Department of Biomedical Engineering, Boston University, Boston, United States.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States.
Elife. 2023 Aug 11;12:e86453. doi: 10.7554/eLife.86453.
Functional magnetic resonance imaging (fMRI) has proven to be a powerful tool for noninvasively measuring human brain activity; yet, thus far, fMRI has been relatively limited in its temporal resolution. A key challenge is understanding the relationship between neural activity and the blood-oxygenation-level-dependent (BOLD) signal obtained from fMRI, generally modeled by the hemodynamic response function (HRF). The timing of the HRF varies across the brain and individuals, confounding our ability to make inferences about the timing of the underlying neural processes. Here, we show that resting-state fMRI signals contain information about HRF temporal dynamics that can be leveraged to understand and characterize variations in HRF timing across both cortical and subcortical regions. We found that the frequency spectrum of resting-state fMRI signals significantly differs between voxels with fast versus slow HRFs in human visual cortex. These spectral differences extended to subcortex as well, revealing significantly faster hemodynamic timing in the lateral geniculate nucleus of the thalamus. Ultimately, our results demonstrate that the temporal properties of the HRF impact the spectral content of resting-state fMRI signals and enable voxel-wise characterization of relative hemodynamic response timing. Furthermore, our results show that caution should be used in studies of resting-state fMRI spectral properties, because differences in fMRI frequency content can arise from purely vascular origins. This finding provides new insight into the temporal properties of fMRI signals across voxels, which is crucial for accurate fMRI analyses, and enhances the ability of fast fMRI to identify and track fast neural dynamics.
功能磁共振成像(fMRI)已被证明是一种强大的工具,可用于非侵入性地测量人类大脑活动;然而,到目前为止,fMRI 在时间分辨率方面相对有限。一个关键的挑战是理解神经活动与从 fMRI 获得的血氧水平依赖(BOLD)信号之间的关系,通常由血流动力学响应函数(HRF)建模。HRF 的时间在大脑和个体之间变化,这混淆了我们对潜在神经过程时间的推断能力。在这里,我们表明静息态 fMRI 信号包含有关 HRF 时间动态的信息,可以利用这些信息来理解和描述皮质和皮质下区域中 HRF 时间的变化。我们发现,在人类视觉皮层中,具有快速与慢速 HRF 的体素之间,静息态 fMRI 信号的频谱显著不同。这些频谱差异也扩展到了皮质下区域,揭示了丘脑外侧膝状体中更快的血液动力学时间。最终,我们的结果表明,HRF 的时间特性会影响静息态 fMRI 信号的频谱内容,并能够实现相对血液动力学响应时间的体素特征化。此外,我们的结果表明,在静息态 fMRI 频谱特性的研究中应谨慎行事,因为 fMRI 频率内容的差异可能纯粹来自血管起源。这一发现为跨体素 fMRI 信号的时间特性提供了新的见解,这对于准确的 fMRI 分析至关重要,并增强了快速 fMRI 识别和跟踪快速神经动力学的能力。