Abstract
In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, ‘spontaneous’ neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a ‘representation-rich’ approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Narrative thinking lingers in spontaneous thought
Nature Communications Open Access 06 August 2022
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
References
Uddin, L. Q. Bring the noise: reconceptualizing spontaneous neural activity. Trends Cogn. Sci. 24, 734–746 (2020).
Zhang, D. & Raichle, M. E. Disease and the brain’s dark energy. Nat. Rev. Neurol. 6, 15–28 (2010).
Becker, R., Van De Ville, D. & Kleinschmidt, A. Alpha oscillations reduce temporal long-range dependence in spontaneous human brain activity. J. Neurosci. 38, 755–764 (2018).
Allaman, L., Mottaz, A., Kleinschmidt, A. & Guggisberg, A. G. Spontaneous network coupling enables efficient task performance without local task-induced activations. J. Neurosci. 40, 9663–9675 (2020).
Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013).
Smith, S. M. et al. Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl Acad. Sci. USA 106, 13040–13045 (2009).
Tavor, I. et al. Task-free MRI predicts individual differences in brain activity during task performance. Science 352, 216–220 (2016). This study shows that individual differences in brain activity during task performance can be predicted on the basis of its neural profile off-task (that is, during rest).
Rudoy, J. D., Voss, J. L., Westerberg, C. E. & Paller, K. A. Strengthening individual memories by reactivating them during sleep. Science 326, 1079–1079 (2009).
Rasch, B., Büchel, C., Gais, S. & Born, J. Odor cues during slow-wave sleep prompt declarative memory consolidation. Science 315, 1426–1429 (2007).
Wang, B. et al. Targeted memory reactivation during sleep elicits neural signals related to learning content. J. Neurosci. 39, 6728–6736 (2019).
Cairney, S. A., El Marj, N. & Staresina, B. P. Memory consolidation is linked to spindle-mediated information processing during sleep. Curr. Biol. 28, 948–954.e4 (2018).
Pajani, A., Kok, P., Kouider, S. & de Lange, F. P. Spontaneous activity patterns in primary visual cortex predispose to visual hallucinations. J. Neurosci. 35, 12947–12953 (2015).
Chew, B. et al. Endogenous fluctuations in the dopaminergic midbrain drive behavioral choice variability. Proc. Natl Acad. Sci. USA 116, 18732–18737 (2019).
Tambini, A. & Davachi, L. Awake reactivation of prior experiences consolidates memories and biases cognition. Trends Cogn. Sci. 23, 876–890 (2019).
Higgins, C. et al. Replay bursts in humans coincide with activation of the default mode and parietal alpha networks. Neuron 109, 882–893.e7 (2021). This study shows that human replays happen in bursts and are coupled with activation of the DMN, as well as high-frequency power increase in the temporal lobe.
Yeshurun, Y., Nguyen, M. & Hasson, U. The default mode network: where the idiosyncratic self meets the shared social world. Nat. Rev. Neurosci. 22, 181–192 (2021).
Sutherland, G. R. & McNaughton, B. Memory trace reactivation in hippocampal and neocortical neuronal ensembles. Curr. Opin. Neurobiol. 10, 180–186 (2000).
Tambini, A. & Davachi, L. Persistence of hippocampal multivoxel patterns into postencoding rest is related to memory. Proc. Natl Acad. Sci. USA 110, 19591–19596 (2013).
Kriegeskorte, N., Mur, M. & Bandettini, P. A. Representational similarity analysis-connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).
Diedrichsen, J. & Kriegeskorte, N. Representational models: a common framework for understanding encoding, pattern-component, and representational-similarity analysis. PLoS Comput. Biol. 13, e1005508 (2017).
Eldar, E., Lièvre, G., Dayan, P. & Dolan, R. J. The roles of online and offline replay in planning. eLife 9, e56911 (2020).
Kurth-Nelson, Z., Economides, M., Dolan, R. J. & Dayan, P. Fast sequences of non-spatial state representations in humans. Neuron 91, 194–204 (2016).
Liu, Y., Dolan, R. J., Kurth-Nelson, Z. & Behrens, T. E. J. Human replay spontaneously reorganizes experience. Cell 178, 640–652 (2019). This study shows that human replay (measured with MEG) exhibits similarities to rodent replay, while representing sensory and structural information independently, facilitating generalization in a novel context.
Liu, Y., Mattar, M. G., Behrens, T. E., Daw, N. D. & Dolan, R. J. Experience replay is associated with efficient nonlocal learning. Science 372, eabf1357 (2021).
Schuck, N. W. & Niv, Y. Sequential replay of nonspatial task states in the human hippocampus. Science 364, eaaw5181 (2019). This study shows that human replay (measured with fMRI) can be captured in the hippocampus and is related to maintaining a neural representation of task space in the orbitofrontal cortex.
Burgess, N. & O’Keefe, J. Neuronal computations underlying the firing of place cells and their role in navigation. Hippocampus 6, 749–762 (1996).
Skaggs, W. E. & McNaughton, B. L. Replay of neuronal firing sequences in rat hippocampus during sleep following spatial experience. Science 271, 1870–1873 (1996).
Nádasdy, Z., Hirase, H., Czurkó, A., Csicsvari, J. & Buzsáki, G. Replay and time compression of recurring spike sequences in the hippocampus. J. Neurosci. 19, 9497–9507 (1999).
Louie, K. & Wilson, M. A. Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep. Neuron 29, 145–156 (2001).
Foster, D. J. & Wilson, M. A. Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature 440, 680 (2006).
Diba, K. & Buzsáki, G. Forward and reverse hippocampal place-cell sequences during ripples. Nat. Neurosci. 10, 1241 (2007).
Ji, D. & Wilson, M. A. Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat. Neurosci. 10, 100 (2007).
Davidson, T. J., Kloosterman, F. & Wilson, M. A. Hippocampal replay of extended experience. Neuron 63, 497–507 (2009).
Karlsson, M. P. & Frank, L. M. Awake replay of remote experiences in the hippocampus. Nat. Neurosci. 12, 913 (2009).
Gupta, A. S., van der Meer, M. A., Touretzky, D. S. & Redish, A. D. Hippocampal replay is not a simple function of experience. Neuron 65, 695–705 (2010).
Carr, M. F., Jadhav, S. P. & Frank, L. M. Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval. Nat. Neurosci. 14, 147 (2011).
Ambrose, R. E., Pfeiffer, B. E. & Foster, D. J. Reverse replay of hippocampal place cells is uniquely modulated by changing reward. Neuron 91, 1124–1136 (2016).
Ólafsdóttir, H. F., Carpenter, F. & Barry, C. Coordinated grid and place cell replay during rest. Nat. Neurosci. 19, 792 (2016).
O’Neill, J., Boccara, C. N., Stella, F., Schoenenberger, P. & Csicsvari, J. Superficial layers of the medial entorhinal cortex replay independently of the hippocampus. Science 355, 184–188 (2017).
Wu, C.-T., Haggerty, D., Kemere, C. & Ji, D. Hippocampal awake replay in fear memory retrieval. Nat. Neurosci. 20, 571–580 (2017).
Buzsáki, G. Hippocampal sharp wave-ripple: a cognitive biomarker for episodic memory and planning. Hippocampus 25, 1073–1188 (2015). This study shows that hippocampal SWRs are implicated in many cognitive processes, the deficit of which occurs in certain psychiatric conditions, such as schizophrenia.
Jadhav, S. P., Kemere, C., German, P. W. & Frank, L. M. Awake hippocampal sharp-wave ripples support spatial memory. Science 336, 1454–1458 (2012).
Foster, D. J. Replay comes of age. Annu. Rev. Neurosci. 40, 581–602 (2017).
Ólafsdóttir, H. F., Bush, D. & Barry, C. The role of hippocampal replay in memory and planning. Curr. Biol. 28, R37–R50 (2018).
Wittkuhn, L. & Schuck, N. W. Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nat. Commun. 12, 1795 (2021).
Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT Press, 2018).
Sutton, R. S. Dyna, an integrated architecture for learning, planning, and reacting. ACM Sigart Bull. 2, 160–163 (1991).
Mattar, M. G. & Daw, N. D. Prioritized memory access explains planning and hippocampal replay. Nat. Neurosci. 21, 1609 (2018).
Guggenmos, M., Sterzer, P. & Cichy, R. M. Multivariate pattern analysis for MEG: a comparison of dissimilarity measures. NeuroImage 173, 434–447 (2018).
Norman, K. A., Polyn, S. M., Detre, G. J. & Haxby, J. V. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10, 424–430 (2006).
Haxby, J. V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001).
Peelen, M. V. & Downing, P. E. Using multi-voxel pattern analysis of fMRI data to interpret overlapping functional activations. Trends Cogn. Sci. 11, 4–5 (2007).
Liu, Y. et al. Temporally delayed linear modelling (TDLM) measures replay in both animals and humans. eLife 10, e66917 (2021). This study shows that fast neural sequences of spontaneous reactivations can be captured non-invasively in the human brain, and the same method can be used to quantify rodent replay in electrophysiology recordings.
Harris, K. D. Nonsense correlations in neuroscience. Preprint at bioRxiv https://doi.org/10.1101/2020.11.29.402719 (2020).
Doll, B. B., Duncan, K. D., Simon, D. A., Shohamy, D. & Daw, N. D. Model-based choices involve prospective neural activity. Nat. Neurosci. 18, 767 (2015).
Eldar, E., Bae, G. J., Kurth-Nelson, Z., Dayan, P. & Dolan, R. J. Magnetoencephalography decoding reveals structural differences within integrative decision processes. Nat. Hum. Behav. 2, 670–681 (2018).
Kurth-Nelson, Z., Barnes, G., Sejdinovic, D., Dolan, R. & Dayan, P. Temporal structure in associative retrieval. eLife 4, e04919 (2015).
Momennejad, I., Otto, A. R., Daw, N. D. & Norman, K. A. Offline replay supports planning in human reinforcement learning. eLife 7, e32548 (2018).
Wimmer, G. E., Liu, Y., Vehar, N., Behrens, T. E. J. & Dolan, R. J. Episodic memory retrieval success is associated with rapid replay of episode content. Nat. Neurosci. 23, 1025–1033 (2020).
Wimmer, G. E. & Shohamy, D. Preference by association: how memory mechanisms in the hippocampus bias decisions. Science 338, 270–273 (2012).
Wise, T., Liu, Y., Chowdhury, F. & Dolan, R. J. Model-based aversive learning in humans is supported by preferential task state reactivation. Sci. Adv. 7, eabf9616 (2021).
Wittkuhn, L., Chien, S., Hall-McMaster, S. & Schuck, N. W. Replay in minds and machines. Neurosci. Biobehav. Rev. 129, 367–388 (2021).
Schapiro, A. C., McDevitt, E. A., Rogers, T. T., Mednick, S. C. & Norman, K. A. Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance. Nat. Commun. 9, 3920 (2018).
Genzel, L. et al. A consensus statement: defining terms for reactivation analysis. Phil. Trans. R. Soc. B 375, 20200001 (2020).
Tambini, A., Ketz, N. & Davachi, L. Enhanced brain correlations during rest are related to memory for recent experiences. Neuron 65, 280–290 (2010).
Schönauer, M. et al. Decoding material-specific memory reprocessing during sleep in humans. Nat. Commun. 8, 15404 (2017).
Shanahan, L. K., Gjorgieva, E., Paller, K. A., Kahnt, T. & Gottfried, J. A. Odor-evoked category reactivation in human ventromedial prefrontal cortex during sleep promotes memory consolidation. eLife 7, e39681 (2018).
Deuker, L. et al. Memory consolidation by replay of stimulus-specific neural activity. J. Neurosci. 33, 19373–19383 (2013).
Antony, J. W. & Schapiro, A. C. Active and effective replay: systems consolidation reconsidered again. Nat. Rev. Neurosci. 20, 506–507 (2019).
Schuck, N. W. et al. Human orbitofrontal cortex represents a cognitive map of state space. Neuron 91, 1402–1412 (2016).
Kornysheva, K. et al. Neural competitive queuing of ordinal structure underlies skilled sequential action. Neuron 101, 1166–1180.e3 (2019).
Hahamy, A., Wilf, M., Rosin, B., Behrmann, M. & Malach, R. How do the blind ‘see’? The role of spontaneous brain activity in self-generated perception. Brain 144, 340–353 (2021).
Shohamy, D. & Daw, N. D. Integrating memories to guide decisions. Curr. Opin. Behav. Sci. 5, 85–90 (2015).
Dolan, R. J. & Dayan, P. Goals and habits in the brain. Neuron 80, 312–325 (2013). This article provides a road map for the neuroscience research of decision-making, with implications for the ‘representation-rich’ paradigm.
Behrens, T. E. J. et al. What is a cognitive map? organizing knowledge for flexible behavior. Neuron 100, 490–509 (2018). This article discusses the functional relevance of a cognitive map, especially in terms of its computational role in human cognition as well as artificial intelligence.
Vaz, A. P., Wittig, J. H., Inati, S. K. & Zaghloul, K. A. Replay of cortical spiking sequences during human memory retrieval. Science 367, 1131–1134 (2020).
Norman, Y. et al. Hippocampal sharp-wave ripples linked to visual episodic recollection in humans. Science 365, eaax1030 (2019).
Ólafsdóttir, H. F., Barry, C., Saleem, A. B., Hassabis, D. & Spiers, H. J. Hippocampal place cells construct reward related sequences through unexplored space. eLife 4, e06063 (2015).
Moneta, N., Garvert, M. M., Heekeren, H. R. & Schuck, N. W. Parallel representation of context and multiple context-dependent values in ventro-medial prefrontal cortex. Preprint at bioRxiv https://doi.org/10.1101/2021.03.17.435844 (2021).
Wang, F., Schoenbaum, G. & Kahnt, T. Interactions between human orbitofrontal cortex and hippocampus support model-based inference. PLoS Biol. 18, e3000578 (2020).
Schuck, N. W. et al. Medial prefrontal cortex predicts internally driven strategy shifts. Neuron 86, 331–340 (2015).
Woodward, N. D. & Cascio, C. J. Resting-state functional connectivity in psychiatric disorders. JAMA Psychiatry 72, 743–744 (2015).
Snyder, A. Z. & Raichle, M. E. A brief history of the resting state: the Washington University perspective. Neuroimage 62, 902–910 (2012).
Smith, S. M. et al. Resting-state fMRI in the human connectome project. Neuroimage 80, 144–168 (2013).
Raichle, M. E. & Snyder, A. Z. A default mode of brain function: a brief history of an evolving idea. Neuroimage 37, 1083–1090 (2007).
Gusnard, D. A. & Raichle, M. E. Searching for a baseline: functional imaging and the resting human brain. Nat. Rev. Neurosci. 2, 685–694 (2001).
Raichle, M. E. The brain’s default mode network. Annu. Rev. Neurosci. 38, 433–447 (2015).
Buckner, R. L. & DiNicola, L. M. The brain’s default network: updated anatomy, physiology and evolving insights. Nat. Rev. Neurosci. 20, 593–608 (2019).
Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R. & Buckner, R. L. Functional-anatomic fractionation of the brain’s default network. Neuron 65, 550–562 (2010).
Raichle, M. E. et al. A default mode of brain function. Proc. Natl Acad. Sci. USA 98, 676–682 (2001).
Buckner, R. L. The serendipitous discovery of the brain’s default network. Neuroimage 62, 1137–1145 (2012).
Agnati, L. F., Guidolin, D., Battistin, L., Pagnoni, G. & Fuxe, K. The neurobiology of imagination: possible role of interaction-dominant dynamics and default mode network. Front. Psychol. 4, 296 (2013).
Smallwood, J. & Schooler, J. W. The science of mind wandering: empirically navigating the stream of consciousness. Annu. Rev. Psychol. 66, 487–518 (2015).
Schacter, D. L., Addis, D. R. & Buckner, R. L. Remembering the past to imagine the future: the prospective brain. Nat. Rev. Neurosci. 8, 657–661 (2007).
Hassabis, D. & Maguire, E. A. Deconstructing episodic memory with construction. Trends Cogn. Sci. 11, 299–306 (2007).
Meyer, M. L., Davachi, L., Ochsner, K. N. & Lieberman, M. D. Evidence that default network connectivity during rest consolidates social information. Cereb. Cortex 29, 1910–1920 (2019).
Constantinescu, A. O., O’Reilly, J. X. & Behrens, T. E. J. Organizing conceptual knowledge in humans with a gridlike code. Science 352, 1464 (2016). This study provides evidence for grid-like coding of non-physical space in the brain areas that constitute the DMN.
Park, S. A., Miller, D. S. & Boorman, E. D. Novel inferences in a multidimensional social network use a grid-like code. Preprint at bioRxiv https://doi.org/10.1101/2020.05.29.124651 (2020).
Baldassano, C., Hasson, U. & Norman, K. A. Representation of real-world event schemas during narrative perception. J. Neurosci. 38, 9689–9699 (2018).
Vidaurre, D., Myers, N. E., Stokes, M., Nobre, A. C. & Woolrich, M. W. Temporally unconstrained decoding reveals consistent but time-varying stages of stimulus processing. Cereb. Cortex 29, 863–874 (2019).
Kaplan, R. et al. Hippocampal sharp-wave ripples influence selective activation of the default mode network. Curr. Biol. 26, 686–691 (2016). This study shows a selective coupling between hippocampal ripples and activation of the DMN in the monkey brain.
Liu, X. et al. Multimodal neural recordings with Neuro-FITM uncover diverse patterns of cortical–hippocampal interactions. Nat. Neurosci. 24, 886–896 (2021).
Wang, M., Foster, D. J. & Pfeiffer, B. E. Alternating sequences of future and past behavior encoded within hippocampal theta oscillations. Science 370, 247 (2020).
Buzsáki, G. & Moser, E. I. Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nat. Neurosci. 16, 130 (2013). This article provides a synthesis of the hippocampal–entorhinal system in supporting both navigation and memory.
Redish, A. D. Vicarious trial and error. Nat. Rev. Neurosci. 17, 147 (2016).
Sylvester, C. M. et al. Individual-specific functional connectivity of the amygdala: a substrate for precision psychiatry. Proc. Natl Acad. Sci. USA 117, 3808–3818 (2020).
McCutcheon, R. A., Marques, T. R. & Howes, O. D. Schizophrenia — an overview. JAMA Psychiatry 77, 201–210 (2020).
Suh, J., Foster, D. J., Davoudi, H., Wilson, M. A. & Tonegawa, S. Impaired hippocampal ripple-associated replay in a mouse model of schizophrenia. Neuron 80, 484–493 (2013).
Altimus, C., Harrold, J., Jaaro-Peled, H., Sawa, A. & Foster, D. J. Disordered ripples are a common feature of genetically distinct mouse models relevant to schizophrenia. Mol. Neuropsychiatry 1, 52–59 (2015).
Zeidman, P. & Maguire, E. A. Anterior hippocampus: the anatomy of perception, imagination and episodic memory. Nat. Rev. Neurosci. 17, 173–182 (2016).
Adams, R. A. et al. Impaired theta phase coupling underlies frontotemporal dysconnectivity in schizophrenia. Brain 143, 1261–1277 (2020).
Titone, D., Ditman, T., Holzman, P. S., Eichenbaum, H. & Levy, D. L. Transitive inference in schizophrenia: impairments in relational memory organization. Schizophr. Res. 68, 235–247 (2004).
Nour, M. M., Liu, Y., Arumuham, A., Kurth-Nelson, Z. & Dolan, R. Impaired neural replay of inferred relationships in schizophrenia. Cell 184, 4315–4328 (2021). This study is the first demonstration of augmented ripple power, cognitive map deficit and diminished replay in PScz.
Whitfield-Gabrieli, S. & Ford, J. M. Default mode network activity and connectivity in psychopathology. Annu. Rev. Clin. Psychol. 8, 49–76 (2012).
Huys, Q. J. et al. Interplay of approximate planning strategies. Proc. Natl Acad. Sci. USA 112, 3098–3103 (2015).
Huys, Q. J. et al. Bonsai trees in your head: how the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Comput. Biol. 8, e1002410 (2012).
Heller, A. S. & Bagot, R. C. Is hippocampal replay a mechanism for anxiety and depression? JAMA Psychiatry 77, 431–432 (2020).
Lewis, P. A., Knoblich, G. & Poe, G. How memory replay in sleep boosts creative problem-solving. Trends Cogn. Sci. 22, 491–503 (2018).
Klinzing, J. G., Niethard, N. & Born, J. Mechanisms of systems memory consolidation during sleep. Nat. Neurosci. 22, 1598–1610 (2019).
Wei, Y., Krishnan, G. P., Marshall, L., Martinetz, T. & Bazhenov, M. Stimulation augments spike sequence replay and memory consolidation during slow-wave sleep. J. Neurosci. 40, 811–824 (2020).
Tamminen, J., Lambon Ralph, M. A. & Lewis, P. A. The role of sleep spindles and slow-wave activity in integrating new information in semantic memory. J. Neurosci. 33, 15376–15381 (2013).
Wilson, M. A. & McNaughton, B. L. Reactivation of hippocampal ensemble memories during sleep. Science 265, 676–679 (1994).
Pfeiffer, B. E. The content of hippocampal “replay”. Hippocampus 30, 6–18 (2020). Human replay (measured with MEG) is found to bear many similarities to rodent replay, and here is shown to represent sensory and structural information independently, thus facilitating generalization in a novel context.
Zielinski, M. C., Shin, J. D. & Jadhav, S. P. Hippocampal theta sequences in REM sleep during spatial learning. Preprint at bioRxiv https://doi.org/10.1101/2021.04.15.439854 (2021).
McNamee, D. C., Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. Flexible modulation of sequence generation in the entorhinal–hippocampal system. Nat. Neurosci. 24, 851–862 (2021).
Krause, E. L. & Drugowitsch, J. A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum. Neuron 110, 722–733.e8 (2021).
Stella, F., Baracskay, P., O’Neill, J. & Csicsvari, J. Hippocampal reactivation of random trajectories resembling Brownian diffusion. Neuron 102, 450–461.e7 (2019).
Pereira, S. I. R. & Lewis, P. A. Sleeping through brain excitation and inhibition. Nat. Neurosci. 23, 1037–1039 (2020).
Belal, S. et al. Identification of memory reactivation during sleep by EEG classification. Neuroimage 176, 203–214 (2018).
Higgins, C. Uncovering Temporal Structure in Neural Data with Statistical Machine Learning Models (Univ. Oxford, 2019).
Kriegeskorte, N. et al. Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60, 1126–1141 (2008).
Barron, H. C., Mars, R. B., Dupret, D., Lerch, J. P. & Sampaio-Baptista, C. Cross-species neuroscience: closing the explanatory gap. Phil. Trans. R. Soc. B 376, 20190633 (2021).
Barron, H. C. et al. Neuronal computation underlying inferential reasoning in humans and mice. Cell 183, 228–243.e21 (2020).
Akam, T., Costa, R. & Dayan, P. Simple plans or sophisticated habits? State, transition and learning interactions in the two-step task. PLoS Comput. Biol. 11, e1004648 (2015).
Miranda, B., Malalasekera, W. M. N., Behrens, T. E., Dayan, P. & Kennerley, S. W. Combined model-free and model-sensitive reinforcement learning in non-human primates. PLoS Comput. Biol. 16. e1007944 (2020).
Mack, M. L., Preston, A. R. & Love, B. C. Ventromedial prefrontal cortex compression during concept learning. Nat. Commun. 11, 46 (2020).
Morton, N. W., Schlichting, M. L. & Preston, A. R. Representations of common event structure in medial temporal lobe and frontoparietal cortex support efficient inference. Proc. Natl Acad. Sci. USA 117, 29338–29345 (2020).
Al Roumi, F., Marti, S., Wang, L., Amalric, M. & Dehaene, S. Mental compression of spatial sequences in human working memory using numerical and geometrical primitives. Neuron 109, 2627–2639.e4 (2021).
Bernardi, S. et al. The geometry of abstraction in hippocampus and prefrontal cortex. Cell 183, 954–967.e21 (2020).
Higgins, I. et al. Darla: improving zero-shot transfer in reinforcement learning. Proc. Int. Conf. Mach. Learn. 70, 1480–1490 (2017).
Whittington, J. C. et al. The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation. Cell 183, 1249–1263.e23 (2020). This study describes a unifying computational model of the hippocampal–entorhinal system for generalization and inference in an arbitrary relational graph (including physical space and memory).
Schwartenbeck, P. et al. Generative replay for compositional visual understanding in the prefrontal-hippocampal circuit. Preprint at bioRxiv https://doi.org/10.1101/2021.06.06.447249 (2021).
Kragel, P. A., Knodt, A. R., Hariri, A. R. & LaBar, K. S. Decoding spontaneous emotional states in the human brain. PLoS Biol. 14, e2000106 (2016).
Tusche, A., Smallwood, J., Bernhardt, B. C. & Singer, T. Classifying the wandering mind: revealing the affective content of thoughts during task-free rest periods. Neuroimage 97, 107–116 (2014).
Van Calster, L., D’Argembeau, A., Salmon, E., Peters, F. & Majerus, S. Fluctuations of attentional networks and default mode network during the resting state reflect variations in cognitive states: evidence from a novel resting-state experience sampling method. J. Cogn. Neurosci. 29, 95–113 (2017).
Smallwood, J. et al. The neural correlates of ongoing conscious thought. iScience 24, 102132 (2021).
Colgin, L. L. Rhythms of the hippocampal network. Nat. Rev. Neurosci. 17, 239 (2016).
Ólafsdóttir, H. F., Carpenter, F. & Barry, C. Task demands predict a dynamic switch in the content of awake hippocampal replay. Neuron 96, e926 (2017).
Gillespie, A. K. et al. Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice. Neuron 109, 3149–3163.e6 (2021).
Chadwick, A., van Rossum, M. C. & Nolan, M. F. Independent theta phase coding accounts for CA1 population sequences and enables flexible remapping. eLife 4, e03542 (2015).
Skaggs, W. E., McNaughton, B. L., Wilson, M. A. & Barnes, C. A. Theta phase precession in hippocampal neuronal populations and the compression of temporal sequences. Hippocampus 6, 149–172 (1996).
Wikenheiser, A. M. & Redish, A. D. Hippocampal theta sequences reflect current goals. Nat. Neurosci. 18, 289–294 (2015).
Johnson, A. & Redish, A. D. Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. J. Neurosci. 27, 12176–12189 (2007).
Kay, K. et al. Constant sub-second cycling between representations of possible futures in the hippocampus. Cell 180, 552–567.e25 (2020).
Schultz, W., Dayan, P. & Montague, P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).
Doll, B. B., Simon, D. A. & Daw, N. D. The ubiquity of model-based reinforcement learning. Curr. Opin. Neurobiol. 22, 1075–1081 (2012).
Daw, N. D. & Dayan, P. The algorithmic anatomy of model-based evaluation. Phil. Trans. R. Soc. B 369, 20130478 (2014).
Siegel, S. & Allan, L. G. The widespread influence of the Rescorla-Wagner model. Psychon. Bull. Rev. 3, 314–321 (1996).
Sutton, R. S. & Barto, A. G. in Proceedings of the Ninth Annual Conference of the Cognitive Science Society (1987).
Gallistel, C. R., LoLordo, V. M., Rozin, P. & Seligman, M. E. P. Robert A. Rescorla (1940–2020). Am. Psychol. 76, 391–392 (2021).
Vikbladh, O. M. et al. Hippocampal contributions to model-based planning and spatial memory. Neuron 102, 683–693.e4 (2019).
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P. & Dolan, R. J. Model-based influences on humans’ choices and striatal prediction errors. Neuron 69, 1204–1215 (2011).
Kool, W., Cushman, F. A. & Gershman, S. J. When does model-based control pay off? PLoS Comput. Biol. 12, e1005090 (2016).
Acknowledgements
The authors thank E. Wimmer, C. Higgins and P. Schwartenbeck for helpful discussions and fruitful collaborations regarding the work and ideas presented in this Review. This work was supported by the Fundamental Research Funds for the Central Universities (to Y.L.), a Wellcome Trust Investigator Award (098362/Z/12/Z to R.J.D.), a UCL Welcome PhD Fellowship for Clinicians (102186/B/13/Z to M.M.N.), a Wellcome Trust Senior Research Fellowship (104765/Z/14/Z to T.B.), a Principal Research Fellowship (219525/Z/19/Z to T.B.), a James S. McDonnell Foundation Award (JSMF220020372 to T.B.), an Independent Research Group Grant from the Max Planck Society (M.TN.A.BILD0004 to N.W.S.) and a Starting Grant from the European Union (ERC-2019-StG REPLAY-852669 to N.W.S.). M.M.N. is a predoctoral fellow of the International Max Planck Research School on Computational Methods in Psychiatry and Ageing Research (https://www.mps-ucl-centre.mpg.de/en/comp2psych). The Max Planck UCL Centre for Computational Psychiatry and Ageing Research is supported by UCL and the Max Planck Society. The Wellcome Centre for Human Neuroimaging is supported by core funding from the Wellcome Trust (203147/Z/16/Z). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z).
Author information
Authors and Affiliations
Contributions
Y.L. researched data for article and contributed substantially to discussion of the content, writing, review and editing of the manuscript before submission. T.E.J.B. contributed substantially to discussion of the content of the manuscript and contributed to the writing, review and editing of the manuscript. R.J.D. contributed to the writing, review and editing of the manuscript. M.M.N. and N.W.S. contributed to the writing of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Reviews Neuroscience thanks J. Andrews-Hanna and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Glossary
- Task
-
An experiment designed to manipulate an assumed cognitive process.
- Off-task
-
Period without explicit task demand (for example, during rest).
- Functional connectivity
-
Temporal dependency of neuronal activation (for example, correlation) between anatomically separated brain regions.
- Power
-
The strength of a signal in a given frequency band.
- Decoding
-
Reading out task-related information from neural activity.
- Pairwise multivoxel correlation
-
Correlation between all pairs of voxels of interest using the entire time course of the functional MRI signal.
- Representational similarity analysis
-
Measure of the similarity of neural activity among different conditions.
- Multivoxel patterns
-
Neural activity profile of multiple voxels in the brain.
- Transition matrix
-
A matrix that stores the probability of transition from state s to state s′.
- Regressors
-
Independent variables in a regression model.
- Multivariate decision boundary
-
A region of a problem space where the output label of a classifier is ambiguous.
- Charles Bonnet syndrome
-
A condition where visual hallucinations occur as a result of vision loss.
- Resting states
-
The states when an explicit task is not being performed.
- Brownian diffusive spatial trajectories
-
Trajectories whose movement is random in space.
- Superdiffusive dynamics
-
Random movement but with sudden jumps.
Rights and permissions
About this article
Cite this article
Liu, Y., Nour, M.M., Schuck, N.W. et al. Decoding cognition from spontaneous neural activity. Nat Rev Neurosci 23, 204–214 (2022). https://doi.org/10.1038/s41583-022-00570-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41583-022-00570-z
This article is cited by
-
Intrinsic dopamine and acetylcholine dynamics in the striatum of mice
Nature (2023)
-
Brain–heart interaction disruption in major depressive disorder: disturbed rhythm modulation of the cardiac cycle on brain transient theta bursts
European Archives of Psychiatry and Clinical Neuroscience (2023)
-
Narrative thinking lingers in spontaneous thought
Nature Communications (2022)
