Delft Centre for Systems and Control, Delft University of Technology, Delft, Netherlands.
Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, United States of America.
PLoS One. 2022 Jul 27;17(7):e0268752. doi: 10.1371/journal.pone.0268752. eCollection 2022.
Resting-state blood-oxygen-level-dependent (BOLD) signal acquired through functional magnetic resonance imaging is a proxy of neural activity and a key mechanism for assessing neurological conditions. Therefore, practical tools to filter out artefacts that can compromise the assessment are required. On the one hand, a variety of tailored methods to preprocess the data to deal with identified sources of noise (e.g., head motion, heart beating, and breathing, just to mention a few) are in place. But, on the other hand, there might be unknown sources of unstructured noise present in the data. Therefore, to mitigate the effects of such unstructured noises, we propose a model-based filter that explores the statistical properties of the underlying signal (i.e., long-term memory). Specifically, we consider autoregressive fractional integrative process filters. Remarkably, we provide evidence that such processes can model the signals at different regions of interest to attain stationarity. Furthermore, we use a principled analysis where a ground-truth signal with statistical properties similar to the BOLD signal under the injection of noise is retrieved using the proposed filters. Next, we considered preprocessed (i.e., the identified sources of noise removed) resting-state BOLD data of 98 subjects from the Human Connectome Project. Our results demonstrate that the proposed filters decrease the power in the higher frequencies. However, unlike the low-pass filters, the proposed filters do not remove all high-frequency information, instead they preserve process-related higher frequency information. Additionally, we considered four different metrics (power spectrum, functional connectivity using the Pearson's correlation, coherence, and eigenbrains) to infer the impact of such filter. We provided evidence that whereas the first three keep most of the features of interest from a neuroscience perspective unchanged, the latter exhibits some variations that could be due to the sporadic activity filtered out.
静息态血氧水平依赖（BOLD）信号是通过功能磁共振成像获得的，它是神经活动的一种代理，也是评估神经状况的关键机制。因此，需要实用的工具来过滤掉可能影响评估的伪影。一方面，有各种各样的定制方法来预处理数据，以处理已识别的噪声源（例如，头部运动、心跳和呼吸，仅举几例）。但是，另一方面，数据中可能存在未知的非结构化噪声源。因此，为了减轻这种非结构化噪声的影响，我们提出了一种基于模型的滤波器，该滤波器探索了基础信号（即长期记忆）的统计特性。具体来说，我们考虑自回归分数积分过程滤波器。值得注意的是，我们提供了证据表明，这些过程可以对不同感兴趣区域的信号进行建模，以达到平稳状态。此外，我们使用了一种有原则的分析方法，使用类似 BOLD 信号的统计特性的地面真实信号，并在噪声注入的情况下使用所提出的滤波器进行检索。接下来，我们考虑了来自人类连接组计划的 98 名受试者的预处理（即已去除已识别噪声源）静息态 BOLD 数据。我们的结果表明，所提出的滤波器降低了较高频率的功率。然而，与低通滤波器不同，所提出的滤波器不会去除所有高频信息，而是保留与过程相关的较高频率信息。此外，我们还考虑了四个不同的指标（功率谱、使用 Pearson 相关的功能连接、相干性和特征脑）来推断这种滤波器的影响。我们提供的证据表明，尽管前三个指标从神经科学的角度来看保留了大多数感兴趣的特征不变，但后者表现出一些变化，这可能是由于过滤掉的零星活动所致。
