期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 117, 期 14, 页码 8115-8125出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1911240117
关键词
TMS-EEG; fMRI; resting-state networks; cognition
资金
- Harvard-MIT BROAD Institute [6600024-5500000895]
- Berenson-Allen Foundation
- Sidney R. Baer Jr. Foundation
- NIH [R01 MH113929, R01HD069776, R01NS073601, R21 MH099196, R21 NS082870, R21 NS085491, R21 HD07616, P01 AG031720-06A1, R01 MH117063-01, R01 AG060981-01, R01 MH115949, R01AG060987, R01 NS073601]
- Harvard Clinical and Translational Science Center (National Center for Research Resources) [UL1 RR025758]
- Harvard Clinical and Translational Science Center (National Center for Advancing Translational Sciences) [UL1 RR025758]
- Beth Israel Deaconess Medical Center via the Chief Academic Officer Award 2017
- Defence Advanced Research Projects Agency [HR001117S0030]
- Citizens United for Research in Epilepsy foundation
- Football Players Health Study at Harvard University
- G. Harold and Leila Y. Mathers Charitable Foundation
- Nancy Lurie Marks Foundation
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.
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