English(EN) Bayesian Uncertainty Quantification for fMRI Functional Connectivity via Simulation-Based Inference

新的贝叶斯框架量化fMRI连接不确定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-28 04:00

研究人员开发了一个新的贝叶斯框架来量化fMRI功能连接数据中的不确定性。该框架使用耦合的Ornstein-Uhlenbeck过程对BOLD动力学进行建模，并采用顺序神经网络后验估计来解释扫描仪噪声和真实的神经变异性。研究结果为优化fMRI扫描持续时间和空间分辨率提供了指导，表明像7T这样的更高场强扫描仪可以在显著更短的时间内实现与3T扫描仪相当的精度。 AI

影响提供了优化神经影像采集的方法，可能提高临床生物标志物的可靠性并降低成本。

排序理由详细介绍用于分析神经影像数据的新统计框架的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Simon Carter, Zeming Kuang, Lilianne R. Mujica-Parodi, Helmut H. Strey · 2026-09-28 04:00

用于fMRI功能连接的基于仿真的贝叶斯不确定性量化

arXiv:2609.30445v1 Announce Type: new Abstract: Optimizing fMRI scan duration and spatial resolution is critical for experimental design, yet traditional correlation-based approaches cannot quantify uncertainty or disentangle scanner measurement noise from true neural variability…

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用于fMRI功能连接的基于仿真的贝叶斯不确定性量化

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