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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All code used to analyze the data in this study is available from http://github.com/macshine/state_space/.
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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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.).
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
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.
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.
The low-dimensional embedding space, comparing tPC1 with tPC4 and tPC5 (n = 100 subjects).
Embedding space (tPC1–3) following regression of the task block structure (n = 100 subjects).
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).
Schematic figure detailing the steps conducted to compare low-dimensional embedding and time-varying network topology.
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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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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