发表机构
Stony Brook University; Santa Fe Institute（石溪大学; 圣塔菲研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出贝叶斯框架量化fMRI连接性不确定性，发现7T较3T显著缩短所需扫描时间，为协议优化提供依据。
AI 中文摘要
优化fMRI扫描时长和空间分辨率对于实验设计至关重要，然而传统的基于相关性的方法无法量化不确定性，也无法将扫描仪的测量噪声与受试者间的真实神经变异性区分开来。缺乏原则性的不确定性界限，研究人员无法知晓某个协议是否足够长以可靠地估计连接性，也无法判断受试者间的差异反映的是生物学变异还是噪声。我们提出一个贝叶斯框架，将BOLD动态建模为耦合的Ornstein-Uhlenbeck过程，利用序贯神经后验估计获得连接性后验，同时考虑BOLD频谱中与频率无关的测量噪声。将该框架应用于N=28名健康对照者（55次扫描）在7T下的数据，使用功能网络图谱（65个DMN区域），该框架量化了其不确定性来源：扫描仪噪声、受试者变异性和采集时长。空间分析确定每个ROI平均46个体素，约为典型区域大小的一半，足以达到渐近精度的90%。在单受试者层面，7T在约7分钟内达到其会话内精度平台，而3T需要10分钟，所需扫描时间减少40%，首次提供了高场强所赋予扫描时间优势的直接、基于模型的量化。在群体层面，3T所需的每受试者扫描时间约为7T的37倍，才能使汇总曲线收敛，证实了高场强在每个时间尺度上的一致优势。这些发现共同为协议优化提供了具体的、针对扫描仪的建议，对降低采集成本和改善基于连接性的临床生物标志物的可靠性具有直接影响。我们提供代码，使研究人员能够从他们自己的数据中推导出这些界限。
英文摘要
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 across subjects. Without principled uncertainty bounds, researchers cannot know whether a protocol is long enough to reliably estimate connectivity, or whether between-subject differences reflect biological variation or noise. We present a Bayesian framework modeling BOLD dynamics as coupled Ornstein-Uhlenbeck processes, using Sequential Neural Posterior Estimation to obtain connectivity posteriors while accounting for frequency-independent measurement noise across the BOLD spectrum. Applied to N = 28 healthy controls (55 scans) at 7T using a functional network atlas (65 DMN regions), the framework quantifies uncertainty across its sources: scanner noise, subject variability, and acquisition length. Spatial analysis identifies a mean of 46 voxels per ROI, roughly half of typical region sizes, as sufficient to achieve 90% of asymptotic precision. At the single-subject level, 7T reaches its within-session precision plateau in approximately 7 minutes versus 10 minutes for 3T, a 40% reduction in required scan time, providing the first direct, model-based quantification of the scan-time advantage conferred by higher field strength. At the population level, 3T requires roughly 37 times more per-subject scan time than 7T for the pooled curves to converge, confirming a consistent advantage of higher field strength at every timescale. Together these findings provide concrete, scanner-specific guidance for protocol optimization, with direct implications for reducing acquisition costs and improving the reliability of connectivity-based clinical biomarkers. We provide code enabling researchers to derive these bounds from their own data.
Comments29 pages, 4 figures
