Department of Biomedical Engineering, University of Minnesota, MN 55455, USA.
Clin Neurophysiol. 2010 Aug;121(8):1240-50. doi: 10.1016/j.clinph.2010.02.153. Epub 2010 Apr 8.
Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been pursued in an effort to achieve greater spatio-temporal resolution of imaging dynamic brain activity. We report a data-driven approach to image spatio-temporal features of neural oscillatory activity and event-related activity from continuously recorded EEG and fMRI signals.
This approach starts with using the independent component analysis (ICA) to decompose the spatio-temporal EEG data into a linear combination of scalp potential maps and time courses. The time course of each independent component (IC) is used to construct a regressor to fit the fMRI time series. The resultant fMRI map then feeds back as a spatial constraint to the estimation of the source distribution underlying the corresponding IC's scalp map. The estimated source distributions multiplied by the corresponding IC time courses are summed across all ICs, giving rise to the reconstructed spatio-temporal brain activity. Functional connectivity between cortical areas can be further revealed from the imaged source signals using phase synchrony measures. We tested the method using both simulated oscillatory activity and event-related neural activity at various cortical regions. We also used this method to study the alpha-band EEG modulations in an eyes-open-eyes-closed human experiment.
In the simulation study, reliable reconstruction of the localization, time-frequency feature and cortical functional connection were achieved for the simulated oscillatory and event-related activities. In the experimental study, the alpha rhythmic modulation was localized mainly in the occipital visual area and the parieto-occipital sulcus. Within these regions, time-frequency analysis and phase-synchronization analysis indicated increased alpha power and alpha-band phase-synchronization in eyes-closed condition versus eyes-open condition.
Our results suggest that the proposed approach is well suited to image continuously oscillatory activities and their functional connectivity.
Such ability promises to facilitate the investigation of the long-term neural behaviors and large-scale cortical interactions involved in spontaneous brain activity and cognitive tasks.
脑电图（EEG）与功能磁共振成像（fMRI）的整合旨在提高成像动态脑活动的时空分辨率。我们报告了一种数据驱动的方法，用于从连续记录的 EEG 和 fMRI 信号中对神经振荡活动和事件相关活动的时空特征进行成像。
该方法首先使用独立成分分析（ICA）将时空 EEG 数据分解为头皮电位图和时间序列的线性组合。每个独立成分（IC）的时间历程用于构建回归器以拟合 fMRI 时间序列。然后，所得 fMRI 图作为空间约束反馈到相应 IC 的头皮图的源分布估计。将估计的源分布乘以相应的 IC 时间历程，然后对所有 IC 进行求和，从而产生重建的时空大脑活动。还可以使用相位同步度量从成像源信号中进一步揭示皮质区域之间的功能连接。我们使用各种皮质区域的模拟振荡活动和事件相关神经活动来测试该方法。我们还使用该方法研究了睁眼-闭眼人体实验中的 alpha 波段 EEG 调制。
在模拟研究中，对于模拟的振荡和事件相关活动，成功实现了定位、时频特征和皮质功能连接的可靠重建。在实验研究中，alpha 节律调制主要定位于枕叶视觉区和顶枕沟。在这些区域内，时频分析和相位同步分析表明，闭眼条件下的 alpha 功率和 alpha 波段相位同步增加。
我们的结果表明，所提出的方法非常适合对连续振荡活动及其功能连接进行成像。
这种能力有望促进对自发脑活动和认知任务中涉及的长期神经行为和大规模皮质相互作用的研究。
