个体特定的休息状态网络预测了药物耐药性的语言主导地位
Mervyn Lim Jun Rui1,2,3, Zhang Shaoshi1, Shreya Pande1
1Computational Brain Imaging Group, Yong Loo Lin School of Medicine, National University of Singapore.
medRxiv : the preprint server for health sciences
|December 8, 2025
概括
这项研究开发了一种新的脑绘图模型,用于预测患者使用休息状态fMRI的语言主导. 该模型为手术规划提供了一个实用的,非侵入性的工具.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 发病学 (Epileptology) 是一个专业的学科.
背景情况:
- 识别语言优势对于有效的手术规划至关重要.
- 目前的方法可能具有侵入性或缺乏个体特异性.
- 精确的功能性脑图绘制提供了一个潜在的解决方案.
研究的目的:
- 为了确定个体特异性皮质网络拓是否可以预测药物耐药性的语言主导地位.
- 为此目的评估一种新型的精确功能性脑图绘制方法.
主要方法:
- 开发了一种基于国家卫生研究院 (NIH) 数据的多会期层次贝叶斯模型 (MS-HBM).
- 应用该模型来估计药物耐药患者的个体特异性静止状态网络.
- 使用神经成像数据验证了模型对语言主导的预测准确性.
主要成果:
- 在病数据上接受培训的MS-HBM超出了群体平均网络的表现,并且很好地对独立的数据集进行了概括.
- 个体特定网络与内电刺激期间的皮质活动更紧密地结合,而不是与群体平均网络相比.
- 个体特定的语言网络地形准确地预测了左边,双边和右边的语言主导地位 (AUCs从0.72到0.83).
结论:
- 个体特定的皮质网络拓可以可靠地预测药物耐药性的语言主导地位.
- 开发的MS-HBM为手术前的语言映射提供了一个实用的,非侵入性的工具.
- 这种方法增强了精确的功能性大脑映射,用于手术规划.
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