C HARACTERISTICS OF S AMPLES IN A GING R ESEARCH 79

The primary aim of this dissertation was to investigate the brain-behavior relationship in healthy aging participants. Next to new insights in how brain measures are related to behavior performance, some previous results could not be replicated with our dataset. Moreover, associations between neural and behavioral parameters found in our studies depicted rather small effect sizes and did not survive a correction for multiple comparisons. We think this discrepancy can be due to several reasons, which are mainly caused by an overarching problem: the absence of a clear definition for healthy aging. To date, a consensual characterization is missing and terms like active aging (WHO, 2002) or successful aging (e.g., Baltes & Baltes, 1990; Bowling & Dieppe, 2005; Rowe & Kahn, 1997) are used synonymously. Terminologies and definitions may roughly be categorized into approaches reflecting psychosocial or biomedical components or combination of those (Bowling & Dieppe, 2005). While biomedical theories focus more on mental and/or physical deterioration

  GENERAL DISCUSSION  

interpretations, healthy aging is mostly seen as a multidimensional construct and not just the absence of disease. However, a consensus about which specific components comprise such a multidimensional design is still missing. Additional difficulties arise since the term “healthy aging” is used in literature as a process and as an outcome measure. Thus, a first step towards a definition of healthy aging is a clearer answer to the question of what the relevant and interesting measurements are and how to obtain them. Moreover, the change of focus of healthy aging research, away from solely describing age-related functional decline, towards the examination of how cognitive functioning can be maintained and stabilized, is of great importance for the near future. Additionally, it is essential that the construct healthy aging has to be relevant for the community. This is crucial because discrepancies between elderly adults’ subjective perceptions and their scores on objective measures can occur. To illustrate, a study by von Faber and colleagues (2001) demonstrated that subjective evaluations differ from quantitative measures, since significantly more participants saw themselves as successfully aged, than what was found in the quantitative results. Therefore, participative research should be promoted to include the perspective from older adults.

In order to achieve the goal of a distinct definition of a healthy aging concept, the biomedical and psychosocial models should be combined to enable a broader view of the construct healthy aging. Within an interdisciplinary team, perspectives from different academic disciplines and opinions from laypersons, such as elderly people, should be incorporated to find a consensual concept relevant to researchers, clinicians, and older adults. Moreover, what should be used as screening measures for a sample (e.g., what inclusion and exclusion criteria a person should meet) and what predictors should be investigated, needs to be stated more precisely.

We suggest that, with a clearer view about sample characteristics in healthy aging (e.g., age- range and cognitive status), future research may be able to reduce the likelihood of finding

  GENERAL DISCUSSION  

results that are only applicable in one study but not in the other. To illustrate, different cutoff scores (as low as 24) for the MMSE are used for exclusion criteria throughout studies of healthy aging. Additionally, there is no clear-cut point from which participants are “classified” as older. Hence, studies can vary extremely in age-ranges (e.g., ranging from ~55 years to 85 years, including also middle aged participants). In comparison, the findings presented in this dissertation are based on measurements of the first time point of the LHAB database project. The project is characterized by strict exclusion criteria (e.g., MMSE>26) to prohibit participants with risk of mild cognitive impairment (MCI). Additionally, included participants had to be over 64 years of age resulting in a small age-range (ranging from age ~65 years to – 85 years) of the investigated sample. Furthermore, the sample was recruited from the close Zurich area and can be described as well-educated. Therefore, we believe that the absence of brain-behavioral associations, found in previous studies, could be explained by lower variability in our data. We base our assumption on findings, which indicate that inter- individual variability within cognitive performance (e.g., processing speed, memory, executive functions) increases with age among older participants (Christensen, 2001) or when comparing younger with older subjects (Hultsch, MacDonald, & Dixon, 2002; Sylvain-Roy & Belleville, 2014). In general, we could argue that we diminished variability within our data by analyzing a small age-ranged healthy group of elderly people.

Besides the absence of a clear definition of healthy aging, which could lead to differences in sample characteristics, the diversity in sample size and applied tests could further contribute to differences in brain-behavior associations. In studies investigating associations between brain measures and behavior performance, sample size can vary substantially, which could lead to differences in statistical power (reported sample sizes range from N~50 to N~900) (Kennedy & Raz, 2009; Vernooij et al., 2009). Additionally, rather small samples of younger

  GENERAL DISCUSSION  

on functional brain connectivity in aging based their assumption on group comparisons between young and older participants (Dennis & Thompson, 2014). Comparing extreme groups could lead to an overestimation of age-related differences. Further, cohort effects, which are likely to be a problem in cross-sectional studies, may confound results.

Moreover, applied tests to describe a cognitive construct can differ between studies, which could lead to different associations. To illustrate, composite scores for certain cognitive domains are widely used when analyzing brain-behavior relationships (e.g., Andrews-Hanna et al., 2007; Vernooij et al., 2009). In contrast, we evaluated tests separately, especially for EF scores. Analyzing tests separately seems to be appropriate because of the substantial inconsistencies in the definition and conceptualization of EF and its subcomponents (Miyake et al., 2000).

In conclusion, by applying strict inclusion and exclusion criteria, the LHAB database project contributes to the evaluation of healthy aging. We hypothesize, and anticipate identifying, single or a combination of different predictors (e.g., leisure time activity, nutrition), which may be the cause for healthy aging trajectories (e.g., stability over time in a measured component like cognition or physical functioning).