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Concurrent/Interleaved TMS–fMRI as an MR-Guided Framework for Target Engagement

计算机科学 操作化 桥接(联网) 透视图(图形) 磁刺激 神经调节 脑刺激 数据科学 转化研究 人机交互 风险分析(工程) 开放式研究 生物标志物 心理学 神经科学 人工智能 价值网络 协议(科学) 医学 价值(数学)
作者
Chiara Di Fazio,Sara Palermo
出处
期刊:Applied sciences [Multidisciplinary Digital Publishing Institute]
卷期号:16 (9): 4135-4135
标识
DOI:10.3390/app16094135
摘要

Concurrent/interleaved transcranial magnetic stimulation combined with functional MRI (TMS–fMRI) enables causal perturbation of targeted cortical regions while measuring whole-brain MR-based responses during stimulation. This perspective argues that the main translational value of concurrent/interleaved TMS–fMRI lies in operationalizing target engagement and network-level propagation as measurable endpoints, bridging stimulation “dose” to clinically meaningful effects. Rather than proposing a validated gold-standard protocol, we frame concurrent/interleaved TMS–fMRI as a measurement-driven translational approach in which MRI-informed targeting and MR-based readouts can be integrated to quantify target engagement under clearly specified methodological and quality-control conditions. This perspective specifically aims to make explicit an intermediate verification step that remains only partially formalized in current clinical neuromodulation workflows. We propose that MRI-based neuronavigation should move beyond template coordinates toward individualized anatomical and network-informed targeting, with the aim of improving precision, reproducibility, and safety. Building on the field’s evolution from technical feasibility to emerging clinical applications, we outline a staged framework from feasibility to biomarker potential, summarize representative protocol archetypes, and provide pragmatic recommendations for reporting and study design to improve comparability. This framework is intended to guide future concurrent/interleaved TMS–fMRI studies toward biomarker-ready designs and more clinically informative network neuromodulation. We further distinguish offline MRI-informed targeting from potential future real-time or closed-loop implementations, and we emphasize that current biomarker claims should remain proportional to the still heterogeneous evidence base.
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