Optimizing transcranial focused ultrasound parameters: A methodological advancement in non-invasive brain stimulation for next-gen clinical applications

Neuroscience informatics Pub Date : 2025-05-05 DOI:10.1016/j.neuri.2025.100204

Sachin Gupta , Mustafa Mudhafar , Yogini Dilip Borole , V. Mahalakshmi , Janjhyam Venkata Naga Ramesh , Muhammad Attique Khan

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Abstract

Background: Transcranial-focused ultrasound (FUS), a non-invasive neuromodulation method, is gaining popularity for treating neurological and psychiatric disorders. However, changing stimulation settings for precise brain targeting remains challenging.

Methods: Existing techniques have spatial resolution, skull acoustic transmission, and parameter selection issues that reduce clinical efficacy. These problems reduce tFUS application repeatability and safety. To address these challenges, this research proposes a novel computational-experimental strategy that combines advanced computational modeling (IACM) with in vivo validation. The proposed design uses subject-specific skull acoustic simulations, Deep Learning (DL)-based parameter optimization, and real-time feedback to increase stimulation accuracy and efficacy.

Results: The recommended approach allows customized transcutaneous electrical nerve stimulation (tFUS) by modifying frequency, intensity, and targeting. Neuromodulation becomes better while staying safe. It should be adaptable enough for research and clinical usage to create neurostimulation precision medicine.

Comparative analysis: The study shows that the proposed framework improves spatial precision, skull transmission effect variability, and neuromodulation efficacy compared to existing methods.

Conclusion: This approach enables the development next-generation non-invasive brain stimulation devices with more therapeutic uses. Non-invasive brain stimulation (NIBS) technologies, including tFUS, TMS, and tDCS, may now accurately affect neurological and psychiatric diseases. However, these approaches are susceptible to inter-subject variability, poor targeting, and skull deformities. Artificial intelligence-driven real-time optimization frameworks like the Integrating Advanced Computational Modeling (IACM) framework are needed to overcome these constraints.

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优化经颅聚焦超声参数：用于下一代临床应用的无创脑刺激的方法学进展

背景：经颅聚焦超声（Transcranial-focused ultrasound， FUS）作为一种非侵入性的神经调节方法，在神经和精神疾病的治疗中越来越受欢迎。然而，改变刺激设置来精确定位大脑仍然具有挑战性。方法：现有技术存在空间分辨率、颅骨声透射、参数选择等问题，降低了临床疗效。这些问题降低了tFUS应用的可重复性和安全性。为了应对这些挑战，本研究提出了一种新的计算实验策略，将先进的计算建模（IACM）与体内验证相结合。所提出的设计使用特定受试者的颅骨声学模拟、基于深度学习（DL）的参数优化和实时反馈来提高刺激的准确性和效果。结果：推荐的方法允许通过改变频率、强度和目标来定制经皮神经电刺激（tFUS）。在保持安全的情况下，神经调节会变得更好。它应该有足够的适应性用于研究和临床应用，以创造神经刺激精准医学。对比分析：研究表明，与现有方法相比，所提出的框架提高了空间精度、颅骨传递效应变异性和神经调节效果。结论：该方法使下一代无创脑刺激装置的开发具有更多的治疗用途。非侵入性脑刺激（NIBS）技术，包括tFUS、TMS和tDCS，现在可以准确地影响神经和精神疾病。然而，这些方法容易受到主体间变异性、靶向性差和颅骨畸形的影响。需要人工智能驱动的实时优化框架，如集成高级计算建模（IACM）框架来克服这些限制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Neuroscience informatics Surgery, Radiology and Imaging, Information Systems, Neurology, Artificial Intelligence, Computer Science Applications, Signal Processing, Critical Care and Intensive Care Medicine, Health Informatics, Clinical Neurology, Pathology and Medical Technology

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审稿时长

57 days