IEEE Trans Med Imaging. 2019 Nov;38(11):2523-2532. doi: 10.1109/TMI.2019.2904555. Epub 2019 Mar 12.
Intrinsic neural activity ubiquitously persists in all physiological states. However, how intrinsic brain activity (iBA) changes over a short time remains unknown. To uncover the brain dynamics' theoretic underpinning, electrophysiological relevance, and neuromodulation, we identified iBA dynamics on simulated data, electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) data, and repetitive transcranial magnetic stimulation (rTMS) fMRI data using sliding-window analysis. The temporal variability (dynamics) of iBA were quantified using the variance of the amplitude of low-frequency fluctuations (ALFF) over time. We first used simulated fMRI data to examine the effects of various parameters including window length, and step size on dynamic ALFF. Second, using EEG-fMRI data, we found that the heteromodal association cortex had the most variable dynamics while the limbic regions had the least, consistent with previous findings. In addition, the temporal variability of dynamic ALFF depended on EEG power fluctuations. Moreover, using rTMS fMRI data, we found that the temporal variability of dynamic ALFF could be modulated by rTMS. Taken together, these results provide evidence about the theory, relevance, and adjustability of iBA dynamics.
内在神经活动普遍存在于所有生理状态中。然而，内在脑活动 (iBA) 在短时间内如何变化尚不清楚。为了揭示大脑动力学的理论基础、电生理学相关性和神经调制，我们使用滑动窗口分析在模拟数据、脑电图-功能磁共振成像 (EEG-fMRI) 数据和重复经颅磁刺激 (rTMS) fMRI 数据上识别 iBA 动力学。通过随时间变化的低频波动 (ALFF) 幅度的方差来量化 iBA 的时间可变性 (动态)。我们首先使用模拟 fMRI 数据检查了各种参数（包括窗口长度和步长）对动态 ALFF 的影响。其次，使用 EEG-fMRI 数据，我们发现异模态联合皮层具有最可变的动力学，而边缘区域的动力学最小，这与先前的发现一致。此外，动态 ALFF 的时间可变性取决于 EEG 功率波动。此外，使用 rTMS fMRI 数据，我们发现 rTMS 可以调节动态 ALFF 的时间可变性。总之，这些结果为 iBA 动力学的理论、相关性和可调节性提供了证据。
