School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, India.
Department of Computer Science and Engineering, Indian Institute of Technology Bhilai, Bhilai, Chhattisgarh, India.
Hum Brain Mapp. 2024 Jun 15;45(9):e26606. doi: 10.1002/hbm.26606.
Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly being used to infer the functional organization of the brain. Blood oxygen level-dependent (BOLD) features related to spontaneous neuronal activity, are yet to be clearly understood. Prior studies have hypothesized that rs-fMRI is spontaneous event-related and these events convey crucial information about the neuronal activity in estimating resting state functional connectivity (FC). Attempts have been made to extract these temporal events using a predetermined threshold. However, the thresholding methods in addition to being very sensitive to noise, may consider redundant events or exclude the low-valued inflection points. Here, we extract the event-related temporal onsets from the rs-fMRI time courses using a zero-frequency resonator (ZFR). The ZFR reflects the transient behavior of the BOLD events at its output. The conditional rate (CR) of the BOLD events occurring in a time course with respect to a seed time course is used to derive static FC. The temporal activity around the estimated events called high signal-to-noise ratio (SNR) segments are also obtained in the rs-fMRI time course and are then used to compute static and dynamic FCs during rest. Coactivation pattern (CAP) is the dynamic FC obtained using the high SNR segments driven by the ZFR. The static FC demonstrates that the ZFR-based CR distinguishes the coactivation and non-coactivation scores well in the distribution. CAP analysis demonstrated the stable and longer dwell time dominant resting state functional networks with high SNR segments driven by the ZFR. Static and dynamic FC analysis underpins that the ZFR-driven temporal onsets of BOLD events derive reliable and consistent FCs in the resting brain using a subset of the time points.
静息态功能磁共振成像（rs-fMRI）越来越多地被用于推断大脑的功能组织。与自发神经元活动相关的血氧水平依赖（BOLD）特征尚不清楚。先前的研究假设 rs-fMRI 是自发事件相关的，这些事件在估计静息状态功能连接（FC）时传递有关神经元活动的关键信息。已经尝试使用预定的阈值提取这些时间事件。然而，除了对噪声非常敏感之外，阈值方法还可能考虑冗余事件或排除低值拐点。在这里，我们使用零频率谐振器（ZFR）从 rs-fMRI 时间序列中提取事件相关的时间起始。ZFR 反映了其输出处 BOLD 事件的瞬态行为。与种子时间序列相比，在时间序列中发生的 BOLD 事件的条件率（CR）用于得出静态 FC。在 rs-fMRI 时间序列中还获得了围绕估计事件的时间活动，称为高信噪比（SNR）段，然后用于在休息期间计算静态和动态 FC。共激活模式（CAP）是使用 ZFR 驱动的高 SNR 段获得的动态 FC。静态 FC 表明，基于 ZFR 的 CR 在分布中很好地区分了共激活和非共激活评分。CAP 分析表明，使用 ZFR 驱动的高 SNR 段的稳定且更长停留时间主导的静息状态功能网络。静态和动态 FC 分析支持 ZFR 驱动的 BOLD 事件的时间起始在使用时间点子集的静息大脑中得出可靠且一致的 FC。
