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Free keywords:
heart rate variability, brain signal variability, aging, BOLD fMRI, EEG
Abstract:
The world population is rapidly aging. In Germany for example, the percentage of individuals 60 years and older is projected to be 38% in 20501. Longer lifetimes entail more progressive impairment of brain and body. It is therefore a crucial question how to assess and quantify these frequently occurring alterations associated with aging. In order to address this question, the overarching goal of this dissertation is to explore and characterize bodily and neural signals which reflect effects of aging across the adult lifespan. To this end, I performed two studies as lead investigator and contributed to three more large-scale collaborative studies.
In Study 1 (Kumral et al., 2019), I investigated the relationship of heart rate variability (HRV) to brain structure (gray matter) and resting state (rs) brain activity (functional connectivity) in a well-characterized sample of healthy subjects across the adult lifespan (N=388). For Study 2 (Koenig et al., 2020), I contributed to a mega analysis testing the association between cortical thickness and heart-rate variability (HRV) at rest, also across the lifespan (N=1218). In Study 3 (Kumral et al., 2020), I examined whether different measures of brain signal variability – identified with hemodynamic (functional magnetic resonance imaging; fMRI) or electrophysiological (EEG) methods – reflect the same underlying physiology in healthy younger and older adults (N=189). Lastly, during my dissertation work, I was part of the Mind-Body-Emotion group in Leipzig, which established two publicly available – and now widely used – datasets (Datasets 1 and 2; Babayan et al., 2019, Mendes et al., 2019), which include structural and functional MRI, EEG data as well as a range of physiological and behavioral measures.
In Study 1, I showed that age-related decreases in resting HRV are accompanied by age-dependent and age-invariant alterations in brain function, particularly located along cortical midline structures. In Study 2, we found that the age-related decrease of resting HRV was associated with cortical thinning in prefrontal brain structures. In Study 3, I demonstrated age differences in brain signal variability obtained with rs-fMRI and rs-EEG, respectively. Surprisingly, the two measures of neural variability showed no significant correlation, but rather seemed to provide complementary information on the state of the aging brain.
The present dissertation provides evidence that measures of cardiovascular and neural signal variability may be useful biomarkers for neurocognitive health (and disease) in aging. With these measures, we can further specify the dynamic interplay of the human body and the brain in relation to individual health-related factors.
