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Proc Natl Acad Sci U S A. 2020 Mar 19. pii: 201911240. doi: 10.1073/pnas.1911240117. [Epub ahead of print]

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

Author information

1
Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Interventional Cognitive Neurology, Beth Israel Deaconess Medical Center-Harvard Medical School, Boston, MA 02120.
2
Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02139.
3
Guttmann Brain Health Institut, Guttmann Institut, Universitat Autònoma de Barcelona, 08916 Barcelona, Spain.
4
Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Interventional Cognitive Neurology, Beth Israel Deaconess Medical Center-Harvard Medical School, Boston, MA 02120; esantarn@bidmc.harvard.edu.
5
Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy.

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.

KEYWORDS:

TMS-EEG; cognition; fMRI; resting-state networks

PMID:
32193345
DOI:
10.1073/pnas.1911240117

Conflict of interest statement

The authors declare no competing interest.

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