Cao Lei, Kohut Stephen J, Frederick Blaise deB
Behavioral Neuroimaging Laboratory, McLean Hospital, Belmont, MA, United States.
McLean Imaging Center, McLean Hospital, Belmont, MA, United States.
Front Neuroimaging. 2023 Jan 19;1:1031991. doi: 10.3389/fnimg.2022.1031991. eCollection 2022.
Resting-state fMRI (rs-fMRI) is often used to infer regional brain interactions from the degree of temporal correlation between spontaneous low-frequency fluctuations, thought to reflect local changes in the BOLD signal due to neuronal activity. One complication in the analysis and interpretation of rs-fMRI data is the existence of non-neuronal low frequency physiological noise (systemic low frequency oscillations; sLFOs) which occurs within the same low frequency band as the signal used to compute functional connectivity. Here, we demonstrate the use of a time lag mapping technique to estimate and mitigate the effects of the sLFO signal on resting state functional connectivity of awake squirrel monkeys.
Twelve squirrel monkeys (6 male/6 female) were acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Rs-fMRI data was preprocessed using an in-house pipeline and sLFOs were detected using a seed regressor generated by averaging BOLD signal across all voxels in the brain, which was then refined recursively within a time window of -16-12 s. The refined regressor was then used to estimate the voxel-wise sLFOs; these regressors were subsequently included in the general linear model to remove these moving hemodynamic components from the rs-fMRI data using general linear model filtering. Group level independent component analysis (ICA) with dual regression was used to detect resting-state networks and compare networks before and after sLFO denoising.
Results show sLFOs constitute ~64% of the low frequency fMRI signal in squirrel monkey gray matter; they arrive earlier in regions in proximity to the middle cerebral arteries (e.g., somatosensory cortex) and later in regions close to draining vessels (e.g., cerebellum). Dual regression results showed that the physiological noise was significantly reduced after removing sLFOs and the extent of reduction was determined by the brain region contained in the resting-state network.
These results highlight the need to estimate and remove sLFOs from fMRI data before further analysis.
静息态功能磁共振成像（rs-fMRI）常用于根据自发低频波动之间的时间相关性程度来推断局部脑区之间的相互作用，这种波动被认为反映了由于神经元活动导致的血氧水平依赖（BOLD）信号的局部变化。rs-fMRI数据分析和解释中的一个复杂问题是存在非神经元性低频生理噪声（全身性低频振荡；sLFOs），其出现在与用于计算功能连接性的信号相同的低频波段内。在此，我们展示了一种时间滞后映射技术的应用，以估计并减轻sLFO信号对清醒松鼠猴静息态功能连接性的影响。
12只松鼠猴（6只雄性/6只雌性）适应了清醒扫描程序；使用9.4特斯拉扫描仪获取全脑功能磁共振成像图像。rs-fMRI数据使用内部流程进行预处理，sLFOs通过对全脑所有体素的BOLD信号进行平均生成种子回归器来检测，然后在-16至12秒的时间窗口内进行递归优化。优化后的回归器随后用于估计体素水平的sLFOs；这些回归器随后被纳入一般线性模型，使用一般线性模型滤波从rs-fMRI数据中去除这些移动的血液动力学成分。采用具有双重回归的组水平独立成分分析（ICA）来检测静息态网络，并比较sLFO去噪前后的网络。
结果显示，sLFOs在松鼠猴灰质中占低频功能磁共振成像信号的约64%；它们在靠近大脑中动脉的区域（如体感皮层）出现得较早，而在靠近引流血管的区域（如小脑）出现得较晚。双重回归结果表明，去除sLFOs后生理噪声显著降低，降低程度取决于静息态网络中包含的脑区。
这些结果强调了在进一步分析之前，需要从功能磁共振成像数据中估计并去除sLFOs。
