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

Learning to play golf has high demands on attention and therefore may counteract age-related changes of functional brain networks. This cross-sectional study compared source connectivity in the Default Mode Network (DMN) between elderly golf novices and non-golfers. Four-minute resting-state electroencephalography (128 channels) from 22 elderly people (mean age 67 ± 4.3 years, 55% females) were recorded after completing a 22-week golf learning program or after having continued with normal life. Source connectivity was assessed after co-registration of EEG data with native MRI within pre-defined portions of the DMN in the beta band (14-25 Hz). Non-golfers had significantly higher source connectivity values in the anterior DMN compared to non-golfers. Exploratory correlation analyses did not indicate an association to cognitive performance in either group. Inverse correlations between a marker of external attention with source connectivity of the anterior DMN may suggest a trend in the golf group only, but have to be replicated in future studies. Clinical relevance of these findings remains to be elucidated, but the observed difference in the anterior DMN may provide a starting point to further investigate if and how learning golf may have an impact on physiological age-related cognitive changes.

Classification characteristics of fine motor experts based on electroencephalographic and force tracking data.

The application of machine learning techniques provides a data-driven approach for a deeper understanding of the development and expressions of expertise. In extension to the common procedure of comparing experts' and novices' performances in expertise-domain-related tasks we applied conventional classification algorithms. We distinguished between tasks for each participant and between groups, i.e., experts or novices, based on electroencephalographic (EEG) activity patterns and force output variables during four different force modulation tasks. The tasks under investigation involved sinusoidal and steady force tracking tasks, which were performed with the left and right hand. Classification of tasks based on EEG patterns as well as force output was possible with high accuracy in novices and experts, whereas classification of group membership, i.e., experts or novices, was at chance level. In follow-up analyses, we found a high degree of individuality in the EEG patterns of the experts, implying the long-term development of specialized central processing during fine motor tasks in fine motor experts. Taken together, the results suggest that continuous practice in the work context leads to the development of a highly individual and task-specific central control pattern.

Classification of visuomotor tasks based on electroencephalographic data depends on age-related differences in brain activity patterns.

Classification of physiological data provides a data driven approach to study central aspects of motor control, which changes with age. To implement such results in real-life applications for elderly it is important to identify age-specific characteristics of movement classification. We compared task-classification based on EEG derived activity patterns related to brain network characteristics between older and younger adults performing force tracking with two task characteristics (sinusoidal; constant) with the right or left hand. We extracted brain network patterns with dynamic mode decomposition (DMD) and classified the tasks on an individual level using linear discriminant analysis (LDA). Next, we compared the models' performance between the groups. Studying brain activity patterns, we identified signatures of altered motor network function reflecting dedifferentiated and compensational brain activation in older adults. We found that the classification performance of the body side was lower in older adults. However, classification performance with respect to task characteristics was better in older adults. This may indicate a higher susceptibility of brain network mechanisms to task difficulty in elderly. Signatures of dedifferentiation and compensation refer to an age-related reorganization of functional brain networks, which suggests that classification of visuomotor tracking tasks is influenced by age-specific characteristics of brain activity patterns. In addition to insights into central aspects of fine motor control, the results presented here are relevant in application-oriented areas such as brain computer interfaces.