Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
24 04 2023
24 04 2023
Historique:
received:
17
10
2022
accepted:
14
04
2023
medline:
26
4
2023
pubmed:
25
4
2023
entrez:
24
04
2023
Statut:
epublish
Résumé
One of the most well-established tools for modeling the brain is the functional connectivity network, which is constructed from pairs of interacting brain regions. While powerful, the network model is limited by the restriction that only pairwise dependencies are considered and potentially higher-order structures are missed. Here, we explore how multivariate information theory reveals higher-order dependencies in the human brain. We begin with a mathematical analysis of the O-information, showing analytically and numerically how it is related to previously established information theoretic measures of complexity. We then apply the O-information to brain data, showing that synergistic subsystems are widespread in the human brain. Highly synergistic subsystems typically sit between canonical functional networks, and may serve an integrative role. We then use simulated annealing to find maximally synergistic subsystems, finding that such systems typically comprise ≈10 brain regions, recruited from multiple canonical brain systems. Though ubiquitous, highly synergistic subsystems are invisible when considering pairwise functional connectivity, suggesting that higher-order dependencies form a kind of shadow structure that has been unrecognized by established network-based analyses. We assert that higher-order interactions in the brain represent an under-explored space that, accessible with tools of multivariate information theory, may offer novel scientific insights.
Identifiants
pubmed: 37095282
doi: 10.1038/s42003-023-04843-w
pii: 10.1038/s42003-023-04843-w
pmc: PMC10125999
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
451Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2023. The Author(s).
Références
Barabási, A. L., Pósfai, M. Network Science (Cambridge University Press, 2016).
Menczer, F., Fortunato, S. & Davis, C. A. A First Course in Network Science (Cambridge University Press, 2020).
Sporns, O. & Kötter, R. Motifs in brain networks. PLOS Biol. 2, e369 (2004).
pubmed: 15510229
pmcid: 524253
doi: 10.1371/journal.pbio.0020369
Watts, D. J. & Strogatz, S. H. Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998).
pubmed: 9623998
doi: 10.1038/30918
Fortunato, S. Community detection in graphs. Phys. Rep. 486, 75–174 (2010).
doi: 10.1016/j.physrep.2009.11.002
Betzel, R. F. Community detection in network neuroscience. https://arxiv.org/abs/2011.06723 (2020).
Battiston, F. et al. Networks beyond pairwise interactions: structure and dynamics. Phys. Rep. 874, 1–92 (2020).
doi: 10.1016/j.physrep.2020.05.004
Battiston, F. et al. The physics of higher-order interactions in complex systems. Nat. Phys. 17, 1093–1098 (2021)
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).
pubmed: 8197179
pmcid: 43925
doi: 10.1073/pnas.91.11.5033
Tononi, G., Edelman, G. M. & Sporns, O. Complexity and coherency: integrating information in the brain. Trends Cogn. Sci. 2, 474–484 (1998).
pubmed: 21227298
doi: 10.1016/S1364-6613(98)01259-5
Tononi, G. & Edelman, G. M. Schizophrenia and the mechanisms of conscious integration. Brain Res. Rev. 31, 391–400 (2000).
pubmed: 10719167
doi: 10.1016/S0165-0173(99)00056-9
Timme, N. M. et al. High-degree neurons feed cortical computations. PLOS Comput. Biol. 12, e1004858 (2016).
pubmed: 27159884
pmcid: 4861348
doi: 10.1371/journal.pcbi.1004858
Faber, S. P., Timme, N. M., Beggs, J. M. & Newman, E. L. Computation is concentrated in rich clubs of local cortical networks. Netw. Neurosci. 3, 1–21 (2018).
Sherrill, S. P., Timme, N. M., Beggs, J. M. & Newman, E. L. Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures. PLOS Comput. Biol. 17, e1009196 (2021).
pubmed: 34252081
pmcid: 8297941
doi: 10.1371/journal.pcbi.1009196
Sherrill, S. P., Timme, N. M., Beggs, J. M. & Newman, E. L. Correlated activity favors synergistic processing in local cortical networks in vitro at synaptically relevant timescales. Netw. Neurosci. 4, 678–697 (2020).
pubmed: 32885121
pmcid: 7462423
doi: 10.1162/netn_a_00141
Scagliarini, T., Marinazzo, D., Guo, Y., Stramaglia, S. & Rosas, F. E. Quantifying high-order interdependencies on individual patterns via the local O-information: theory and applications to music analysis. Phys. Rev. Res. 4, 013184 (2022).
doi: 10.1103/PhysRevResearch.4.013184
Varley, T. F., Sporns, O., Schaffelhofer, S., Scherberger, H. & Dann, B. Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior. Proc. Natl Acad. Scie. USA 120, e2207677120 (2023).
Rosas, F. E. et al. Reconciling emergences: an information-theoretic approach to identify causal emergence in multivariate data. PLOS Comput. Biol. 16, e1008289 (2020).
pubmed: 33347467
pmcid: 7833221
doi: 10.1371/journal.pcbi.1008289
Varley, T., Sporns, O., Puce, A. & Beggs, J. Differential effects of propofol and ketamine on critical brain dynamics. PLOS Comput. Biol. 16, e1008418 (2020).
pubmed: 33347455
pmcid: 7785236
doi: 10.1371/journal.pcbi.1008418
Luppi, A. I. et al. A synergistic workspace for human consciousness revealed by integrated information Decomposition. https://doi.org/10.1101/2020.11.25.398081 (2020).
Luppi, A. I. et al. A synergistic core for human brain evolution and cognition. Nat. Neurosci. 25, 771–782 (2022).
Gatica, M. et al. High-order interdependencies in the aging brain. Brain Connect. 1, 734–744 (2021).
Luppi, A. I. et al. What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena. Neurosci. Conscious. 2021, niab027 (2021).
Rosas, F., Mediano, P. A. M., Gastpar, M. & Jensen, H. J. Quantifying high-order interdependencies via multivariate extensions of the mutual information. Phys. Rev. E 100, 032305 (2019).
pubmed: 31640038
doi: 10.1103/PhysRevE.100.032305
Lizier, J. T., Flecker, B. & Williams, P. L. Towards a synergy-based approach to measuring information modification. https://arxiv.org/abs/1303.3440 (2013).
Newman, E. L., Varley, T. F., Parakkattu, V. K., Sherrill, S. P. & Beggs, J. M. Revealing the dynamics of neural information processing with multivariate information decomposition. Entropy 24, 930 (2022).
pubmed: 35885153
pmcid: 9319160
doi: 10.3390/e24070930
Williams, P. L. & Beer, R. D. Nonnegative decomposition of multivariate information. https://arxiv.org/abs/1004.2515 (2010).
Gutknecht, A. J., Wibral, M. & Makkeh, A. Bits and pieces: understanding information decomposition from part-whole relationships and formal logic. Proc. R Soc. A Math. Phys. Eng. Sci. 477, 20210110 (2021).
Kolchinsky, A. A novel approach to the partial information decomposition. Entropy 24, 403 (2022).
pubmed: 35327914
pmcid: 8947370
doi: 10.3390/e24030403
Kay, J. W., Schulz, J. M. & Phillips, W. A. A comparison of partial information decompositions using data from real and simulated layer 5b pyramidal cells. Entropy 24, 1021 (2022).
pubmed: 35893001
pmcid: 9394329
doi: 10.3390/e24081021
James, R. G., Ellison, C. J. & Crutchfield, J. P. Anatomy of a bit: information in a time series observation. Chaos: Interdiscip. J. Nonlinear Sci. 21, 037109 (2011).
doi: 10.1063/1.3637494
Deco, G., Tononi, G., Boly, M. & Kringelbach, M. L. Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 16, 430–439 (2015).
pubmed: 26081790
doi: 10.1038/nrn3963
Shine, J. M. Neuromodulatory influences on integration and segregation in the brain. Trends Cogn. Sci. 23, 572–583 (2019).
pubmed: 31076192
doi: 10.1016/j.tics.2019.04.002
Massimini, M. et al. Breakdown of cortical effective connectivity during sleep. Science (N. Y., NY) 309, 2228–2232 (2005).
doi: 10.1126/science.1117256
Casali, A. G. et al. A theoretically based index of consciousness independent of sensory processing and behavior. Sci. Transl. Med. 5, 198ra105–198ra105 (2013).
pubmed: 23946194
doi: 10.1126/scitranslmed.3006294
Sarasso, S. et al. Consciousness and complexity: a consilience of evidence. Neurosci. Conscious. https://doi.org/10.1093/nc/niab023 (2021).
Luppi, A. I. et al. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nat. Commun. 10, 1–12 (2019).
doi: 10.1038/s41467-019-12658-9
Luppi, A. I. et al. LSD alters dynamic integration and segregation in the human brain. NeuroImage 227, 117653 (2021).
pubmed: 33338615
doi: 10.1016/j.neuroimage.2020.117653
McGhee, G. R. Theoretical morphology: the concept and its applications. Short. Courses Paleontol. 4, 87–102 (1991).
doi: 10.1017/S2475263000002130
Avena-Koenigsberger, A., Goñi, J., Solé, R. & Sporns, O. Network morphospace. J. R. Soc. Interface 12, 20140881 (2015).
pubmed: 25540237
pmcid: 4305402
doi: 10.1098/rsif.2014.0881
Varley, T. F., Denny, V., Sporns, O. & Patania, A. Topological analysis of differential effects of ketamine and propofol anaesthesia on brain dynamics. R. Soc. Open Sci. 8, 201971 (2021).
pubmed: 34168888
pmcid: 8220281
doi: 10.1098/rsos.201971
Cover, T. M. & Thomas, J. A. Elements of Information Theory (Wiley, 2012).
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).
doi: 10.1002/j.1538-7305.1948.tb01338.x
Friston, K. J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2, 56–78 (1994).
doi: 10.1002/hbm.460020107
van Diessen, E. et al. Opportunities and methodological challenges in EEG and MEG resting state functional brain network research. Clin. Neurophysiol. 126, 1468–1481 (2015).
pubmed: 25511636
doi: 10.1016/j.clinph.2014.11.018
Ursino, M., Ricci, G., Magosso, E. Transfer entropy as a measure of brain connectivity: a critical analysis with the help of neural mass models. Front. Comput. Neurosci. 14, 45 (2020).
Barnett, L., Muthukumaraswamy, S. D., Carhart-Harris, R. L. & Seth, A. K. Decreased directed functional connectivity in the psychedelic state. NeuroImage 209, 116462 (2020).
pubmed: 31857204
doi: 10.1016/j.neuroimage.2019.116462
Fornito, A., Zalesky, A., Bullmore, E. Fundamentals of Brain Network Analysis (Academic Press, 2016).
Sporns, O. Networks of the Brain (The MIT Press, 2010).
Abdallah, S. A. & Plumbley, M. D. A measure of statistical complexity based on predictive information with application to finite spin systems. Phys. Lett. A 376, 275–281 (2012).
doi: 10.1016/j.physleta.2011.10.066
Williams, P. L. & Beer, R. D. Generalized measures of information transfer. https://arxiv.org/abs/1102.1507 (2011).
Stramaglia, S., Scagliarini, T., Daniels, B. C. & Marinazzo, D. Quantifying dynamical high-order interdependencies from the O-information: an application to neural spiking dynamics. Front. Physiol. 11, 595736 (2021).
Sporns, O., Tononi, G. & Edelman, G. M. Theoretical neuroanatomy and the connectivity of the cerebral cortex. Behav. Brain Res. 135, 69–74 (2002).
pubmed: 12356436
doi: 10.1016/S0166-4328(02)00157-2
Ay, N., Olbrich, E., Bertschinger, N. & Jost, J. A unifying framework for complexity measures of finite systems. ECCS’06 : Proceedings of the European Conference on Complex Systems 2006. (2006).
Scagliarini, T. et al. Gradients of O-information: low-order descriptors of high-order dependencies. http://arxiv.org/abs/2207.03581 (2022).
Van Essen, D. C. et al. The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013).
pubmed: 23684880
doi: 10.1016/j.neuroimage.2013.05.041
Royer, J. et al. An open MRI dataset for multiscale neuroscience. Sci. Data 9, 569 (2021).
Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).
pubmed: 28981612
doi: 10.1093/cercor/bhx179
Colenbier, N. et al. Disambiguating the role of blood flow and global signal with partial information decomposition. NeuroImage 213, 116699 (2020).
pubmed: 32179104
doi: 10.1016/j.neuroimage.2020.116699
Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).
pubmed: 21653723
doi: 10.1152/jn.00338.2011
Griffith, V. & Harel, J. Irreducibility is minimum synergy among parts. https://arxiv.org/abs/1311.7442 (2013).
Santoro, A., Battiston, F., Petri, G., & Amico, E. Higher-order organization of multivariate time series. Nat. Phys. 19, 221–229(2023).
Zamani Esfahlani, F. et al. High-amplitude cofluctuations in cortical activity drive functional connectivity. Proc. Natl Acad. Sci. USA 117, 28393–28401 (2020).
pubmed: 33093200
pmcid: 7668041
doi: 10.1073/pnas.2005531117
Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O. & Betzel, R. F. Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nat. Neurosci. 23, 1644–1654 (2020).
pubmed: 33077948
doi: 10.1038/s41593-020-00719-y
Betzel, R. F., Cutts, S. A., Greenwell, S., Faskowitz, J. & Sporns, O. Individualized event structure drives individual differences in whole-brain functional connectivity. NeuroImage 252, 118993 (2022).
pubmed: 35192942
doi: 10.1016/j.neuroimage.2022.118993
Varley, T. F., Pope, M., Puxeddu, M. G., Faskowitz, J. & Sporns, O. Partial entropy decomposition reveals higher-order structures in human brain activity. http://arxiv.org/abs/2301.05307 (2023).
Ince, R. A. A. The partial entropy decomposition: decomposing multivariate entropy and mutual information via pointwise common surprisal. https://arxiv.org/abs/1702.01591 (2017).
Finn, C. & Lizier, J. T. Generalised measures of multivariate information content. Entropy 22, 216 (2020).
pubmed: 33285991
pmcid: 7851747
doi: 10.3390/e22020216
Varley, T. F. Decomposing past and future: integrated information decomposition based on shared probability mass exclusions. https://arxiv.org/abs/2202.12992 (2022).
Timme, N. M. et al. Criticality maximizes complexity in neural tissue. Front. Physiol. 7, 425 (2016).
Rosas, F. E. et al. Disentangling high-order mechanisms and high-order behaviours in complex systems. Nat. Phys. 18, 476–477 (2022).
Varley, T. F. & Kaminski, P. Untangling Synergistic Effects of Intersecting Social Identities with Partial Information Decomposition. Entropy 24, 1387 (2022).
Sizemore, A. E., Phillips-Cremins, J., Ghrist, R. & Bassett, D. S. The importance of the whole: topological data analysis for the network neuroscientist. Netw. Neurosci. 3, 656–673 (2019).
pubmed: 31410372
pmcid: 6663305
doi: 10.1162/netn_a_00073
Saggar, M. et al. Towards a new approach to reveal dynamical organization of the brain using topological data analysis. Nat. Commun. 9, 1399 (2018).
Billings, J., Saggar, M., Hlinka, J., Keilholz, S. & Petri, G. Simplicial and topological descriptions of human brain dynamics. Netw. Neurosci. 5, 549–568 (2021).
pubmed: 34189377
pmcid: 8233107
Stolz, B. J., Emerson, T., Nahkuri, S., Porter, M. A. & Harrington, H. A. Topological data analysis of task-based fMRI data from experiments on schizophrenia. J. Phys. Complex. 2, 035006 (2021).
doi: 10.1088/2632-072X/abb4c6
Varley, T. F. & Hoel, E. Emergence as the conversion of information: a unifying theory. Philos. Trans. R. Soc. A: Math., Phys. Eng. Sci. 380, 20210150 (2022).
doi: 10.1098/rsta.2021.0150
Wollstadt, P., Schmitt, S. & Wibral, M. A rigorous information-theoretic definition of redundancy and relevancy in feature selection based on partial information decomposition. https://arxiv.org/abs/2105.04187 (2021).
Novelli, L., Wollstadt, P., Mediano, P., Wibral, M. & Lizier, J. T. Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing. Netw. Neurosci. 3, 827–847 (2019).
pubmed: 31410382
pmcid: 6663300
doi: 10.1162/netn_a_00092
Liu, T. T., Nalci, A. & Falahpour, M. The global signal in fMRI: nuisance or information? NeuroImage 150, 213–229 (2017).
pubmed: 28213118
doi: 10.1016/j.neuroimage.2017.02.036
Novelli, L. & Razi, A. A mathematical perspective on edge-centric functional connectivity. http://arxiv.org/abs/2106.10631 (2021).
Lizier, J. T. JIDT: an information-theoretic toolkit for studying the dynamics of complex systems. https://arxiv.org/pdf/1408.3270.pdf (2014).
Bossomaier, T., Barnett, L., Harré, M. & Lizier. J. T. An Introduction to Transfer Entropy: Information Flow in Complex Systems (Springer, 2016).
Faes, L. et al. A new framework for the time- and frequency-domain assessment of high-order interactions in networks of random processes. IEEE Trans. Signal. Process. 70 (IEEE, 2022).
Hlinkaa, J., Paluša, M., Vejmelkaa, M., Mantini, D. & Corbetta, M. Functional connectivity in resting-state fMRI: Is linear correlation sufficient? NeuroImage 54, 2218–2225 (2011).
doi: 10.1016/j.neuroimage.2010.08.042
Liégeois, R., Yeo, B. T. T. & Van De Ville, D. Interpreting null models of resting-state functional MRI dynamics: not throwing the model out with the hypothesis. NeuroImage 243, 118518 (2021).
pubmed: 34469853
doi: 10.1016/j.neuroimage.2021.118518
Schulz, M. A. et al. Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets. Nat. Commun. 11, 4238 (2020).
pubmed: 32843633
pmcid: 7447816
doi: 10.1038/s41467-020-18037-z
Barrett, A. B. Exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. Phys. Rev. E 91, 052802 (2015).
doi: 10.1103/PhysRevE.91.052802
Sporns, O., Faskowitz, J., Teixeira, A. S., Cutts, S. A. & Betzel, R. F. Dynamic expression of brain functional systems disclosed by fine-scale analysis of edge time series. Netw. Neurosci. 5, 405–433 (2021).
pubmed: 34189371
pmcid: 8233118
doi: 10.1162/netn_a_00182
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).
pubmed: 23668970
doi: 10.1016/j.neuroimage.2013.04.127
Robinson, E. C. et al. MSM: a new flexible framework for Multimodal Surface Matching. NeuroImage 100, 414–426 (2014).
pubmed: 24939340
doi: 10.1016/j.neuroimage.2014.05.069
Parkes, L., Fulcher, B., Yücel, M. & Fornito, A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415–436 (2018).
pubmed: 29278773
doi: 10.1016/j.neuroimage.2017.12.073
Cruces, R. R. et al. Micapipe: a pipeline for multimodal neuroimaging and connectome analysis. Neuroimage 263, 119612 (2022).
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953).
doi: 10.1063/1.1699114