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Human cognition involves the dynamic integration of neural activity and neuromodulatory systems

Nature Neuroscience volume 22, pages 289–296 (2019)Cite this article

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A Publisher Correction to this article was published on 21 February 2019

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Abstract

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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Fig. 1: Spatiotemporal PCA across multiple cognitive tasks.
Fig. 2: The low-dimensional signature across cognitive tasks.
Fig. 3: The cognitive relevance of the low-dimensional embedding space.
Fig. 4: The low-dimensional integrative core of the brain across cognitive tasks.
Fig. 5: The neurochemical signature of integrated 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.

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.

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Acknowledgements

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.).

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Authors and Affiliations

  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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Contributions

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.

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Correspondence to James M. Shine.

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Integrated supplementary information

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.

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.

Supplementary Figure 3 Low-dimensional embedding space.

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

Supplementary Figure 4 The effect of regressing task structure.

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

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).

Supplementary Figure 6 Analysis pipeline.

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

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.

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Supplementary Figures 1–7 and Supplementary Tables 1–2

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Shine, J.M., Breakspear, M., Bell, P.T. et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat Neurosci 22, 289–296 (2019). https://doi.org/10.1038/s41593-018-0312-0

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