Abstract
Compensation implies the recruitment of additional neuronal resources to prevent the detrimental effect of age-related neuronal decline on cognition. Recently suggested statistical models comprise behavioral performance, brain activation, and measures related to aging- or disease-specific pathological burden to characterize compensation. Higher chronological age as well as the APOE ε4 allele are risk factors for Alzheimer's disease. A more biological approach to characterize aging compared with chronological age is the brain age gap estimation (BrainAGE), taking into account structural brain characteristics. We utilized this estimate in an fMRI experiment together with APOE variant as measures related to pathological burden and aimed at identifying compensatory regions during working memory (WM) processing in a group of 34 healthy older adults. According to published compensation criteria, better performance along with increased brain activation would indicate successful compensation. We examined the moderating effects of BrainAGE on the relationship between task performance and brain activation in prefrontal cortex, as previous studies suggest predominantly frontal compensatory activation. Then we statistically compared them to the effects of chronological age (CA) tested in a previous study. Moreover, we examined the effects of adding APOE variant as a further moderator. Herewith, we strived to uncover neuronal compensation in healthy older adults at risk for neurodegenerative disease. Higher BrainAGE alone was not associated with an increased recruitment in prefrontal cortex. When adding APOE variant as a second moderator, we found an interaction of BrainAGE and APOE variant, such that ε4 carriers recruited right inferior frontal gyrus with higher BrainAGE to maintain WM performance, thus showing a pattern compatible with successful neuronal compensation. Exploratory analyses yielded similar patterns in left inferior and bilateral middle frontal gyrus. These results contrast those from a previous study, where we found no indication of compensation in prefrontal cortex in ε4 carriers with increasing CA. We conclude that BrainAGE together with APOE variant can help to reveal potential neuronal compensation in healthy older adults. Previous results on neuronal compensation in frontal areas corroborate our findings. Compensatory brain regions could be targeted in affected individuals by training or stimulation protocols to maintain cognitive functioning as long as possible.
Highlights
Neuronal compensation as an individual’s reaction to cognitive challenge with the aim to maintain cognitive performance has been increasingly investigated over the past years in healthy aging and beginning neurodegenerative disease
We showed that ε4 carriers activated medial frontal and inferior frontal areas to a greater extent compared to non- ε4 carriers during a working memory (WM) task, pointing to successful compensation in genetically burdened individuals
The aim of the current study was to investigate potential compensatory recruitment in healthy older individuals with a compensation model comprising the components task performance, brain activation and two measures related to pathological burden in aging, BrainAGE, and APOE variant
Summary
Neuronal compensation as an individual’s reaction to cognitive challenge with the aim to maintain cognitive performance has been increasingly investigated over the past years in healthy aging and beginning neurodegenerative disease. Several theoretical frameworks describe compensation as a flexible recruitment of additional neuronal resources when existing networks reach their capacity limits and are not sufficient anymore for successful cognitive performance (Cabeza, 2002; Davis et al, 2008; Park and Reuter-Lorenz, 2009; Stern, 2009; Reuter-Lorenz and Park, 2014). Together with these frameworks, a debate on the interpretation of increased brain activation as compensatory arose (Price and Friston, 1999; Friston and Price, 2003; Grady, 2012). To establish a statistical model of compensation, three components are necessary: task performance, a measure of brain activation as well as a measure related to pathological or disease burden for the condition investigated (Cabeza and Dennis, 2013; Gregory et al, 2017)
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