Integrative, segregative, and degenerate harmonics of the structural connectome.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
14 Aug 2024
14 Aug 2024
Historique:
received:
24
01
2024
accepted:
01
08
2024
medline:
15
8
2024
pubmed:
15
8
2024
entrez:
14
8
2024
Statut:
epublish
Résumé
Unifying integration and segregation in the brain has been a fundamental puzzle in neuroscience ever since the conception of the "binding problem." Here, we introduce a framework that places integration and segregation within a continuum based on a fundamental property of the brain-its structural connectivity graph Laplacian harmonics and a new feature we term the gap-spectrum. This framework organizes harmonics into three regimes-integrative, segregative, and degenerate-that together account for various group-level properties. Integrative and segregative harmonics occupy the ends of the continuum, and they share properties such as reproducibility across individuals, stability to perturbation, and involve "bottom-up" sensory networks. Degenerate harmonics are in the middle of the continuum, and they are subject-specific, flexible, and involve "top-down" networks. The proposed framework accommodates inter-subject variation, sensitivity to changes, and structure-function coupling in ways that offer promising avenues for studying cognition and consciousness in the brain.
Identifiants
pubmed: 39143303
doi: 10.1038/s42003-024-06669-6
pii: 10.1038/s42003-024-06669-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
986Informations de copyright
© 2024. The Author(s).
Références
Revonsuo, A. & Newman, J. Binding and consciousness. Conscious. Cogn. 8, 173–185 (1999).
pubmed: 10448000
doi: 10.1006/ccog.1999.0384
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. 91, 5033–5037 (1994).
pubmed: 8197179
pmcid: 43925
doi: 10.1073/pnas.91.11.5033
Friston, K. J. Functional integration in the brain. Human Brain Function 2nd edn Academic Press, San Diego 971–997 (2004).
Sporns, O., Chialvo, D. R., Kaiser, M. & Hilgetag, C. C. Organization, development and function of complex brain networks. Trends Cogn. Sci. 8, 418–425 (2004).
pubmed: 15350243
doi: 10.1016/j.tics.2004.07.008
Van Den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775–15786 (2011).
pubmed: 22049421
pmcid: 6623027
doi: 10.1523/JNEUROSCI.3539-11.2011
Sporns, O. Network attributes for segregation and integration in the human brain. Curr. Opin. Neurobiol. 23, 162–171 (2013).
pubmed: 23294553
doi: 10.1016/j.conb.2012.11.015
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
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).
pubmed: 19190637
doi: 10.1038/nrn2575
Bassett, D. S. & Sporns, O. Network neuroscience. Nat. Neurosci. 20, 353–364 (2017).
pubmed: 28230844
pmcid: 5485642
doi: 10.1038/nn.4502
Zamani Esfahlani, F., Faskowitz, J., Slack, J., Mišić, B. & Betzel, R. F. Local structure-function relationships in human brain networks across the lifespan. Nat. Commun. 13, 1–16 (2022).
doi: 10.1038/s41467-022-29770-y
Liégeois, R., Santos, A., Matta, V., Van De Ville, D. & Sayed, A. H. Revisiting correlation-based functional connectivity and its relationship with structural connectivity. Netw. Neurosci. 4, 1235–1251 (2020).
pubmed: 33409438
pmcid: 7781609
doi: 10.1162/netn_a_00166
Lioi, G., Gripon, V., Brahim, A., Rousseau, F. & Farrugia, N. Gradients of connectivity as graph fourier bases of brain activity. Netw. Neurosci. 5, 322–336 (2021).
pubmed: 34189367
pmcid: 8233110
doi: 10.1162/netn_a_00183
Abdelnour, F., Voss, H. U. & Raj, A. Network diffusion accurately models the relationship between structural and functional brain connectivity networks. NeuroImage 90, 335–347 (2014).
pubmed: 24384152
doi: 10.1016/j.neuroimage.2013.12.039
Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl Acad. Sci. 113, 12574–12579 (2016).
pubmed: 27791099
pmcid: 5098630
doi: 10.1073/pnas.1608282113
Chung, F. R. Spectral graph theory, vol. 92 (American Mathematical Soc., 1997).
Deslauriers-Gauthier, S., Zucchelli, M., Frigo, M. & Deriche, R. A unified framework for multimodal structure-function mapping based on eigenmodes. Med. Image Anal. 66, 101799 (2020).
pubmed: 32889301
doi: 10.1016/j.media.2020.101799
Müller, E. J., Munn, B. R., Aquino, K. M., Shine, J. M. & Robinson, P. A. The music of the hemispheres: Cortical eigenmodes as a physical basis for large-scale brain activity and connectivity patterns. Front. Hum. Neurosci. 16, 1062487 (2022).
pubmed: 36504620
pmcid: 9729350
doi: 10.3389/fnhum.2022.1062487
Rué-Queralt, J. et al. The coupling between the spatial and temporal scales of neural processes revealed by a joint time-vertex connectome spectral analysis. NeuroImage 280, 120337 (2023).
pubmed: 37604296
doi: 10.1016/j.neuroimage.2023.120337
Huang, W. et al. Graph frequency analysis of brain signals. IEEE J. Sel. Top. Signal Process. 10, 1189–1203 (2016).
pubmed: 28439325
pmcid: 5400112
doi: 10.1109/JSTSP.2016.2600859
Ortega, A., Frossard, P., Kovačević, J., Moura, J. M. & Vandergheynst, P. Graph signal processing: Overview, challenges, and applications. Proc. IEEE 106, 808–828 (2018).
doi: 10.1109/JPROC.2018.2820126
Preti, M. G. & Van De Ville, D. Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nat. Commun. 10, 4747 (2019).
pubmed: 31628329
pmcid: 6800438
doi: 10.1038/s41467-019-12765-7
Xie, X., Cai, C., Damasceno, P. F., Nagarajan, S. S. & Raj, A. Emergence of canonical functional networks from the structural connectome. NeuroImage 237, 118190 (2021).
pubmed: 34022382
doi: 10.1016/j.neuroimage.2021.118190
Yeo, B. T. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–65 (2011).
pubmed: 21653723
doi: 10.1152/jn.00338.2011
Atasoy, S., Donnelly, I. & Pearson, J. Human brain networks function in connectome-specific harmonic waves. Nat. Commun. 7, 10340 (2016).
pubmed: 26792267
pmcid: 4735826
doi: 10.1038/ncomms10340
Abdelnour, F., Dayan, M., Devinsky, O., Thesen, T. & Raj, A. Functional brain connectivity is predictable from anatomic network’s Laplacian eigen-structure. NeuroImage 172, 728–739 (2018).
pubmed: 29454104
doi: 10.1016/j.neuroimage.2018.02.016
Irion, J. & Saito, N. Applied and computational harmonic analysis on graphs and networks. In Wavelets and Sparsity XVI, vol. 9597, 336–350 (SPIE, 2015).
Verma, P., Nagarajan, S. & Raj, A. Spectral graph theory of brain oscillations–revisited and improved. NeuroImage 249, 118919 (2022).
pubmed: 35051584
doi: 10.1016/j.neuroimage.2022.118919
Shaffer, W. H. Degenerate modes of vibration and perturbations in polyatomic molecules. Rev. Mod. Phys. 16, 245 (1944).
doi: 10.1103/RevModPhys.16.245
Dulock, V. A. & McIntosh, H. V. On the degeneracy of the two-dimensional harmonic oscillator. Am. J. Phys. 33, 109–118 (1965).
doi: 10.1119/1.1971258
Marrec, L. & Jalan, S. Analysing degeneracies in networks spectra. Europhys. Lett. 117, 48001 (2017).
doi: 10.1209/0295-5075/117/48001
Kanehisa, H. Degenerate modes of symmetric instability. J. Meteorol. Soc. Jpn. Ser. II 86, 557–562 (2008).
doi: 10.2151/jmsj.86.557
Jakob, M. & Stenholm, S. Variational functions in degenerate open quantum systems. Phys. Rev. A 69, 042105 (2004).
doi: 10.1103/PhysRevA.69.042105
de Micheli, F. & Zanelli, J. Quantum degenerate systems. Journal of mathematical physics53 (2012).
Royer, J. et al. An open mri dataset for multiscale neuroscience. Sci. Data 9, 1–12 (2022).
doi: 10.1038/s41597-022-01682-y
Cruces, R. R. et al. Micapipe: A pipeline for multimodal neuroimaging and connectome analysis. NeuroImage 263, 119612 (2022).
pubmed: 36070839
doi: 10.1016/j.neuroimage.2022.119612
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
Lin, C.-T. et al. Multi-tasking deep network for tinnitus classification and severity prediction from multimodal structural mr images. J. Neural Eng. 20, 016017 (2023).
doi: 10.1088/1741-2552/acab33
Jiang, T. Brainnetome: a new -ome to understand the brain and its disorders. NeuroImage 80, 263–272 (2013).
pubmed: 23571422
doi: 10.1016/j.neuroimage.2013.04.002
Duff, I. S. & Koster, J. On algorithms for permuting large entries to the diagonal of a sparse matrix. SIAM J. Matrix Anal. Appl. 22, 973–996 (2001).
doi: 10.1137/S0895479899358443
Behjat, H., Tarun, A., Abramian, D., Larsson, M. & Van De Ville, D. Voxel-wise brain graphs from diffusion mri: Intrinsic eigenspace dimensionality and application to functional mri. IEEE Open Journal of Engineering in Medicine and Biology (2023).
de Lange, S. C., de Reus, M. A. & van den Heuvel, M. P. The laplacian spectrum of neural networks. Front. Comput. Neurosci. 7, 189 (2014).
pubmed: 24454286
pmcid: 3888935
doi: 10.3389/fncom.2013.00189
Fiedler, M. Laplacian of graphs and algebraic connectivity. Banach Cent. Publ. 1, 57–70 (1989).
doi: 10.4064/-25-1-57-70
Spielman, D. Spectral graph theory. Combin. Sci. Comput. 18, 18 (2012).
de Lange, S. C., van den Heuvel, M. P. & de Reus, M. A. The role of symmetry in neural networks and their laplacian spectra. NeuroImage 141, 357–365 (2016).
pubmed: 27475289
doi: 10.1016/j.neuroimage.2016.07.051
Lizier, J. T., Bauer, F., Atay, F. M. & Jost, J. Analytic relationship of relative synchronizability to network structure and motifs. Proc. Natl Acad. Sci. 120, e2303332120 (2023).
pubmed: 37669393
pmcid: 10500263
doi: 10.1073/pnas.2303332120
Gudbjartsson, H. & Patz, S. The rician distribution of noisy mri data. Magn. Reson. Med. 34, 910–914 (1995).
pubmed: 8598820
pmcid: 2254141
doi: 10.1002/mrm.1910340618
Den Dekker, A. & Sijbers, J. Data distributions in magnetic resonance images: A review. Phys. Med. 30, 725–741 (2014).
doi: 10.1016/j.ejmp.2014.05.002
Hearne, J. Sensitivity analysis of parameter combinations. Appl. Math. Model. 9, 106–108 (1985).
doi: 10.1016/0307-904X(85)90121-0
Borgonovo, E. & Plischke, E. Sensitivity analysis: A review of recent advances. Eur. J. Oper. Res. 248, 869–887 (2016).
doi: 10.1016/j.ejor.2015.06.032
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010).
pubmed: 19819337
doi: 10.1016/j.neuroimage.2009.10.003
Bassett, D. S. & Bullmore, E. T. Small-world brain networks revisited. Neuroscientist 23, 499–516 (2017).
pubmed: 27655008
doi: 10.1177/1073858416667720
Landsman, N. P. Born rule and its interpretation. In Compendium of quantum physics, 64–70 (Springer, 2009).
Medaglia, J. D. et al. Functional alignment with anatomical networks is associated with cognitive flexibility. Nat. Hum. Behav. 2, 156–164 (2018).
pubmed: 30498789
doi: 10.1038/s41562-017-0260-9
Luppi, A. I. et al. Distributed harmonic patterns of structure-function dependence orchestrate human consciousness. Commun. Biol. 6, 117 (2023).
pubmed: 36709401
pmcid: 9884288
doi: 10.1038/s42003-023-04474-1
Orfanidis, S. J.Introduction to signal processing (Prentice-Hall, Inc., 1995).
Richman, J. S. & Moorman, J. R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.-Heart Circ. Physiol. 278, H2039–H2049 (2000).
pubmed: 10843903
doi: 10.1152/ajpheart.2000.278.6.H2039
Richman, J. S., Lake, D. E. & Moorman, J. R. Sample entropy. In Methods in enzymology, vol. 384, 172–184 (Elsevier, 2004).
Martínez-Cagigal, V. Sample entropy. https://www.mathworks.com/matlabcentral/fileexchange/69381-sample-entropy MathWorks (2018).
Hamilton, N. E. & Ferry, M. ggtern: Ternary diagrams using ggplot2. J. Stat. Softw. 87, 1–17 (2018).
doi: 10.18637/jss.v087.c03
lynch4815. Ternary plots. https://github.com/lynch4815/ternary_plots (Accessed: 2024-04-23).
Wang, M. B., Owen, J. P., Mukherjee, P. & Raj, A. Brain network eigenmodes provide a robust and compact representation of the structural connectome in health and disease. PLoS Comput. Biol. 13, e1005550 (2017).
pubmed: 28640803
pmcid: 5480812
doi: 10.1371/journal.pcbi.1005550
Bauer, F. & Jost, J. Bipartite and neighborhood graphs and the spectrum of the normalized graph laplacian. arXiv preprint arXiv:0910.3118 (2009).
Jost, J., Mulas, R. & Münch, F. Spectral gap of the largest eigenvalue of the normalized graph laplacian. Communications in Mathematics and Statistics 1–11 (2021).
Robinson, P. Discrete-network versus modal representations of brain activity: why a sparse regions-of-interest approach can work for analysis of continuous dynamics. Phys. Rev. E 88, 054702 (2013).
doi: 10.1103/PhysRevE.88.054702
Belkin, M. & Niyogi, P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Comput. 15, 1373–1396 (2003).
doi: 10.1162/089976603321780317
Huang, W. et al. A graph signal processing perspective on functional brain imaging. Proc. IEEE 106, 868–885 (2018).
doi: 10.1109/JPROC.2018.2798928
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
Bolton, T. A. & Van De Ville, D. Dynamics of brain activity captured by graph signal processing of neuroimaging data to predict human behaviour. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 549–553 (IEEE, 2020).
Zarkali, A. et al. Organisational and neuromodulatory underpinnings of structural-functional connectivity decoupling in patients with parkinson’s disease. Commun. Biol. 4, 86 (2021).
pubmed: 33469150
pmcid: 7815846
doi: 10.1038/s42003-020-01622-9
Feng, G. et al. Spatial and temporal pattern of structure-function coupling of human brain connectome with development. bioRxiv 2023–09 (2023).
Monroe, D. C., DuBois, S. L., Rhea, C. K. & Duffy, D. M. Age-related trajectories of brain structure–function coupling in female roller derby athletes. Brain Sci. 12, 22 (2021).
pubmed: 35053766
pmcid: 8774127
doi: 10.3390/brainsci12010022
Ye, C., Huang, J., Lv, H., Lu, J. & Ma, T. Decoupling of brain activity from connectome in multiple sclerosis and neuromyelitis optica [abstract]. In International Society for Magnetic Resonance in Medicine (2020).
Zhou, B. et al. Structural and functional connectivity abnormalities of the default mode network in patients with Alzheimer’s disease and mild cognitive impairment within two independent datasets. Methods 205, 29–38 (2022).
pubmed: 35671900
doi: 10.1016/j.ymeth.2022.06.001
McFadden, J. Integrating information in the brain’s em field: the cemi field theory of consciousness. Neurosci. Conscious. 2020, niaa016 (2020).
pubmed: 32995043
pmcid: 7507405
doi: 10.1093/nc/niaa016
Preti, M. G., Bolton, T. A., Griffa, A. & Van De Ville, D. Graph signal processing for neurogimaging to reveal dynamics of brain structure-function coupling. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5 (IEEE, 2023).
Finn, E. S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).
pubmed: 26457551
pmcid: 5008686
doi: 10.1038/nn.4135
Mantwill, M., Gell, M., Krohn, S. & Finke, C. Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome. Commun. Biol. 5, 261 (2022).
pubmed: 35332230
pmcid: 8948277
doi: 10.1038/s42003-022-03185-3
Van De Ville, D., Farouj, Y., Preti, M. G., Liégeois, R. & Amico, E. When makes you unique: temporality of the human brain fingerprint. Sci. Adv. 7, eabj0751 (2021).
pubmed: 34652937
doi: 10.1126/sciadv.abj0751
Ravindra, V., Drineas, P. & Grama, A. Constructing compact signatures for individual fingerprinting of brain connectomes. Front. Neurosci. 15, 549322 (2021).
pubmed: 33889066
pmcid: 8055927
doi: 10.3389/fnins.2021.549322
Jalbrzikowski, M. et al. Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart. Hum. Brain Mapp. 41, 4187–4199 (2020).
pubmed: 32652852
pmcid: 7502841
doi: 10.1002/hbm.25118
Cai, B. et al. Functional connectome fingerprinting: identifying individuals and predicting cognitive functions via autoencoder. Hum. Brain Mapp. 42, 2691–2705 (2021).
pubmed: 33835637
pmcid: 8127140
doi: 10.1002/hbm.25394
Byrge, L. & Kennedy, D. P. High-accuracy individual identification using a “thin slice” of the functional connectome. Netw. Neurosci. 3, 363–383 (2019).
pubmed: 30793087
pmcid: 6370471
doi: 10.1162/netn_a_00068
Yeh, F.-C. et al. Local connectome fingerprinting reveals the uniqueness of individual white matter architecture. BioR xiv43778 (2016).
Munsell, B. C. et al. Personalized connectome fingerprints: Their importance in cognition from childhood to adult years. Neuroimage 221, 117122 (2020).
pubmed: 32634596
doi: 10.1016/j.neuroimage.2020.117122
Ciarrusta, J. et al. The developing brain structural and functional connectome fingerprint. Dev. Cogn. Neurosci. 55, 101117 (2022).
pubmed: 35662682
pmcid: 9344310
doi: 10.1016/j.dcn.2022.101117
Kumar, K., Desrosiers, C., Siddiqi, K., Colliot, O. & Toews, M. Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis. NeuroImage 158, 242–259 (2017).
pubmed: 28684331
doi: 10.1016/j.neuroimage.2017.06.083
Griffa, A., Amico, E., Liégeois, R., Van De Ville, D. & Preti, M. G. Brain structure-function coupling provides signatures for task decoding and individual fingerprinting. NeuroImage 250, 118970 (2022).
pubmed: 35124226
doi: 10.1016/j.neuroimage.2022.118970
Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336–349 (2012).
pubmed: 22498897
doi: 10.1038/nrn3214
Raj, A. & Chen, Y.-h The wiring economy principle: connectivity determines anatomy in the human brain. PloS one 6, e14832 (2011).
pubmed: 21915250
pmcid: 3168442
doi: 10.1371/journal.pone.0014832
Revonsuo, A. Binding and the phenomenal unity of consciousness. Conscious. Cogn. 8, 173–185 (1999).
pubmed: 10448000
doi: 10.1006/ccog.1999.0384
Sepulcre, J. Functional streams and cortical integration in the human brain. Neuroscientist 20, 499–508 (2014).
pubmed: 24737695
doi: 10.1177/1073858414531657
Sepulcre, J. Integration of visual and motor functional streams in the human brain. Neurosci. Lett. 567, 68–73 (2014).
pubmed: 24699175
doi: 10.1016/j.neulet.2014.03.050
Ursino, M., Cuppini, C. & Magosso, E. Neurocomputational approaches to modelling multisensory integration in the brain: a review. Neural Netw. 60, 141–165 (2014).
pubmed: 25218929
doi: 10.1016/j.neunet.2014.08.003