of the brain, which in turn could influence performanceat higher cognitive levels. Prediction studies revealed moderate to low overall prediction accuracies,with measures of functional within-subject variability showing superior predictive performancefor younger and structural measures for older adults. Surprisingly, whole-brain and randomnetwork approaches outperformed EF-specific networks in predicting EF abilities, suggesting thatbroader network properties may be more indicative of individual differences in EF than previouslythought.The findings highlight the complex interplay between structural and functional brain changes andcognitive aging, emphasizing an age-dependent modality specificity in the neural predictors of EFperformance. The differential effectiveness of global versus EF-specific networks in predicting EFunderscores the potential value of considering global brain characteristics and combining multiplemetrics to enhance predictive accuracy. The modest to low prediction accuracies call for furtherresearch into developing more effective biomarkers for EF abilities, considering broader networkdynamics, and adopting adaptive behavioral testing approaches to capture the full performancespectrum of healthy aging.
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