Genetic influences on hub connectivity of the human connectome.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
09 07 2021
09 07 2021
Historique:
received:
21
03
2021
accepted:
03
06
2021
entrez:
10
7
2021
pubmed:
11
7
2021
medline:
21
7
2021
Statut:
epublish
Résumé
Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted magnetic resonance imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.
Identifiants
pubmed: 34244483
doi: 10.1038/s41467-021-24306-2
pii: 10.1038/s41467-021-24306-2
pmc: PMC8271018
doi:
Types de publication
Journal Article
Observational Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4237Subventions
Organisme : NIMH NIH HHS
ID : U54 MH091657
Pays : United States
Informations de copyright
© 2021. The Author(s).
Références
Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186 (2009).
pubmed: 19190637
doi: 10.1038/nrn2575
Fornito, A., Zalesky, A. & Bullmore, E. Fundamentals of Brain Network Analysis (Academic Press, 2016).
Harriger, L., van den Heuvel, M. P. & Sporns, O. Rich club organization of macaque cerebral cortex and its role in network communication. PLoS ONE 7, e46497 (2012).
pubmed: 23029538
pmcid: 3460908
doi: 10.1371/journal.pone.0046497
Towlson, E. K., Vértes, P. E., Ahnert, S. E., Schafer, W. R. & Bullmore, E. T. The rich club of the C. elegans neuronal connectome. J. Neurosci. 33, 6380 (2013).
pubmed: 23575836
pmcid: 4104292
doi: 10.1523/JNEUROSCI.3784-12.2013
van den Heuvel, M. P. & Sporns, O. Rich-club organization of the human connectome. J. Neurosci. 31, 15775 (2011).
pubmed: 22049421
pmcid: 6623027
doi: 10.1523/JNEUROSCI.3539-11.2011
van den Heuvel, M. P. & Sporns, O. Network hubs in the human brain. Trends Cogn. Sci. 17, 683 (2013).
pubmed: 24231140
doi: 10.1016/j.tics.2013.09.012
Fulcher, B. D. & Fornito, A. A transcriptional signature of hub connectivity in the mouse connectome. Proc. Natl. Acad. Sci USA 113, 1513302113 (2016).
doi: 10.1073/pnas.1513302113
Mišić, B. et al. Network-level structure-function relationships in numan neocortex. Cereb. Cortex 26, 3285 (2016).
pubmed: 27102654
pmcid: 4898678
doi: 10.1093/cercor/bhw089
Buckner, R. L. et al. Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimeras disease. J. Neurosci. 29, 1860 (2009).
pubmed: 19211893
pmcid: 2750039
doi: 10.1523/JNEUROSCI.5062-08.2009
Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336 (2012).
doi: 10.1038/nrn3214
pubmed: 22498897
Arnatkevičiūtė, A., Fulcher, B. D., Pocock, R. & Fornito, A. Hub connectivity, neuronal diversity, and gene expression in the Caenorhabditis elegans connectome. PLoS Comput. Biol. 14, e1005989 (2018).
pubmed: 29432412
pmcid: 5825174
doi: 10.1371/journal.pcbi.1005989
Mueller, S. et al. Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586 (2013).
pubmed: 23395382
pmcid: 3746075
doi: 10.1016/j.neuron.2012.12.028
Jung, R. E. & Haier, R. J. The parieto-frontal integration theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav. Brain Sci. 30, 135 (2007).
pubmed: 17655784
doi: 10.1017/S0140525X07001185
Crossley, N. A. et al. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137, 2382 (2014).
pubmed: 25057133
pmcid: 4107735
doi: 10.1093/brain/awu132
Reardon, P. K. et al. Normative brain size variation and brain shape diversity in humans. Science 360, 1222 (2018).
pubmed: 29853553
pmcid: 7485526
doi: 10.1126/science.aar2578
Hill, J. et al. Similar patterns of cortical expansion during human development and evolution. Proc. Natl. Acad. Sci. USA 107, 13135 (2010).
pubmed: 20624964
pmcid: 2919958
doi: 10.1073/pnas.1001229107
Ardesch, D. J. et al. Evolutionary expansion of connectivity between multimodal association areas in the human brain compared with chimpanzees. Proc. Natl. Acad. Sci. USA 116, 7101 (2019).
pubmed: 30886094
pmcid: 6452697
doi: 10.1073/pnas.1818512116
Buckner, R. L. & Krienen, F. M. The evolution of distributed association networks in the human brain. Trends Cogn. Sci. 17, 648 (2013).
pubmed: 24210963
doi: 10.1016/j.tics.2013.09.017
Thompson, P. M. Genetic influences on brain structure. Nat. Neurosci. 4, 1253 (2001).
pubmed: 11694885
doi: 10.1038/nn758
Yamamoto, N., Tamada, A. & Murakami, F. Wiring of the brain by a range of guidance cues. Progress Neurobiol. 68, 393 (2003).
doi: 10.1016/S0301-0082(02)00129-6
Kolodkin, A. L. & Tessier-Lavigne, M. Mechanisms and molecules of neuronal wiring: a primer. Cold Spring Harb. Perspect. Biol. 3, a001727 (2011).
pubmed: 21123392
pmcid: 3098670
doi: 10.1101/cshperspect.a001727
Fornito, A. et al. Genetic influences on cost-efficient organization of human cortical functional networks. J. Neurosci. 31, 3261 (2011).
pubmed: 21368038
pmcid: 6623940
doi: 10.1523/JNEUROSCI.4858-10.2011
Vértes, P. E. et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos. Trans. R. Soc. B 371, 735 (2016).
doi: 10.1098/rstb.2015.0362
Fornito, A., Arnatkevičiūtė, A. & Fulcher, B. D. Bridging the gap between connectome and transcriptome. Trends Cogn. Sci. 23, 34 (2019).
pubmed: 30455082
doi: 10.1016/j.tics.2018.10.005
Arnatkevičiūtė, A., Fulcher, B. D. & Fornito, A. Uncovering the transcriptional correlates of hub connectivity in neural networks. Front. Neural Circuits 13, 47 (2019).
pubmed: 31379515
pmcid: 6659348
doi: 10.3389/fncir.2019.00047
Baker, S. T. E. et al. Developmental changes in brain network hub connectivity in late adolescence. J. Neurosci. 35, 9078 (2015).
pubmed: 26085632
pmcid: 6605159
doi: 10.1523/JNEUROSCI.5043-14.2015
Oldham, S. & Fornito, A. The development of brain network hubs. Dev. Cogn. Neurosci. 36, 100607 (2018).
pubmed: 30579789
pmcid: 6969262
doi: 10.1016/j.dcn.2018.12.005
Ercsey-Ravasz, M. et al. A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80, 184 (2013).
pubmed: 24094111
pmcid: 3954498
doi: 10.1016/j.neuron.2013.07.036
Song, H. F., Kennedy, H. & Wang, X.-J. Spatial embedding of structural similarity in the cerebral cortex. Proc. Natl. Acad. Sci. USA 111, 16580 (2014).
pubmed: 25368200
pmcid: 4246295
doi: 10.1073/pnas.1414153111
Henderson, J. & Robinson, P. Relations between the geometry of cortical gyrification and white-matter network architecture. Brain Connect. 4, 112 (2014).
pubmed: 24437717
doi: 10.1089/brain.2013.0183
Horvát, S. et al. Spatial embedding and wiring cost constrain the functional layout of the cortical network of rodents and primates. PLoS Biol. 14, e1002512 (2016).
pubmed: 27441598
pmcid: 4956175
doi: 10.1371/journal.pbio.1002512
Vértes, P. E. et al. Simple models of human brain functional networks. Proc. Natl. Acad. Sci. USA 109, 5868 (2012).
pubmed: 22467830
pmcid: 3326510
doi: 10.1073/pnas.1111738109
Betzel, R. F. et al. Generative models of the human connectome. Neuroimage 124, 1054 (2016).
doi: 10.1016/j.neuroimage.2015.09.041
pubmed: 26427642
Goulas, A., Betzel, R. F. & Hilgetag, C. C. Spatiotemporal ontogeny of brain wiring. Sci. Adv. 5, eaav9694 (2019).
pubmed: 31206020
pmcid: 6561744
doi: 10.1126/sciadv.aav9694
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62 (2013).
pubmed: 23684880
doi: 10.1016/j.neuroimage.2013.05.041
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171 (2016).
pubmed: 27437579
pmcid: 4990127
doi: 10.1038/nature18933
van den Heuvel, M. P., Kahn, R. S., Goni, J. & Sporns, O. High-cost, high-capacity backbone for global brain communication. Proc. Natl. Acad. Sci. USA 109, 11372 (2012).
pubmed: 22711833
pmcid: 3396547
doi: 10.1073/pnas.1203593109
Ji, J. L. et al. Mapping the human brainas cortical-subcortical functional network organization. NeuroImage 185, 35 (2019).
pubmed: 30291974
doi: 10.1016/j.neuroimage.2018.10.006
Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391 (2012).
pubmed: 22996553
pmcid: 4243026
doi: 10.1038/nature11405
Arnatkevičiūtė, A., Fulcher, B. D. & Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage 189, 353–367 (2019).
doi: 10.1016/j.neuroimage.2019.01.011
pubmed: 30648605
Lau, H. Y. G., Fornito, A. & Fulcher, B. D. Scaling of gene transcriptional gradients with brain size across mouse development. Neuroimage 224, 117395 (2021).
Burt, J. B. et al. Hierarchy of transcriptomic specialization across human cortex captured by structural neuroimaging topography. Nat. Neurosci. 21, 1251 (2018).
pubmed: 30082915
pmcid: 6119093
doi: 10.1038/s41593-018-0195-0
Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37 (2016).
pubmed: 26687838
doi: 10.1016/j.neuron.2015.11.013
Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).
pubmed: 30545854
pmcid: 6413317
doi: 10.1126/science.aat7615
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechtol. 36, 70 (2018).
doi: 10.1038/nbt.4038
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl. Acad. Sci. USA 112, 7285 (2015).
pubmed: 26060301
pmcid: 4466750
doi: 10.1073/pnas.1507125112
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955 (2017).
pubmed: 28846088
pmcid: 5623139
doi: 10.1038/nmeth.4407
Amunts, K. et al. Bigbrain: an ultrahigh-resolution 3D human brain model. Science 340, 1472 (2013).
pubmed: 23788795
doi: 10.1126/science.1235381
Paquola, C. et al. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol. 17, e3000284 (2019).
pubmed: 31107870
pmcid: 6544318
doi: 10.1371/journal.pbio.3000284
Fulcher, B. D., Arnatkeviciute, A. & Fornito, A. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat. Commun. 12, 2669 (2021).
pubmed: 33976144
pmcid: 8113439
doi: 10.1038/s41467-021-22862-1
Ramón y Cajal, S. Histology of the Nervous System of Man and Vertebrates (Oxford University Press, 1995).
Wei, Y. et al. Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nat. Commun. 10, 4839 (2019).
pubmed: 31649260
pmcid: 6813316
doi: 10.1038/s41467-019-12764-8
Henderson, J. A. & Robinson, P. A. Geometric effects on complex network structure in the cortex. Phys. Rev. Lett. 107, 018102 (2011).
pubmed: 21797575
doi: 10.1103/PhysRevLett.107.018102
Bertolero, M. A. et al. The human brain’s network architecture is genetically encoded by modular pleiotropy. Preprint at https://arxiv.org/abs/1905.07606 (2019).
Barbas, H. General cortical and special prefrontal connections: principles from structure to function. Annu. Rev. Neurosci. 38, 269 (2015).
pubmed: 25897871
doi: 10.1146/annurev-neuro-071714-033936
Gollo, L. L. et al. Fragility and volatility of structural hubs in the human connectome. Nat. Neurosci. 21, 1107 (2018).
doi: 10.1038/s41593-018-0188-z
pubmed: 30038275
Bassett, D. S. et al. Efficient physical embedding of topologically complex information processing networks in brains and computer circuits. PLoS Comput. Biol. 6, e1000748 (2010).
pubmed: 20421990
pmcid: 2858671
doi: 10.1371/journal.pcbi.1000748
Beul, S. F., Barbas, H. & Hilgetag, C. C. A predictive structural model of the primate connectome. Sci. Rep. 7, 43176 (2017).
pubmed: 28256558
pmcid: 5335700
doi: 10.1038/srep43176
Goulas, A., Majka, P., Rosa, M. G. P. & Hilgetag, C. C. A blueprint of mammalian cortical connectomes. PLoS Biol. 17, e2005346 (2019).
pubmed: 30901324
pmcid: 6456226
doi: 10.1371/journal.pbio.2005346
Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105 (2013).
pubmed: 23668970
doi: 10.1016/j.neuroimage.2013.04.127
Anderson, J., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20, 870 (2003).
doi: 10.1016/S1053-8119(03)00336-7
Smith, S. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208 (2004).
doi: 10.1016/j.neuroimage.2004.07.051
pubmed: 15501092
Andersson, J. & Sotiropoulos, S. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125, 1063 (2016).
doi: 10.1016/j.neuroimage.2015.10.019
pubmed: 26481672
Andersson, J., Graham, M., Zsoldos, E. & Sotiropoulos, S. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. Neuroimage 141, 556 (2016).
pubmed: 27393418
doi: 10.1016/j.neuroimage.2016.06.058
Andersson, J. et al. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: within volume movement. Neuroimage 152, 450 (2017).
pubmed: 28284799
doi: 10.1016/j.neuroimage.2017.02.085
Oldham, S. et al. The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics. NeuroImage. 222, 117252 (2020).
pubmed: 32800991
doi: 10.1016/j.neuroimage.2020.117252
Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45 (2017).
doi: 10.1109/42.906424
Notter, M. et al. Parcellation fragmenter. https://github.com/miykael/parcellation_fragmenter (2018).
Tournier, J.-D., Calamante, F. & Connelly, A. MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22, 53 (2012).
doi: 10.1002/ima.22005
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782 (2012).
pubmed: 21979382
doi: 10.1016/j.neuroimage.2011.09.015
Tournier, J.-D., Calamante, F. & Connelly, A. Improved probabilistic streamlines tractography by 2 nd order integration over fibre orientation distributions. Ismrm 88, 2010 (2010).
Smith, R. E., Tournier, J.-D., Calamante, F. & Connelly, A. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62, 1924 (2012).
pubmed: 22705374
doi: 10.1016/j.neuroimage.2012.06.005
Smith, R. E., Tournier, J.-D., Calamante, F. & Connelly, A. SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119, 338 (2015).
pubmed: 26163802
doi: 10.1016/j.neuroimage.2015.06.092
Jones, D. K. Challenges and limitations of quantifying brain connectivity in vivo with diffusion MRI. Imaging Med. 2, 341 (2010).
doi: 10.2217/iim.10.21
Zalesky, A. et al. Connectome sensitivity or specificity: which is more important? Neuroimage 142, 407 (2016).
pubmed: 27364472
doi: 10.1016/j.neuroimage.2016.06.035
Thomas, C. et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc. Natl. Acad. Sci. USA 111, 16574 (2014).
pubmed: 25368179
pmcid: 4246325
doi: 10.1073/pnas.1405672111
Maier-Hein, K. H., Neher, P. F., Houde, J.-C., Cote, M.-A. & Descoteaux, M. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8, 1349 (2017).
pubmed: 29116093
pmcid: 5677006
doi: 10.1038/s41467-017-01285-x
Zalesky, A. & Fornito, A. A dti-derived measure of cortico-cortical connectivity. IEEE Trans. Med. Imaging 28, 1023 (2009).
pubmed: 19150781
doi: 10.1109/TMI.2008.2012113
de Reus, M. A. & van den Heuvel, M. P. Estimating false positives and negatives in brain networks. Neuroimage 70, 402 (2013).
pubmed: 23296185
doi: 10.1016/j.neuroimage.2012.12.066
Betzel, R. F., Griffa, A., Hagmann, P. & Mišić, B. Distance-dependent consensus thresholds for generating group-representative structural brain networks. Proc. Natl. Acad. Sci. USA 3, 475 (2018).
Roberts, J. A., Perry, A., Roberts, G., Mitchell, P. B. & Breakspear, M. Consistency-based thresholding of the human connectome. Neuroimage 145, 118 (2016).
pubmed: 27666386
doi: 10.1016/j.neuroimage.2016.09.053
Colizza, V., Flammini, A., Serrano, M. A. & Vespignani, A. Detecting rich-club ordering in complex networks. Nat. Phys. 2, 110 (2006).
doi: 10.1038/nphys209
Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059 (2010).
doi: 10.1016/j.neuroimage.2009.10.003
pubmed: 19819337
Roberts, J. A. et al. The contribution of geometry to the human connectome. Neuroimage 124, 379 (2016).
pubmed: 26364864
doi: 10.1016/j.neuroimage.2015.09.009
Betzel, R. F. & Bassett, D. S. Specificity and robustness of long-distance connections in weighted, interareal connectomes. Proc. Natl. Acad. Sci. USA 115, E4880 (2018).
pubmed: 29739890
pmcid: 6003515
doi: 10.1073/pnas.1720186115
Opsahl, T., Colizza, V., Panzarasa, P. & Ramasco, J. J. Prominence and control: the weighted rich-club effect. Phys. Rev. Lett. 101, 168702 (2008).
pubmed: 18999722
doi: 10.1103/PhysRevLett.101.168702
Alstott, J., Panzarasa, P., Rubinov, M., Bullmore, E. T. & Vértes, P. E. A unifying framework for measuring weighted rich clubs. Sci. Rep. 4, 1 (2014).
doi: 10.1038/srep07258
Estrada, E. & Hatano, N. Communicability in complex networks. Phys. Rev. E 77, 036111 (2008).
doi: 10.1103/PhysRevE.77.036111
Goñi, J. et al. Resting-brain functional connectivity predicted by analytic measures of network communication. Proc. Natl. Acad. Sci. USA 111, 833 (2014).
pubmed: 24379387
doi: 10.1073/pnas.1315529111
Boker, S. et al. OpenMx: an open source extended structural equation modeling framework. Psychometrika 76, 306 (2011).
pubmed: 23258944
pmcid: 3525063
doi: 10.1007/s11336-010-9200-6
Neale, M. C. et al. OpenMx 2.0: extended structural equation and statistical modeling. Psychometrika 81, 535 (2016).
pubmed: 25622929
doi: 10.1007/s11336-014-9435-8
Akaike, H. Information Theory and an Extension of the Maximum Likelihood Principle (Springer, 1998).
Arloth, J., Bader, D. M., Röh, S. & Altmann, A. Re-Annotator: annotation pipeline for microarray probe sequences. PLoS ONE 10, e0139516 (2015).
pubmed: 26426330
pmcid: 4591122
doi: 10.1371/journal.pone.0139516
Miller, J. A. et al. Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq. BMC Genomics 15, 154 (2014).
pubmed: 24564186
pmcid: 4007560
doi: 10.1186/1471-2164-15-154
Gillis, J., Mistry, M. & Pavlidis, P. Gene function analysis in complex data sets using ErmineJ. Nat. Protoc. 5, 1148 (2010).
pubmed: 20539290
doi: 10.1038/nprot.2010.78
Zoubarev, A. et al. Gemma: a resource for the reuse, sharing and meta-analysis of expression profiling data. Bioinformatics 28, 2272 (2012).
pubmed: 22782548
pmcid: 3426847
doi: 10.1093/bioinformatics/bts430
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289 (1995).
Rubinov, M. Constraints and spandrels of interareal connectomes. Nat. Commun. 7, 13812 (2016).
pubmed: 27924867
pmcid: 5151054
doi: 10.1038/ncomms13812
Chen, Y., Wang, S., Hilgetag, C. C. & Zhou, C. Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency. PLoS Comput. Biol. 13, 1 (2017).
doi: 10.1371/journal.pcbi.1005776
Chen, Y., Wang, S., Hilgetag, C. C. & Zhou, C. Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems. PLoS Comput. Biol. 9, 1 (2013).
doi: 10.1371/journal.pcbi.1002937
Beul, S. F., Goulas, A. & Hilgetag, C. C. Comprehensive computational modelling of the development of mammalian cortical connectivity underlying an architectonic type principle. PLoS Comput. Biol. 14, e1006550 (2018).
pubmed: 30475798
pmcid: 6261046
doi: 10.1371/journal.pcbi.1006550
Arnatkeviciute, A. Data files to support reproducing analyses in “Genetic influences on hub connectivity of the human connectome” [Data set]. (Version v2). https://doi.org/10.5281/zenodo.4733297 (2021).
Arnatkeviciute, A. Reproducing figures for “Genetic influences on hub connectivity of the human connectome” (Version v1). https://doi.org/10.5281/zenodo.4724407 (2021).