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A synergistic core for human brain evolution and cognition

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

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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.

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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.

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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/.

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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

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  1. Andrea I. Luppi
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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.

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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.

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Nature Neuroscience thanks Michael Hawrylycz, Stefano Panzeri, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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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.

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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

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