The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here we investigated the spatial, dynamic, and molecular signatures of system-wide neural activity across a range of cognitive tasks. We found that neuronal activity converged onto a low-dimensional manifold that facilitates the execution of diverse task states. Flow within this attractor space was associated with dissociable cognitive functions, unique patterns of network-level topology, and individual differences in fluid intelligence. The axes of the low-dimensional neurocognitive architecture aligned with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between neural activity, neuromodulatory systems, and cognitive function.

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Code availability

All code used to analyze the data in this study is available from http://github.com/macshine/state_space/.

Data availability

Data were provided by the Human Connectome Project (HCP); the Washington University, University of Minnesota, and Oxford University Consortium (Principal Investigators David Van Essen and Kamil Ugurbil; grant no. 1U54MH091657) funded by 16 NIH institutes and centers that support the NIH Blueprint for Neuroscience Research; and the McDonnell Center for Systems Neuroscience at Washington University. This project also made use of Connectome DB and Connectome Workbench, developed under the auspices of the HCP (HCP 1200 Subject Release, http://www.humanconnectome.org/). Neurotransmitter receptor data from the Allen Human Brain Atlas (2010 Allen Institute for Brain Science; available from: human.brain-map.org) were obtained from neurosynth.org.

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Change history

  • 21 February 2019

    In the version of this article initially published, Kaylena A. Ehgoetz Martens’ name was misspelled as Kayla. The error has been corrected in the HTML and PDF versions of the article.


  1. 1.

    Breakspear, M. Dynamic models of large-scale brain activity. Nat. Neurosci. 20, 340–352 (2017).

  2. 2.

    Tononi, G. & Edelman, G. M. Consciousness and complexity. Science 282, 1846–1851 (1998).

  3. 3.

    Krienen, F. M., Yeo, B. T. T. & Buckner, R. L. Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture. Phil. Trans. R. Soc. Lond. B 369, 20130526 (2014).

  4. 4.

    Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S. & Petersen, S. E. Intrinsic and task-evoked network architectures of the human brain. Neuron 83, 238–251 (2014).

  5. 5.

    Ghosh, A., Rho, Y., McIntosh, A. R., Kötter, R. & Jirsa, V. K. Noise during rest enables the exploration of the brain’s dynamic repertoire. PLoS Comput. Biol. 4, e1000196 (2008).

  6. 6.

    Tononi, G., Sporns, O. & Edelman, G. M. Measures of degeneracy and redundancy in biological networks. Proc. Natl Acad. Sci. USA 96, 3257–3262 (1999).

  7. 7.

    Cunningham, J. P. & Yu, B. M. Dimensionality reduction for large-scale neural recordings. Nat. Neurosci. 17, 1500–1509 (2014).

  8. 8.

    Kato, S. et al. Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell 163, 656–669 (2015).

  9. 9.

    Nichols, A. L. A., Eichler, T., Latham, R. & Zimmer, M. A global brain state underlies C. elegans sleep behavior. Science 356, eaam6851 (2017).

  10. 10.

    Lemon, W. C. et al. Whole-central nervous system functional imaging in larval Drosophila. Nat. Commun. 6, 7924 (2015).

  11. 11.

    Stitt, I. et al. Dynamic reconfiguration of cortical functional connectivity across brain states. Sci. Rep. 7, 8797 (2017).

  12. 12.

    Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).

  13. 13.

    Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).

  14. 14.

    Hasson, U., Nir, Y., Levy, I., Fuhrmann, G. & Malach, R. Intersubject synchronization of cortical activity during natural vision. Science 303, 1634–1640 (2004).

  15. 15.

    Kennel, M. B. & Abarbanel, H. D. I. False neighbors and false strands: a reliable minimum embedding dimension algorithm. Phys. Rev. E 66, 026209 (2002).

  16. 16.

    Dosenbach, N. U. F. et al. A core system for the implementation of task sets. Neuron 50, 799–812 (2006).

  17. 17.

    Broomhead, D. S. & King, G. P. Extracting qualitative dynamics from experimental data. Physica D 20, 217–236 (1986).

  18. 18.

    Friston, K. J., Frith, C. D., Liddle, P. F. & Frackowiak, R. S. Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13, 5–14 (1993).

  19. 19.

    Woodman, M. M. & Jirsa, V. K. Emergent dynamics from spiking neuron networks through symmetry breaking of connectivity. PLoS ONE 8, e64339 (2013).

  20. 20.

    Ott, E. & Antonsen, T. M. Low dimensional behavior of large systems of globally coupled oscillators. Chaos 18, 037113 (2008).

  21. 21.

    Al-Aidroos, N., Said, C. P. & Turk-Browne, N. B. Top-down attention switches coupling between low-level and high-level areas of human visual cortex. Proc. Natl Acad. Sci. USA 109, 14675–14680 (2012).

  22. 22.

    Cocchi, L., Gollo, L. L., Zalesky, A. & Breakspear, M. Criticality in the brain: a synthesis of neurobiology, models and cognition. Prog. Neurobiol. 158, 132–152 (2017).

  23. 23.

    Pillai, A. S. & Jirsa, V. K. Symmetry breaking in space-time hierarchies shapes brain dynamics and behavior. Neuron 94, 1010–1026 (2017).

  24. 24.

    Gray, J. R., Chabris, C. F. & Braver, T. S. Neural mechanisms of general fluid intelligence. Nat. Neurosci. 6, 316–322 (2003).

  25. 25.

    Poldrack, R. A. et al. Discovering relations between mind, brain, and mental disorders using topic mapping. PLoS Comput. Biol. 8, e1002707 (2012).

  26. 26.

    Shine, J. M. & Poldrack, R. A. Principles of dynamic network reconfiguration across diverse brain states. Neuroimage 180, 396–405 (2018).

  27. 27.

    Shine, J. M. et al. The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92, 544–554 (2016).

  28. 28.

    Le Van Quyen, M. Disentangling the dynamic core: a research program for a neurodynamics at the large-scale. Biol. Res. 36, 67–88 (2003).

  29. 29.

    DeSalvo, M. N., Douw, L., Takaya, S., Liu, H. & Stufflebeam, S. M. Task-dependent reorganization of functional connectivity networks during visual semantic decision making. Brain Behav. 4, 877–885 (2014).

  30. 30.

    Boly, M. et al. Stimulus set meaningfulness and neurophysiological differentiation: a functional magnetic resonance imaging study. PLoS ONE 10, e0125337 (2015).

  31. 31.

    Gollo, L. L., Zalesky, A., Hutchison, R. M., van den Heuvel, M. & Breakspear, M. Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations. Phil. Trans. R. Soc. Lond. B 370, 20140165 (2015).

  32. 32.

    Honey, C. J. et al. Slow cortical dynamics and the accumulation of information over long timescales. Neuron 76, 423–434 (2012).

  33. 33.

    Robbins, T. W. & Arnsten, A. F. T. The neuropsychopharmacology of fronto-executive function: monoaminergic modulation. Annu. Rev. Neurosci. 32, 267–287 (2009).

  34. 34.

    Aston-Jones, G. & Cohen, J. D. An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28, 403–450 (2005).

  35. 35.

    van den Heuvel, M. P. & Sporns, O. An anatomical substrate for integration among functional networks in human cortex. J. Neurosci. 33, 14489–14500 (2013).

  36. 36.

    Puig, M. V., Gulledge, A. T., Lambe, E. K. & Gonzalez-Burgos, G. Editorial: neuromodulation of executive circuits. Front. Neural Circuits 9, 58 (2015).

  37. 37.

    Brezina, V. Beyond the wiring diagram: signalling through complex neuromodulator networks. Phil. Trans. R. Soc. Lond. B 365, 2363–2374 (2010).

  38. 38.

    Avery, M. C. & Krichmar, J. L. Neuromodulatory systems and their interactions: a review of models, theories, and experiments. Front. Neural Circuits 11, 108 (2017).

  39. 39.

    Shine, J. M., Aburn, M. J., Breakspear, M. & Poldrack, R. A. The modulation of neural gain facilitates a transition between functional segregation and integration in the brain. eLife 7, e31130 (2018).

  40. 40.

    Gu, S. et al. Controllability of structural brain networks. Nat. Commun. 6, 8414 (2015).

  41. 41.

    Yeh, F.-C. et al. Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 178, 57–68 (2018).

  42. 42.

    Tu, C. et al. Warnings and caveats in brain controllability. Neuroimage 176, 83–91 (2018).

  43. 43.

    Huys, R., Perdikis, D. & Jirsa, V. K. Functional architectures and structured flows on manifolds: a dynamical framework for motor behavior. Psychol. Rev. 121, 302–336 (2014).

  44. 44.

    Bell, P. T. & Shine, J. M. Subcortical contributions to large-scale network communication. Neurosci. Biobehav. Rev. 71, 313–322 (2016).

  45. 45.

    Fulcher, B. D. & Fornito, A. A transcriptional signature of hub connectivity in the mouse connectome. Proc. Natl Acad. Sci. USA 113, 1435–1440 (2016).

  46. 46.

    van den Heuvel, M. P., Bullmore, E. T. & Sporns, O. Comparative connectomics. Trends Cogn. Sci. 20, 345–361 (2016).

  47. 47.

    Poldrack, R. A. & Yarkoni, T. From brain maps to cognitive ontologies: informatics and the search for mental structure. Annu. Rev. Psychol. 67, 587–612 (2016).

  48. 48.

    Moran, R. J. et al. Free energy, precision and learning: the role of cholinergic neuromodulation. J. Neurosci. 33, 8227–8236 (2013).

  49. 49.

    Cohen, J. D., Braver, T. S. & Brown, J. W. Computational perspectives on dopamine function in prefrontal cortex. Curr. Opin. Neurobiol. 12, 223–229 (2002).

  50. 50.

    Vidaurre, D., Smith, S. M. & Woolrich, M. W. Brain network dynamics are hierarchically organized in time. Proc. Natl Acad. Sci. USA 114, 12827–12832 (2017).

  51. 51.

    Glasser, M. F. et al. The minimal preprocessing pipelines for the human connectome project. Neuroimage 80, 105–124 (2013).

  52. 52.

    Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320–341 (2014).

  53. 53.

    Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101 (2007).

  54. 54.

    Diedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E. & Ramnani, N. A probabilistic MR atlas of the human cerebellum. Neuroimage 46, 39–46 (2009).

  55. 55.

    McIntosh, A. R. & Lobaugh, N. J. Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23, S250–S263 (2004).

  56. 56.

    Shine, J. M. et al. Estimation of dynamic functional connectivity using multiplication of temporal derivatives. Neuroimage 122, 399–407 (2015).

  57. 57.

    Thompson, W. H., Richter, C. G., Plavén-Sigray, P. & Fransson, P. Simulations to benchmark time-varying connectivity methods for fMRI. PLoS Comput. Biol. 14, e1006196 (2018).

  58. 58.

    Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010).

  59. 59.

    Guimerà, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).

  60. 60.

    Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N. & Petersen, S. E. Evidence for hubs in human functional brain networks. Neuron 79, 798–813 (2013).

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We would like to thank T. Verstynen for the diffusion data, and D. Bassett for the controllability code. The funding for the study was provided by an NHMRC CJ Martin Fellowship (GNT1072403) and a University of Sydney SOAR Fellowship (J.M.S.).

Author information


  1. Brain and Mind Center, The University of Sydney, Sydney, New South Wales, Australia

    • James M. Shine
    • , Kaylena A. Ehgoetz Martens
    •  & Richard Shine
  2. QIMR Berghofer, Brisbane, Queensland, Australia

    • Michael Breakspear
  3. Metro North Mental Health Service, Brisbane, Queensland, Australia

    • Michael Breakspear
  4. Hunter Medical Research Institute, University of Newcastle, Callaghan, New South Wales, Australia

    • Michael Breakspear
  5. University of Queensland, Brisbane, Queensland, Australia

    • Peter T. Bell
  6. Macquarie University, Sydney, New South Wales, Australia

    • Richard Shine
  7. University of Illinois, Champagne, IL, USA

    • Oluwasanmi Koyejo
  8. Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA

    • Olaf Sporns
  9. Department of Psychology, Stanford University, Stanford, CA, USA

    • Russell A. Poldrack


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J.M.S. and O.S. conceived of the idea. J.M.S., M.B., O.S., and R.A.P. designed the analysis plan. J.M.S. ran the analyses and wrote the first draft of the manuscript. M.B., P.T.B., K.A.E.M., O.S., R.S., and R.A.P. provided critical methodological and conceptual input. All authors provided critical feedback on the manuscript, including editing of the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to James M. Shine.

Integrated supplementary information

  1. Supplementary Figure 1 Intersubject correlation analysis.

    Left: scatter plot comparing the regional loadings from PC1 with similar values derived using the intersubject correlation (ISC) approach (for example, from Hasson et al., 2004)—the spatial correlation between the weighted ISC and PC1 was 0.58; however; the relationship between the 2 variables was not linear. Right: conjunction analysis comparing the results of the ISC approach with the spatial loadings of PC1: regions in yellow were present in both ISC and PC1, whereas regions in blue were uniquely associated with ISC and regions in orange were uniquely associated with PC1. Briefly, this analysis involves calculating a regression coefficient for each pair of similar regions across all 4,950 subject pairs from the discovery cohort (for comparison, the original study in 2003 was performed in 5 subjects, leaving 10 unique subject pairs). Regions with strong regression coefficients across the 4,950 subject pairs can be said to have strong intersubject correlations. We calculated a Pearson’s correlation between the mean regional weighting across the discovery cohort and the spatial loadings of the top five PCs. We observed a significant positive correlation between the regional weightings associated with the ISC map and spatial loadings from PC1 (r = 0.58), PC2 (r = 0.25), PC3 (r = 0.20), and tPC5 (r = 0.13), suggesting that there was significant, but not selective, overlap between the ISC approach and PCA.

  2. Supplementary Figure 2 Bootstrapping analysis.

    Results of the bootstrapping analysis that shows that 4–5 tasks are required to discover the same underlying principal component (tPC1) that recurs across task blocks; black line denotes the mean similarity and error bars denote standard error across 100 iterations.

  3. Supplementary Figure 3 Low-dimensional embedding space.

    The low-dimensional embedding space, comparing tPC1 with tPC4 and tPC5 (n = 100 subjects).

  4. Supplementary Figure 4 The effect of regressing task structure.

    Embedding space (tPC1–3) following regression of the task block structure (n = 100 subjects).

  5. Supplementary Figure 5 Relationship between the low-dimensional manifold and fluid intelligence.

    Strength of Pearson’s correlation between fluid intelligence (number of correct items on Raven’s progressive matrices; measured outside of the magnetic resonance imaging scanner) and the loading of tPC1 (collapsed onto low-dimensional manifold using the relative phase of tPC1—see lower right inset). Thick lines denote statistical significance (P <0.05; randomized null model), whereas dotted lines denote non-significance (P >0.05; n = 100 subjects).

  6. Supplementary Figure 6 Analysis pipeline.

    Schematic figure detailing the steps conducted to compare low-dimensional embedding and time-varying network topology.

  7. Supplementary Figure 7 Individual differences in low-dimensional flow.

    Similarity of the tPC1-tPC5 time series across 100 subjects from the discovery cohort; mean time series is presented in black; colored blocks represent each of the 7 tasks from the HCP. As per Fig. 1, Pearson’s correlations were used to compare the mean tPC with the cross-task block structure.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–7 and Supplementary Tables 1–2

  2. Reporting Summary

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