Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.
Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.
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DOI：
10.3389/fnins.2014.00258
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发表时间：
2014
影响因子：
4.3
通讯作者：
Clayden JD
中科院分区：
文献类型：
--
作者：
Deligianni F;Centeno M;Carmichael DW;Clayden JD
全脑功能连接体有望了解人类大脑在认知，发育和病理状态下的活动。所谓的静息态（rs）功能磁共振成像研究有助于在宏观尺度上将大脑视为一组相互作用的区域。相互作用被定义为由血氧水平依赖（BOLD）对比度驱动的基于相关性的信号测量。了解这些测量的神经生理学基础对于传达有关大脑功能的有用信息非常重要。BOLD功能磁共振成像和神经生理学测量之间的局部耦合相对较好地定义，有证据表明，伽马（范围）频率EEG信号是BOLD功能磁共振成像在认知过程中的变化最密切的相关。然而，目前尚不清楚全脑网络在休息期间的相互作用如何，其中低频信号被认为发挥了关键作用。同步EEG-fMRI提供了以高时空分辨率观察脑网络动态的机会。我们利用这些测量结果来比较来自rs-fMRI和EEG频带限制功率（BLP）的连接体。要合并这种多模式信息，就需要制定一个适当的统计框架。我们的Hilbert包络的源本地化的EEG信号跨频带的协方差矩阵的协方差矩阵来自rs-fMRI的统计预测的基础上稀疏典型相关分析（sCCA）的手段。随后，我们确定了有助于这种关系的最突出的连接。我们比较全脑功能连接组的基础上，他们的测地线距离可靠地估计预测的性能。从EEG连接体预测fMRI的性能在所有频带上都比从fMRI预测EEG好得多，而在低频EEG频带中导出的连接体类似于最佳的rs-fMRI连接。
Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.
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影响因子：
5.7
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
Duyn, Jeff H.
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
Bellec, Pierre;Rosa-Neto, Pedro;Evans, Alan C.
通讯作者：
Evans, Alan C.
探索人类大脑功能的科学
DOI：
10.1073/pnas.0911855107
发表时间：
2010-03-09
影响因子：
11.1
作者：
Biswal, Bharat B.;Mennes, Maarten;Milham, Michael P.
通讯作者：
Milham, Michael P.