Personalized Network-Guided Neuromodulation Enhances Human Working Memory

The next frontier in cognitive neuromodulation is defined by personalized and adaptive protocols, necessitating approaches tailored to individual functional neuroanatomy and brain-state fluctuations. Here, we introduce an adaptive neuromodulation framework that integrates individualized network targeting with real-time decoding of brain states to precisely target working memory functional networks. Using concurrent transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI), we first mapped participant-specific networks and identified personalized targets. A real-time decoder then tracked stimulation-evoked neural dynamics to empirically determine the optimal frequency (i.e., the best-performing within a tested set of 5, 10, and 20 Hz) and a corresponding suboptimal frequency for each individual. In a multi-session crossover study, only the optimal-frequency stimulation significantly improved working memory, with the decoder's output predicting behavioral gains. A key finding is the substantial inter-individual variability in the optimal frequency, providing evidence against the notion of a universal "best" frequency. Our results demonstrate that cognitive enhancement is governed by the precise interaction between stimulation target and frequency. This work provides a causal demonstration of personalized, network-based neuromodulation and offers proof of concept for a generalizable, biomarker-driven framework, representing a step toward advancing cognitive therapeutics. Trial Registration: This study is registered at ClinicalTrials.gov (identifier: NCT04402294).