Censoring high-motion volumes in fMRI is common practice to reduce effects of head motion on functional connectivity (FC). Although aggressive censoring removes more noise, it causes extensive data loss, creating a tradeoff that may ultimately improve or degrade FC accuracy. Here, we evaluate how censoring affects FC estimation and downstream brain-wide association studies (BWAS). Using extensively sampled participants from the Human Connectome Project (HCP) Retest dataset, we establish individual "ground truth" FC and assess the accuracy of FC estimated from 5-30 minute scans. We find that censoring degrades FC accuracy, with more aggressive censoring being more detrimental, particularly among participants exhibiting above-average motion. In these participants, aggressive censoring reduces FC accuracy by 30% for 30-minute scans denoised with ICA-FIX, an advanced denoising method, and by 3% for scans denoised with conventional confound regression. These effects reflect substantial data loss (34%) that outweighs comparatively modest noise reductions: 7% with ICA-FIX and 18% with confound regression. Compensating for this would require substantially longer scans (62% with confound regression; 76% with ICA-FIX), inflating data collection budgets. Introducing a repeated measures framework to separate motion trait from artifact, we find that standard QC metrics are dominated by motion trait and overstate motion bias, which is effectively mitigated with less aggressive censoring. Finally, using data from nearly 1,000 HCP participants, we demonstrate that unreliable FC substantially attenuates BWAS correlations: by ~30% under optimal conditions (longer ICA-FIX scans with no censoring) but exceeding 75% in short, aggressively censored scans. Our findings support the use of advanced denoising methods, limiting censoring, and collecting longer scans to maximize fidelity of FC and BWAS.

翻译：在功能磁共振成像（fMRI）中删减高运动帧是降低头部运动对功能连接（FC）影响的常见做法。尽管激进的删减能去除更多噪声，但会导致大量数据丢失，从而形成一种权衡，可能最终提高或降低FC的准确性。本文评估了删减如何影响FC估计及下游的全脑关联研究（BWAS）。利用人类连接组计划（HCP）重测数据集中深度采样的参与者，我们建立了个体“真实”FC，并评估了基于5至30分钟扫描数据估计的FC的准确性。我们发现删减会降低FC准确性，且更激进的删减危害更大，尤其是在表现出高于平均运动水平的参与者中。对于这些参与者，在使用先进去噪方法ICA-FIX去噪的30分钟扫描中，激进删减使FC准确性降低30%；而在使用传统混杂回归去噪的扫描中，准确性降低3%。这些效应反映了大量数据损失（34%）超过了相对有限的噪声减少：ICA-FIX减少7%，混杂回归减少18%。为弥补此损失需要显著延长扫描时间（混杂回归需延长62%；ICA-FIX需延长76%），从而大幅增加数据采集成本。通过引入重复测量框架以区分运动特质与伪影，我们发现标准质量控制指标主要由运动特质主导，并高估了运动偏差，而采用较不激进的删减可有效缓解此偏差。最后，利用近1000名HCP参与者的数据，我们证明不可靠的FC会显著削弱BWAS相关性：在最优条件下（较长的ICA-FIX扫描且无删减）削弱约30%，但在短时、激进删减的扫描中削弱超过75%。我们的研究结果支持使用先进去噪方法、限制删减以及采集更长的扫描数据，以最大化FC和BWAS的保真度。