# Individualized AI-driven Neuromodulation (iNM)

The individualized AI-driven fMRI neuromodulation platform iNM abandons a single fixed set of brain targets and instead builds a neurofunctional twin from each participant's own anatomy and function, reinforcing the tongue sensorimotor network in a closed loop at 2-second resolution. In healthy participants, an unsupervised hidden Markov model showed target-state occupancy rising from 82% under control to 97%, swallow-related interference falling from 18% to 3%, and connectivity among motor, sensory and attention networks strengthening by 53% to 74%, with effects persisting after modulation stopped.

The individualized AI-driven fMRI neuromodulation platform iNM abandons a single fixed set of brain targets and instead builds a neurofunctional twin from each participant's own anatomy and function, reinforcing the tongue sensorimotor network in a closed loop at 2-second resolution. In healthy participants, an unsupervised hidden Markov model showed target-state occupancy rising from 82% under control to 97%, swallow-related interference falling from 18% to 3%, and connectivity among motor, sensory and attention networks strengthening by 53% to 74%, with effects persisting after modulation stopped.