Brain Network–Informed Optimization of Individualized tACS Targets for Working Memory Modulation

Abstract

Working memory underpins complex task execution and human–machine interaction, yet conventional transcranial alternating current stimulation (tACS) commonly targets the left dorsolateral prefrontal cortex (DLPFC) without accounting for inter-individual neural specificity or task-related activation, which may contribute to variable and poorly reproducible effects. In this study, we proposed a brain network–informed strategy to optimize individualized tACS targets for working-memory enhancement. High-density electroencephalography was recorded during a graded working-memory task, and task-related functional brain networks were constructed using the phase lag index. A modified K-order structural entropy algorithm was applied to quantify network topology and identify individual hub nodes showing load-dependent enhancement and significant associations with behavioral performance as candidate stimulation targets. In a within-subject, three-condition crossover design, participants received sham stimulation, conventional DLPFC-targeted stimulation, and network-guided stimulation centered on the individualized hub node. Compared with sham and conventional stimulation, the network-guided condition showed a more consistent improvement trend in working-memory performance under high load. These findings support a task-state brain network–based framework for translating quantitative network features into individualized stimulation site selection, providing a feasible and transferable pathway for precision neuromodulation in cognitive engineering and neuroergonomics.

Keywords: Working Memory, Transcranial Alternating Current Stimulation, Functional Brain Network, Neuromodulation

DOI: 10.54941/ahfe1007397