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