Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A synergistic core for human brain evolution and cognition

Nature Neuroscience volume 25, pages 771–782 (2022)Cite this article

Subjects

Abstract

How does the organization of neural information processing enable humans’ sophisticated cognition? Here we decompose functional interactions between brain regions into synergistic and redundant components, revealing their distinct information-processing roles. Combining functional and structural neuroimaging with meta-analytic results, we demonstrate that redundant interactions are predominantly associated with structurally coupled, modular sensorimotor processing. Synergistic interactions instead support integrative processes and complex cognition across higher-order brain networks. The human brain leverages synergistic information to a greater extent than nonhuman primates, with high-synergy association cortices exhibiting the highest degree of evolutionary cortical expansion. Synaptic density mapping from positron emission tomography and convergent molecular and metabolic evidence demonstrate that synergistic interactions are supported by receptor diversity and human-accelerated genes underpinning synaptic function. This information-resolved approach provides analytic tools to disentangle information integration from coupling, enabling richer, more accurate interpretations of functional connectivity, and illuminating how the human neurocognitive architecture navigates the trade-off between robustness and integration.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Synergistic and redundant networks exhibit distinct anatomical and cognitive profiles.
Fig. 2: Distinct cytoarchitectonic and resting-state network profiles for synergy-dominated and redundancy-dominated regions.
Fig. 3: Network analysis indicates global and segregated processing for synergy and redundancy, respectively.
Fig. 4: Redundant interactions are supported by anatomical connections; synergistic interactions connect regions with distinct structural wiring profiles.
Fig. 5: Human brain evolution favored high synergy.
Fig. 6: Synaptic underpinnings of synergy in the human brain.
Fig. 7: Metabolic and molecular underpinnings of synergy in the human brain.

Similar content being viewed by others

Data availability

The HCP DWI data in SRC format are available online (http://brain.labsolver.org/diffusion-mri-data/hcp-dmri-data). The HCP fMRI data are available online (https://www.humanconnectome.org/study/hcp-young-adult/data-releases). Macaque MRI data are available from the PRIME-DE through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC; http:\\fcon_1000.projects.nitrc.org/indi/indiPRIME.html). The PET data that support the findings of this study are available from J.B.R. (james.rowe@mrc-cbu.cam.ac.uk) upon reasonable request for academic (noncommercial) purposes, subject to restrictions required to preserve participant confidentiality. The macaque connectome is available on Zenodo at https://doi.org/10.5281/zenodo.1471588. The CoCoMac database on which it is based, is also available online at http://cocomac.g-node.org/main/index.php?. The GTEx database (release V6p) is available at https://www.gtexportal.org/. The BSS database is available at http://brainspan.org/. Cortical gene expression patterns were taken from the transcriptomic data of the AHBA (http://human.brain-map.org/static/download). Region-wise maps of chimpanzee-to-human cortical expansion and HAR gene expression are available as supplementary materials from Wei et al.34 (https://doi.org/10.1038/s41467-019-12764-8). The NMT anatomical volume and associated probabilistic tissue segmentation maps (GM, WM and CSF) are freely available online: https://afni.nimh.nih.gov/pub/dist/atlases/macaque/nmt and http://github.com/jms290/NMT. The maps of average regional GI are available as supplementary materials from Vaishnavi et al.39 (https://doi.org/10.1073/pnas.1010459107). The genes whose expression is associated with the regional distribution of GI in the human brain are available as supplementary materials from Goyal et al.38 (https://doi.org/10.1016/j.cmet.2013.11.020). Anonymized receptor autoradiography data from Goulas et al.40 are available at https://github.com/AlGoulas/receptor_principles. The measure of cortical wiring distance is available as supplementary information from Paquola et al.32 (https://doi.org/10.1371/journal.pbio.3000979).

Code availability

Data analysis was carried out in MATLAB version 2019a. The Java Information Dynamics Toolbox v1.5 is freely available online at https://github.com/jlizier/jidt. An updated version with MATLAB/Octave code to compute synergy and redundancy from integrated information decomposition of time series with the Gaussian MMI solver is available as Supplementary Software. The CONN toolbox version 17f is freely available at http://www.nitrc.org/projects/conn/. DSI Studio is freely available at https://dsi-studio.labsolver.org/. The Brain Connectivity Toolbox code used for graph-theoretical analyses is freely available at https://sites.google.com/site/bctnet/. The code used for NeuroSynth meta-analysis is freely available at https://www.github.com/gpreti/GSP_StructuralDecouplingIndex. The HRF deconvolution toolbox v2.2 is freely available at https://www.nitrc.org/projects/rshrf/. The Pypreclin pipeline code v1.0.1 is freely available at https://github.com/neurospin/pypreclin. The code for PLS analysis of gene expression profiles is freely available at https://github.com/SarahMorgan/Morphometric_Similarity_SZ. The R package plsgenomics v1.5 is freely available at https://CRAN.R-project.org/package=plsgenomics. The GOrilla platform is available at http://cbl-gorilla.cs.technion.ac.il. The REVIGO platform is available at http://revigo.irb.hr. The code for the dynamic mean-field model is freely available at http://www.gitlab.com/concog/fastdmf. The code for spin-based permutation testing of cortical correlations is freely available at https://github.com/frantisekvasa/rotate_parcellation. The code for gene enrichment relative to an ensemble of null phenotypes is freely available at https://github.com/benfulcher/GeneCategoryEnrichmentAnalysis/wiki/Ensemble-enrichment. FreeSurfer v5.3.0 is available at https://surfer.nmr.mgh.harvard.edu/. SPM12 is available at www.fil.ion.ucl.ac.uk/spm/software/spm12/.

References

  1. Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information (MIT Press, 2010).

  2. Williams, P. L. & Beer, R. D. Nonnegative decomposition of multivariate information. Preprint at http://arxiv.org/abs/1004.2515 (2010).

  3. Timme, N., Alford, W., Flecker, B. & Beggs, J. M. Synergy, redundancy and multivariate information measures: an experimentalist’s perspective. J. Comput. Neurosci. 36, 119–140 (2014).

    Article  PubMed  Google Scholar 

  4. Wibral, M., Priesemann, V., Kay, J. W., Lizier, J. T. & Phillips, W. A. Partial information decomposition as a unified approach to the specification of neural goal functions. Brain Cogn. 112, 25–38 (2017).

    Article  PubMed  Google Scholar 

  5. Del Giudice, M. & Crespi, B. J. Basic functional trade-offs in cognition: an integrative framework. Cognition 179, 56–70 (2018).

    Article  PubMed  Google Scholar 

  6. Mediano, P. A. M. et al. Towards an extended taxonomy of information dynamics via integrated information decomposition. Preprint at https://arxiv.org/abs/2109.13186 (2021).

  7. Quian Quiroga, R. & Panzeri, S. Extracting information from neuronal populations: information theory and decoding approaches. Nat. Rev. Neurosci. 10, 173–185 (2009).

    Article  CAS  PubMed  Google Scholar 

  8. Whitacre, J. M. Biological robustness: paradigms, mechanisms, systems principles. Front. Genet. 3, 1–15 (2012).

    Article  Google Scholar 

  9. Tononi, G., Sporns, O. & Edelman, G. M. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl. Acad. Sci. USA 91, 5033–5037 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Schneidman, E., Still, S., Berry, M. J. & Bialek, W. Network information and connected correlations. Phys. Rev. Lett. 91, 23 (2003).

  11. Reid, A. T. et al. Advancing functional connectivity research from association to causation. Nat. Neurosci. 22, 1751–1760 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Rosas, F. E., Mediano, P. A. M., Gastpar, M. & Jensen, H. J. Quantifying high-order interdependencies via multivariate extensions of the mutual information. Phys. Rev. E 100, 32305 (2019).

    Article  CAS  Google Scholar 

  13. Schreiber, T. Measuring information transfer. Phys. Rev. Lett. 85, 461–464 (2000).

    Article  CAS  PubMed  Google Scholar 

  14. Massey, J. Causality, feedback and directed information. Proc. Int. Symp. Inf. Theory Applic. 27–30 (1990).

  15. Raichle, M. E. The restless brain: how intrinsic activity organizes brain function. Philos. Trans. R. Soc. Lond. B Biol. Sci. 370, 20140172 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Northoff, G., Wainio-Theberge, S. & Evers, K. Is temporo-spatial dynamics the ‘common currency’ of brain and mind? In Quest of ‘spatiotemporal neuroscience’. Phys. Life Rev. 33, 34–54 (2020).

    Article  PubMed  Google Scholar 

  17. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  20. Luppi, A. I. et al. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat. Commun. 10, 4616 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Cover, T. M. & Thomas, J. A. Elements of Information Theory. (Wiley-Interscience, 2005). https://doi.org/10.1002/047174882X

  22. Barrett, A. B. Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. Phys. Rev. E 91, 52802 (2015).

    Article  CAS  Google Scholar 

  23. Morgan, S. E. et al. Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia-related genes. Proc. Natl. Acad. Sci. USA 116, 9604–9609 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

    Article  PubMed  Google Scholar 

  25. Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl. Acad. Sci. USA 113, 12574–12579 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Shine, J. M. et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems. Nat. Neurosci. 22, 289–296 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Preti, M. G. & van de Ville, D. Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nat. Commun. 10, 4747 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Petersen, S. E. & Sporns, O. Brain networks and cognitive architectures. Neuron 88, 207–219 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Shafiei, G. et al. Topographic gradients of intrinsic dynamics across neocortex. eLife 9, 1–24 (2020).

    Article  Google Scholar 

  32. Paquola, C. et al. A multi-scale cortical wiring space links cellular architecture and functional dynamics in the human brain. PLoS Biol. 18, e3000979 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Smaers, J. B., Gómez-Robles, A., Parks, A. N. & Sherwood, C. C. Exceptional evolutionary expansion of prefrontal cortex in great apes and humans. Curr. Biol. 27, 714–720 (2017).

    Article  CAS  PubMed  Google Scholar 

  34. Wei, Y. et al. Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nat. Commun. 10, 4839 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Whitaker, K. J. et al. Adolescence is associated with genomically patterned consolidation of the hubs of the human brain connectome. Proc. Natl. Acad. Sci. USA 113, 9105–9110 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Finnema, S. J. et al. Imaging synaptic density in the living human brain. Sci. Transl. Med. 8, 348ra96 (2016).

  37. Holland, N. et al. Synaptic loss in primary tauopathies revealed by [11C]UCB-J positron emission tomography. Mov. Disord. https://doi.org/10.1002/mds.28188 (2020).

  38. Goyal, M. S., Hawrylycz, M., Miller, J. A., Snyder, A. Z. & Raichle, M. E. Aerobic glycolysis in the human brain is associated with development and neotenous gene expression. Cell Metabolism 19, 49–57 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vaishnavi, S. N. et al. Regional aerobic glycolysis in the human brain. Proc. Natl. Acad. Sci. USA 107, 17757–17762 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Goulas, A. et al. The natural axis of transmitter receptor distribution in the human cerebral cortex. Proc. Natl Acad. Sci. USA 118, e2020574118 (2021).

  41. Zilles, K. & Palomero-Gallagher, N. Multiple transmitter receptors in regions and layers of the human cerebral cortex. Front. Neuroanat. 11, 78 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Goyal, M. S. & Raichle, M. E. Gene expression-based modeling of human cortical synaptic density. Proc. Natl. Acad. Sci. USA 110, 6571–6576 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Mashour, G. A., Roelfsema, P., Changeux, J. P. & Dehaene, S. Conscious processing and the global neuronal workspace hypothesis. Neuron 105, 776–798 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Rosas, F. E., Mediano, P. A. M., Rassouli, B. & Barrett, A. B. An operational information decomposition via synergistic disclosure. J. Phys. A Math. Theor. 53, 485001 (2020).

    Article  Google Scholar 

  45. Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17, 648–665 (2013).

    Article  PubMed  Google Scholar 

  46. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Jones, E. G. & Powell, T. P. S. An anatomical study of converging sensory pathways within the cerebral cortex of the monkey. Brain 93, 793–820 (1970).

    Article  CAS  PubMed  Google Scholar 

  48. Fox, M. D. et al. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. USA 102, 9673–9678 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Raichle, M. E. et al. A default mode of brain function. Proc. Natl. Acad. Sci. USA 98, 676–682 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Vatansever, D., Menon, X. D. K., Manktelow, A. E., Sahakian, B. J. & Stamatakis, E. A. Default mode dynamics for global functional integration. J. Neurosci. 35, 15254–15262 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  52. Cole, M. W. et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16, 1348–1355 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 178, 540–551 (2018).

    Article  PubMed  Google Scholar 

  54. van Essen, D. C. et al. The WU-minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).

    Article  PubMed  Google Scholar 

  55. Luppi, A. I. & Stamatakis, E. A. Combining network topology and information theory to construct representative brain networks. Network Neuroscience 5, 96–124 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Amico, E. et al. Toward an information theoretical description of communication in brain networks. Network Neuroscience 5, 646–665 (2021).

    PubMed  PubMed Central  Google Scholar 

  57. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).

    Article  PubMed  Google Scholar 

  58. Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2, 125–141 (2012).

    Article  PubMed  Google Scholar 

  59. 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).

    Article  PubMed  Google Scholar 

  60. Wu, G.-R. et al. A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Med. Image Anal. 17, 365–374 (2013).

    Article  PubMed  Google Scholar 

  61. Yeh, F. -C., Wedeen, V. J. & Tseng, W. -Y. I. Estimation of fiber orientation and spin density distribution by diffusion deconvolution. Neuroimage 55, 1054–1062 (2011).

    Article  PubMed  Google Scholar 

  62. Yeh, F.-C., Verstynen, T. D., Wang, Y., Fernández-Miranda, J. C. & Tseng, W.-Y. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS ONE 8, 80713 (2013).

    Article  CAS  Google Scholar 

  63. Michael Milham, A. P. et al. An open resource for nonhuman primate imaging. Neuron 100, 61–74 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Tasserie, J. et al. Pypreclin: an automatic pipeline for macaque functional MRI preprocessing. Neuroimage 207, 116353 (2020).

    Article  PubMed  Google Scholar 

  65. Seidlitz, J. et al. A population MRI brain template and analysis tools for the macaque. Neuroimage. 170, 121–131 (2018).

    Article  PubMed  Google Scholar 

  66. Barttfeld, P. et al. Signature of consciousness in the dynamics of resting-state brain activity. Proc. Natl. Acad. Sci. USA 112, 887–892 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Schaefer, A. et al. Local–global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).

    Article  PubMed  Google Scholar 

  68. Tian, Y., Margulies, D., Breakspear, M. & Zalesky, A. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat. Neurosci. 23, 1421–1432 (2020).

    Article  CAS  PubMed  Google Scholar 

  69. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    Article  PubMed  Google Scholar 

  70. Romero-Garcia, R., Atienza, M., Clemmensen, L. H. & Cantero, J. L. Effects of network resolution on topological properties of human neocortex. Neuroimage 59, 3522–3532 (2012).

    Article  PubMed  Google Scholar 

  71. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Kötter, R. & Wanke, E. Mapping brains without coordinates. Philos. Trans. R Soc. B Biol. Sci. 360, 751–766 (2005).

    Article  Google Scholar 

  73. Schulz, M. A. et al. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat. Commun. 11, (2020).

  74. Nozari, E. et al. Is the brain macroscopically linear? A system identification of resting state dynamics. Preprint at arXiv https://doi.org/10.48550/arXiv.2012.12351 (2020).

  75. Bím, J. et al. A nonnegative measure of feature-related information transfer between neural signals. Preprint at bioRxiv, https://doi.org/10.1101/758128 (2020).

  76. Lizier, J., Bertschinger, N., Jost, J. & Wibral, M. Information decomposition of target effects from multi-source interactions: perspectives on previous, current and future work. Entropy 20, 307 (2018).

    Article  PubMed Central  Google Scholar 

  77. Barrett, A. B. & Seth, A. K. Practical measures of integrated information for time-series data. PLoS Comput. Biol. 7, 1001052 (2011).

    Article  CAS  Google Scholar 

  78. Bastos, A. M. et al. Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron 85, 390–401 (2015).

    Article  CAS  PubMed  Google Scholar 

  79. Deco, G., Vidaurre, D. & Kringelbach, M. L. Revisiting the global workspace: orchestration of the functional hierarchical organisation of the human brain. Nat. Hum. Behav. https://doi.org/10.1101/859579 (2021).

  80. Lizier, J. T. JIDT: an information-theoretic toolkit for studying the dynamics of complex systems. Front. Robot. AI 1, 1–37 (2014).

    Article  Google Scholar 

  81. Battiston, F. et al. The physics of higher-order interactions in complex systems. Nat. Phys. 17, 1093–1098 (2021).

    Article  CAS  Google Scholar 

  82. Rosas, F. E. et al. Disentangling high-order mechanisms and high-order behaviours in complex systems. Nat. Physics https://doi.org/10.1038/s41567-022-01548-5 (2022).

  83. Vértes, P. E. et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 20150362 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  85. Rubinov, M. & Sporns, O. Weight-conserving characterization of complex functional brain networks. NeuroImage 56, 2068–2079 (2011).

    Article  PubMed  Google Scholar 

  86. Cruzat, J. et al. The dynamics of human cognition: increasing global integration coupled with decreasing segregation found using iEEG. Neuroimage 172, 492–505 (2018).

    Article  PubMed  Google Scholar 

  87. Newman, M. E. J. Modularity and community structure in networks. Proc. Natl. Acad. Sci. USA 103, 8577–8582 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Luppi, A. I. et al. LSD alters dynamic integration and segregation in the human brain. Neuroimage 227, 117653 (2021).

    Article  PubMed  Google Scholar 

  89. Tagliazucchi, E. et al. Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics. J. R. Soc. Interface 13, 20151027 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Shen, K. et al. A macaque connectome for large-scale network simulations in TheVirtualBrain. Sci. Data 6, 123 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Shen, K., Bezgin, G., Everling, S. & McIntosh, A. R. The virtual macaque brain: a macaque connectome for large-scale network simulations in TheVirtualBrain. https://doi.org/10.5281/zenodo.1471588 (2018).

  92. Cammoun, Letal Mapping the human connectome at multiple scales with diffusion spectrum MRI. J. Neurosci. Methods 203, 386–397 (2012).

    Article  PubMed  Google Scholar 

  93. Deco, G. et al. Whole-brain multimodal neuroimaging model using serotonin receptor maps explains non-linear functional effects of LSD. Curr. Biol. 28, 3065–3074 (2018).

    Article  CAS  PubMed  Google Scholar 

  94. Herzog, R. et al. A mechanistic model of the neural entropy increase elicited by psychedelic drugs. Sci. Rep. 10, 17725 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Luppi, A. I. et al. Whole-brain modelling identifies distinct but convergent paths to unconsciousness in anaesthesia and disorders of consciousness. Commun. Biol. 5, 384 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Mediano, P. A. M., Luppi, A. I., Herzog, R. & Rosas, F. E. FastDMF: fast simulator of the dynamic mean-field model of brain dynamics. https://doi.org/10.5281/zenodo.6373512 (2022).

  97. Wang, P. et al. Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain. Sci. Adv. 5, 1–12 (2019).

    Google Scholar 

  98. Doan, R. N. et al. Mutations in human accelerated regions disrupt cognition and social behavior the homozygosity mapping consortium for autism HHS public access. Cell 167, 341–354 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Kang, H. J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Romero-Garcia, R. et al. Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex. Neuroimage 171, 256–267 (2018).

    Article  PubMed  Google Scholar 

  102. Krishnan, A., Williams, L. J., McIntosh, A. R. & Abdi, H. Partial least-squares methods for neuroimaging: a tutorial and review. Neuroimage 56, 455–475 (2011).

    Article  PubMed  Google Scholar 

  103. Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Supek, F., Bošnjak, M., ˇ Kunca, S. & ˇ Muc, S. Summarizes and visualizes long lists of Gene Ontology terms. PLoS ONE 6, 21800 (2011).

    Article  CAS  Google Scholar 

  105. Fulcher, B. D., Arnatkeviciute, A. & Fornito, A. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat. Commun. 12, 2669 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Váša, F. et al. Adolescent tuning of association cortex in human structural brain networks. Cerebral Cortex 28, 281–294 (2018).

    Article  PubMed  Google Scholar 

  107. Milicevic Sephton, S. et al. Automated radiosynthesis of [11C]UCB-J for imaging synaptic density by positron emission tomography. J. Labelled Comp. Radiopharm. 63, 151–158 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Bajjalieh, S. M., Frantz, G. D., Weimann, J. M., McConnell, S. K. & Scheller, R. H. Differential expression of synaptic vesicle protein 2 (SV2) isoforms. J. Neurosci. 14, 5223–5235 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Burgos, N. et al. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans. Med. Imaging 33, 2332–2341 (2014).

    Article  PubMed  Google Scholar 

  110. Manavaki, R., Hong, Y. & Fryer, T. D. Effect of brain MRI coil attenuation map processing on PET image quantification and uniformity for the GE SIGNA PET/MR. IEEE Nucl. Sci. Symp. Med. Imaging Conf. NSS/MIC (2019).

  111. Wu, Y. & Carson, R. E. Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging. J. Cereb. Blood Flow Metab. 22, 1440–1452 (2002).

    Article  PubMed  Google Scholar 

  112. Koole, M. et al. Quantifying SV2A density and drug occupancy in the human brain using [11C]UCB-J PET imaging and subcortical white matter as reference tissue. Eur. J. Nucl. Med. Mol. Imaging 46, 396–406 (2019).

    Article  CAS  PubMed  Google Scholar 

  113. Rossano, S. et al. Assessment of a white matter reference region for [11C]UCB-J PET quantification. J Cereb. Blood Flow Metab. 40, 1890–1901 (2020).

    Article  CAS  PubMed  Google Scholar 

  114. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. Ser. B 57, 289–300 (1995).

    Google Scholar 

  115. Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. Introduction to Meta-Analysis. 1–421 https://doi.org/10.1002/9780470743386 (Wiley, 2009).

  116. Markello, R. D. & Misic, B. Comparing spatial null models for brain maps. Neuroimage 236, 118052 (2021).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to UCB Pharma for providing the precursor for the radioligand used in PET imaging. We also express our gratitude to the PRIME-DE initiative, to the organizers and managers of PRIME-DE and to all the institutions that contributed to the PRIME-DE dataset (http://fcon_1000.projects.nitrc.org/indi/indiPRIME.html), with special thanks to the Newcastle team. We are also grateful to A. Grigis, J. Tasserie and B. Jarraya for their help with the Pypreclin code, and R. Romero-Garcia for generating and sharing the 500-mm2 subparcellation of the Desikan–Killiany atlas, and the corresponding Von Economo cytoarchitectonics map. We are also grateful to Y. Wei and colleagues for generating and making available the data pertaining to HAR genes and cortical expansion, to N. Vaishnavi and M. Goyal and colleagues for making available data pertaining to regional GI and its associated genes, to C. Paquola and colleagues for making available their data on cortico-cortical wiring distance, and to A. Goulas and colleagues for making available anonymized receptor autoradiography data. We are grateful to S. Morgan, P. Vértes and K. Whitaker for making available their code pertaining to AHBA gene analysis, and to F. Váša for making available the code for spin-based permutation testing. Finally, we thank S. Panzeri for helpful feedback on an earlier draft of our manuscript. This work was supported by grants from the NIHR, Cambridge Biomedical Research Centre and NIHR Senior Investigator Awards (to D.K.M.); the British Oxygen Professorship of the Royal College of Anaesthetists (to D.K.M.); the Canadian Institute for Advanced Research (CIFAR) (RCZB/072 RG93193; to D.K.M. and E.A.S.); the Stephen Erskine Fellowship (Queens’ College, Cambridge; to E.A.S.); and a Gates Scholarship from the Gates Cambridge Trust (OPP 1144 to A.I.L.) and by The Alan Turing Institute under the EPSRC grant EP/N510129/1. P.A.M.M. and D.B. are funded by the Wellcome Trust (grant no. 210920/Z/18/Z). F.R. is funded by the Ad Astra Chandaria foundation. Computing infrastructure at the Wolfson Brain Imaging Centre (WBIC-HPHI) was funded by the MRC research infrastructure award (MR/M009041/1). The PET study was funded by the Cambridge University Centre for Parkinson-Plus; the NIHR Cambridge Biomedical Research Centre (146281); the Wellcome Trust (103838) and the Association of British Neurologists, Patrick Berthoud Charitable Trust (RG99368). Data were provided (in part) by the HCP, WU-Minn Consortium (Principal Investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 National Institutes of Health (NIH) institutes and centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. For the macaque data, primary support for the work by Newcastle University was provided by Wellcome Trust (WT091681MA, WT092606AIA), National Centre for 3Rs (project grant NC/K000802/1; pilot grant NC/K000608/1) and Biotechnology and Biological Sciences Research Council (grant number BB/J009849/1).

Author information

Authors and Affiliations

  1. Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK

    Andrea I. Luppi, David K. Menon & Emmanuel A. Stamatakis

  2. Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

    Andrea I. Luppi, Negin Holland, Tim D. Fryer, James B. Rowe & Emmanuel A. Stamatakis

  3. Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK

    Andrea I. Luppi

  4. The Alan Turing Institute, London, UK

    Andrea I. Luppi

  5. Department of Psychology, University of Cambridge, Cambridge, UK

    Pedro A. M. Mediano & Daniel Bor

  6. Department of Psychology, Queen Mary University of London, London, UK

    Pedro A. M. Mediano & Daniel Bor

  7. Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK

    Fernando E. Rosas

  8. Data Science Institute, Imperial College London, London, UK

    Fernando E. Rosas

  9. Center for Complexity Science, Imperial College London, London, UK

    Fernando E. Rosas

  10. Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK

    Tim D. Fryer & David K. Menon

  11. Department of Psychiatry, University of Cambridge, Cambridge, UK

    John T. O’Brien

  12. Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK

    John T. O’Brien & James B. Rowe

  13. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK

    James B. Rowe

Authors
  1. Andrea I. Luppi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pedro A. M. Mediano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fernando E. Rosas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Negin Holland
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tim D. Fryer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. John T. O’Brien
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. James B. Rowe
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David K. Menon
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Daniel Bor
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Emmanuel A. Stamatakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.I.L. conceived the study, analyzed data and wrote first draft of the manuscript. P.A.M.M. conceived the study, contributed to data analysis and reviewed and edited the manuscript. F.R. contributed to data analysis, and reviewed and edited the manuscript. N.H. acquired PET data, reviewed PET analysis and reviewed the manuscript. T.D.F. preprocessed PET data and reviewed the manuscript. J.T.O. conceived the PET project, reviewed PET analysis and reviewed the manuscript. J.B.R. conceived the PET project, reviewed PET analysis and reviewed the manuscript. D.K.M. reviewed the manuscript. D.B. conceived the study and reviewed and edited the manuscript. E.A.S. conceived the study and reviewed and edited the manuscript.

Corresponding author

Correspondence to Andrea I. Luppi.

Ethics declarations

Competing interests

J.B.R. is a non-remunerated trustee of the Guarantors of Brain and the PSP Association (United Kingdom). J.B.R. provides consultancy to Asceneuron, Biogen and UCB and has research grants from AstraZeneca/MedImmune, Janssen and Lilly as industry partners in the Dementias Platform UK. All other authors declare no competing interests.

Peer review

Peer review information

Nature Neuroscience thanks Michael Hawrylycz, Stefano Panzeri, 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.

Extended data

Extended Data Fig. 1 Synergistic and redundant interactions in the brain.

(a-c) Group average matrices of pairwise functional interactions between brain regions of the Schaefer-232 atlas, quantified by (a) redundancy; (b) synergy; (c) traditional functional connectivity (Pearson correlation). (d) Mean regional density of redundant interactions, after thresholding the group-average redundancy matrix to retain the 5% strongest edges, for display purposes. (e) Mean regional density of synergistic interactions, after thresholding the group-average synergy matrix to retain the 5% strongest edges, for display purposes. (f) Spearman correlation (two-sided CI: [−0.51, −0.28]) between synergy vs. redundancy ranks across cortical regions.

Extended Data Fig. 2 Synergy-redundancy identification and NeuroSynth meta-analysis are robust to the use of alternative methods.

Left: Group-average matrices of redundant and synergistic interactions; Middle: Redundancy-to-synergy gradient scores (synergy rank minus redundancy rank) displayed on medial and lateral brain surfaces (left hemisphere); Right: Results of the NeuroSynth term-based meta-analysis, relating the distribution of redundancy-to-synergy gradient across the brain to a gradient of cognitive domains, from lower-level sensorimotor processing to higher-level cognitive tasks (note that one term, ‘visual semantics’, was excluded from visualisation because it failed to reach the threshold of Z > 3.1, leaving 23 terms). (a) DK-308 parcellation with equally-sized cortical areas (500 mm2), obtained as subdivisions of the Desikan-Killiany cortical atlas. (b) Lausanne-129 parcellation, comprising the DK-114 cortical ROIs, supplemented with 15 subcortical regions. (c) Synergy and redundancy computed without deconvolution of the hemodynamic response function (HRF) from the BOLD signal timeseries. (d) Synergy and redundancy computed from discretised (binary) BOLD signal timeseries.

Extended Data Fig. 3 Robustness of synergy and redundancy network results (efficiency, modularity, and within- vs between-resting state subnetwork comparison) to alternative node and edge definitions.

(a-d) Robustness of network results to the use of the 308-ROI cortical parcellation. (e-h) Robustness of network results to using synergy and redundancy normalised by TDMI. (i-l) Robustness of network results to using synergy and redundancy obtained from discretised signals. For all violin plots: each colored circle represents one subject; white circle: median; central line: mean; box limits, upper and lower quartiles; whiskers, 1.5x inter-quartile range; *** p < 0.001 from paired-sample non-parametric permutation t-test (two-sided); n = 100 unrelated HCP subjects. For all box-plots: white circle, median; box limits, upper and lower quartiles; whiskers, 1.5x inter-quartile range; *** p < 0.001 from two-sample non-parametric permutation t-test (two-sided). For (c-d) and (k-l), Within-RSN n=7178 connections; Between-RSN n=46414 connections. For (g) and (h), Within-RSN n=14784 connections; Between-RSN n=79772 connections.

Extended Data Fig. 4 Robustness of synergy and redundancy structure-function results to alternative node and edge definitions.

(a-c) Robustness of structure-function results to the use of the 308-ROI cortical parcellation. (d-f) Robustness of structure-function results to using synergy and redundancy normalised by TDMI. (g-i) Robustness of structure-function results to using synergy and redundancy obtained from discretised signals. For all violin plots: each colored circle represents one subject; white circle: median; central line: mean; box limits, upper and lower quartiles; whiskers, 1.5x inter-quartile range; *** p < 0.001 from paired-sample non-parametric permutation t-test (two-sided); n = 100 unrelated HCP subjects. For all box-plots: white circle: median; box limits, upper and lower quartiles; whiskers, 1.5x inter-quartile range; *** p < 0.001 from two-sample non-parametric permutation t-test (two-sided). For (b-c), SC+, n=6864 direct connections; SC-, n=88000 connections. For (e-f) and (h-i), SC+, n=5276 direct connections; SC-, n=48548 indirect connections.

Extended Data Fig. 5 Additional validation of synergy and redundancy network results.

(a) Alternative measure of global integration (area under the curve of the size of the largest connected component across thresholds). (b) Alternative structural-functional dissimilarity (mean Hamming distance). For both (a) and (b): *** p < 0.001 from paired-sample non-parametric permutation t-test (two-sided), n=100 unrelated HCP subjects. (c) Comparison of global efficiency of synergy and redundancy networks of each subject with the average global efficiency of 100 synthetic null networks with edges randomly drawn from the distribution between 0 and the empirical TDMI. (d) Comparison of modularity of synergy and redundancy networks of each subject with the average modularity of 100 synthetic null networks with edges randomly drawn from the distribution between 0 and the empirical TDMI. For (c) and (d), *** p < 0.001 (FDR-corrected) from two-sample non-parametric permutation t-test (two-sided); n = 100 unrelated HCP subjects and n=100 synthetic null networks. For all violin plots: each colored circle represents one subject; white circle: median; central line: mean; box limits, upper and lower quartiles; whiskers, 1.5x inter-quartile range.

Extended Data Fig. 6 Validation analysis for human-macaque comparison of synergy and redundancy.

(a-d) Simulation of human fMRI data with same TR as the macaque data shows that human-macaque differences in synergy cannot be attributed solely to TR differences between datasets. (a) The dynamic mean field (DMF) model used to simulate human fMRI data combines macroscale information about neuroanatomy and structural connectivity (from DTI) with excitatory and inhibitory neuronal populations interconnected by AMPA, NMDA and GABA synapses, providing a neurobiologically plausible account of regional neuronal firing rate, which is turned into simulated BOLD signal by means of the Balloon-Windkessel hemodynamic model. (b) Using a TR of 0.72s (the same as the empirical HCP data), the model is fitted to the empirical HCP data by finding the value of the global coupling parameter G that minimises the Kolmogorov-Smirnov distance between the distributions of empirical and simulated functional connectivity dynamics (FCD). The KS distance is minimised for G=1.6, which is the value of G used for subsequent simulations with TR=2.6s (the same TR as the macaque data). (c) The proportion of synergistic information exchange across the brain is significantly higher in simulated human data than in empirical macaque data with the same TR=2.6s (p<0.001). (d) The proportion of redundant information exchange across the brain is also significantly higher in simulated human data than empirical macaque data (p=0.036). Statistical significance assessed with two-sample non-parametric permutation t-test (two-sided); DMF HCP data: n=100 simulations; macaque data: n=19 distinct sessions from 10 individual macaques. (e-f) The human-macaque comparison of synergy and redundancy proportion is robust to bandpass filtering both human and macaque functional MRI data between 0.008−0.09 Hz. The proportion of synergistic information exchange across the brain is significantly higher in humans (p<0.001) (e) whereas the proportion of redundant information exchange across the brain is equivalent in humans and macaques (p=0.943) (f). Statistical significance assessed with two-sample non-parametric permutation t-test (two-sided). Human data: n=100 unrelated HCP subjects. Macaque data: n=19 distinct sessions from 10 individual macaques. (g-h) The proportion of total synergy is significantly higher in humans than macaques (p<0.001) (h), even when only considering humans whose total FC is in the range of values exhibited by macaques (excluding one outlier with extreme value), such that there is no significant difference in total FC between the two groups (p=0.196), shown in (g). Statistical significance assessed with two-sample non-parametric permutation t-test (two-sided). Human data: n=28 unrelated HCP subjects with FC values in the range of the macaque FC values. Macaque data: n=19 distinct sessions from 10 individual macaques (one outlier excluded in (g)). For all violin plots: each colored circle indicates one data-point; white circle: median; central line: mean; box limits, upper and lower quartiles; whiskers, 1.5x inter-quartile range; n.s., p > 0.05; * p < 0.05; *** p < 0.001.

Extended Data Fig. 7 Characterisation of synergistic and redundant network profiles in macaque brains are similar to humans.

(a) Synergistic interactions between regions of the macaque brain. (b) Redundant interactions between regions of the macaque brain. (c) Anatomical connectivity was estimated from axonal tracing and diffusion MRI (Shen et al., 2019), and Spearman correlation coefficient was used to assess the similarity of redundancy and synergy matrices with structural connectivity, after thresholding to ensure equal numbers of connections. (d) The network organisation of synergistic interactions exhibits significantly higher global efficiency than redundant interactions (p < 0.001). (e) The network organisation of redundant interactions exhibits significantly higher segregation (modularity) than synergistic interactions (p < 0.001). (f) Networks of redundant interactions are significantly more correlated with underlying anatomical connectivity than synergistic interactions (p < 0.001). For all tests: *** p < 0.001 from paired-sample non-parametric permutation t-test (two-sided); n=19 distinct sessions from 10 individual macaques (Supplementary Table 7). For all violin plots: each colored circle indicates one data-point; white circle: median; central line: mean; box limits, upper and lower quartiles; whiskers, 1.5x inter-quartile range.

Extended Data Fig. 8 Synergy-redundancy gradient correlates with unadjusted cortical expansion and gene expression.

(a) Significant Spearman correlation (two-sided CI: [0.145, 0.476]) between regional redundancy-to-synergy gradient scores and unadjusted regional cortical expansion from chimpanzee (Pan troglodytes) to human (both on DK-114 cortical atlas, both hemispheres; n =114 cortical regions). (b) Significant Spearman correlation (two-sided CI: [0.109, 0.567]) between regional redundancy-to-synergy gradient scores and unadjusted regional expression of brain-related human-accelerated (HAR) genes (both on left hemisphere of DK-114 atlas: n=57 left-hemisphere regions). (c) Significant Spearman correlation (two-sided CI: [0.010, 0.496]) between regional redundancy-to-synergy gradient scores and unadjusted regional expression of non-brain-related human-accelerated (HAR) genes (both on left hemisphere of DK-114 atlas; n=57 left-hemisphere regions). p_spin indicates the p-value estimated from a spatial permutation test comparing the empirical correlation against 10,000 randomly rotated brain maps with preserved spatial covariance.

Extended Data Fig. 9 Characterisation of PLS components of 20,647 genes from the Allen Institute for Brain Science, for the 308-ROI subdivision of the Desikan-Killiany cortical parcellation.

(a) Spearman correlation (two-sided CI: [0.334, 0.517]; n=308 regions) between the redundancy-to-synergy regional pattern, and the first principal component of PLS (PLS1). (b) Spearman correlation (two-sided CI: [0.216, 0.417]; n=308 regions) between the redundancy-to-synergy regional pattern, and the second principal component of PLS (PLS2). For both (a) and (b), color-bars correspond to scatter-plot axes. (c) The variance explained by the first 2 PLS components is significantly higher than would be expected based on random patterns with preserved spatial autocorrelation, assessed using spin-based permutations (Methods). (d,e) Significant enrichment of HAR-Brain genes in PLS1 and PLS2. (f) Significant HAR-Brain gene enrichment is also observed using an alternative approach: ridge-regularized PLS regression on the binarised cortical pattern of synergy vs redundancy prevalence. (g,h) HAR-Brain gene enrichment in PLS1 and PLS2 is also observed when controlling for spatial autocorrelation using spin-based permutations. (c-h) Statistical significance is assessed via bootstrap resampling of Z-scores; histograms indicate the relative frequency (over 1,000 bootstraps) of the mean Z-score of a random sample of genes of equal size as the HAR-Brain genes. Red vertical line: empirical mean Z-score of HAR-Brain genes.

Extended Data Fig. 10 Enrichment analysis for genes pertaining to synaptic formation, whose regional distribution corresponds to the distribution of aerobic glycolysis in the human brain, as reported by Goyal et al. (2014) (‘aerobic glycolysis genes’).

(a,b) PLS1 and PLS2 are significantly enriched for aerobic glycolysis genes. (c) Enrichment for genes related to aerobic glycolysis is also observed using an alternative approach: ridge-regularised PLS regression on the binarised cortical pattern of synergy vs redundancy prevalence. (d,e) Significant enrichment for genes related to aerobic glycolysis in PLS1 and PLS2 is also observed when controlling for spatial autocorrelation using spin-based permutations. (a-e) Statistical significance is assessed via bootstrap resampling of Z-scores; histograms indicate the relative frequency (over 1,000 bootstraps) of the mean Z-score of a random sample of genes of equal size as the aerobic glycolysis genes. Red vertical line: empirical mean Z-score of aerobic glycolysis genes.

Supplementary information

Reporting Summary

Supplementary Table

Supplementary Table 1 - Prevalence of synergy and redundancy for each canonical resting-state network. Statistical significance assessed via one-sample permutation-based non-parametric t-test (two-sided), FDR-corrected. Supplementary Table 2 - Prevalence of synergy and redundancy for each Von Economo cytoarchitectonic class. Statistical significance assessed via one-sample permutation-based non-parametric t-test (two-sided), FDR-corrected. Supplementary Table 3 - Synergy and redundancy network results for the Schaefer-232 parcellation. Statistical significance assessed via paired-sample permutation-based non-parametric t-test (two-sided). Supplementary Table 4 - Synergy and redundancy network results for alternative definitions of nodes and edges. Statistical significance assessed via paired-sample permutation-based non-parametric t-test (two-sided). Supplementary Table 5 - Synergy and redundancy network results for macaques. Statistical significance assessed via paired-sample permutation-based non-parametric t-test (two-sided). Supplementary Table 6 - Comparison of effect sizes for different measures, for distinguishing the distributions of humans and macaques (macaque data filtered between 0.008 and 0.09 Hz). Statistical significance assessed via z-score test (two-sided). The effect size obtained from each measure is compared against the effect size obtained for synergy, Hedges’ g = 9.98. Positive z-values indicate that synergy has a larger effect size. Supplementary Table 7 - Comparison of effect sizes for different measures, for distinguishing the distributions of humans and macaques (macaque data filtered between 0.0025 and 0.05 Hz). Statistical significance assessed via z-score test (two-sided). The effect size obtained from each measure is compared against the effect size obtained for synergy, Hedges’ g = 4.94. Positive z-values indicate that synergy has a larger effect size. Supplementary Table 8 - Comparison of effect sizes for different measures, for distinguishing the distributions of humans and macaques, with human data trimmed to ensure same number of time points as the macaque data. Statistical significance assessed via z-score test (two-sided). The effect size obtained from each measure is compared against the effect size obtained for synergy, Hedges’ g = 5.90. Positive z-values indicate that synergy has a larger effect size. Supplementary Table 9 - Comparison of effect sizes for different measures, for distinguishing the distributions of humans and macaques, using the discrete estimator for binarized time series. Statistical significance assessed via z-score test (two-sided). The effect size obtained from each measure is compared against the effect size obtained for synergy, Hedges’ g = 14.01. Positive z-values indicate that synergy has a larger effect size. Supplementary Table 10 - Significant enrichment for synaptic transmission and organization is preserved when using advanced null models based on random phenotype ensembles.

Supplementary Software

This library contains Octave (v5.0.0) and MATLAB (>R2016a) functions to compute Integrated Information Decomposition (ΦID) as described in Mediano, P. A. M. et al. Towards an extended taxonomy of information dynamics via integrated information decomposition. Preprint at https://arxiv.org/abs/2109.13186 (2021). The code works with continuous and discrete data using Barrett’s MMI redundancy function, as described in Barrett, A. B. Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. Phys. Rev. E 91, 52802 (2015). This code implements the measures used for further analysis in this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luppi, A.I., Mediano, P.A.M., Rosas, F.E. et al. A synergistic core for human brain evolution and cognition. Nat Neurosci 25, 771–782 (2022). https://doi.org/10.1038/s41593-022-01070-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-022-01070-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing