Brain-computer-interface-based intervention increases brain functional segregation in cognitively normal older adults

Brain-computer interface (BCI)-based cognitive training systems have shown promise in enhancing cognitive performance in cognitively normal older adults. However, the brain network changes underlying these behavioural improvements remain poorly understood. To address this gap, we investigated topological alterations in intrinsic brain functional networks following BCI-based training and their behavioural relevance in cognitively normal older adults using resting-state functional magnetic resonance imaging and graph theoretical analysis. Compared to a non-intervention waitlist (WL) group, the intervention (INT) group did not show significant behavioural improvements. However, they exhibited positive changes in brain network organisation. Specifically, the INT group demonstrated a reduced nodal participation coefficient, indicating enhanced strength of a node's connections within its community, primarily within control and subcortical networks, as well as increased system segregation after training. Additionally, the modular organisation of the brain functional network in the INT group became more segregated and more aligned with a young adult-based partition template (quantified using the adjusted Rand index) compared to the WL group. Importantly, decreased participation coefficients, particularly in subcortical regions, were associated with language improvement, while increases in the adjusted Rand index were linked to enhancements in everyday memory function. These findings suggest that BCI-based cognitive training may contribute to maintaining brain network organisation in cognitively normal ageing by enhancing functional network segregation, potentially supporting cognitive performance. This study provides insights into the neural mechanisms underlying the effectiveness of BCI-based cognitive training for cognitively normal ageing.