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个体化扰动人类连接组揭示了与认知相关的网络动力学的可重复生物标志物。

Individualized perturbation of the human connectome reveals reproducible biomarkers of network dynamics relevant to cognition.

机构信息

Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Interventional Cognitive Neurology, Beth Israel Deaconess Medical Center-Harvard Medical School, Boston, MA 02120.

Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02139.

出版信息

Proc Natl Acad Sci U S A. 2020 Apr 7;117(14):8115-8125. doi: 10.1073/pnas.1911240117. Epub 2020 Mar 19.

Abstract

Large-scale brain networks are often described using resting-state functional magnetic resonance imaging (fMRI). However, the blood oxygenation level-dependent (BOLD) signal provides an indirect measure of neuronal firing and reflects slow-evolving hemodynamic activity that fails to capture the faster timescale of normal physiological function. Here we used fMRI-guided transcranial magnetic stimulation (TMS) and simultaneous electroencephalography (EEG) to characterize individual brain dynamics within discrete brain networks at high temporal resolution. TMS was used to induce controlled perturbations to individually defined nodes of the default mode network (DMN) and the dorsal attention network (DAN). Source-level EEG propagation patterns were network-specific and highly reproducible across sessions 1 month apart. Additionally, individual differences in high-order cognitive abilities were significantly correlated with the specificity of TMS propagation patterns across DAN and DMN, but not with resting-state EEG dynamics. Findings illustrate the potential of TMS-EEG perturbation-based biomarkers to characterize network-level individual brain dynamics at high temporal resolution, and potentially provide further insight on their behavioral significance.

摘要

大规模脑网络通常使用静息态功能磁共振成像(fMRI)来描述。然而,血氧水平依赖(BOLD)信号提供了神经元放电的间接测量,反映了缓慢演变的血液动力学活动,无法捕捉正常生理功能的更快时间尺度。在这里,我们使用 fMRI 引导的经颅磁刺激(TMS)和同时的脑电图(EEG)以高时间分辨率在离散脑网络内表征个体脑动态。TMS 用于对默认模式网络(DMN)和背侧注意网络(DAN)的个体定义节点进行受控干扰。源级 EEG 传播模式具有网络特异性,并且在相隔 1 个月的会话中具有高度可重复性。此外,高阶认知能力的个体差异与 TMS 在 DAN 和 DMN 中的传播模式的特异性显著相关,但与静息状态 EEG 动力学无关。研究结果表明,TMS-EEG 扰动生物标志物具有以高时间分辨率表征网络级个体脑动态的潜力,并可能为其行为意义提供进一步的见解。

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