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神经生物标志物用于识别无形皮肤炎和评估针治疗反应使用休息状态fMRI
In-Seon Lee1, Da-Eun Yoon1, Seoyoung Lee2
1College of Korean Medicine, Kyung Hee University, Seoul, Republic of Korea.
Journal of asthma and allergy
|April 23, 2024
概括
研究人员开发了一种新的深度学习模型,以识别阿尔茨海默病 (AD) 患者并预测针治疗反应. 这种神经生物标志物有助于诊断AD和个性化治疗.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 对阿尔茨海默病 (AD) 患者的的脑机制存在有限的研究.
- 对于阿尔茨海默病患者,没有研究过神经生物标志物,特别是关于的处理.
研究的目的:
- 开发一种基于深度学习模型的AD神经签名.
- 在AD患者和健康对照者 (HC) 之间进行歧视.
- 区分AD患者对针反应高和低的区别.
主要方法:
- 招募了41名AD患者和40名HC患者.
- 测量了静止状态的功能性MRI信号,并应用了38个感兴趣的功能区域.
- 利用长期短期记忆 (LSTM) 网络进行分类和预测模型训练.
主要成果:
- 该模型在识别AD患者与HC患者方面取得了显著的预测准确性,突出了特定的大脑区域,如SMA,PCC,极,先和dlPFC.
- 该模型还区分了针的高和低响应者,涉及诸如语言 - 副海马 - 状,SMA,额头,PCC,前,副中心叶片和初级运动/体感皮质等区域.
结论:
- 一个基于深度学习的神经生物标志物成功开发和验证.
- 这种生物标志物可以区分AD和HC,并预测针治疗的反应.
- 由生物标志物识别的内在神经异常可以促进AD诊断和个性化治疗策略.
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These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

