Precision TMS through the integration of neuroimaging and machine learning: optimizing stimulation targets for personalized treatment

作者：Bing Liu, Changwei Hu, Panxiao Bao · 发表于：Frontiers in Human Neuroscience · 年份：2025 · DOI：10.3389/fnhum.2025.1682852 · 被引用次数：10 · 研究领域：Transcranial Magnetic Stimulation Studies、Pain Management and Treatment、Migraine and Headache Studies

Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique based on electromagnetic induction, modulates cortical excitability by inducing currents with a magnetic field. TMS has demonstrated significant clinical potential in the treatment of various neuropsychiatric disorders, including depression, anxiety, and Parkinson's disease. However, conventional TMS targeting methods that rely on anatomical landmarks do not adequately account for individual differences in brain structure and functional networks, leading to considerable variability in treatment responses. In recent years, advances in neuroimaging techniques-such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI)-together with the application of machine learning (ML) and artificial intelligence (AI) algorithms in big data analysis, have provided novel approaches for precise TMS targeting and individualized treatment. This review summarizes the latest developments in the integration of multimodal neuroimaging and AI technologies for precision neuromodulation with TMS. It focuses on critical issues such as imaging resolution, AI model generalizability, real-time feedback modulation, as well as data privacy and ethical considerations. Future prospects including closed-loop TMS control systems, cross-modal data fusion, and AI-assisted brain-computer interfaces (BCIs) are also discussed. Overall, AI-driven personalized TMS strategies hold promise for markedly enhancing ...