Exploring the Complexity of Healthy Aging: A Multi-Time-Scale Analysis to Investigate the Reorganization of Functional Networks and Associated Cognitive Changes across the Lifespan

Abstract

“Your brain is constantly rewiring itself, adapting with each new experience. It’s a dynamic, ever-changing network of connections, shaped by the world around you.” - David Eagleman This thesis investigates the reorganization of brain networks during the aging process, particularly in the transition from young to middle-aged to older adults. The study utilizes the CAMCAN dataset, a comprehensive cross-sectional dataset with multimodal data, including pre-processed resting-state fMRI (rs-fMRI) data. The aim is to understand how brain networks evolve with age and identify key brain regions and network properties that undergo changes. Data-driven statistical and graph-theoretic measures are employed to study modular segregation and integration in the brain. The results reveal characteristic nodes forming stable cores and flexible peripheries in both young and old age groups. Notably, regions within the Default Mode network (DMN) show a negative correlation with modularity in the old age group, while regions from the Limbic, SensoriMotor (SMN), and Salience networks display a positive correlation. Machine learning models based on flexibility scores further validate the relevance of these regions, providing promising insights for future investigations. Additionally, the study uncovers age-related changes in brain connectivity and network properties. Modularity increases with age, indicating greater functional specialization in the aging brain, accompanied by a decrease in flexibility, suggesting reduced adaptability to changing cognitive demands. The negative correlation between flexibility and modularity across all age groups implies that as the brain becomes less modular, it becomes more flexible in its organization. Certain brain regions show significant connectivity alterations, with increased participation coefficient in some frontal and temporal regions and decreased participation coefficient in several frontal and parietal regions. Hemispheric differences indicate that age-related connectivity changes may vary between hemispheres. Furthermore, the complexity of the relationship between cognitive abilities, task performance, and brain network dynamics is highlighted. The absence of strong correlations between task scores and network measures at the nodal level, along with the weak correlation between Cattell scores and global flexibility, underscore the multifaceted nature of these associations. Age alone cannot fully account for the observed dynamics, suggesting the involvement of other factors in shaping the relationship between cognition and brain network measures. However, this study has limitations. The use of cross-sectional data hinders the exploration of individual changes over time, and longitudinal data would provide more robust insights. The dataset’s relatively small size and the categorization of age into discrete groups may limit the generalizability of the findings. Moreover, relying solely on resting-state fMRI data may not fully capture the dynamic nature of brain function during various cognitive processes. Additionally, causal inferences should be made with caution, as the study is based on observational data, and other factors may influence the observed brain network changes. In conclusion, this thesis contributes to understanding age-related changes in brain networks and their impact on cognitive aging. The findings highlight the importance of specific brain regions in maintaining functional networks during aging and underscore the complexity of brain network dynamics. Addressing the limitations in future research will enhance our knowledge of brain aging and the interplay between brain networks, cognitive abilities, and behavior.