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.
Your institute does not have access to this article
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Kanwisher, N. & Wojciulik, E. Visual attention: insights from brain imaging. Nat. Rev. Neurosci. 1, 91–100 (2000).
Poldrack, R. A. The role of fMRI in cognitive neuroscience: where do we stand? Curr. Opin. Neurobiol. 18, 223–227 (2008).
Polanía, R., Nitsche, M. A. & Ruff, C. C. Studying and modifying brain function with non-invasive brain stimulation. Nat. Neurosci. 21, 174–187 (2018).
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).
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).
Bestmann, S. & Feredoes, E. Combined neurostimulation and neuroimaging in cognitive neuroscience: past, present, and future. Ann. N. Y. Acad. Sci. 1296, 11–30 (2013).
Silvanto, J., Muggleton, N. & Walsh, V. State-dependency in brain stimulation studies of perception and cognition. Trends Cogn. Sci. 12, 447–454 (2008).
Romei, V., Thut, G. & Silvanto, J. Information-based approaches of noninvasive transcranial brain stimulation. Trends Neurosci. 39, 782–795 (2016).
Pinto, L. et al. Task-dependent changes in the large-scale dynamics and necessity of cortical regions. Neuron 104, 810–824.e9 (2019).
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).
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.
Steriade, M. Corticothalamic resonance, states of vigilance and mentation. Neuroscience 101, 243–276 (2000).
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).
Petersen, S. E. & Sporns, O. Brain networks and cognitive architectures. Neuron 88, 207–219 (2015).
McGinley, M. J. et al. Waking state: rapid variations modulate neural and behavioral responses. Neuron 87, 1143–1161 (2015).
Siegel, M., Donner, T. H. & Engel, A. K. Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121–134 (2012).
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).
Heeger, D. J. & Ress, D. What does fMRI tell us about neuronal activity? Nat. Rev. Neurosci. 3, 142–151 (2002).
Liu, A. et al. Immediate neurophysiological effects of transcranial electrical stimulation. Nat. Commun. 9, 5092 (2018).
Hallett, M. Transcranial magnetic stimulation: a primer. Neuron 55, 187–199 (2007).
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).
Diana, M. et al. Rehabilitating the addicted brain with transcranial magnetic stimulation. Nat. Rev. Neurosci. 18, 685–693 (2017).
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).
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).
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).
Miniussi, C., Harris, J. A. & Ruzzoli, M. Modelling non-invasive brain stimulation in cognitive neuroscience. Neurosci. Biobehav. Rev. 37, 1702–1712 (2013).
Pitcher, D., Parkin, B. & Walsh, V. Transcranial magnetic stimulation and the understanding of behavior. Annu. Rev. Psychol. 72, 97–121 (2021).
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).
Rogasch, N. C. & Fitzgerald, P. B. Assessing cortical network properties using TMS–EEG. Hum. Brain Mapp. 34, 1652–1669 (2013).
Bergmann, T. O. et al. Concurrent TMS-fMRI for causal network perturbation and proof of target engagement. Neuroimage 237, 118093 (2021).
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).
Woods, A. J. et al. A technical guide to tDCS, and related non-invasive brain stimulation tools. Clin. Neurophysiol. 127, 1031–1048 (2016).
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).
Purpura, D. P. & Mcmurtry, J. G. Intracellular activities and evoked potential changes during polarization of motor cortex. J. Neurophysiol. 28, 166–185 (1965).
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).
McDonnell, M. D. & Abbott, D. What is stochastic resonance? Definitions, misconceptions, debates, and its relevance to biology. PLoS Comput. Biol. 5, e1000348 (2009).
Antal, A. & Herrmann, C. S. Transcranial alternating current and random noise stimulation: possible mechanisms. Neural Plast. 2016, e3616807 (2016).
Bland, N. S. & Sale, M. V. Current challenges: the ups and downs of tACS. Exp. Brain Res. 237, 3071–3088 (2019).
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).
Johnson, L. et al. Dose-dependent effects of transcranial alternating current stimulation on spike timing in awake nonhuman primates. Sci. Adv. 6, eaaz2747 (2020).
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).
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).
Nitsche, M. A. et al. Transcranial direct current stimulation: state of the art 2008. Brain Stimul. 1, 206–223 (2008).
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).
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).
Huang, Y.-Z. et al. Plasticity induced by non-invasive transcranial brain stimulation: a position paper. Clin. Neurophysiol. 128, 2318–2329 (2017).
Karabanov, A. et al. Consensus paper: probing homeostatic plasticity of human cortex with non-invasive transcranial brain stimulation. Brain Stimul. 8, 993–1006 (2015).
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).
Lefaucheur, J.-P. et al. Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clin. Neurophysiol. 128, 56–92 (2017).
Silvanto, J. in The Oxford Handbook of Transcranial Stimulation 2nd Edn (eds Wassermann, E. M. et al.) (Oxford Univ. Press, 2021).
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.
Paulus, W. & Rothwell, J. C. Membrane resistance and shunting inhibition: where biophysics meets state-dependent human neurophysiology. J. Physiol. 594, 2719–2728 (2016).
Bikson, M. & Rahman, A. Origins of specificity during tDCS: anatomical, activity-selective, and input-bias mechanisms. Front. Hum. Neurosci. 7, 688 (2013).
McCormick, D. A., McGinley, M. & Salkoff, D. Brain state dependent activity in the cortex and thalamus. Curr. Opin. Neurobiol. 31, 133–140 (2015).
Nir, Y. et al. Regional slow waves and spindles in human sleep. Neuron 70, 153–169 (2011).
Murphy, M. et al. Propofol anesthesia and sleep: a high-density EEG study. Sleep 34, 283–291A (2011).
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).
Husain, A. M. Electroencephalographic assessment of coma. J. Clin. Neurophysiol. 23, 208–220 (2006).
Frohlich, J., Toker, D. & Monti, M. M. Consciousness among delta waves: a paradox? Brain https://doi.org/10.1093/brain/awab095 (2021).
Uhrig, L. et al. Resting-state dynamics as a cortical signature of anesthesia in monkeys. Anesthesiology 129, 942–958 (2018).
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).
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).
Wang, D.-S. & Orser, B. A. Inhibition of learning and memory by general anesthetics. Can. J. Anesth. Can. Anesth. 58, 167–177 (2011).
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).
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).
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).
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).
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).
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).
Logothetis, N. K. et al. The effects of electrical microstimulation on cortical signal propagation. Nat. Neurosci. 13, 1283–1291 (2010).
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).
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.
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).
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).
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).
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.
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.
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).
Darracq, M. et al. Evoked alpha power is reduced in disconnected consciousness during sleep and anesthesia. Sci. Rep. 8, 1–10 (2018).
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).
Ferrarelli, F. et al. Breakdown in cortical effective connectivity during midazolam-induced loss of consciousness. Proc. Natl Acad. Sci. USA 107, 2681–2686 (2010).
Rosanova, M. et al. Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients. Brain 135, 1308–1320 (2012).
Massimini, M. et al. Cortical reactivity and effective connectivity during REM sleep in humans. Cogn. Neurosci. 1, 176–183 (2010).
Sarasso, S. et al. Consciousness and complexity during unresponsiveness induced by propofol, xenon, and ketamine. Curr. Biol. 25, 3099–3105 (2015).
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).
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).
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).
Facchin, L. et al. Slow waves promote sleep-dependent plasticity and functional recovery after stroke. J. Neurosci. 40, 8637–8651 (2020).
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).
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).
Chauvette, S., Seigneur, J. & Timofeev, I. Sleep oscillations in the thalamocortical system induce long-term neuronal plasticity. Neuron 75, 1105–1113 (2012).
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.
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.
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).
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).
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).
Moore, T. & Zirnsak, M. Neural mechanisms of selective visual attention. Annu. Rev. Psychol. 68, 47–72 (2017).
Fiebelkorn, I. C. & Kastner, S. Functional specialization in the attention network. Annu. Rev. Psychol. 71, 221–249 (2020).
Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat. Rev. Neurosci. 3, 201–215 (2002).
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).
Slotnick, S. D., Schwarzbach, J. & Yantis, S. Attentional inhibition of visual processing in human striate and extrastriate cortex. Neuroimage 19, 1602–1611 (2003).
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).
Jensen, O., Kaiser, J. & Lachaux, J.-P. Human gamma-frequency oscillations associated with attention and memory. Trends Neurosci. 30, 317–324 (2007).
Noudoost, B., Chang, M. H., Steinmetz, N. A. & Moore, T. Top-down control of visual attention. Curr. Opin. Neurobiol. 20, 183–190 (2010).
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).
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).
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.
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.
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).
Rosanova, M. et al. Natural frequencies of human corticothalamic circuits. J. Neurosci. 29, 7679–7685 (2009).
Samaha, J., Gosseries, O. & Postle, B. R. Distinct oscillatory frequencies underlie excitability of human occipital and parietal cortex. J. Neurosci. 37, 2824–2833 (2017).
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.
Blankenburg, F. et al. Studying the role of human parietal cortex in visuospatial attention with concurrent TMS–fMRI. Cereb. Cortex 20, 2702–2711 (2010).
Morishima, Y. et al. Task-specific signal transmission from prefrontal cortex in visual selective attention. Nat. Neurosci. 12, 85–91 (2009).
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).
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).
Stefan, K. Induction of plasticity in the human motor cortex by paired associative stimulation. Brain 123, 572–584 (2000).
Wolters, A. et al. A temporally asymmetric Hebbian rule governing plasticity in the human motor cortex. J. Neurophysiol. 89, 2339–2345 (2003).
Malenka, R. C. & Bear, M. F. LTP and LTD: an embarrassment of riches. Neuron 44, 5–21 (2004).
Nicoll, R. A. A brief history of long-term potentiation. Neuron 93, 281–290 (2017).
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.
Stefan, K., Wycislo, M. & Classen, J. Modulation of associative human motor cortical plasticity by attention. J. Neurophysiol. 92, 66–72 (2004).
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).
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).
Driver, J. & Mattingley, J. B. Parietal neglect and visual awareness. Nat. Neurosci. 1, 17–22 (1998).
Chun, M. M., Golomb, J. D. & Turk-Browne, N. B. A taxonomy of external and internal attention. Annu. Rev. Psychol. 62, 73–101 (2011).
Baddeley, A. Working memory: theories, models, and controversies. Annu. Rev. Psychol. 63, 1–29 (2012).
Xu, Y. Reevaluating the sensory account of visual working memory storage. Trends Cogn. Sci. 21, 794–815 (2017).
Serences, J. T. Neural mechanisms of information storage in visual short-term memory. Vis. Res. 128, 53–67 (2016).
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).
Miller, E. K., Lundqvist, M. & Bastos, A. M. Working memory 2.0. Neuron 100, 463–475 (2018).
Romo, R. & Rossi-Pool, R. Turning touch into perception. Neuron 105, 16–33 (2020).
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.
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).
Zokaei, N., Ning, S., Manohar, S., Feredoes, E. & Husain, M. Flexibility of representational states in working memory. Front. Hum. Neurosci. 8, 853 (2014).
Stokes, M. G. ‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework. Trends Cogn. Sci. 19, 394–405 (2015).
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).
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).
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).
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).
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).
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).
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.
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.
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).
Luber, B. et al. Remediation of sleep-deprivation–induced working memory impairment with fMRI-guided transcranial magnetic stimulation. Cereb. Cortex 18, 2077–2085 (2008).
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).
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).
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).
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).
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).
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).
Popov, T. et al. Cross-frequency interactions between frontal theta and posterior alpha control mechanisms foster working memory. Neuroimage 181, 728–733 (2018).
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.
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.
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.
Bestmann, S. et al. Influence of uncertainty and surprise on human corticospinal excitability during preparation for action. Curr. Biol. 18, 775–780 (2008).
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).
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).
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).
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).
Klein, P.-A., Olivier, E. & Duque, J. Influence of reward on corticospinal excitability during movement preparation. J. Neurosci. 32, 18124–18136 (2012).
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).
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).
Derosiere, G., Vassiliadis, P. & Duque, J. Advanced TMS approaches to probe corticospinal excitability during action preparation. Neuroimage 213, 116746 (2020).
Duque, J., Greenhouse, I., Labruna, L. & Ivry, R. B. Physiological markers of motor inhibition during human behavior. Trends Neurosci. 40, 219–236 (2017).
Bikson, M. et al. Rigor and reproducibility in research with transcranial electrical stimulation: an NIMH-sponsored workshop. Brain Stimul. 11, 465–480 (2018).
Khalighinejad, N. et al. A basal forebrain-cingulate circuit in macaques decides it is time to act. Neuron 105, 370–384.e8 (2020).
King, J.-R. & Dehaene, S. Characterizing the dynamics of mental representations: the temporal generalization method. Trends Cogn. Sci. 18, 203–210 (2014).
Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).
Lafon, B. et al. Low frequency transcranial electrical stimulation does not entrain sleep rhythms measured by human intracranial recordings. Nat. Commun. 8, 1199 (2017).
Alekseichuk, I. et al. Electric field dynamics in the brain during multi-electrode transcranial electric stimulation. Nat. Commun. 10, 2573 (2019).
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).
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).
Fenno, L., Yizhar, O. & Deisseroth, K. The development and application of optogenetics. Annu. Rev. Neurosci. 34, 389–412 (2011).
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).
Grossman, N. et al. Noninvasive deep brain stimulation via temporally interfering electric fields. Cell 169, 1029–1041.e16 (2017).
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).
Karabanov, A., Thielscher, A. & Siebner, H. Transcranial brain stimulation: closing the loop between brain and stimulation. Curr. Opin. Neurol. 29, 397–404 (2016).
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).
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).
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).
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).
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).
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.
Fisher, R. S. & Velasco, A. L. Electrical brain stimulation for epilepsy. Nat. Rev. Neurol. 10, 261–270 (2014).
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).
Brittain, J., Probert-Smith, P., Aziz, T. & Brown, P. Tremor suppression by rhythmic transcranial current stimulation. Curr. Biol. 23, 436–440 (2013).
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).
Schreglmann, S. R. et al. Non-invasive suppression of essential tremor via phase-locked disruption of its temporal coherence. Nat. Commun. 12, 363 (2021).
Bouthour, W. et al. Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nat. Rev. Neurol. 15, 343–352 (2019).
Lebedev, M. A. & Nicolelis, M. A. L. Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation. Physiol. Rev. 97, 767–837 (2017).
Shanechi, M. M. Brain–machine interfaces from motor to mood. Nat. Neurosci. 22, 1554–1564 (2019).
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).
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.
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).
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).
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).
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).
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).
Benabid, A. L. Deep brain stimulation for Parkinson’s disease. Curr. Opin. Neurobiol. 13, 696–706 (2003).
Brunelin, J. et al. Transcranial direct current stimulation for obsessive-compulsive disorder: a systematic review. Brain Sci. 8, 37 (2018).
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).
Grover, S., Nguyen, J. A., Viswanathan, V. & Reinhart, R. M. G. High-frequency neuromodulation improves obsessive-compulsive behavior. Nat. Med. 27, 232–238 (2021).
Gold, A. K. et al. Clinical applications of transcranial magnetic stimulation in bipolar disorder. Brain Behav. 9, e01419 (2019).
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).
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).
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).
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).
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).
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).
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).
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).
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.
The authors declare no competing interests.
Peer review information
Nature Reviews Neuroscience thanks J. Duque, who co-reviewed with G. Derosiere, C. Ruff, A. Sack and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- 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.
(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.
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.
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).
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.
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.
About this article
Cite this article
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