Three groups of healthy older participants who differed with respect to their training history participated in a high-density EEG measurement (multi-domain group: participants who had participated in the Hotel Plastisse multi-domain training; visuomotor group: participants who had participated in the Hotel Plastisse visuomotor training; control group: participants with no specific training history). The idea of the study in Chapter 7 was to examine whether the different training histories were associated with differences in behavioral performance as well as with differences in functional brain network characteristics. Therefore, graph-theoretical measures representing the efficiency of functional brain networks were calculated. In terms of behavioral performance, the multi-domain group performed significantly better than the visuomotor and the control groups on a multi-domain task. In terms of the functional brain network features, the multi-domain group showed significantly higher connectivity in a network of the theta band encompassing visual, motor, executive, and memory-associated brain areas compared to the visuomotor group. In addition, the multi-domain group showed significantly enhanced processing efficiency reflected by higher mean weighted degree (strength) compared to the visuomotor group. There were no differences in functional brain networks between the multi-domain and the control group. The results show expertise- dependent differences in task-related functional brain networks. These network differences were evident even a year after the acquisition of the different expertise levels.
Given that brain networks are subjected to aging (Sun et al., 2012), identifying factors that are associated with the efficiency of functional brain networks contributes to insights into how aging can be positively influenced. There are only few studies that show expertise- dependent brain network alterations during task performance (Balser et al., 2014; Bernardi et
al., 2013; Duan et al., 2012) and most of these studies were conducted with young study participants. For example, professional racing car drivers showed enhanced functional connectivity in task-related brain areas during motor reaction and visuospatial tasks when compared to naive drivers (Bernardi et al., 2013). Regular practice in mediation and yoga was associated with increased small-world topology in middle aged adults compared to adults with no practice history (Gard et al., 2014). More formal exercise and cognitive training have shown network alterations during resting state (young participants: Langer, von Bastian, et al., 2013; older participants: Voss et al., 2010).
The specificity of such experience-dependent network alterations remains a topic of further investigation. With regard to training, it is generally assumed that training transfer relates on an overlap of cognitive and neural processes targeted by the trained and transfer tasks (Buschkuehl et al., 2012; Dahlin et al., 2008; Jonides, 2004). Dahlin et al. (2008) nicely showed that the activation of the striatum elicited by both the training and the transfer task was crucial for transfer. With regard to the network alterations associated with expertise in multi-domain tasks, enhanced connectivity in the network subserving the simultaneous administration of visuomotor function, inhibition, and spatial navigation might also benefit another task drawing on the same or parts of the network, as for example another spatial navigation task. However, as far as we know, no study has investigated whether training-induced brain network efficiency benefits other brain networks activated for performance on the transfer tasks. This question can be approximated by further analyses of the EEG data. Actually, EEG was measured during performance on a training task of each Hotel Plastisse training condition (inhibition, visuomotor function, spatial navigation, multi-domain). Only networks of the multi-domain task are reported in chapter 7. One could further investigate if the multi-domain group that showed enhanced performance on the multi-domain task shows also enhanced performance on the other tasks of the different single-domain conditions. This would further give insight into how the
simultaneous training of three functions benefitted each individual function. Furthermore, one could compare performance and functional brain networks between the multi-domain and the visuomotor group during performance on the visuomotor task. This analysis would show whether single-domain training on the trained task led to the same pattern as was found for the multi-domain training group during multi-domain performance (higher connectivity and efficiency in a task-related network of visual and motor regions). Last, one could investigate whether the different training histories led to task-independent functional network changes during resting state. A resting state measurement was included at the beginning and at the end of the EEG measurement session, but has not been analyzed thus far.
While superior performance of the multi-domain training group was nicely paralleled by functional network alterations when compared to the visuomotor function training group, performance differences between the multi-domain training group and the control group were not accompanied by brain network differences. One could speculate that the control group recruited similar brain networks, but recruitment was inefficient since the control group performed significantly worse. Hence, different mechanisms might be operating during task performance depending on whether participants had participated in the training or were completely naive.
To conclude, the multi-domain group showed increased efficiency in a task-related functional brain network during performance on the trained task. This finding is in line with other cross-sectional studies that found experience-dependent network differences (Balser et al., 2014; Bernardi et al., 2013; Duan et al., 2012; Gard et al., 2014). Future investigations of training-induced and experience-dependent brain network alterations will provide further insights into neuroplasticity during the aging process.