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State-dependent effects of neural stimulation on brain function and cognition

Nature Reviews Neuroscience volume 23, pages 459–475 (2022)Cite this article

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Abstract

Invasive and non-invasive brain stimulation methods are widely used in neuroscience to establish causal relationships between distinct brain regions and the sensory, cognitive and motor functions they subserve. When combined with concurrent brain imaging, such stimulation methods can reveal patterns of neuronal activity responsible for regulating simple and complex behaviours at the level of local circuits and across widespread networks. Understanding how fluctuations in physiological states and task demands might influence the effects of brain stimulation on neural activity and behaviour is at the heart of how we use these tools to understand cognition. Here we review the concept of such ‘state-dependent’ changes in brain activity in response to neural stimulation, and consider examples from research on altered states of consciousness (for example, sleep and anaesthesia) and from task-based manipulations of selective attention and working memory. We relate relevant findings from non-invasive methods used in humans to those obtained from direct electrical and optogenetic stimulation of neuronal ensembles in animal models. Given the widespread use of brain stimulation as a research tool in the laboratory and as a means of augmenting or restoring brain function, consideration of the influence of changing physiological and cognitive states is crucial for increasing the reliability of these interventions.

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Fig. 1: Different cognitive and neural states modify the effects of local brain stimulation.
Fig. 2: Brain stimulation applied during different states of consciousness yields distinct effects on brain activity.
Fig. 3: Effects of attention on brain stimulation-triggered activity and neural plasticity.
Fig. 4: Brain stimulation interacts with latent working memory representations.

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References

  1. Kanwisher, N. & Wojciulik, E. Visual attention: insights from brain imaging. Nat. Rev. Neurosci. 1, 91–100 (2000).

    Article  CAS  PubMed  Google Scholar 

  2. Poldrack, R. A. The role of fMRI in cognitive neuroscience: where do we stand? Curr. Opin. Neurobiol. 18, 223–227 (2008).

    Article  CAS  PubMed  Google Scholar 

  3. Polanía, R., Nitsche, M. A. & Ruff, C. C. Studying and modifying brain function with non-invasive brain stimulation. Nat. Neurosci. 21, 174–187 (2018).

    Article  PubMed  CAS  Google Scholar 

  4. Bassett, D. S. et al. Reflections on the past two decades of neuroscience. Nat. Rev. Neurosci. https://doi.org/10.1038/s41583-020-0363-6 (2020).

    Article  PubMed  Google Scholar 

  5. Bergmann, T. O., Karabanov, A., Hartwigsen, G., Thielscher, A. & Siebner, H. R. Combining non-invasive transcranial brain stimulation with neuroimaging and electrophysiology: current approaches and future perspectives. Neuroimage 140, 4–19 (2016).

    Article  PubMed  Google Scholar 

  6. Bestmann, S. & Feredoes, E. Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future. Ann. N. Y. Acad. Sci. 1296, 11–30 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Silvanto, J., Muggleton, N. & Walsh, V. State-dependency in brain stimulation studies of perception and cognition. Trends Cogn. Sci. 12, 447–454 (2008).

    Article  PubMed  Google Scholar 

  8. Romei, V., Thut, G. & Silvanto, J. Information-based approaches of noninvasive transcranial brain stimulation. Trends Neurosci. 39, 782–795 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Pinto, L. et al. Task-dependent changes in the large-scale dynamics and necessity of cortical regions. Neuron 104, 810–824.e9 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Li, L. M. et al. Brain state and polarity dependent modulation of brain networks by transcranial direct current stimulation. Hum. Brain Mapp. 40, 904–915 (2019).

    Article  PubMed  Google Scholar 

  11. Zagha, E. & McCormick, D. A. Neural control of brain state. Curr. Opin. Neurobiol. 29, 178–186 (2014). This article provides an overview of studies on cortical states beyond stereotypical exemplars, identifies key mechanisms of state modulation and poses questions for future research.

    Article  CAS  PubMed  Google Scholar 

  12. Steriade, M. Corticothalamic resonance, states of vigilance and mentation. Neuroscience 101, 243–276 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Peterson, E. J. & Voytek, B. Alpha oscillations control cortical gain by modulating excitatory-inhibitory background activity. Preprint at bioRxiv https://doi.org/10.1101/185074 (2017).

    Article  Google Scholar 

  14. Petersen, S. E. & Sporns, O. Brain networks and cognitive architectures. Neuron 88, 207–219 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. McGinley, M. J. et al. Waking state: rapid variations modulate neural and behavioral responses. Neuron 87, 1143–1161 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Siegel, M., Donner, T. H. & Engel, A. K. Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121–134 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Heeger, D. J. & Ress, D. What does fMRI tell us about neuronal activity? Nat. Rev. Neurosci. 3, 142–151 (2002).

    Article  CAS  PubMed  Google Scholar 

  19. Liu, A. et al. Immediate neurophysiological effects of transcranial electrical stimulation. Nat. Commun. 9, 5092 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Hallett, M. Transcranial magnetic stimulation: a primer. Neuron 55, 187–199 (2007).

    Article  CAS  PubMed  Google Scholar 

  21. Rossini, P. M. et al. Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: basic principles and procedures for routine clinical and research application. An updated report from an I.F.C.N. committee. Clin. Neurophysiol. 126, 1071–1107 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Diana, M. et al. Rehabilitating the addicted brain with transcranial magnetic stimulation. Nat. Rev. Neurosci. 18, 685–693 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Silvanto, J. & Cattaneo, Z. Common framework for “virtual lesion” and state-dependent TMS: the facilitatory/suppressive range model of online TMS effects on behavior. Brain Cogn. 119, 32–38 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ilić, T. V. et al. Short-interval paired-pulse inhibition and facilitation of human motor cortex: the dimension of stimulus intensity. J. Physiol. 545, 153–167 (2002).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Silvanto, J. & Muggleton, N. G. New light through old windows: moving beyond the “virtual lesion” approach to transcranial magnetic stimulation. Neuroimage 39, 549–552 (2008).

    Article  PubMed  Google Scholar 

  26. Miniussi, C., Harris, J. A. & Ruzzoli, M. Modelling non-invasive brain stimulation in cognitive neuroscience. Neurosci. Biobehav. Rev. 37, 1702–1712 (2013).

    Article  PubMed  Google Scholar 

  27. Pitcher, D., Parkin, B. & Walsh, V. Transcranial magnetic stimulation and the understanding of behavior. Annu. Rev. Psychol. 72, 97–121 (2021).

    Article  PubMed  Google Scholar 

  28. Siebner, H. R., Hartwigsen, G., Kassuba, T. & Rothwell, J. C. How does transcranial magnetic stimulation modify neuronal activity in the brain? Implications for studies of cognition. Cortex 45, 1035–1042 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Rogasch, N. C. & Fitzgerald, P. B. Assessing cortical network properties using TMS–EEG. Hum. Brain Mapp. 34, 1652–1669 (2013).

    Article  PubMed  Google Scholar 

  30. Bergmann, T. O. et al. Concurrent TMS-fMRI for causal network perturbation and proof of target engagement. Neuroimage 237, 118093 (2021).

    Article  PubMed  Google Scholar 

  31. Filmer, H. L., Mattingley, J. B. & Dux, P. E. Modulating brain activity and behaviour with tDCS: rumours of its death have been greatly exaggerated. Cortex 123, 141–151 (2020).

    Article  PubMed  Google Scholar 

  32. Woods, A. J. et al. A technical guide to tDCS, and related non-invasive brain stimulation tools. Clin. Neurophysiol. 127, 1031–1048 (2016).

    Article  CAS  PubMed  Google Scholar 

  33. van Boekholdt, L., Kerstens, S., Khatoun, A., Asamoah, B. & Mc Laughlin, M. tDCS peripheral nerve stimulation: a neglected mode of action? Mol. Psychiatry https://doi.org/10.1038/s41380-020-00962-6 (2020).

    Article  PubMed  Google Scholar 

  34. Purpura, D. P. & Mcmurtry, J. G. Intracellular activities and evoked potential changes during polarization of motor cortex. J. Neurophysiol. 28, 166–185 (1965).

    Article  CAS  PubMed  Google Scholar 

  35. Nitsche, M. A. & Paulus, W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J. Physiol. 527, 633–639 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. McDonnell, M. D. & Abbott, D. What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS Comput. Biol. 5, e1000348 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Antal, A. & Herrmann, C. S. Transcranial alternating current and random noise stimulation: possible mechanisms. Neural Plast. 2016, e3616807 (2016).

    Article  CAS  Google Scholar 

  38. Bland, N. S. & Sale, M. V. Current challenges: the ups and downs of tACS. Exp. Brain Res. 237, 3071–3088 (2019).

    Article  PubMed  Google Scholar 

  39. Krause, M. R., Vieira, P. G., Csorba, B. A., Pilly, P. K. & Pack, C. C. Transcranial alternating current stimulation entrains single-neuron activity in the primate brain. Proc. Natl Acad. Sci. USA 116, 5747–5755 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Johnson, L. et al. Dose-dependent effects of transcranial alternating current stimulation on spike timing in awake nonhuman primates. Sci. Adv. 6, eaaz2747 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Huang, W. A. et al. Transcranial alternating current stimulation entrains alpha oscillations by preferential phase synchronization of fast-spiking cortical neurons to stimulation waveform. Nat. Commun. 12, 3151 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Beliaeva, V., Savvateev, I., Zerbi, V. & Polania, R. Toward integrative approaches to study the causal role of neural oscillations via transcranial electrical stimulation. Nat. Commun. 12, 2243 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Nitsche, M. A. et al. Transcranial direct current stimulation: state of the art 2008. Brain Stimul. 1, 206–223 (2008).

    Article  PubMed  Google Scholar 

  44. Jamil, A. et al. Current intensity- and polarity-specific online and aftereffects of transcranial direct current stimulation: an fMRI study. Hum. Brain Mapp. 41, 1644–1666 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Choi, C.-H., Iordanishvili, E., Shah, N. J. & Binkofski, F. Magnetic resonance spectroscopy with transcranial direct current stimulation to explore the underlying biochemical and physiological mechanism of the human brain: a systematic review. Hum. Brain Mapp. 42, 2642–2671 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Huang, Y.-Z. et al. Plasticity induced by non-invasive transcranial brain stimulation: a position paper. Clin. Neurophysiol. 128, 2318–2329 (2017).

    Article  PubMed  Google Scholar 

  47. Karabanov, A. et al. Consensus paper: probing homeostatic plasticity of human cortex with non-invasive transcranial brain stimulation. Brain Stimul. 8, 993–1006 (2015).

    Article  PubMed  Google Scholar 

  48. Lefaucheur, J.-P. et al. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): an update (2014–2018). Clin. Neurophysiol. 131, 474–528 (2020).

    Article  PubMed  Google Scholar 

  49. Lefaucheur, J.-P. et al. Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clin. Neurophysiol. 128, 56–92 (2017).

    Article  PubMed  Google Scholar 

  50. Silvanto, J. in The Oxford Handbook of Transcranial Stimulation 2nd Edn (eds Wassermann, E. M. et al.) (Oxford Univ. Press, 2021).

  51. Silvanto, J., Muggleton, N. G., Cowey, A. & Walsh, V. Neural adaptation reveals state-dependent effects of transcranial magnetic stimulation. Eur. J. Neurosci. 25, 1874–1881 (2007). This seminal article shows that neural adaptation modulates the behavioural and perceptual effects of TMS. The study inspired subsequent work on state-dependent TMS effects.

    Article  PubMed  Google Scholar 

  52. Paulus, W. & Rothwell, J. C. Membrane resistance and shunting inhibition: where biophysics meets state-dependent human neurophysiology. J. Physiol. 594, 2719–2728 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bikson, M. & Rahman, A. Origins of specificity during tDCS: anatomical, activity-selective, and input-bias mechanisms. Front. Hum. Neurosci. 7, 688 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  54. McCormick, D. A., McGinley, M. & Salkoff, D. Brain state dependent activity in the cortex and thalamus. Curr. Opin. Neurobiol. 31, 133–140 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Nir, Y. et al. Regional slow waves and spindles in human sleep. Neuron 70, 153–169 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Murphy, M. et al. Propofol anesthesia and sleep: a high-density EEG study. Sleep 34, 283–291A (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Huang, Z., Liu, X., Mashour, G. A. & Hudetz, A. G. Timescales of Intrinsic BOLD signal dynamics and functional connectivity in pharmacologic and neuropathologic states of unconsciousness. J. Neurosci. 38, 2304–2317 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Husain, A. M. Electroencephalographic assessment of coma. J. Clin. Neurophysiol. 23, 208–220 (2006).

    Article  PubMed  Google Scholar 

  59. Frohlich, J., Toker, D. & Monti, M. M. Consciousness among delta waves: a paradox? Brain https://doi.org/10.1093/brain/awab095 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Uhrig, L. et al. Resting-state dynamics as a cortical signature of anesthesia in monkeys. Anesthesiology 129, 942–958 (2018).

    Article  PubMed  Google Scholar 

  61. Gao, Y.-R. et al. Time to wake up: studying neurovascular coupling and brain-wide circuit function in the un-anesthetized animal. Neuroimage 153, 382–398 (2017).

    Article  PubMed  Google Scholar 

  62. Abel, T., Havekes, R., Saletin, J. M. & Walker, M. P. Sleep, plasticity and memory from molecules to whole-brain networks. Curr. Biol. 23, R774–R788 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Wang, D.-S. & Orser, B. A. Inhibition of learning and memory by general anesthetics. Can. J. Anesth. Can. Anesth. 58, 167–177 (2011).

    Article  Google Scholar 

  64. Moliadze, V., Zhao, Y., Eysel, U. & Funke, K. Effect of transcranial magnetic stimulation on single-unit activity in the cat primary visual cortex. J. Physiol. 553, 665–679 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Allen, E. A., Pasley, B. N., Duong, T. & Freeman, R. D. Transcranial magnetic stimulation elicits coupled neural and hemodynamic consequences. Science 317, 1918–1921 (2007).

    Article  CAS  PubMed  Google Scholar 

  66. de Labra, C. et al. Changes in visual responses in the feline dLGN: selective thalamic suppression induced by transcranial magnetic stimulation of V1. Cereb. Cortex 17, 1376–1385 (2007).

    Article  PubMed  Google Scholar 

  67. Pasley, B. N., Allen, E. A. & Freeman, R. D. State-dependent variability of neuronal responses to transcranial magnetic stimulation of the visual cortex. Neuron 62, 291–303 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kim, T., Allen, E. A., Pasley, B. N. & Freeman, R. D. Transcranial magnetic stimulation changes response selectivity of neurons in the visual cortex. Brain Stimul. 8, 613–623 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Romero, M. C., Davare, M., Armendariz, M. & Janssen, P. Neural effects of transcranial magnetic stimulation at the single-cell level. Nat. Commun. 10, 1–11 (2019).

    Article  CAS  Google Scholar 

  70. Logothetis, N. K. et al. The effects of electrical microstimulation on cortical signal propagation. Nat. Neurosci. 13, 1283–1291 (2010).

    Article  CAS  PubMed  Google Scholar 

  71. Premereur, E., Dromme, I. C. V., Romero, M. C., Vanduffel, W. & Janssen, P. Effective connectivity of depth-structure–selective patches in the lateral bank of the macaque intraparietal sulcus. PLoS Biol. 13, e1002072 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Murris, S. R., Arsenault, J. T. & Vanduffel, W. Frequency- and state-dependent network effects of electrical stimulation targeting the ventral tegmental area in macaques. Cereb. Cortex https://doi.org/10.1093/cercor/bhaa007 (2020). This study was among the first to systematically explore the global haemodynamic effects of electrical stimulation over a range of stimulation frequencies in anaesthetized and awake macaques.

    Article  PubMed  PubMed Central  Google Scholar 

  73. Liang, Z. et al. Mapping the functional network of medial prefrontal cortex by combining optogenetics and fMRI in awake rats. Neuroimage 117, 114–123 (2015).

    Article  PubMed  Google Scholar 

  74. Sellers, K. K., Bennett, D. V., Hutt, A. & Fröhlich, F. Anesthesia differentially modulates spontaneous network dynamics by cortical area and layer. J. Neurophysiol. 110, 2739–2751 (2013).

    Article  PubMed  Google Scholar 

  75. Sellers, K. K., Bennett, D. V., Hutt, A., Williams, J. H. & Fröhlich, F. Awake vs. anesthetized: layer-specific sensory processing in visual cortex and functional connectivity between cortical areas. J. Neurophysiol. 113, 3798–3815 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Gersner, R., Kravetz, E., Feil, J., Pell, G. & Zangen, A. Long-term effects of repetitive transcranial magnetic stimulation on markers for neuroplasticity: differential outcomes in anesthetized and awake animals. J. Neurosci. 31, 7521–7526 (2011). This is one of the first studies to investigate long-term neuroplasticity effects of repeated rTMS interventions in anaesthetized and awake rats. It reveals a number of opposing outcomes depending on the anaesthetic state.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Massimini, M. et al. Breakdown of cortical effective connectivity during sleep. Science 309, 2228–2232 (2005). This pioneering study uses combined TMS and EEG in humans to demonstrate dramatic alterations in cortical effective connectivity during sleep.

    Article  CAS  PubMed  Google Scholar 

  78. Nieminen, J. O. et al. Consciousness and cortical responsiveness: a within-state study during non-rapid eye movement sleep. Sci. Rep. 6, 1–10 (2016).

    Article  CAS  Google Scholar 

  79. Darracq, M. et al. Evoked alpha power is reduced in disconnected consciousness during sleep and anesthesia. Sci. Rep. 8, 1–10 (2018).

    Article  CAS  Google Scholar 

  80. Lee, M. et al. Connectivity differences between consciousness and unconsciousness in non-rapid eye movement sleep: a TMS–EEG study. Sci. Rep. 9, 1–9 (2019).

    CAS  Google Scholar 

  81. Ferrarelli, F. et al. Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness. Proc. Natl Acad. Sci. USA 107, 2681–2686 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Rosanova, M. et al. Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients. Brain 135, 1308–1320 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Massimini, M. et al. Cortical reactivity and effective connectivity during REM sleep in humans. Cogn. Neurosci. 1, 176–183 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Sarasso, S. et al. Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine. Curr. Biol. 25, 3099–3105 (2015).

    Article  CAS  PubMed  Google Scholar 

  85. Kirov, R., Weiss, C., Siebner, H. R., Born, J. & Marshall, L. Slow oscillation electrical brain stimulation during waking promotes EEG theta activity and memory encoding. Proc. Natl Acad. Sci. USA 106, 15460–15465 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Marshall, L., Kirov, R., Brade, J., Mölle, M. & Born, J. Transcranial electrical currents to probe EEG brain rhythms and memory consolidation during sleep in humans. PLoS ONE 6, e16905 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Durkin, J. et al. Cortically coordinated NREM thalamocortical oscillations play an essential, instructive role in visual system plasticity. Proc. Natl Acad. Sci. USA 114, 10485–10490 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Facchin, L. et al. Slow waves promote sleep-dependent plasticity and functional recovery after stroke. J. Neurosci. 40, 8637–8651 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Ali, M. M., Sellers, K. K. & Fröhlich, F. Transcranial alternating current stimulation modulates large-scale cortical network activity by network resonance. J. Neurosci. 33, 11262–11275 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Schmidt, S. L., Iyengar, A. K., Foulser, A. A., Boyle, M. R. & Fröhlich, F. Endogenous cortical oscillations constrain neuromodulation by weak electric fields. Brain Stimul. 7, 878–889 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Chauvette, S., Seigneur, J. & Timofeev, I. Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity. Neuron 75, 1105–1113 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Li, G., Henriquez, C. S. & Fröhlich, F. Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation. PLoS Comput. Biol. 13, e1005797 (2017). The authors introduce a biophysical thalamic model that displays oscillatory regimes which recapitulate different sleep–wake states, and which are shown to constrain the extent to which periodic external stimulation entrains oscillations.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  93. Noreika, V. et al. Alertness fluctuations when performing a task modulate cortical evoked responses to transcranial magnetic stimulation. Neuroimage 223, 117305 (2020). The authors use EEG to characterize rapid, non-linear changes in cortical reactivity to TMS pulses during fluctuations in alertness in human participants.

    Article  PubMed  Google Scholar 

  94. Derosière, G., Billot, M., Ward, E. T. & Perrey, S. Adaptations of motor neural structures’ activity to lapses in attention. Cereb. Cortex 25, 66–74 (2015).

    Article  PubMed  Google Scholar 

  95. Magnin, M. et al. Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans. Proc. Natl Acad. Sci. USA 107, 3829–3833 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Tagliazucchi, E. & Laufs, H. Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron 82, 695–708 (2014).

    Article  CAS  PubMed  Google Scholar 

  97. Moore, T. & Zirnsak, M. Neural mechanisms of selective visual attention. Annu. Rev. Psychol. 68, 47–72 (2017).

    Article  CAS  PubMed  Google Scholar 

  98. Fiebelkorn, I. C. & Kastner, S. Functional specialization in the attention network. Annu. Rev. Psychol. 71, 221–249 (2020).

    Article  PubMed  Google Scholar 

  99. Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215 (2002).

    Article  CAS  PubMed  Google Scholar 

  100. Gregoriou, G. G., Gotts, S. J., Zhou, H. & Desimone, R. High-frequency, long-range coupling between prefrontal and visual cortex during attention. Science 324, 1207–1210 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Slotnick, S. D., Schwarzbach, J. & Yantis, S. Attentional inhibition of visual processing in human striate and extrastriate cortex. Neuroimage 19, 1602–1611 (2003).

    Article  PubMed  Google Scholar 

  102. Li, X., Lu, Z.-L., Tjan, B. S., Dosher, B. A. & Chu, W. Blood oxygenation level-dependent contrast response functions identify mechanisms of covert attention in early visual areas. Proc. Natl Acad. Sci. USA 105, 6202–6207 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Jensen, O., Kaiser, J. & Lachaux, J.-P. Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci. 30, 317–324 (2007).

    Article  CAS  PubMed  Google Scholar 

  104. Noudoost, B., Chang, M. H., Steinmetz, N. A. & Moore, T. Top-down control of visual attention. Curr. Opin. Neurobiol. 20, 183–190 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Ciaramitaro, V. M., Buracas, G. T. & Boynton, G. M. Spatial and cross-modal attention alter responses to unattended sensory information in early visual and auditory human cortex. J. Neurophysiol. 98, 2399–2413 (2007).

    Article  PubMed  Google Scholar 

  106. Kelly, S. P., Lalor, E. C., Reilly, R. B. & Foxe, J. J. Increases in alpha oscillatory power reflect an active retinotopic mechanism for distracter suppression during sustained visuospatial attention. J. Neurophysiol. 95, 3844–3851 (2006).

    Article  PubMed  Google Scholar 

  107. Briggs, F., Mangun, G. R. & Usrey, W. M. Attention enhances synaptic efficacy and the signal-to-noise ratio in neural circuits. Nature 499, 476–480 (2013). This study shows that the deployment of spatial attention rapidly and transiently modulates the synaptic efficacy of thalamic stimulation in driving visual cortical neurons in the macaque brain.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Ruff, D. A. & Cohen, M. R. Attention increases spike count correlations between visual cortical areas. J. Neurosci. 36, 7523–7534 (2016). The authors show that spatial attentional allocation increases the after-effects of microstimulation across two cortical areas in the macaque brain.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Herring, J. D., Thut, G., Jensen, O. & Bergmann, T. O. Attention modulates TMS-locked alpha oscillations in the visual cortex. J. Neurosci. 35, 14435–14447 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Rosanova, M. et al. Natural frequencies of human corticothalamic circuits. J. Neurosci. 29, 7679–7685 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Samaha, J., Gosseries, O. & Postle, B. R. Distinct oscillatory frequencies underlie excitability of human occipital and parietal cortex. J. Neurosci. 37, 2824–2833 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Capotosto, P. et al. Task and regions specific top-down modulation of alpha rhythms in parietal cortex. Cereb. Cortex 27, 4815–4822 (2017). This study finds that the effect of parietal rTMS on neural oscillatory activity and behavioural performance depends on the nature of the cognitive task in which human participants engage.

    Article  PubMed  Google Scholar 

  113. Blankenburg, F. et al. Studying the role of human parietal cortex in visuospatial attention with concurrent TMS–fMRI. Cereb. Cortex 20, 2702–2711 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  114. Morishima, Y. et al. Task-specific signal transmission from prefrontal cortex in visual selective attention. Nat. Neurosci. 12, 85–91 (2009).

    Article  CAS  PubMed  Google Scholar 

  115. Heinen, K., Feredoes, E., Weiskopf, N., Ruff, C. C. & Driver, J. Direct evidence for attention-dependent influences of the frontal eye-fields on feature-responsive visual cortex. Cereb. Cortex 24, 2815–2821 (2014).

    Article  PubMed  Google Scholar 

  116. Sack, A. T. et al. Imaging the brain activity changes underlying impaired visuospatial judgments: simultaneous fMRI, TMS, and behavioral studies. Cereb. Cortex 17, 2841–2852 (2007).

    Article  PubMed  Google Scholar 

  117. Stefan, K. Induction of plasticity in the human motor cortex by paired associative stimulation. Brain 123, 572–584 (2000).

    Article  PubMed  Google Scholar 

  118. Wolters, A. et al. A temporally asymmetric Hebbian rule governing plasticity in the human motor cortex. J. Neurophysiol. 89, 2339–2345 (2003).

    Article  PubMed  Google Scholar 

  119. Malenka, R. C. & Bear, M. F. LTP and LTD: an embarrassment of riches. Neuron 44, 5–21 (2004).

    Article  CAS  PubMed  Google Scholar 

  120. Nicoll, R. A. A brief history of long-term potentiation. Neuron 93, 281–290 (2017).

    Article  CAS  PubMed  Google Scholar 

  121. Kamke, M. R. et al. Visual attentional load influences plasticity in the human motor cortex. J. Neurosci. 32, 7001–7008 (2012). This study demonstrates that high visual attentional load abolishes the plasticity effects normally produced by two different excitatory TMS protocols in humans.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Stefan, K., Wycislo, M. & Classen, J. Modulation of associative human motor cortical plasticity by attention. J. Neurophysiol. 92, 66–72 (2004).

    Article  PubMed  Google Scholar 

  123. Antal, A., Terney, D., Poreisz, C. & Paulus, W. Towards unravelling task-related modulations of neuroplastic changes induced in the human motor cortex. Eur. J. Neurosci. 26, 2687–2691 (2007).

    Article  PubMed  Google Scholar 

  124. Kamke, M. R. et al. Visual spatial attention has opposite effects on bidirectional plasticity in the human motor cortex. J. Neurosci. 34, 1475–1480 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Driver, J. & Mattingley, J. B. Parietal neglect and visual awareness. Nat. Neurosci. 1, 17–22 (1998).

    Article  CAS  PubMed  Google Scholar 

  126. Chun, M. M., Golomb, J. D. & Turk-Browne, N. B. A taxonomy of external and internal attention. Annu. Rev. Psychol. 62, 73–101 (2011).

    Article  PubMed  Google Scholar 

  127. Baddeley, A. Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29 (2012).

    Article  PubMed  Google Scholar 

  128. Xu, Y. Reevaluating the sensory account of visual working memory storage. Trends Cogn. Sci. 21, 794–815 (2017).

    Article  PubMed  Google Scholar 

  129. Serences, J. T. Neural mechanisms of information storage in visual short-term memory. Vis. Res. 128, 53–67 (2016).

    Article  PubMed  Google Scholar 

  130. Woodward, T. S., Feredoes, E., Metzak, P. D., Takane, Y. & Manoach, D. S. Epoch-specific functional networks involved in working memory. Neuroimage 65, 529–539 (2013).

    Article  PubMed  Google Scholar 

  131. Miller, E. K., Lundqvist, M. & Bastos, A. M. Working memory 2.0. Neuron 100, 463–475 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Romo, R. & Rossi-Pool, R. Turning touch into perception. Neuron 105, 16–33 (2020).

    Article  CAS  PubMed  Google Scholar 

  133. Rose, N. S. et al. Reactivation of latent working memories with transcranial magnetic stimulation. Science 354, 1136–1139 (2016). This study applies multivariate decoding of EEG data to show that a TMS pulse can briefly reactivate the otherwise latent representation of an item held in working memory.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Lewis-Peacock, J. A., Drysdale, A. T., Oberauer, K. & Postle, B. R. Neural evidence for a distinction between short-term memory and the focus of attention. J. Cogn. Neurosci. 24, 61–79 (2012).

    Article  PubMed  Google Scholar 

  135. Zokaei, N., Ning, S., Manohar, S., Feredoes, E. & Husain, M. Flexibility of representational states in working memory. Front. Hum. Neurosci. 8, 853 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Stokes, M. G. ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework. Trends Cogn. Sci. 19, 394–405 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Zokaei, N., Manohar, S., Husain, M. & Feredoes, E. Causal evidence for a privileged working memory state in early visual cortex. J. Neurosci. 34, 158–162 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Akrami, A., Kopec, C. D., Diamond, M. E. & Brody, C. D. Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature 554, 368–372 (2018).

    Article  CAS  PubMed  Google Scholar 

  139. van de Ven, V., Jacobs, C. & Sack, A. T. Topographic contribution of early visual cortex to short-term memory consolidation: a transcranial magnetic stimulation study. J. Neurosci. 32, 4–11 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  140. Cattaneo, Z., Vecchi, T., Pascual-Leone, A. & Silvanto, J. Contrasting early visual cortical activation states causally involved in visual imagery and short-term memory. Eur. J. Neurosci. 30, 1393–1400 (2009).

    Article  PubMed  Google Scholar 

  141. Rademaker, R. L., van de Ven, V. G., Tong, F. & Sack, A. T. The impact of early visual cortex transcranial magnetic stimulation on visual working memory precision and guess rate. PLoS ONE 12, e0175230 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  142. van Lamsweerde, A. E. & Johnson, J. S. Assessing the effect of early visual cortex transcranial magnetic stimulation on working memory consolidation. J. Cogn. Neurosci. 29, 1226–1238 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  143. Ezzyat, Y. et al. Direct brain stimulation modulates encoding states and memory performance in humans. Curr. Biol. 27, 1251–1258 (2017). This study uses multivariate classification of human intracranial EEG data to stratify the quality of encoding of memory stimuli, and shows that the effects of electrical brain stimulation on recall depend on the initial encoding state.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Feredoes, E., Heinen, K., Weiskopf, N., Ruff, C. & Driver, J. Causal evidence for frontal involvement in memory target maintenance by posterior brain areas during distracter interference of visual working memory. Proc. Natl Acad. Sci. 108, 17510–17515 (2011). This combined TMS and fMRI study finds that stimulation over the DLPFC increases blood oxygen level-dependent activity in category-specific cortex during a working memory task, but only when visual distractors are present.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Ranganath, C., DeGutis, J. & D’Esposito, M. Category-specific modulation of inferior temporal activity during working memory encoding and maintenance. Cogn. Brain Res. 20, 37–45 (2004).

    Article  Google Scholar 

  146. Luber, B. et al. Remediation of sleep-deprivation–induced working memory impairment with fMRI-guided transcranial magnetic stimulation. Cereb. Cortex 18, 2077–2085 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Kundu, B., Sutterer, D. W., Emrich, S. M. & Postle, B. R. Strengthened effective connectivity underlies transfer of working memory training to tests of short-term memory and attention. J. Neurosci. 33, 8705–8715 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Gill, J., Shah-Basak, P. P. & Hamilton, R. It’s the thought that counts: examining the task-dependent effects of transcranial direct current stimulation on executive function. Brain Stimul. 8, 253–259 (2015).

    Article  PubMed  Google Scholar 

  149. Albouy, P., Weiss, A., Baillet, S. & Zatorre, R. J. Selective entrainment of theta oscillations in the dorsal stream causally enhances auditory working memory performance. Neuron 94, 193–206.e5 (2017).

    Article  CAS  PubMed  Google Scholar 

  150. Beynel, L. et al. Online repetitive transcranial magnetic stimulation during working memory in younger and older adults: a randomized within-subject comparison. PLoS ONE 14, e0213707 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Metzak, P. et al. Constrained principal component analysis reveals functionally connected load-dependent networks involved in multiple stages of working memory. Hum. Brain Mapp. 32, 856–871 (2011).

    Article  PubMed  Google Scholar 

  152. Jensen, O. & Tesche, C. D. Frontal theta activity in humans increases with memory load in a working memory task. Eur. J. Neurosci. 15, 1395–1399 (2002).

    Article  PubMed  Google Scholar 

  153. Popov, T. et al. Cross-frequency interactions between frontal theta and posterior alpha control mechanisms foster working memory. Neuroimage 181, 728–733 (2018).

    Article  PubMed  Google Scholar 

  154. Reinhart, R. M. G. & Nguyen, J. A. Working memory revived in older adults by synchronizing rhythmic brain circuits. Nat. Neurosci. 22, 820–827 (2019). This study demonstrates that tACS can ‘rescue’ behavioural performance and oscillatory phase–amplitude coupling in older adults (60–76 years) performing a working memory task.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Peters, J. C. et al. Concurrent human TMS-EEG-fMRI enables monitoring of oscillatory brain state-dependent gating of cortico-subcortical network activity. Commun. Biol. 3, 40 (2020). This study combines TMS, EEG and fMRI to show that neural oscillatory activity before stimulation constrains the propagation of TMS pulses within the cortico-subcortical motor network.

    Article  PubMed  PubMed Central  Google Scholar 

  156. Papadopoulos, L., Lynn, C. W., Battaglia, D. & Bassett, D. S. Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state. PLoS Comput. Biol. 16, e1008144 (2020). This computational study explores the influence of ongoing oscillatory state and structural connectivity on stimulation-induced activity in a large-scale brain model.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  157. Bestmann, S. et al. Influence of uncertainty and surprise on human corticospinal excitability during preparation for action. Curr. Biol. 18, 775–780 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Sale, M. V., Nydam, A. S. & Mattingley, J. B. Stimulus uncertainty enhances long-term potentiation-like plasticity in human motor cortex. Cortex 88, 32–41 (2017).

    Article  PubMed  Google Scholar 

  159. Leitão, J., Thielscher, A., Tünnerhoff, J. & Noppeney, U. Concurrent TMS-fMRI reveals interactions between dorsal and ventral attentional systems. J. Neurosci. 35, 11445–11457 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  160. Tran, D. M. D., McNair, N. A., Harris, J. A. & Livesey, E. J. Expected TMS excites the motor system less effectively than unexpected stimulation. Neuroimage 226, 117541 (2021).

    Article  PubMed  Google Scholar 

  161. Gupta, N. & Aron, A. R. Urges for food and money spill over into motor system excitability before action is taken. Eur. J. Neurosci. 33, 183–188 (2011).

    Article  PubMed  Google Scholar 

  162. Klein, P.-A., Olivier, E. & Duque, J. Influence of reward on corticospinal excitability during movement preparation. J. Neurosci. 32, 18124–18136 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Chiu, Y.-C., Cools, R. & Aron, A. R. Opposing effects of appetitive and aversive cues on go/no-go behavior and motor excitability. J. Cogn. Neurosci. 26, 1851–1860 (2014).

    Article  PubMed  Google Scholar 

  164. Filmer, H. L., Varghese, E., Hawkins, G. E., Mattingley, J. B. & Dux, P. E. Improvements in attention and decision-making following combined behavioral training and brain stimulation. Cereb. Cortex 27, 3675–3682 (2017).

    PubMed  Google Scholar 

  165. Derosiere, G., Vassiliadis, P. & Duque, J. Advanced TMS approaches to probe corticospinal excitability during action preparation. Neuroimage 213, 116746 (2020).

    Article  PubMed  Google Scholar 

  166. Duque, J., Greenhouse, I., Labruna, L. & Ivry, R. B. Physiological markers of motor inhibition during human behavior. Trends Neurosci. 40, 219–236 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Bikson, M. et al. Rigor and reproducibility in research with transcranial electrical stimulation: an NIMH-sponsored workshop. Brain Stimul. 11, 465–480 (2018).

    Article  PubMed  Google Scholar 

  168. Khalighinejad, N. et al. A basal forebrain-cingulate circuit in macaques decides it is time to act. Neuron 105, 370–384.e8 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. King, J.-R. & Dehaene, S. Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18, 203–210 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  170. Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Lafon, B. et al. Low frequency transcranial electrical stimulation does not entrain sleep rhythms measured by human intracranial recordings. Nat. Commun. 8, 1199 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  172. Alekseichuk, I. et al. Electric field dynamics in the brain during multi-electrode transcranial electric stimulation. Nat. Commun. 10, 2573 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  173. Tehovnik, E. J., Tolias, A. S., Sultan, F., Slocum, W. M. & Logothetis, N. K. Direct and indirect activation of cortical neurons by electrical microstimulation. J. Neurophysiol. 96, 512–521 (2006).

    Article  CAS  PubMed  Google Scholar 

  174. Kringelbach, M. L., Jenkinson, N., Owen, S. L. F. & Aziz, T. Z. Translational principles of deep brain stimulation. Nat. Rev. Neurosci. 8, 623–635 (2007).

    Article  CAS  PubMed  Google Scholar 

  175. Fenno, L., Yizhar, O. & Deisseroth, K. The development and application of optogenetics. Annu. Rev. Neurosci. 34, 389–412 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Folloni, D. et al. Manipulation of subcortical and deep cortical activity in the primate brain using transcranial focused ultrasound stimulation. Neuron 101, 1109–1116.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  177. Grossman, N. et al. Noninvasive deep brain stimulation via temporally interfering electric fields. Cell 169, 1029–1041.e16 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Thut, G. et al. Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions: a position paper. Clin. Neurophysiol. 128, 843–857 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  179. Karabanov, A., Thielscher, A. & Siebner, H. Transcranial brain stimulation: closing the loop between brain and stimulation. Curr. Opin. Neurol. 29, 397–404 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  180. Zrenner, C., Belardinelli, P., Müller-Dahlhaus, F. & Ziemann, U. Closed-loop neuroscience and non-invasive brain stimulation: a tale of two loops. Front. Cell. Neurosci. 10, 92 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  181. Madsen, K. H. et al. No trace of phase: corticomotor excitability is not tuned by phase of pericentral mu-rhythm. Brain Stimul. 12, 1261–1270 (2019).

    Article  PubMed  Google Scholar 

  182. Schaworonkow, N., Triesch, J., Ziemann, U. & Zrenner, C. EEG-triggered TMS reveals stronger brain state-dependent modulation of motor evoked potentials at weaker stimulation intensities. Brain Stimul. 12, 110–118 (2019).

    Article  PubMed  Google Scholar 

  183. Torrecillos, F. et al. Motor cortex inputs at the optimum phase of beta cortical oscillations undergo more rapid and less variable corticospinal propagation. J. Neurosci. 40, 369–381 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Peles, O., Werner-Reiss, U., Bergman, H., Israel, Z. & Vaadia, E. Phase-specific microstimulation differentially modulates beta oscillations and affects behavior. Cell Rep. 30, 2555–2566.e3 (2020).

    Article  CAS  PubMed  Google Scholar 

  185. Zanos, S., Rembado, I., Chen, D. & Fetz, E. E. Phase-locked stimulation during cortical beta oscillations produces bidirectional synaptic plasticity in awake monkeys. Curr. Biol. 28, 2515–2526.e4 (2018). This study demonstrates that the particular phase of neural oscillations at which a burst of electrical stimulation is delivered results in transient, opposing plasticity effects in monkeys.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Fisher, R. S. & Velasco, A. L. Electrical brain stimulation for epilepsy. Nat. Rev. Neurol. 10, 261–270 (2014).

    Article  PubMed  Google Scholar 

  187. Kokkinos, V., Sisterson, N. D., Wozny, T. A. & Richardson, R. M. Association of closed-loop brain stimulation neurophysiological features with seizure control among patients with focal epilepsy. JAMA Neurol. 76, 800–808 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  188. Brittain, J., Probert-Smith, P., Aziz, T. & Brown, P. Tremor suppression by rhythmic transcranial current stimulation. Curr. Biol. 23, 436–440 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Opri, E. et al. Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor. Sci. Transl. Med. 21, eaay7680 (2020).

    Article  Google Scholar 

  190. Schreglmann, S. R. et al. Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence. Nat. Commun. 12, 363 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  191. Bouthour, W. et al. Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nat. Rev. Neurol. 15, 343–352 (2019).

    Article  PubMed  Google Scholar 

  192. Lebedev, M. A. & Nicolelis, M. A. L. Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation. Physiol. Rev. 97, 767–837 (2017).

    Article  PubMed  Google Scholar 

  193. Shanechi, M. M. Brain–machine interfaces from motor to mood. Nat. Neurosci. 22, 1554–1564 (2019).

    Article  CAS  PubMed  Google Scholar 

  194. Qiao, S., Sedillo, J. I., Brown, K. A., Ferrentino, B. & Pesaran, B. A causal network analysis of neuromodulation in the mood processing network. Neuron 107, 972–985.e6 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Scangos, K. W. et al. Closed-loop neuromodulation in an individual with treatment-resistant depression. Nat. Med. 27, 1696–1700 (2021). This study provides a proof-of-principle approach for monitoring patient-tailored biomarkers of affective state to guide delivery of invasive, closed-loop brain stimulation for treatment-resistant depression.

    Article  CAS  PubMed  Google Scholar 

  196. Krack, P., Hariz, M. I., Baunez, C., Guridi, J. & Obeso, J. A. Deep brain stimulation: from neurology to psychiatry? Trends Neurosci. 33, 474–484 (2010).

    Article  CAS  PubMed  Google Scholar 

  197. Sullivan, C. R. P., Olsen, S. & Widge, A. S. Deep brain stimulation for psychiatric disorders: from focal brain targets to cognitive networks. Neuroimage 225, 117515 (2021).

    Article  PubMed  Google Scholar 

  198. Hitti, F. L., Yang, A. I., Cristancho, M. A. & Baltuch, G. H. Deep brain stimulation is effective for treatment-resistant depression: a meta-analysis and meta-regression. J. Clin. Med. 9, 2796 (2020).

    Article  PubMed Central  Google Scholar 

  199. McClintock, S. M. et al. Consensus recommendations for the clinical application of repetitive transcranial magnetic stimulation (rTMS) in the treatment of depression. J. Clin. Psychiatry 78, 16cs10905 (2017).

    Google Scholar 

  200. Brunoni, A. R. et al. Transcranial direct current stimulation for acute major depressive episodes: meta-analysis of individual patient data. Br. J. Psychiatry 208, 522–531 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  201. Benabid, A. L. Deep brain stimulation for Parkinson’s disease. Curr. Opin. Neurobiol. 13, 696–706 (2003).

    Article  CAS  PubMed  Google Scholar 

  202. Brunelin, J. et al. Transcranial direct current stimulation for obsessive-compulsive disorder: a systematic review. Brain Sci. 8, 37 (2018).

    Article  PubMed Central  Google Scholar 

  203. Cocchi, L. et al. Transcranial magnetic stimulation in obsessive-compulsive disorder: a focus on network mechanisms and state dependence. Neuroimage Clin. 19, 661–674 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  204. Grover, S., Nguyen, J. A., Viswanathan, V. & Reinhart, R. M. G. High-frequency neuromodulation improves obsessive-compulsive behavior. Nat. Med. 27, 232–238 (2021).

    Article  CAS  PubMed  Google Scholar 

  205. Gold, A. K. et al. Clinical applications of transcranial magnetic stimulation in bipolar disorder. Brain Behav. 9, e01419 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  206. Scangos, K. W., Makhoul, G. S., Sugrue, L. P., Chang, E. F. & Krystal, A. D. State-dependent responses to intracranial brain stimulation in a patient with depression. Nat. Med. 27, 229–231 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Sathappan, A. V., Luber, B. M. & Lisanby, S. H. The dynamic duo: combining noninvasive brain stimulation with cognitive interventions. Prog. Neuropsychopharmacol. Biol. Psychiatry 89, 347–360 (2019).

    Article  PubMed  Google Scholar 

  208. Dedoncker, J., Baeken, C., De Raedt, R. & Vanderhasselt, M.-A. Combined transcranial direct current stimulation and psychological interventions: state of the art and promising perspectives for clinical psychology. Biol. Psychol. 158, 107991 (2021).

    Article  PubMed  Google Scholar 

  209. Segrave, R. A., Arnold, S., Hoy, K. & Fitzgerald, P. B. Concurrent cognitive control training augments the antidepressant efficacy of tDCS: a pilot study. Brain Stimul. 7, 325–331 (2014).

    Article  CAS  PubMed  Google Scholar 

  210. Bajbouj, M. et al. PsychotherapyPlus: augmentation of cognitive behavioral therapy (CBT) with prefrontal transcranial direct current stimulation (tDCS) in major depressive disorder — study design and methodology of a multicenter double-blind randomized placebo-controlled trial. Eur. Arch. Psychiatry Clin. Neurosci. 268, 797–808 (2018).

    Article  PubMed  Google Scholar 

  211. Uyl, T. E., den, Gladwin, T. E., Lindenmeyer, J. & Wiers, R. W. A clinical trial with combined transcranial direct current stimulation and attentional bias modification in alcohol-dependent patients. Alcohol. Clin. Exp. Res. 42, 1961–1969 (2018).

    Article  Google Scholar 

  212. Claus, E. D., Klimaj, S. D., Chavez, R., Martinez, A. D. & Clark, V. P. A randomized trial of combined tDCS over right inferior frontal cortex and cognitive bias modification: null effects on drinking and alcohol approach bias. Alcohol. Clin. Exp. Res. 43, 1591–1599 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  213. Monnart, A. et al. Treatment of resistant depression: a pilot study assessing the efficacy of a tDCS-mindfulness program compared with a tDCS-relaxation program. Front. Psychiatry 10, 730 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by a grant from the Australian National Health and Medical Research Council awarded to J.B.M. and P.E.D. (GNT1129715). J.B.M. was supported by a National Health and Medical Research Council Investigator Award (GNT2010141), and by the Australian Research Council Centre of Excellence for Integrative Brain Function (Australian Research Council Centre of Excellence grant CE140100007). The authors thank D. Lloyd for assistance with the figures, K. Garner for helpful discussion, and A. Harris, W. Harrison, D. Rangelov, R. Rideaux and R. Tweedale for their comments on earlier drafts of the manuscript.

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  1. Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia

    Claire Bradley & Jason B. Mattingley

  2. Department of Psychology, University of Toronto, Toronto, ON, Canada

    Abbey S. Nydam

  3. School of Psychology, The University of Queensland, St Lucia, QLD, Australia

    Paul E. Dux & Jason B. Mattingley

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C.B., A.S.N. and J.B.M. researched data for the article, provided substantial contributions to discussion of its content, and wrote, reviewed and edited the manuscript before submission. P.E.D. provided a substantial contribution to discussion of the article’s content and reviewed and edited the manuscript before submission.

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Glossary

Cognitive states

Finite periods of information processing or mental activity which, in conjunction with their associated neural activity, regulate functions such as attention, learning, memory, thought and reasoning.

Electroencephalography

(EEG). A non-invasive imaging technique that uses electrodes placed on the scalp to record stimulus-evoked or endogenous electrical activity in the brain with millisecond precision.

Functional MRI

(fMRI). A non-invasive imaging technique used to measure changes in metabolic activity in the brain associated with local and distributed fluctuations in the blood oxygen level-dependent signal.

Stochastic resonance

A phenomenon in which the addition of noise to a nonlinear system can, under certain conditions, improve performance or output-signal quality.

Entrainment

The alignment or synchronization of brain activity in response to rhythmic sensory stimuli or brain stimulation, and which may play a role in regulating perceptual and cognitive states.

Oscillations

Wave-like patterns of periodic brain activity in the 0- to ~200-Hz range, often defined in terms of characteristic ‘frequency bands’ such as delta (0.5–3 Hz), theta (4–7 Hz), alpha (8–14 Hz), beta (15–30 Hz) and gamma (more than 30 Hz).

Plasticity

The capacity of the brain to alter its structure and function, often in response to experience, and which is expressed at different anatomical scales, from individual neurons and synapses to cortical maps and networks.

TMS-evoked potentials

Evoked changes in electrical potential generated in response to a transcranial magnetic stimulation (TMS) pulse and recorded by electroencephalography, resulting in region-specific changes in neural activity generally lasting ~500 ms.

Effective connectivity

Pattern of interactions between different elements of the nervous system in which the direction of functional communication is causally inferred.

Decoding

A data analysis technique that uses a computer algorithm — a ‘classifier’ — to predict which class of stimulus or experimental condition was present in a given trial, based on multivariate features of brain activity for that trial.

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Bradley, C., Nydam, A.S., Dux, P.E. et al. State-dependent effects of neural stimulation on brain function and cognition. Nat Rev Neurosci 23, 459–475 (2022). https://doi.org/10.1038/s41583-022-00598-1

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