Abstract
Transcranial direct current stimulation (tDCS) shows great promise in enhancing neurocognitive abilities. However, the neurostimulation responsiveness varied hugely. Our previous work demonstrates that people receiving tDCS stimulation over Temporoparietal Junction (TPJ) fall into two heterogeneous groups: the positive responders who benefit and the negative responders who hurt from tDCS. The present study investigated whether dynamic brain network properties of resting-state fMRI could predict the pattern. We calculated each subsystem of the default mode network’s dynamic attributes using the multilayer community detection algorithm. Results indicated that the recruitment indexes were significantly different in bilateral aMPFC, PCC, Rsp, and PHC regions between positive responders and negative responders. Our results also confirm the advantages of the dynamic network measures over the static network measures. The study provides a feasible protocol in establishing the pre-stimulation screening procedure using resting-state fMRI.
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References
Das, N., et al.: Cognitive training and transcranial direct current stimulation in mild cognitive impairment: a randomized pilot trial. Front. Neurosci. 13 (2019)
Srivastav, A.K., et al.: tDCS combined with cognitive training in a patient with chronic traumatic head injury. Neurophysiol. Clin. 50(2), 133–134 (2020)
Yang, L.-Z., et al.: Neural and psychological predictors of cognitive enhancement and impairment from neurostimulation. Adv. Sci. 7(4) (2020)
Du, Y., et al.: Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis. Neuroimage 180(Pt B), 632–645 (2018)
Fiorenzato, E., et al.: Dynamic functional connectivity changes associated with dementia in Parkinson’s disease. Brain 142(9), 2860–2872 (2019)
Faghiri, A., et al.: Changing brain connectivity dynamics: from early childhood to adulthood. Hum Brain Mapp. 39(3), 1108–1117 (2018)
Sendi, M.S.E., et al.: Multiple overlapping dynamic patterns of the visual sensory network in schizophrenia. Schizophr Res. 228, 103–111 (2021)
Braun, U., et al.: Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci U S A. 112(37), 11678–83 (2015)
He, C., et al.: Dynamic functional connectivity analysis reveals decreased variability of the default-mode network in developing autistic brain. Autism Res. 11(11), 1479–1493 (2018)
Muldoon, S.F., et al.: Stimulation-based control of dynamic brain networks. PLoS Comput Biol. 12(9), e1005076 (2016)
Hartwright, C.E., et al.: Resting state morphology predicts the effect of theta burst stimulation in false belief reasoning. Hum Brain Mapp. 37(10), 3502–14 (2016)
Anticevic, A., et al.: When less is more: TPJ and default network deactivation during encoding predicts working memory performance. Neuroimage 49(3), 2638–2648 (2010)
Wen, T., et al.: The functional convergence and heterogeneity of social, episodic, and self-referential thought in the default mode network. Cereb Cortex. 30(11), 5915–5929 (2020)
Andrews-Hanna, J.R., et al.: Functional-anatomic fractionation of the brain’s default network. Neuron 65(4), 550–562 (2010)
Mucha, P.J., et al.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980), 876–878 (2010)
He, X., et al.: Disrupted dynamic network reconfiguration of the language system in temporal lobe epilepsy. (2018)
Mattar, M.G., et al.: A functional cartography of cognitive systems. PLoS Comput. Biol. 11(12), e1004533–e1004533 (2015)
Bassett, D.S., et al.: Dynamic reconfiguration of human brain networks during learning. Proc. Natl. Acad. Sci. U. S. A. 108(18), 7641–7646 (2011)
Papadopoulos, L., et al.: Evolution of network architecture in a granular material under compression. Phys. Rev. E 94(3–1), 032908 (2016)
Kuhn, M.: Building predictive models in r using the caret package. J. Stat. Softw. Articles. 28(5), 1–26 (2008)
Acknowledgments
This work was supported by the National Key R&D Program of China (2017YFB1300204), the Key R&D Program of Anhui Province (201904a07020104), the Natural Science Fund of Anhui Province (2008085MC69), Hefei Foreign Cooperation Project (ZR201801020002), Hefei Municipal Natural Science Foundation ( 2021033), Health Commission of Anhui province (AHWJ2021b150), Collaborative Innovation Program of Hefei Science Center, CAS (2020HSC-CIP001). Anhui Province Key Laboratory of Medical Physics and Technology (LMPT201904), Director’s Fund of Hefei Cancer Hospital of CAS (YZJJ2019C14, YZJJ2019A04).
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Lang, JW. et al. (2022). Predicting Neurostimulation Responsiveness with Dynamic Brain Network Measures. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_40
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DOI: https://doi.org/10.1007/978-981-16-3880-0_40
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