Work

Individualized AI-driven Neuromodulation Selectively Enhances and Reorganizes Human Tongue Sensorimotor Networks.

Year: 2026

Type: preprint

Abstract: Precise modulation of brain networks for tongue motor and sensory control (TMSC) is critical for neruorehabilitation. Existing neuromodulation approaches use fixed targets, ignoring individual network architecture. We present an AI-driven, individualized fMRI neuromodulation (iNM) platform adaptively targeting subject-specific TMSC networks in real-time. Using a neurofunctional twin built from each participant's anatomy and function, iNM reinforces targeted activity and suppresses competing states. Unsupervised machine learning and hidden Markov modeling showed iNM increased TMSC-state occupancy from 82% to 97% and suppressed swallow-state interference by 83%. The inferior parietal lobule emerged as a sensorimotor integration hub, with increased magnitude (d′ 90.4 right, 84 left) and spatial expansion (134.8% right, 81.8% left). Cortico-subcortical connectivity strengthened 53% to 74% across motor, sensory, and attention networks and persisted into non-iNM blocks. Thus, iNM may offer precision neurorehabilitation for CNS or PNS disorders, such as amyotrophic lateral sclerosis, and treatment-induced cranial neuropathy in head-and-neck-cancer survivors, respectively.

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.64898/2026.11.04.764393