Suppr超能文献

推进个性化数字疗法:整合音乐疗法、脑波诱导方法和人工智能驱动的生物反馈。

Advancing personalized digital therapeutics: integrating music therapy, brainwave entrainment methods, and AI-driven biofeedback.

作者信息

Jiao Dian

机构信息

Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.

出版信息

Front Digit Health. 2025 Feb 25;7:1552396. doi: 10.3389/fdgth.2025.1552396. eCollection 2025.

Abstract

Mental health disorders and cognitive decline are pressing global concerns, increasing the demand for non-pharmacological interventions targeting emotional dysregulation, memory deficits, and neural dysfunction. This review systematically examines three promising methodologies-music therapy, brainwave entrainment (binaural beats, isochronic tones, multisensory stimulation), and their integration into a unified therapeutic paradigm. Emerging evidence indicates that music therapy modulates affect, reduces stress, and enhances cognition by engaging limbic, prefrontal, and reward circuits. Brainwave entrainment, particularly within the gamma frequency range (30-100 Hz), facilitates neural oscillatory patterns linked to relaxation, concentration, and memory, with 40 Hz showing promise for cognitive enhancement, albeit with individual variability. Synchronized multisensory stimulation, combining auditory and visual inputs at gamma frequencies, has demonstrated potential in enhancing memory and supporting neural integrity, particularly in Alzheimer's disease. However, challenges such as patient response variability, lack of standardization, and scalability hinder widespread implementation. Recent research suggests that a synergistic application of these modalities may optimize therapeutic outcomes by leveraging complementary mechanisms. To actualize this, AI-driven biofeedback, enabling real-time physiological assessment and individualized adjustments-such as tailoring musical complexity, entrainment frequencies, and multisensory components-emerges as a promising solution. This adaptive model enhances treatment accessibility and consistency while maximizing long-term efficacy. Although in early stages, preliminary evidence highlights its transformative potential in reshaping non-pharmacological therapeutic strategies. Advancing this field requires interdisciplinary research, rigorous evaluation, and ethical data stewardship to develop innovative, patient-centered solutions for mental health and cognitive rehabilitation.

摘要

心理健康障碍和认知衰退是全球紧迫关注的问题,这增加了对针对情绪失调、记忆缺陷和神经功能障碍的非药物干预措施的需求。本综述系统地研究了三种有前景的方法——音乐疗法、脑电波诱导(双耳节拍、等时音调、多感官刺激),以及将它们整合为一种统一的治疗模式。新出现的证据表明,音乐疗法通过激活边缘系统、前额叶和奖赏回路来调节情绪、减轻压力并增强认知。脑电波诱导,特别是在伽马频率范围(30 - 100赫兹)内,促进与放松、注意力集中和记忆相关的神经振荡模式,40赫兹的脑电波虽存在个体差异,但显示出认知增强的潜力。同步多感官刺激,即在伽马频率下结合听觉和视觉输入,已证明在增强记忆和支持神经完整性方面具有潜力,特别是在阿尔茨海默病中。然而,诸如患者反应的变异性、缺乏标准化和可扩展性等挑战阻碍了其广泛应用。最近的研究表明,这些方法的协同应用可能通过利用互补机制来优化治疗效果。为实现这一点,人工智能驱动的生物反馈技术能够进行实时生理评估和个性化调整,例如调整音乐复杂性、诱导频率和多感官成分,成为一种有前景的解决方案。这种自适应模型提高了治疗的可及性和一致性,同时使长期疗效最大化。尽管尚处于早期阶段,但初步证据凸显了其在重塑非药物治疗策略方面的变革潜力。推进这一领域需要跨学科研究、严格评估和符合伦理的数据管理,以开发创新的、以患者为中心的心理健康和认知康复解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7553/11893577/fbfac2628607/fdgth-07-1552396-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验