Advanced neuroimaging and AI for personalized non-invasive brain stimulation: from biomarkers to closed-loop strategies

Non-invasive brain stimulation (NIBS) techniques, including transcranial magnetic stimulation (TMS), transcranial direct and alternating current stimulation (tDCS/tACS), and focused ultrasound, have emerged as powerful tools for both probing and modulating human neural circuits. Yet a central challenge has persisted: the considerable variability in individual responses to stimulation, driven by differences in neuroanatomy, brain state, network organisation, and underlying pathophysiology. Advanced neuroimaging now sits at the heart of efforts to address this challenge, offering unprecedented insight into the mechanisms of stimulation, its network-level effects, and the neural features that distinguish responders from non-responders.High-resolution structural and functional MRI, alongside EEG, MEG, and fNIRS, provide detailed windows into individual brain organisation, connectivity, and dynamics before, during, and after stimulation. These modalities are central to identifying biomarkers of target engagement, characterising neuroplastic changes induced by NIBS, and informing individualised targeting strategies. Complementary advances in computational modelling, machine learning, and adaptive closed-loop systems further extend these capabilities, enabling more precise prediction of stimulation outcomes and supporting the move from fixed protocols toward genuinely personalised interventions.This Research Topic brings together work at this intersection, with the goal of accelerating the translation of precision neuromodulation from proof-of-concept to clinical and cognitive neuroscience application. We welcome contributions from researchers working across neuroimaging, computational modelling, AI and machine learning, neurostimulation engineering, and clinical neuroscience.Themes of interest include, but are not limited to:-Neuroimaging studies investigating the mechanisms and network-level effects of NIBS in the human brain.-Biomarkers of target engagement, treatment response, and neuroplasticity following stimulation.-Longitudinal neuroimaging studies evaluating neural changes induced by NIBS over time.-Neuroimaging-guided targeting and individualised dosing for TMS, tDCS/tACS, and focused ultrasound.-Multimodal imaging approaches characterising brain state prior to, during, and after stimulation.-Identification and validation of neuroimaging biomarkers distinguishing responders from non-responders.-Machine learning and AI approaches for predicting individual stimulation response from neuroimaging data.-Computational models of electric field distribution and their interaction with individual neuroanatomy.-Closed-loop brain stimulation systems integrating real-time EEG, fMRI, or fNIRS feedback.-Applications of precision NIBS in neurological and psychiatric conditions, including depression, stroke, and Parkinson's disease.-Ethical, reproducibility, and translational considerations in neuroimaging- and AI-assisted brain stimulation research.-Foundation models, transformers, large language model architectures, transfer learning, and cross-subject generalization applied to neuroimaging and electrophysiological data relevant to individualized stimulation.- Real-time decoding of brain state as a trigger or control signal for closed-loop and state-dependent stimulation, including applications in epilepsy and neurodevelopmental conditions. Topic editors: Liang Shi, Kazuki Sakakura, Adham Atyabi Source: https://www.frontiersin.org/research-topics/82581/advanced-neuroimaging-and-ai-for-personalized-non-invasive-brain-stimulation-from-biomarkers-to-closed-loop-strategies