The diversity and multiplexity of edge communities within and between brain systems

Cell Reports - Tập 37 - Trang 110032 - 2021
Youngheun Jo1, Farnaz Zamani Esfahlani1, Joshua Faskowitz1,2, Evgeny J. Chumin1,3, Olaf Sporns1,2,3,4, Richard F. Betzel1,2,3,4
1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
2Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA
3Network Science Institute, Indiana University, Bloomington, IN 47405, USA
4Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA

Tài liệu tham khảo

Abraham, 2014, Machine learning for neuroimaging with scikit-learn, Front. Neuroinform., 8, 14, 10.3389/fninf.2014.00014 Aerts, 2016, Brain networks under attack: robustness properties and the impact of lesions, Brain, 139, 3063, 10.1093/brain/aww194 Ahn, 2010, Link communities reveal multiscale complexity in networks, Nature, 466, 761, 10.1038/nature09182 Alstott, 2009, Modeling the impact of lesions in the human brain, PLoS Comput. Biol., 5, e1000408, 10.1371/journal.pcbi.1000408 Amico, 2018, The quest for identifiability in human functional connectomes, Sci. Rep., 8, 8254, 10.1038/s41598-018-25089-1 Avants, 2008, Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain, Med. Image Anal., 12, 26, 10.1016/j.media.2007.06.004 Avants, 2009, Advanced normalization tools (ants), Insight J., 2, 1 Bassett, 2017, Network neuroscience, Nat. Neurosci., 20, 353, 10.1038/nn.4502 Bassett, 2010, Efficient physical embedding of topologically complex information processing networks in brains and computer circuits, PLoS Comput. Biol., 6, e1000748, 10.1371/journal.pcbi.1000748 Bassett, 2013, Robust detection of dynamic community structure in networks, Chaos, 23, 013142, 10.1063/1.4790830 Bazzi, 2016, Community detection in temporal multilayer networks, with an application to correlation networks, Multiscale Model. Simul., 14, 1, 10.1137/15M1009615 Beckmann, 2005, Investigations into resting-state connectivity using independent component analysis, Philos. Trans. R. Soc. Lond. B Biol. Sci., 360, 1001, 10.1098/rstb.2005.1634 Bertolero, 2015, The modular and integrative functional architecture of the human brain, Proc. Natl. Acad. Sci. USA, 112, E6798, 10.1073/pnas.1510619112 Bertolero, 2017, The diverse club, Nat. Commun., 8, 1277, 10.1038/s41467-017-01189-w Betzel, 2018, Organizing principles of whole-brain functional connectivity in zebrafish larvae, bioRxiv, 496414 Betzel, 2020, Community detection in network neuroscience, arXiv Betzel, 2017, Multi-scale brain networks, Neuroimage, 160, 73, 10.1016/j.neuroimage.2016.11.006 Betzel, 2018, Specificity and robustness of long-distance connections in weighted, interareal connectomes, Proc. Natl. Acad. Sci. USA, 115, E4880, 10.1073/pnas.1720186115 Betzel, 2017, The modular organization of human anatomical brain networks: Accounting for the cost of wiring, Netw. Neurosci., 1, 42, 10.1162/NETN_a_00002 Betzel, 2018, Non-assortative community structure in resting and task-evoked functional brain networks, bioRxiv, 355016 Betzel, 2018, Diversity of meso-scale architecture in human and non-human connectomes, Nat. Commun., 9, 346, 10.1038/s41467-017-02681-z Betzel, 2019, The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability, Neuroimage, 202, 115990, 10.1016/j.neuroimage.2019.07.003 Betzel, 2021, Individualized event structure drives individual differences in whole-brain functional connectivity, bioRxiv Betzel, 2019, Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography, Nat. Biomed. Eng., 3, 902, 10.1038/s41551-019-0404-5 Blondel, 2008, Fast unfolding of communities in large networks, J. Stat. Mech., P10008, 10.1088/1742-5468/2008/10/P10008 Bullmore, 2009, Complex brain networks: graph theoretical analysis of structural and functional systems, Nat. Rev. Neurosci., 10, 186, 10.1038/nrn2575 Cox, 1996, AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages, Comput. Biomed. Res., 29, 162, 10.1006/cbmr.1996.0014 Craddock, 2013, Imaging human connectomes at the macroscale, Nat. Methods, 10, 524, 10.1038/nmeth.2482 Cui, 2020, Individual variation in functional topography of association networks in youth, Neuron, 106, 340, 10.1016/j.neuron.2020.01.029 Dale, 1999, Cortical surface-based analysis. I. Segmentation and surface reconstruction, Neuroimage, 9, 179, 10.1006/nimg.1998.0395 De Domenico, 2017, Multilayer modeling and analysis of human brain networks, Gigascience, 6, 1, 10.1093/gigascience/gix004 Di Martino, 2014, Unraveling the miswired connectome: a developmental perspective, Neuron, 83, 1335, 10.1016/j.neuron.2014.08.050 Eickhoff, 2018, Imaging-based parcellations of the human brain, Nat. Rev. Neurosci., 19, 672, 10.1038/s41583-018-0071-7 Esteban, 2017, MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites, PLoS ONE, 12, e0184661, 10.1371/journal.pone.0184661 Esteban, 2018, fMRIPrep: A robust preprocessing pipeline for functional MRI, Nat. Methods, 16, 111, 10.1038/s41592-018-0235-4 Evans, 2009, Line graphs, link partitions, and overlapping communities, Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 80, 016105, 10.1103/PhysRevE.80.016105 Faskowitz, 2018, Weighted stochastic block models of the human connectome across the life span, Sci. Rep., 8, 12997, 10.1038/s41598-018-31202-1 Faskowitz, 2020, Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture, Nat. Neurosci., 23, 1644, 10.1038/s41593-020-00719-y Finn, 2015, Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity, Nat. Neurosci., 18, 1664, 10.1038/nn.4135 Fischl, 2004, Automatically parcellating the human cerebral cortex, Cereb. Cortex, 14, 11, 10.1093/cercor/bhg087 Fonov, 2009, Unbiased nonlinear average age-appropriate brain templates from birth to adulthood, Neuroimage, 47, S102, 10.1016/S1053-8119(09)70884-5 Fornito, 2015, The connectomics of brain disorders, Nat. Rev. Neurosci., 16, 159, 10.1038/nrn3901 Fortunato, 2010, Community detection in graphs, Phys. Rep., 486, 75, 10.1016/j.physrep.2009.11.002 Friston, 1993, Functional connectivity: the principal-component analysis of large (PET) data sets, J. Cereb. Blood Flow Metab., 13, 5, 10.1038/jcbfm.1993.4 Gao, 2020, Poincaré embedding reveals edge-based functional networks of the brain, 448 Gordon, 2016, Generation and evaluation of a cortical area parcellation from resting-state correlations, Cereb. Cortex, 26, 288, 10.1093/cercor/bhu239 Gordon, 2017, Individual variability of the system-level organization of the human brain, Cereb. Cortex, 27, 386 Gordon, 2017, Precision functional mapping of individual human brains, Neuron, 95, 791, 10.1016/j.neuron.2017.07.011 Gordon, 2018, Three distinct sets of connector hubs integrate human brain function, Cell Rep., 24, 1687, 10.1016/j.celrep.2018.07.050 Gorgolewski, 2011, Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python, Front. Neuroinform., 5, 13, 10.3389/fninf.2011.00013 Gratton, 2018, Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation, Neuron, 98, 439, 10.1016/j.neuron.2018.03.035 Gratton, 2019, Defining individual-specific functional neuroanatomy for precision psychiatry, Biol. Psychiatry, 88, 28, 10.1016/j.biopsych.2019.10.026 Greenwell, 2021, High-amplitude network co-fluctuations linked to variation in hormone concentrations over menstrual cycle, bioRxiv Greve, 2009, Accurate and robust brain image alignment using boundary-based registration, Neuroimage, 48, 63, 10.1016/j.neuroimage.2009.06.060 Hizanidis, 2016, Chimera-like states in modular neural networks, Sci. Rep., 6, 19845, 10.1038/srep19845 Honey, 2008, Dynamical consequences of lesions in cortical networks, Hum. Brain Mapp., 29, 802, 10.1002/hbm.20579 Hyvärinen, 2000, Independent component analysis: Algorithms and applications, Neural Netw., 13, 411, 10.1016/S0893-6080(00)00026-5 Jenkinson, 2002, Improved optimization for the robust and accurate linear registration and motion correction of brain images, Neuroimage, 17, 825, 10.1006/nimg.2002.1132 Ji, 2019, Mapping the human brain’s cortical-subcortical functional network organization, Neuroimage, 185, 35, 10.1016/j.neuroimage.2018.10.006 Jutla, 2011 Kashtan, 2005, Spontaneous evolution of modularity and network motifs, Proc. Natl. Acad. Sci. USA, 102, 13773, 10.1073/pnas.0503610102 Kenett, 2020, Community structure of the creative brain at rest, Neuroimage, 210, 116578, 10.1016/j.neuroimage.2020.116578 King, 2019, Functional boundaries in the human cerebellum revealed by a multi-domain task battery, Nat. Neurosci., 22, 1371, 10.1038/s41593-019-0436-x Kirschner, 1998, Evolvability, Proc. Natl. Acad. Sci. USA, 95, 8420, 10.1073/pnas.95.15.8420 Klein, 2017, Mindboggling morphometry of human brains, PLoS Comput. Biol., 13, e1005350, 10.1371/journal.pcbi.1005350 Lancichinetti, 2012, Consensus clustering in complex networks, Sci. Rep., 2, 336, 10.1038/srep00336 Liégeois, 2017, Interpreting temporal fluctuations in resting-state functional connectivity MRI, Neuroimage, 163, 437, 10.1016/j.neuroimage.2017.09.012 Lindquist, 2019, Modular preprocessing pipelines can reintroduce artifacts into fMRI data, Hum. Brain Mapp., 40, 2358, 10.1002/hbm.24528 Margulies, 2016, Situating the default-mode network along a principal gradient of macroscale cortical organization, Proc. Natl. Acad. Sci. USA, 113, 12574, 10.1073/pnas.1608282113 Medaglia, 2018, Functional alignment with anatomical networks is associated with cognitive flexibility, Nat. Hum. Behav., 2, 156, 10.1038/s41562-017-0260-9 Meunier, 2009, Hierarchical modularity in human brain functional networks, Front. Neuroinform., 3, 37, 10.3389/neuro.11.037.2009 Meunier, 2010, Modular and hierarchically modular organization of brain networks, Front. Neurosci., 4, 200, 10.3389/fnins.2010.00200 Najafi, 2016, Overlapping communities reveal rich structure in large-scale brain networks during rest and task conditions, Neuroimage, 135, 92, 10.1016/j.neuroimage.2016.04.054 Newman, 2004, Finding and evaluating community structure in networks, Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 69, 026113, 10.1103/PhysRevE.69.026113 Novelli, 2021, A mathematical perspective on edge-centric functional connectivity, arXiv Park, 2013, Structural and functional brain networks: from connections to cognition, Science, 342, 1238411, 10.1126/science.1238411 Parkes, 2018, An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI, Neuroimage, 171, 415, 10.1016/j.neuroimage.2017.12.073 Pessoa, 2014, Understanding brain networks and brain organization, Phys. Life Rev., 11, 400, 10.1016/j.plrev.2014.03.005 Pope, 2021, Modular origins of high-amplitude co-fluctuations in fine-scale functional connectivity dynamics, bioRxiv Porter, 2009, Communities in networks, North Am. Math. Soc., 56, 1082 Power, 2011, Functional network organization of the human brain, Neuron, 72, 665, 10.1016/j.neuron.2011.09.006 Power, 2013, Evidence for hubs in human functional brain networks, Neuron, 79, 798, 10.1016/j.neuron.2013.07.035 Power, 2014, Methods to detect, characterize, and remove motion artifact in resting state fMRI, Neuroimage, 84, 320, 10.1016/j.neuroimage.2013.08.048 Reichardt, 2006, Statistical mechanics of community detection, Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 74, 016110, 10.1103/PhysRevE.74.016110 Reid, 2019, Advancing functional connectivity research from association to causation, Nat. Neurosci., 22, 1751, 10.1038/s41593-019-0510-4 Rogers, 2007, Assessing functional connectivity in the human brain by fMRI, Magn. Reson. Imaging, 25, 1347, 10.1016/j.mri.2007.03.007 Rosvall, 2008, Maps of random walks on complex networks reveal community structure, Proc. Natl. Acad. Sci. USA, 105, 1118, 10.1073/pnas.0706851105 Rubinov, 2010, Complex network measures of brain connectivity: uses and interpretations, Neuroimage, 52, 1059, 10.1016/j.neuroimage.2009.10.003 Satterthwaite, 2013, An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data, Neuroimage, 64, 240, 10.1016/j.neuroimage.2012.08.052 Schaefer, 2018, Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity mri, Cereb. Cortex, 28, 3095, 10.1093/cercor/bhx179 Seitzman, 2019, Trait-like variants in human functional brain networks, Proc. Natl. Acad. Sci. USA, 116, 22851, 10.1073/pnas.1902932116 Shirer, 2012, Decoding subject-driven cognitive states with whole-brain connectivity patterns, Cereb. Cortex, 22, 158, 10.1093/cercor/bhr099 Smith, 2004, Advances in functional and structural MR image analysis and implementation as FSL, Neuroimage, 23, S208, 10.1016/j.neuroimage.2004.07.051 Smith, 2009, Correspondence of the brain’s functional architecture during activation and rest, Proc. Natl. Acad. Sci. USA, 106, 13040, 10.1073/pnas.0905267106 Sporns, 2016, Modular brain networks, Annu. Rev. Psychol., 67, 613, 10.1146/annurev-psych-122414-033634 Sylvester, 2020, Individual-specific functional connectivity of the amygdala: A substrate for precision psychiatry, Proc. Natl. Acad. Sci. USA, 117, 3808, 10.1073/pnas.1910842117 Tewarie, 2016, Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach, Neuroimage, 142, 324, 10.1016/j.neuroimage.2016.07.057 Thomas Yeo, 2011, The organization of the human cerebral cortex estimated by intrinsic functional connectivity, J. Neurophysiol., 106, 1125, 10.1152/jn.00338.2011 Thomas Yeo, 2014, Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex, Neuroimage, 88, 212, 10.1016/j.neuroimage.2013.10.046 Traag, 2011, Narrow scope for resolution-limit-free community detection, Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 84, 016114, 10.1103/PhysRevE.84.016114 Treiber, 2016, Characterization and correction of geometric distortions in 814 diffusion weighted images, PLoS ONE, 11, e0152472, 10.1371/journal.pone.0152472 Tustison, 2010, N4ITK: improved N3 bias correction, IEEE Trans. Med. Imaging, 29, 1310, 10.1109/TMI.2010.2046908 Uddin, 2020, An ‘edgy’ new look, Nat. Neurosci., 23, 1471, 10.1038/s41593-020-00741-0 Uddin, 2019, Towards a universal taxonomy of macro-scale functional human brain networks, Brain Topogr., 32, 926, 10.1007/s10548-019-00744-6 Vaiana, 2020, Multilayer brain networks, J. Nonlinear Sci., 30, 1, 10.1007/s00332-017-9436-8 Váša, 2018, Adolescent tuning of association cortex in human structural brain networks, Cereb. Cortex, 28, 281, 10.1093/cercor/bhx249 Wang, 2017, Evaluation of field map and nonlinear registration methods for correction of susceptibility artifacts in diffusion MRI, Front. Neuroinform., 11, 17, 10.3389/fninf.2017.00017 Wang, 2020, Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists, Neuroimage, 216, 116745, 10.1016/j.neuroimage.2020.116745 Woo, 2015, Neuroimaging-based biomarker discovery and validation, Pain, 156, 1379, 10.1097/j.pain.0000000000000223 Zalesky, 2014, Time-resolved resting-state brain networks, Proc. Natl. Acad. Sci. USA, 111, 10341, 10.1073/pnas.1400181111 Zamani Esfahlani, 2020, Space-independent community and hub structure of functional brain networks, Neuroimage, 211, 116612, 10.1016/j.neuroimage.2020.116612 Zamani Esfahlani, 2021, Edge-centric analysis of time-varying functional brain networks with applications in autism spectrum disorder, bioRxiv Zamani Esfahlani, 2020, High-amplitude co-fluctuations in cortical activity drive functional connectivity, bioRxiv, 800045 Zamani Esfahlani, 2021, Modularity maximization as a flexible and generic framework for brain network exploratory analysis, Neuroimage, 244, 118607, 10.1016/j.neuroimage.2021.118607 Zhang, 2001, Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm, IEEE Trans. Med. Imaging, 20, 45, 10.1109/42.906424 Zuo, 2017, Human connectomics across the life span, Trends Cogn. Sci., 21, 32, 10.1016/j.tics.2016.10.005