Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults

Abstract

Cardiorespiratory fitness was found to influence age-related changes of resting state brain network organization. However, the influence on dedifferentiated involvement of wider and more unspecialized brain regions during task completion is barely understood. We analyzed EEG data recorded during rest and different tasks (sensory, motor, cognitive) with dynamic mode decomposition, which accounts for topological characteristics as well as temporal dynamics of brain networks. As a main feature the dominant spatio-temporal EEG pattern was extracted in multiple frequency bands per participant. To deduce a pattern’s stability, we calculated its proportion of total variance among all activation patterns over time for each task. By comparing fit (N = 15) and less fit older adults (N = 16) characterized by their performance on a 6-min walking test, we found signs of a lower task specificity of the obtained network features for the less fit compared to the fit group. This was indicated by fewer significant differences between tasks in the theta and high beta frequency band in the less fit group. Repeated measures ANOVA revealed that a significantly lower proportion of total variance can be explained by the main pattern in high beta frequency range for the less fit compared to the fit group [F(1,29) = 12.572, p = .001, partial η2 = .300]. Our results indicate that the dedifferentiation in task-related brain activation is lower in fit compared to less fit older adults. Thus, our study supports the idea that cardiorespiratory fitness influences task-related brain network organization in different task domains.

Source connectivity patterns in the default mode network differ between elderly golf-novices and non-golfers

“…www.nature.com/scientificreports/ However, the potential effects of learning to play golf on source connectivity patterns of the DMN remain to be established, preferably in a randomized controlled design with an appropriate sample size. Golf is one of several lifestyle-related factors influencing cognitive performance and functional network characteristics, including cardiovascular fitness 28,50 , leisure time activities like chess or playing an instrument 32 , genetic predisposition [51][52][53] as well as diet 54,55 , which may have influenced our findings. The non-golfing control group did not receive additional treatment to account for the influence of other factors related to the intervention that may also had an effect on results, such as an increased social interaction.…”

“…If the identified electrode bridges of both methods were not identical, the electrodes were additionally visually inspected (difference between both channels) and interpolated, if necessary. Participants were excluded from analysis if more than 15% of all channels or the reference electrode was bridged 50 .…”

Exercise-induced central and peripheral sympathetic activity in a community-based group of epilepsy patients differ from healthy controls

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