Objective: To investigate the causal interplay of brain areas during the performance of a complex bimanual motor task in young and elderly subjects.

Background: Coordination of bimanual movements is crucial for daily activities and controlled by the supplementary motor area (SMA), the premotor (PM) and prefrontal cortex (PFC). The causal interplay of those areas, however, is not yet fully understood. Furthermore, changes within the motor system during physiological aging remain to be discovered.

Methods: 23 young (mean age: 25 ± 2) and 28 elderly (mean age: 61 ± 7) subjects had to memorize a sequence of finger taps that should be executed while the other hand was used to tap a different sequence. Simultaneously, a 128-channel EEG was recorded. Behavioral data was analyzed with respect to errors and performance time and event-related potentials (ERPs) averaged over correctly performed trials. A subsequent source analysis showed distinct activity within the PFC, lateral PM (lPM) and SMA, while the primary motor cortex was not activated. ERP data was then extracted from those sources. To investigate causal interactions between motor sources, we used Dynamic Causal Modeling (DCM) for evoked responses.

Results: Behavioral analysis showed that elderly subjects made more mistakes (elderly: 34.36 %; young: 20.46 %, p = 0.003) and needed more time to accomplish the task (elderly: 3.19 s; young: 2.27 s, p < 0.001). Additionally, analysis of ERP data revealed significant differences: If the memorized rule was performed with the dominant hand, ERPs for young and elderly subjects differed between 500 and 750 ms, with elderly showing more positive potentials. Amplitude differences were mainly located in the ipsilateral hemisphere above the postcentral sulcus and angular gyrus. DCM analysis showed that a fully connected network, comprising coupling between PFC and lPM, PFC and SMA, as well as between lPM and SMA, did explain the data best. Only in the elderly, when the learned rule was performed with the left (non-dominant) hand, Bayesian model selection did not lead to conclusive results.

Conclusions: Our analysis suggests that aging not only affects motor performance but also processing within the motor system. A successive DCM analysis using a larger model space will further uncover differences in the interplay of brain areas and enable us to better understand changes occurring in the aging brain.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/inferring-the-causal-interplay-of-motor-related-brain-areas-in-the-aging-brain-a-dynamic-causal-modeling-dcm-approach/