Goldberg Andrew, Rosario Isabella, Power Jonathan, Horga Guillermo, Wengler Kenneth
New York State Psychiatric Institute, New York, NY, United States.
Department of Psychiatry, Weill Cornell Medicine, New York, NY, United States.
Imaging Neurosci (Camb). 2024;2. doi: 10.1162/imag_a_00326. Epub 2024 Oct 28.
Intrinsic neural timescales (INT) reflect the time window of neural integration within a brain region and can be measured via resting-state functional magnetic resonance imaging (rs-fMRI). Despite the potential relevance of INT to cognition, brain organization, and neuropsychiatric illness, the influences of physiological artifacts on rs-fMRI INT have not been systematically considered. Two artifacts, head motion and respiration, pose serious issues in rs-fMRI studies. Here, we described their impact on INT estimation and tested the ability of two denoising strategies for mitigating these artifacts, high-motion frame censoring and global signal regression (GSR). We used a subset of the Human Connectome Project Young Adult (HCP-YA) dataset with runs annotated for breathing patterns (Lynch et al., 2020) and at least one "clean" (reference) run that had minimal head motion and no respiration artifacts; other runs from the same participants ( ) were labeled as "non-clean." We found that non-clean runs exhibited brain-wide increases in INT compared with their respective clean runs and that the magnitude of error in INT between non-clean and clean runs correlated with the amount of head motion. Importantly, effect sizes were comparable with INT effects reported in the clinical literature. GSR and high-motion frame censoring improved the similarity between INT maps from non-clean runs and their respective clean run. Using a pseudo-random frame-censoring approach, we uncovered a relationship between the number of censored frames and both the mean INT and mean error, suggesting that frame censoring itself biases INT estimation. A group-level correction procedure reduced this bias and improved similarity between non-clean runs and their respective clean run. Based on our findings, we offer recommendations for rs-fMRI INT studies, which include implementing GSR and high-motion frame censoring with Lomb-Scargle interpolation of censored frames, and performing group-level correction of the bias introduced by frame censoring.
内在神经时间尺度（INT）反映了大脑区域内神经整合的时间窗口，可通过静息态功能磁共振成像（rs-fMRI）进行测量。尽管INT与认知、大脑组织和神经精神疾病潜在相关，但生理伪影对rs-fMRI INT的影响尚未得到系统考虑。两种伪影，头部运动和呼吸，在rs-fMRI研究中构成严重问题。在此，我们描述了它们对INT估计的影响，并测试了两种减轻这些伪影的去噪策略的能力，即高运动帧截断和全局信号回归（GSR）。我们使用了人类连接组计划青年成人（HCP-YA）数据集的一个子集，其中的运行标注了呼吸模式（林奇等人，2020年），并且至少有一个头部运动最小且无呼吸伪影的“干净”（参考）运行；来自同一参与者的其他运行被标记为“不干净”。我们发现，与各自的干净运行相比，不干净的运行在全脑范围内INT增加，并且不干净和干净运行之间INT的误差大小与头部运动量相关。重要的是，效应大小与临床文献中报道的INT效应相当。GSR和高运动帧截断提高了不干净运行的INT图与其各自干净运行之间的相似性。使用伪随机帧截断方法，我们发现截断帧数与平均INT和平均误差之间存在关系，这表明帧截断本身会使INT估计产生偏差。一种组水平的校正程序减少了这种偏差，并提高了不干净运行与其各自干净运行之间的相似性。基于我们的发现，我们为rs-fMRI INT研究提供了建议，包括实施GSR和高运动帧截断，并对截断帧进行 Lomb-Scargle 插值，以及对帧截断引入的偏差进行组水平校正。
