Just like a bustling city, the human brain is a complex network that never rests. Scientists have made exciting discoveries by studying how this network behaves during periods of rest and cognitive engagement. In this study, researchers focused on a unique approach called the task aftereffect model. It involves comparing the resting-state brain networks before and after a cognitive task to gain insight into cognitive decline in healthy aging and mild cognitive impairment (MCI) patients. Using electroencephalography recordings, they found that MCI patients showed a greater increase in global network integration compared to healthy controls after a cognitive task. This suggests that MCI patients rely on a more interconnected network to maintain or improve task performance. The findings imply that MCI patients might engage in compensatory activation to overcome cognitive challenges. Interestingly, there were no significant differences in network topology between the two groups during the pre-task resting state. These results highlight the relevance of the task aftereffect model in identifying abnormalities in network organization associated with cognitive decline and offer valuable insights into brain health in MCI patients. To dive deeper into this fascinating research, check out the full article!

The view of the human brain as a complex network has led to considerable advances in understanding the brain’s network organization during rest and task, in both health and disease. Here, we propose that examining brain networks within the task aftereffect model, in which we compare resting-state networks immediately before and after a cognitive engagement task, may enhance differentiation between those with normal cognition and those with increased risk for cognitive decline. We validated this model by comparing the pre- and post-task resting-state functional network organization of neurologically intact elderly and those with mild cognitive impairment (MCI) derived from electroencephalography recordings. We have demonstrated that a cognitive task among MCI patients induced, compared to healthy controls, a significantly higher increment in global network integration with an increased number of vertices taking a more central role within the network from the pre- to post-task resting state. Such modified network organization may aid cognitive performance by increasing the flow of information through the most central vertices among MCI patients who seem to require more communication and recruitment across brain areas to maintain or improve task performance. This could indicate that MCI patients are engaged in compensatory activation, especially as both groups did not differ in their task performance. In addition, no significant group differences were observed in network topology during the pre-task resting state. Our findings thus emphasize that the task aftereffect model is relevant for enhancing the identification of network topology abnormalities related to cognitive decline, and also for improving our understanding of inherent differences in brain network organization for MCI patients, and could therefore represent a valid marker of cortical capacity and/or cortical health.

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