Genetic fingerprinting with heritable phenotypes of the resting-state brain network topology.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
30 Sep 2024
30 Sep 2024
Historique:
received:
23
03
2024
accepted:
29
08
2024
medline:
1
10
2024
pubmed:
1
10
2024
entrez:
30
9
2024
Statut:
epublish
Résumé
Cognitive, behavioral, and disease traits are influenced by both genetic and environmental factors. Individual differences in these traits have been associated with graph theoretical properties of resting-state networks, indicating that variations in connectome topology may be driven by genetics. In this study, we establish the heritability of global and local graph properties of resting-state networks derived from functional MRI (fMRI) and magnetoencephalography (MEG) using a large sample of twins and non-twin siblings from the Human Connectome Project. We examine the heritability of MEG in the source space, providing a more accurate estimate of genetic influences on electrophysiological networks. Our findings show that most graph measures are more heritable for MEG compared to fMRI and the heritability for MEG is greater for amplitude compared to phase synchrony in the delta, high beta, and gamma frequency bands. This suggests that the fast neuronal dynamics in MEG offer unique insights into the genetic basis of brain network organization. Furthermore, we demonstrate that brain network features can serve as genetic fingerprints to accurately identify pairs of identical twins within a cohort. These results highlight novel opportunities to relate individual connectome signatures to genetic mechanisms underlying brain function.
Identifiants
pubmed: 39349968
doi: 10.1038/s42003-024-06807-0
pii: 10.1038/s42003-024-06807-0
doi:
Types de publication
Journal Article
Twin Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
1221Informations de copyright
© 2024. The Author(s).
Références
Seguin, C., Sporns, O. & Zalesky, A. Brain network communication: concepts, models and applications. Nat Rev Neurosci, https://doi.org/10.1038/s41583-023-00718-5 (2023).
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
Bastos, A. M. & Schoffelen, J. M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front. Syst. Neurosci. 9, 175 (2015).
pubmed: 26778976
O’Neill, G. C., Barratt, E. L., Hunt, B. A., Tewarie, P. K. & Brookes, M. J. Measuring electrophysiological connectivity by power envelope correlation: A technical review on MEG methods. Phys. Med Biol. 60, R271–295, (2015).
pubmed: 26447925
doi: 10.1088/0031-9155/60/21/R271
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
Cohen, J. R., & D’Esposito, M. The segregation and integration of distinct brain networks and their relationship to cognition. J. Neurosci. 36, 12083–12094 (2016).
pubmed: 27903719
pmcid: 5148214
doi: 10.1523/JNEUROSCI.2965-15.2016
Wang, R. et al. Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities. Proc. Natl Acad. Sci. USA 118, (2021).
Hilger, K. & Markett, S. Personality network neuroscience: Promises and challenges on the way toward a unifying framework of individual variability. Netw. Neurosci. 5, 631–645 (2021).
pubmed: 34746620
pmcid: 8567832
Perovnik, M., Rus, T., Schindlbeck, K. A. & Eidelberg, D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat. Rev. Neurol. 19, 73–90 (2023).
pubmed: 36539533
doi: 10.1038/s41582-022-00753-3
Deco, G. & Kringelbach, M. L. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron 84, 892–905 (2014).
pubmed: 25475184
doi: 10.1016/j.neuron.2014.08.034
Khazaee, A., Ebrahimzadeh, A. & Babajani-Feremi, A. Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clin. Neurophysiol. 126, 2132–2141 (2015).
pubmed: 25907414
doi: 10.1016/j.clinph.2015.02.060
Pourmotabbed, H., Wheless, J. W. & Babajani-Feremi, A. Lateralization of epilepsy using intra-hemispheric brain networks based on resting-state MEG data. Hum. Brain Mapp. 41, 2964–2979 (2020).
pubmed: 32400923
pmcid: 7336137
doi: 10.1002/hbm.24990
Piper, R. J. et al. Towards network-guided neuromodulation for epilepsy. Brain 145, 3347–3362 (2022).
pubmed: 35771657
pmcid: 9586548
doi: 10.1093/brain/awac234
Corona, L. et al. Non-invasive mapping of epileptogenic networks predicts surgical outcome. Brain 146, 1916–1931 (2023).
pubmed: 36789500
pmcid: 10151194
doi: 10.1093/brain/awac477
Satterthwaite, T. D., Xia, C. H. & Bassett, D. S. Personalized neuroscience: common and individual-specific features in functional brain networks. Neuron 98, 243–245 (2018).
pubmed: 29673476
doi: 10.1016/j.neuron.2018.04.007
Chabris, C. F., Lee, J. J., Cesarini, D., Benjamin, D. J. & Laibson, D. I. The fourth law of behavior genetics. Curr. Dir. Psychol. Sci. 24, 304–312 (2015).
pubmed: 26556960
pmcid: 4635473
doi: 10.1177/0963721415580430
Brainstorm, C. et al. Analysis of shared heritability in common disorders of the brain. Science 360, https://doi.org/10.1126/science.aap8757 (2018).
Congdon, E., Poldrack, R. A. & Freimer, N. B. Neurocognitive phenotypes and genetic dissection of disorders of brain and behavior. Neuron 68, 218–230 (2010).
pubmed: 20955930
pmcid: 4123421
doi: 10.1016/j.neuron.2010.10.007
Arnatkeviciute, A., Fulcher, B. D., Bellgrove, M. A. & Fornito, A. Where the genome meets the connectome: Understanding how genes shape human brain connectivity. Neuroimage 244, 118570 (2021).
pubmed: 34508898
doi: 10.1016/j.neuroimage.2021.118570
Kochunov, P. et al. Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data. Neuroimage 111, 300–311 (2015).
pubmed: 25747917
doi: 10.1016/j.neuroimage.2015.02.050
Arnatkeviciute, A. et al. Genetic influences on hub connectivity of the human connectome. Nat. Commun. 12, 4237 (2021).
pubmed: 34244483
pmcid: 8271018
doi: 10.1038/s41467-021-24306-2
van Pelt, S., Boomsma, D. I. & Fries, P. Magnetoencephalography in twins reveals a strong genetic determination of the peak frequency of visually induced gamma-band synchronization. J. Neurosci. 32, 3388–3392 (2012).
pubmed: 22399760
pmcid: 6621035
doi: 10.1523/JNEUROSCI.5592-11.2012
Van ’t Ent, D., Van Soelen, I. L., Stam, K. J., De Geus, E. J. & Boomsma, D. I. Genetic influence demonstrated for MEG-recorded somatosensory evoked responses. Psychophysiology 47, 1040–1046 (2010).
pubmed: 20409017
Blokland, G. A. et al. Heritability of working memory brain activation. J. Neurosci. 31, 10882–10890 (2011).
pubmed: 21795540
pmcid: 3163233
doi: 10.1523/JNEUROSCI.5334-10.2011
van ‘t Ent, D., van Soelen, I. L., Stam, C. J., De Geus, E. J. & Boomsma, D. I. Strong resemblance in the amplitude of oscillatory brain activity in monozygotic twins is not caused by “trivial” similarities in the composition of the skull. Hum. Brain Mapp. 30, 2142–2145 (2009).
pubmed: 18819108
doi: 10.1002/hbm.20656
Smit, D. J., Posthuma, D., Boomsma, D. I. & Geus, E. J. Heritability of background EEG across the power spectrum. Psychophysiology 42, 691–697 (2005).
pubmed: 16364064
doi: 10.1111/j.1469-8986.2005.00352.x
Colclough, G. L. et al. The heritability of multi-modal connectivity in human brain activity. Elife 6, https://doi.org/10.7554/eLife.20178 (2017).
Sinclair, B. et al. Heritability of the network architecture of intrinsic brain functional connectivity. Neuroimage 121, 243–252 (2015).
pubmed: 26226088
doi: 10.1016/j.neuroimage.2015.07.048
Ge, T., Holmes, A. J., Buckner, R. L., Smoller, J. W. & Sabuncu, M. R. Heritability analysis with repeat measurements and its application to resting-state functional connectivity. Proc. Natl Acad. Sci. USA 114, 5521–5526 (2017).
pubmed: 28484032
pmcid: 5448225
doi: 10.1073/pnas.1700765114
Anderson, K. M. et al. Heritability of individualized cortical network topography. Proc. Natl Acad. Sci. USA 118, https://doi.org/10.1073/pnas.2016271118 (2021).
Posthuma, D. et al. Genetic components of functional connectivity in the brain: the heritability of synchronization likelihood. Hum. Brain Mapp. 26, 191–198 (2005).
pubmed: 15929086
pmcid: 6871713
doi: 10.1002/hbm.20156
Babajani-Feremi, A., Noorizadeh, N., Mudigoudar, B. & Wheless, J. W. Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology. Neuroimage Clin. 19, 990–999 (2018).
pubmed: 30003036
pmcid: 6039837
doi: 10.1016/j.nicl.2018.06.017
Schutte, N. M. et al. Heritability of resting state EEG functional connectivity patterns. Twin Res Hum. Genet 16, 962–969 (2013).
pubmed: 23931641
doi: 10.1017/thg.2013.55
Smit, D. J. et al. Endophenotypes in a dynamically connected brain. Behav. Genet 40, 167–177 (2010).
pubmed: 20111993
pmcid: 2829652
doi: 10.1007/s10519-009-9330-8
Smit, D. J., Stam, C. J., Posthuma, D., Boomsma, D. I. & de Geus, E. J. Heritability of “small-world” networks in the brain: a graph theoretical analysis of resting-state EEG functional connectivity. Hum. Brain Mapp. 29, 1368–1378 (2008).
pubmed: 18064590
doi: 10.1002/hbm.20468
Brookes, M. J. et al. Measuring functional connectivity using MEG: methodology and comparison with fcMRI. Neuroimage 56, 1082–1104 (2011).
pubmed: 21352925
doi: 10.1016/j.neuroimage.2011.02.054
Schoffelen, J. M. & Gross, J. Source connectivity analysis with MEG and EEG. Hum. Brain Mapp. 30, 1857–1865 (2009).
pubmed: 19235884
pmcid: 6870611
doi: 10.1002/hbm.20745
Pourmotabbed, H., de Jongh Curry, A. L., Clarke, D. F., Tyler-Kabara, E. C. & Babajani-Feremi, A. Reproducibility of graph measures derived from resting-state MEG functional connectivity metrics in sensor and source spaces. Hum. Brain Mapp. 43, 1342–1357 (2022).
pubmed: 35019189
pmcid: 8837594
doi: 10.1002/hbm.25726
Ball, T. M., Goldstein-Piekarski, A. N., Gatt, J. M. & Williams, L. M. Quantifying person-level brain network functioning to facilitate clinical translation. Transl. Psychiatry 7, e1248 (2017).
pubmed: 29039851
pmcid: 5682602
doi: 10.1038/tp.2017.204
Kuntzelman, K. & Miskovic, V. Reliability of graph metrics derived from resting-state human EEG. Psychophysiology 54, 51–61 (2017).
pubmed: 28000256
doi: 10.1111/psyp.12600
Gratton, C. et al. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98, 439–452 e435 (2018).
pubmed: 29673485
pmcid: 5912345
doi: 10.1016/j.neuron.2018.03.035
Shehzad, Z. et al. The resting brain: unconstrained yet reliable. Cereb. Cortex 19, 2209–2229 (2009).
pubmed: 19221144
pmcid: 3896030
doi: 10.1093/cercor/bhn256
Horien, C., Shen, X., Scheinost, D. & Constable, R. T. The individual functional connectome is unique and stable over months to years. Neuroimage 189, 676–687 (2019).
pubmed: 30721751
doi: 10.1016/j.neuroimage.2019.02.002
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
da Silva Castanheira, J., Orozco Perez, H. D., Misic, B. & Baillet, S. Brief segments of neurophysiological activity enable individual differentiation. Nat. Commun. 12, 5713 (2021).
pubmed: 34588439
pmcid: 8481307
doi: 10.1038/s41467-021-25895-8
Bari, S., Amico, E., Vike, N., Talavage, T. M. & Goni, J. Uncovering multi-site identifiability based on resting-state functional connectomes. Neuroimage 202, 115967 (2019).
pubmed: 31352124
doi: 10.1016/j.neuroimage.2019.06.045
Fraschini, M., Hillebrand, A., Demuru, M., Didaci, L. & Marcialis, G. L. An EEG-based biometric system using eigenvector centrality in resting state brain networks. IEEE Signal Process. Lett. 22, 666–670 (2015).
doi: 10.1109/LSP.2014.2367091
Kong, W., Wang, L., Xu, S., Babiloni, F. & Chen, H. EEG fingerprints: Phase synchronization of EEG signals as biomarker for subject identification. IEEE Access 7, 121165–121173 (2019).
doi: 10.1109/ACCESS.2019.2931624
Sareen, E. et al. Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations. Neuroimage 240, 118331 (2021).
pubmed: 34237444
doi: 10.1016/j.neuroimage.2021.118331
Demuru, M. et al. Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs. Sci. Rep. 7, 9685 (2017).
pubmed: 28852152
pmcid: 5575140
doi: 10.1038/s41598-017-10235-y
Miranda-Dominguez, O. et al. Heritability of the human connectome: A connectotyping study. Netw. Neurosci. 2, 175–199 (2018).
pubmed: 30215032
pmcid: 6130446
doi: 10.1162/netn_a_00029
Jansen, P. R. et al. Polygenic scores for neuropsychiatric traits and white matter microstructure in the pediatric population. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 4, 243–250 (2019).
pubmed: 30243642
Wang, T. et al. Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations. Neuroimage Clin. 14, 441–449 (2017).
pubmed: 28275544
pmcid: 5328751
doi: 10.1016/j.nicl.2017.02.011
Van Essen, D. C. et al. The Human Connectome Project: a data acquisition perspective. Neuroimage 62, 2222–2231 (2012).
pubmed: 22366334
doi: 10.1016/j.neuroimage.2012.02.018
Larson-Prior, L. J. et al. Adding dynamics to the Human Connectome Project with MEG. Neuroimage 80, 190–201 (2013).
pubmed: 23702419
doi: 10.1016/j.neuroimage.2013.05.056
Brookes, M. J. et al. Investigating the electrophysiological basis of resting state networks using magnetoencephalography. Proc. Natl Acad. Sci. USA 108, 16783–16788 (2011).
pubmed: 21930901
pmcid: 3189080
doi: 10.1073/pnas.1112685108
Nentwich, M. et al. Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI. Neuroimage 218, 117001 (2020).
pubmed: 32492509
doi: 10.1016/j.neuroimage.2020.117001
Chang, C. & Chen, J. E. Multimodal EEG-fMRI: advancing insight into large-scale human brain dynamics. Curr Opin. Biomed. Eng. 18, https://doi.org/10.1016/j.cobme.2021.100279 (2021).
Engel, A. K., Gerloff, C., Hilgetag, C. C. & Nolte, G. Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron 80, 867–886 (2013).
pubmed: 24267648
doi: 10.1016/j.neuron.2013.09.038
Fan, L. et al. The Human Brainnetome Atlas: A new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016).
pubmed: 27230218
pmcid: 4961028
doi: 10.1093/cercor/bhw157
Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2, 125–141 (2012).
pubmed: 22642651
doi: 10.1089/brain.2012.0073
Muschelli, J. et al. Reduction of motion-related artifacts in resting state fMRI using aCompCor. Neuroimage 96, 22–35 (2014).
pubmed: 24657780
doi: 10.1016/j.neuroimage.2014.03.028
Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. Neuroimage 80, 144–168 (2013).
pubmed: 23702415
doi: 10.1016/j.neuroimage.2013.05.039
Hillebrand, A. et al. Direction of information flow in large-scale resting-state networks is frequency-dependent. Proc. Natl Acad. Sci. USA 113, 3867–3872 (2016).
pubmed: 27001844
pmcid: 4833227
doi: 10.1073/pnas.1515657113
Nissen, I. A. et al. Identifying the epileptogenic zone in interictal resting-state MEG source-space networks. Epilepsia 58, 137–148 (2017).
pubmed: 27888520
doi: 10.1111/epi.13622
Vinck, M., Oostenveld, R., van Wingerden, M., Battaglia, F. & Pennartz, C. M. An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55, 1548–1565 (2011).
pubmed: 21276857
doi: 10.1016/j.neuroimage.2011.01.055
Brookes, M. J., Woolrich, M. W. & Barnes, G. R. Measuring functional connectivity in MEG: a multivariate approach insensitive to linear source leakage. Neuroimage 63, 910–920 (2012).
pubmed: 22484306
doi: 10.1016/j.neuroimage.2012.03.048
Hipp, J. F., Hawellek, D. J., Corbetta, M., Siegel, M. & Engel, A. K. Large-scale cortical correlation structure of spontaneous oscillatory activity. Nat. Neurosci. 15, 884–890 (2012).
pubmed: 22561454
doi: 10.1038/nn.3101
Kochunov, P. et al. Homogenizing estimates of heritability among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data. Front Neuroinform 13, 16 (2019).
pubmed: 30914942
pmcid: 6422938
doi: 10.3389/fninf.2019.00016
Chen, X. et al. Accelerated estimation and permutation inference for ACE modeling. Hum. Brain Mapp. 40, 3488–3507 (2019).
pubmed: 31037793
pmcid: 6680147
doi: 10.1002/hbm.24611
Menardi, A. et al. Heritability of brain resilience to perturbation in humans. Neuroimage 235, 118013 (2021).
pubmed: 33794357
doi: 10.1016/j.neuroimage.2021.118013
Gu, Z., Jamison, K. W., Sabuncu, M. R. & Kuceyeski, A. Heritability and interindividual variability of regional structure-function coupling. Nat. Commun. 12, 4894 (2021).
pubmed: 34385454
pmcid: 8361191
doi: 10.1038/s41467-021-25184-4
Suarez, L. E., Markello, R. D., Betzel, R. F. & Misic, B. Linking structure and function in macroscale brain networks. Trends Cogn. Sci. 24, 302–315 (2020).
pubmed: 32160567
doi: 10.1016/j.tics.2020.01.008
Murphy, K. & Fox, M. D. Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage 154, 169–173 (2017).
pubmed: 27888059
doi: 10.1016/j.neuroimage.2016.11.052
Chai, X. J., Castanon, A. N., Ongur, D. & Whitfield-Gabrieli, S. Anticorrelations in resting state networks without global signal regression. Neuroimage 59, 1420–1428 (2012).
pubmed: 21889994
doi: 10.1016/j.neuroimage.2011.08.048
Chang, C. & Glover, G. H. Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. Neuroimage 47, 1448–1459 (2009).
pubmed: 19446646
doi: 10.1016/j.neuroimage.2009.05.012
Couvy-Duchesne, B. et al. Heritability of head motion during resting state functional MRI in 462 healthy twins. Neuroimage 102, 424–434 (2014).
pubmed: 25132021
doi: 10.1016/j.neuroimage.2014.08.010
Messe, A. et al. Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex. Neuroimage 276, 120212 (2023).
pubmed: 37269959
doi: 10.1016/j.neuroimage.2023.120212
Chu, C. J. et al. EEG functional connectivity is partially predicted by underlying white matter connectivity. Neuroimage 108, 23–33 (2015).
pubmed: 25534110
doi: 10.1016/j.neuroimage.2014.12.033
Vidaurre, D. et al. Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. Nat. Commun. 9, 2987 (2018).
pubmed: 30061566
pmcid: 6065434
doi: 10.1038/s41467-018-05316-z
Martin-Buro, M. C., Garces, P. & Maestu, F. Test-retest reliability of resting-state magnetoencephalography power in sensor and source space. Hum. Brain Mapp. 37, 179–190 (2016).
pubmed: 26467848
doi: 10.1002/hbm.23027
Lew, B. J., Fitzgerald, E. E., Ott, L. R., Penhale, S. H. & Wilson, T. W. Three-year reliability of MEG resting-state oscillatory power. Neuroimage 243, 118516 (2021).
pubmed: 34454042
doi: 10.1016/j.neuroimage.2021.118516
Birn, R. M. et al. The effect of scan length on the reliability of resting-state fMRI connectivity estimates. Neuroimage 83, 550–558 (2013).
pubmed: 23747458
doi: 10.1016/j.neuroimage.2013.05.099
Siems, M., Pape, A. A., Hipp, J. F. & Siegel, M. Measuring the cortical correlation structure of spontaneous oscillatory activity with EEG and MEG. Neuroimage 129, 345–355 (2016).
pubmed: 26827813
doi: 10.1016/j.neuroimage.2016.01.055
Coquelet, N. et al. Comparing MEG and high-density EEG for intrinsic functional connectivity mapping. Neuroimage 210, 116556 (2020).
pubmed: 31972279
doi: 10.1016/j.neuroimage.2020.116556
Deco, G. et al. Dynamical consequences of regional heterogeneity in the brain’s transcriptional landscape. Sci Adv 7, https://doi.org/10.1126/sciadv.abf4752 (2021).
Betzel, R. F. et al. Structural, geometric and genetic factors predict interregional brain connectivity patterns probed by electrocorticography. Nat. Biomed. Eng. 3, 902–916 (2019).
pubmed: 31133741
doi: 10.1038/s41551-019-0404-5
Stier, C. et al. Heritability of Magnetoencephalography phenotypes among patients with genetic generalized epilepsy and their siblings. Neurology 97, e166–e177 (2021).
pubmed: 34045271
pmcid: 8279565
doi: 10.1212/WNL.0000000000012144
Guo, W. et al. Increased cerebellar functional connectivity with the default-mode network in unaffected siblings of schizophrenia patients at rest. Schizophr. Bull. 41, 1317–1325 (2015).
pubmed: 25956897
pmcid: 4601712
doi: 10.1093/schbul/sbv062
Zhang, J. et al. Functional connectivity in people at clinical and familial high risk for schizophrenia. Psychiatry Res 328, 115464 (2023).
pubmed: 37690192
doi: 10.1016/j.psychres.2023.115464
Martin, C. G., He, B. J. & Chang, C. State-related neural influences on fMRI connectivity estimation. Neuroimage 244, 118590 (2021).
pubmed: 34560268
doi: 10.1016/j.neuroimage.2021.118590
Colclough, G. L. et al. How reliable are MEG resting-state connectivity metrics? Neuroimage 138, 284–293 (2016).
pubmed: 27262239
doi: 10.1016/j.neuroimage.2016.05.070
Arslan, S. et al. Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex. Neuroimage 170, 5–30 (2018).
pubmed: 28412442
doi: 10.1016/j.neuroimage.2017.04.014
Abbas, K. et al. Tangent functional connectomes uncover more unique phenotypic traits. iScience 26, 107624 (2023).
pubmed: 37694156
pmcid: 10483051
doi: 10.1016/j.isci.2023.107624
Luppi, A. I. & Stamatakis, E. A. Combining network topology and information theory to construct representative brain networks. Netw. Neurosci. 5, 96–124 (2021).
pubmed: 33688608
pmcid: 7935031
doi: 10.1162/netn_a_00170
Tait, L., Ozkan, A., Szul, M. J. & Zhang, J. A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high-resolution atlas: Performance, precision, and parcellation. Hum. Brain Mapp. 42, 4685–4707 (2021).
pubmed: 34219311
pmcid: 8410546
doi: 10.1002/hbm.25578
Colclough, G. L., Brookes, M. J., Smith, S. M. & Woolrich, M. W. A symmetric multivariate leakage correction for MEG connectomes. Neuroimage 117, 439–448 (2015).
pubmed: 25862259
doi: 10.1016/j.neuroimage.2015.03.071
Hallquist, M. N., Hwang, K. & Luna, B. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82, 208–225 (2013).
pubmed: 23747457
doi: 10.1016/j.neuroimage.2013.05.116
Valente, G., Castellanos, A. L., Hausfeld, L., De Martino, F. & Formisano, E. Cross-validation and permutations in MVPA: Validity of permutation strategies and power of cross-validation schemes. Neuroimage 238, 118145 (2021).
pubmed: 33961999
doi: 10.1016/j.neuroimage.2021.118145
Xia, M., Wang, J. & He, Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One 8, e68910 (2013).
pubmed: 23861951
pmcid: 3701683
doi: 10.1371/journal.pone.0068910