Abstract
There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers’ choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Code Availability
Python code for the portrait divergence is freely available online (https://github.com/bagrow/network-portrait-divergence). MATLAB code for the Orthogonal Minimum Spanning Tree thresholding is freely available online (https://github.com/stdimitr/topological_filtering_networks).
References
Alderson, T. H., Bokde, A. L. W., Kelso, J. A. S., et al. (2020). Metastable neural dynamics underlies cognitive performance across multiple behavioural paradigms. Human Brain Mapping, 41, 3212–3234. https://doi.org/10.1002/hbm.25009
Andellini, M., Cannatà, V., Gazzellini, S., et al. (2015). Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review. Journal of Neuroscience Methods, 253, 183–192. https://doi.org/10.1016/j.jneumeth.2015.05.020
Arslan, S., Ktena, S. I., Makropoulos, A., et al. (2018). Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex. NeuroImage, 170, 5–30. https://doi.org/10.1016/j.neuroimage.2017.04.014
Bagrow, J. P., & Bollt, E. M. (2019). An information-theoretic, all-scales approach to comparing networks. Applied Network Science, 4, 45. https://doi.org/10.1007/s41109-019-0156-x
Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20, 353–364. https://doi.org/10.1038/nn.4502
Battiston, F., Nicosia, V., Chavez, M., & Latora, V. (2017). Multilayer motif analysis of brain networks. Chaos, 27, 047404. https://doi.org/10.1063/1.4979282
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (methodological), 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x
Bertolero, M. A., & Bassett, D. S. (2020). On the nature of explanations offered by network science: A perspective from and for practicing neuroscientists. Topics in Cognitive Science, 12, 1272–1293. https://doi.org/10.1111/tops.12504
Boccaletti, S., Bianconi, G., Criado, R., et al. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544, 1–122. https://doi.org/10.1016/j.physrep.2014.07.001
Braun, U., Schäfer, A., Walter, H., et al. (2015). Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proceedings of the National Academy of Sciences of the United States of America, 112, 11678–11683. https://doi.org/10.1073/pnas.1422487112
Brookes, M. J., Tewarie, P. K., Hunt, B. A. E., et al. (2016). A multi-layer network approach to MEG connectivity analysis. NeuroImage, 132, 425–438. https://doi.org/10.1016/j.neuroimage.2016.02.045
Buldú, J. M., & Porter, M. A. (2018). Frequency-based brain networks: From a multiplex framework to a full multilayer description. Netw Neurosci, 2, 418–441. https://doi.org/10.1162/netn_a_00033
Carmon, J., Heege, J., Necus, J. H., et al. (2020). Reliability and comparability of human brain structural covariance networks. NeuroImage, 220, 117104. https://doi.org/10.1016/j.neuroimage.2020.117104
Chen, X., Liao, X., Dai, Z., Lin, Q., Wang, Z., Li, K., & He, Y. (2018). Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models. Human Brain Mapping, 39(11), 4545–4564. https://doi.org/10.1002/hbm.24305
Crossley, N. A., Mechelli, A., Scott, J., et al. (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain, 137, 2382–2395. https://doi.org/10.1093/brain/awu132
De Domenico, M., Sasai, S., & Arenas, A. (2016). Mapping multiplex hubs in human functional brain networks. Frontiers in Neuroscience, 10, 326. https://doi.org/10.3389/fnins.2016.00326
De Domenico, M. (2017). Multilayer modeling and analysis of human brain networks. Gigascience, 6, 1–8. https://doi.org/10.1093/gigascience/gix004
Dimitriadis, S. I., Antonakakis, M., Simos, P., et al. (2017a). Data-driven topological filtering based on orthogonal minimal spanning trees: application to multigroup magnetoencephalography resting-state connectivity. Brain Connectivity, 7, 661–670. https://doi.org/10.1089/brain.2017.0512
Dimitriadis, S. I., Drakesmith, M., Bells, S., et al. (2017b). Improving the reliability of network metrics in structural brain networks by integrating different network weighting strategies into a single graph. Frontiers in Neuroscience, 11, 694. https://doi.org/10.3389/fnins.2017.00694
Dimitriadis, S. I., Salis, C., Tarnanas, I., & Linden, D. E. (2017c). Topological filtering of dynamic functional brain networks unfolds informative Chronnectomics: a novel data-driven thresholding scheme based on Orthogonal Minimal Spanning Trees (OMSTs). Frontiers in Neuroinformatics, 11, 28. https://doi.org/10.3389/fninf.2017.00028
Dimitriadis, S. I., Lancaster, T. M., Perry, G., et al. (2021). Global brain flexibility during working memory is reduced in a high-genetic-risk group for schizophrenia. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. https://doi.org/10.1016/j.bpsc.2021.01.007
Dimitriadis, S. I., López, M. E., Bruña, R., et al. (2018a). How to build a functional connectomic biomarker for mild cognitive impairment from source reconstructed MEG resting-state activity: the combination of ROI representation and connectivity estimator matters. Frontiers in Neuroscience, 12, 306. https://doi.org/10.3389/fnins.2018.00306
Dimitriadis, S. I., Routley, B., Linden, D. E., & Singh, K. D. (2018b). Reliability of static and dynamic network metrics in the resting-state: A MEG-beamformed connectivity analysis. Frontiers in Neuroscience, 12, 506. https://doi.org/10.3389/fnins.2018.00506
Dimitriadis, S. I., Zouridakis, G., Rezaie, R., et al. (2015). Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury. NeuroImage: Clinical, 9, 519–531. https://doi.org/10.1016/j.nicl.2015.09.011
Dimitriadis, S. I. (2021). Reconfiguration of αmplitude driven dominant coupling modes (DoCM) mediated by α-band in adolescents with schizophrenia spectrum disorders. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 108, 110073. https://doi.org/10.1016/j.pnpbp.2020.110073
Eickhoff, S., Nichols, T. E., Van Horn, J. D., & Turner, J. A. (2016). Sharing the wealth: Neuroimaging data repositories. NeuroImage, 124, 1065–1068. https://doi.org/10.1016/j.neuroimage.2015.10.079
Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2018). Imaging-based parcellations of the human brain. Nature Reviews Neuroscience, 19, 672–686. https://doi.org/10.1038/s41583-018-0071-7
Fornito, A., Zalesky, A., & Breakspear, M. (2015). The connectomics of brain disorders. Nature Reviews Neuroscience, 16, 159–172. https://doi.org/10.1038/nrn3901
Garcés, P., Pereda, E., Hernández-Tamames, J. A., et al. (2016). Multimodal description of whole brain connectivity: A comparison of resting state MEG, fMRI, and DWI. Human Brain Mapping, 37, 20–34. https://doi.org/10.1002/hbm.22995
Geerligs, L., & Cam-Can, & Henson, R. N. (2016). Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation. NeuroImage, 135, 16–31. https://doi.org/10.1016/j.neuroimage.2016.04.047
Gifford, G., Crossley, N., Kempton, M. J., et al. (2020). Resting state fMRI based multilayer network configuration in patients with schizophrenia. Neuroimage Clin, 25, 102169. https://doi.org/10.1016/j.nicl.2020.102169
Golestani, A. M., Chang, C., Kwinta, J. B., et al. (2015). Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test-retest reliability and effect of fMRI sampling rate. NeuroImage, 104, 266–277. https://doi.org/10.1016/j.neuroimage.2014.10.031
Guillon, J., Attal, Y., Colliot, O., et al. (2017). Loss of brain inter-frequency hubs in Alzheimer’s disease. Science and Reports, 7, 10879. https://doi.org/10.1038/s41598-017-07846-w
Hallquist, M. N., & Hillary, F. G. (2019). Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world. Network Neuroscience, 3, 1–26. https://doi.org/10.1162/netn_a_00054
Hindriks, R., Adhikari, M. H., Murayama, Y., et al. (2016). Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage, 127, 242–256. https://doi.org/10.1016/j.neuroimage.2015.11.055
Hocke, L. M., Tong, Y., Lindsey, K. P., & de Frederick, B. B. (2016). Comparison of peripheral near-infrared spectroscopy low-frequency oscillations to other denoising methods in resting state functional MRI with ultrahigh temporal resolution. Magnetic Resonance in Medicine, 76, 1697–1707. https://doi.org/10.1002/mrm.26038
Joseph, A. C., Joseph, S. E., & Chen, G. (2014). Cross-border portfolio investment networks and indicators for financial crises. Science and Reports, 4, 3991. https://doi.org/10.1038/srep03991
Kalcher, K., Boubela, R. N., Huf, W., et al. (2014). The spectral diversity of resting-state fluctuations in the human brain. PLoS One1, 9, e93375. https://doi.org/10.1371/journal.pone.0093375
Korhonen, O., Zanin, M., & Papo, D. (2021). Principles and open questions in functional brain network reconstruction. Human Brain Mapping, 42, 3680–3711. https://doi.org/10.1002/hbm.25462
Laumann, T. O., Snyder, A. Z., Mitra, A., et al. (2017). On the stability of BOLD fMRI correlations. Cerebral Cortex, 27, 4719–4732. https://doi.org/10.1093/cercor/bhw265
Loued-Khenissi, L., Döll, O., & Preuschoff, K. (2018). An overview of functional magnetic resonance imaging techniques for organizational research. Organizational Research Methods, 22, 109442811880263. https://doi.org/10.1177/1094428118802631
Luppi, A. I., Gellersen, H. M., Peattie, A. R. D., Manktelow, A. E., Menon, D. K., Dimitriadis, S. I., & Stamatakis, E. A. (2021). Searching for consistent brain network topologies across the garden of (shortest) forking paths. bioRxiv 2021.07.13.452257; https://doi.org/10.1101/2021.07.13.452257
Luppi, A. I., & Stamatakis, E. A. (2021). Combining network topology and information theory to construct representative brain networks. Netw Neurosci, 5, 96–124. https://doi.org/10.1162/netn_a_00170
Lv, H., Wang, Z., Tong, E., et al. (2018). Resting-state functional MRI: everything that nonexperts have always wanted to know. AJNR. American Journal of Neuroradiology, 39, 1390–1399. https://doi.org/10.3174/ajnr.A5527
Mandke, K., Meier, J., Brookes, M. J., O’Dea, R. D., Van Mieghem, P., Stam, C. J., et al. (2018). Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations. NeuroImage, 166, 371–384. https://doi.org/10.1016/j.neuroimage.2017.11.016
Mark, C. I., Mazerolle, E. L., & Chen, J. J. (2015). Metabolic and vascular origins of the BOLD effect: Implications for imaging pathology and resting-state brain function. Journal of Magnetic Resonance Imaging, 42, 231–246. https://doi.org/10.1002/jmri.24786
Maturana-Candelas, A., Gómez, C., Poza, J., et al. (2019). EEG characterization of the alzheimer’s disease continuum by means of multiscale entropies. Entropy, 21, 544. https://doi.org/10.3390/e21060544
Messaritaki, E., Dimitriadis, S. I., & Jones, D. K. (2019). Optimization of graph construction can significantly increase the power of structural brain network studies. NeuroImage, 199, 495–511. https://doi.org/10.1016/j.neuroimage.2019.05.052
Mišić, B., & Sporns, O. (2016). From regions to connections and networks: New bridges between brain and behavior. Current Opinion in Neurobiology, 40, 1–7. https://doi.org/10.1016/j.conb.2016.05.003
Muldoon, S. F., & Bassett, D. S. (2016). Network and multilayer network approaches to understanding human brain dynamics. Philosophy in Science, 83, 710–720. https://doi.org/10.1086/687857
Naro, A., Maggio, M. G., Leo, A., & Calabrò, R. S. (2021). Multiplex and multilayer network EEG analyses: a novel strategy in the differential diagnosis of patients with chronic disorders of consciousness. International Journal of Neural Systems, 31, 2050052. https://doi.org/10.1142/S0129065720500525
Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., et al. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nature Neuroscience, 20(3), 299–303. https://doi.org/10.1038/nn.4500
Nikolaou, F., Orphanidou, C., Papakyriakou, P., et al. (2016). Spontaneous physiological variability modulates dynamic functional connectivity in resting-state functional magnetic resonance imaging. Philosophical Transactions. Series a, Mathematical, Physical, and Engineering Sciences, 374. https://doi.org/10.1098/rsta.2015.0183
Noble, S., Scheinost, D., & Constable, R. T. (2019). A decade of test-retest reliability of functional connectivity: A systematic review and meta-analysis. NeuroImage, 203, 116157. https://doi.org/10.1016/j.neuroimage.2019.116157
Noble, S., Spann, M. N., Tokoglu, F., et al. (2017). Influences on the test-retest reliability of functional connectivity MRI and its relationship with behavioral utility. Cerebral Cortex, 27, 5415–5429. https://doi.org/10.1093/cercor/bhx230
Parkes, L., Satterthwaite, T. D., & Bassett, D. S. (2020). Towards precise resting-state fMRI biomarkers in psychiatry: Synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment. Current Opinion in Neurobiology, 65, 120–128. https://doi.org/10.1016/j.conb.2020.10.016
Pereda, E., Quiroga, R. Q., & Bhattacharya, J. (2005). Nonlinear multivariate analysis of neurophysiological signals. Progress in Neurobiology, 77, 1–37. https://doi.org/10.1016/j.pneurobio.2005.10.003
Poldrack, R. A., Laumann, T. O., Koyejo, O., et al. (2015). Long-term neural and physiological phenotyping of a single human. Nature Communications, 6, 8885. https://doi.org/10.1038/ncomms9885
Prichard, D., & Theiler, J. (1994). Generating surrogate data for time series with several simultaneously measured variables. Physical Review Letters, 73, 951–954. https://doi.org/10.1103/PhysRevLett.73.951
Pusil, S., López, M. E., Cuesta, P., et al. (2019). Hypersynchronization in mild cognitive impairment: The “X” model. Brain, 142, 3936–3950. https://doi.org/10.1093/brain/awz320
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Savva, A. D., Mitsis, G. D., & Matsopoulos, G. K. (2019). Assessment of dynamic functional connectivity in resting-state fMRI using the sliding window technique. Brain and Behavior: A Cognitive Neuroscience Perspective, 9, e01255. https://doi.org/10.1002/brb3.1255
Shehzad, Z., Kelly, A. M. C., Reiss, P. T., Gee, D. G., Gotimer, K., Uddin, L. Q., Lee, S. H., Margulies, D. S., Roy, A. K., Biswal, B. B., Petkova, E., Castellanos, F. X., & Milham, M. P. (2009). The resting brain: Unconstrained yet reliable. Cerebral Cortex, 19(2209–2229), 477. https://doi.org/10.1093/cercor/bhn256
Schreiber, T., & Schmitz, A. (2000). Surrogate time series. Physica d: Nonlinear Phenomena, 142, 346–382. https://doi.org/10.1016/S0167-2789(00)00043-9
Shah, L. M., Cramer, J. A., Ferguson, M. A., et al. (2016). Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state. Brain and Behavior: A Cognitive Neuroscience Perspective, 6, e00456. https://doi.org/10.1002/brb3.456
Shirer, W. R., Jiang, H., Price, C. M., Ng, B., & Greicius, M. D. (2015). Optimization of rs-fMRI Pre-processing for enhanced signal-noise separation, test-retest reliability, and group discrimination. NeuroImage, 117, 67–79. https://doi.org/10.1016/j.neuroimage.2015.05.015
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., et al. (2013). Functional connectomics from resting-state fMRI. Trends in Cognitive Sciences, 17(12), 666–682.
Smith, S. M., Nichols, T. E., Vidaurre, D., et al. (2015). A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nature Neuroscience, 18, 1565–1567. https://doi.org/10.1038/nn.4125
Somandepalli, K., Kelly, C., Reiss, P. T., et al. (2015). Short-term test-retest reliability of resting state fMRI metrics in children with and without attention-deficit/hyperactivity disorder. Developmental Cognitive Neuroscience, 15, 83–93. https://doi.org/10.1016/j.dcn.2015.08.003
Song, J., Desphande, A. S., Meier, T. B., et al. (2012). Age-related differences in test-retest reliability in resting-state brain functional connectivity. PLoS One, 7, e49847. https://doi.org/10.1371/journal.pone.0049847
Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annual Review of Psychology, 67, 613–640. https://doi.org/10.1146/annurev-psych-122414-033634
Sporns, O. (2011). Networks of the brain. MIT Press, Cambridge.
Sporns, O. (2014). Contributions and challenges for network models in cognitive neuroscience. Nature Neuroscience, 17, 652–660. https://doi.org/10.1038/nn.3690
Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience, 15, 683–695. https://doi.org/10.1038/nrn3801
Székely, G. J., & Rizzo, M. L. (2013). The distance correlation-test of independence in high dimension. Journal of Multivariate Analysis, 117, 193–213. https://doi.org/10.1016/j.jmva.2013.02.012
Termenon, M., Jaillard, A., Delon-Martin, C., & Achard, S. (2016). Reliability of graph analysis of resting state fMRI using test-retest dataset from the Human Connectome Project. NeuroImage, 142, 172–187. https://doi.org/10.1016/j.neuroimage.2016.05.062
Tewarie, P., Hillebrand, A., van Dijk, B. W., et al. (2016). Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach. NeuroImage, 142, 324–336. https://doi.org/10.1016/j.neuroimage.2016.07.057
Theiler, J., Eubank, S., Longtin, A., et al. (1992). Testing for nonlinearity in time series: The method of surrogate data. Physica d: Nonlinear Phenomena, 58, 77–94. https://doi.org/10.1016/0167-2789(92)90102-S
Tulay, E. E., Metin, B., Tarhan, N., & Arıkan, M. K. (2019). Multimodal neuroimaging: Basic concepts and classification of neuropsychiatric diseases. Clinical EEG and Neuroscience, 50, 20–33. https://doi.org/10.1177/1550059418782093
Van Dijk, K. R. A., Hedden, T., Venkataraman, A., et al. (2010). Intrinsic functional connectivity as a tool for human connectomics: Theory, properties, and optimization. Journal of Neurophysiology, 103, 297–321. https://doi.org/10.1152/jn.00783.2009
Van Mieghem, P. (2016). Interconnectivity structure of a general interdependent network. Physical Review E, 93, 042305. https://doi.org/10.1103/PhysRevE.93.042305
Wang, J., Ren, Y., Hu, X., et al. (2017). Test-retest reliability of functional connectivity networks during naturalistic fMRI paradigms. Human Brain Mapping, 38, 2226–2241. https://doi.org/10.1002/hbm.23517
Wang, J.-H., Zuo, X.-N., Gohel, S., et al. (2011). Graph theoretical analysis of functional brain networks: Test-retest evaluation on short- and long-term resting-state functional MRI data. PLoS One, 6, e21976. https://doi.org/10.1371/journal.pone.0021976
Wei, J., Wang, X., Cui, X., Wang, B., Xue, J., Niu, Y., et al. (2022). Functional integration and segregation in a multilayer network model of patients with schizophrenia. Brain sciences, 12(3). https://doi.org/10.3390/brainsci12030368
Williamson, B. J., De Domenico, M., & Kadis, D. S. (2021). Multilayer connector hub mapping reveals key brain regions supporting expressive language. Brain Connect, 11, 45–55. https://doi.org/10.1089/brain.2020.0776
Yuen, N. H., Osachoff, N., & Chen, J. J. (2019). Intrinsic frequencies of the resting-state fMRI signal: the frequency dependence of functional connectivity and the effect of mode mixing. Frontiers in Neuroscience, 13, 900. https://doi.org/10.3389/fnins.2019.00900
Yu, M., Engels, M. M. A., Hillebrand, A., et al. (2017). Selective impairment of hippocampus and posterior hub areas in Alzheimer’s disease: An MEG-based multiplex network study. Brain, 140, 1466–1485. https://doi.org/10.1093/brain/awx050
Zalesky, A., Fornito, A., Cocchi, L., et al. (2014). Time-resolved resting-state brain networks. Proceedings of the National Academy of Sciences of the United States of America, 111, 10341–10346. https://doi.org/10.1073/pnas.1400181111
Zhang, Z., Telesford, Q. K., Giusti, C., et al. (2016). Choosing wavelet methods, filters, and lengths for functional brain network construction. PLoS One, 11, e0157243. https://doi.org/10.1371/journal.pone.0157243
Zhang, W., Braden, B. B., Miranda, G., Shu, K., Wang, S., Liu, H., & Wang, Y. (2021). Integrating multimodal and longitudinal neuroimaging data with multi-source network representation learning. Neuroinformatics, 20(2), 301–316. https://doi.org/10.1007/s12021-021-09523-w
Acknowledgements
SID is supported by a Beatriu de Pinós fellowship (2020 BP 00116). SID was supported by MRC grant MR/K004360/1 and a Marie Sklodowska-Curie COFUND EU-UK Research Fellowship.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Preprint
This study has been submitted to biorxiv as a preprint research article and can be found at the following link:
https://www.biorxiv.org/content/10.1101/2021.10.10.463799v1
Conflict of Interest
The author declare that there is no conflict of interest regarding the publication of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
ESM 1
(DOCX 748 KB)
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Dimitriadis, S.I. Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher’s Choice Paths. Neuroinform 21, 71–88 (2023). https://doi.org/10.1007/s12021-022-09610-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12021-022-09610-6