Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness.
Brain; Consciousness/physiology; Frontal Lobe/diagnostic imaging; Humans; Magnetic Resonance Imaging; Persistent Vegetative State; computational biology; disorders of consciousness; dynamic connectivity; eigenmodes; fMRI; global neuronal workspace; human; mesocircuit; systems biology
Abstract :
[en] Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.
Disciplines :
Neurosciences & behavior
Author, co-author :
Panda, Rajanikant ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Thibaut, Aurore ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Lopez-Gonzalez, Ane; Computational Neuroscience Group, Center for Brain and Cognition, Universitat
Escrichs, Anira ; Computational Neuroscience Group, Center for Brain and Cognition, Universitat
Bahri, Mohamed Ali; GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège,
Hillebrand, Arjan ; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical
Deco, Gustavo; Computational Neuroscience Group, Center for Brain and Cognition, Universitat ; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain. ; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain ; School of Psychological Sciences, Monash University, Melbourne, Australia.
Laureys, Steven ; Centre Hospitalier Universitaire de Liège - CHU > > Centre du Cerveau² ; CERVO Research Center, Laval University, Québec, Canada.
Gosseries, Olivia ; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Annen, Jitka ✱; Université de Liège - ULiège > GIGA > GIGA Consciousness - Coma Science Group
Tewarie, Prejaas ✱; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Clinical ; Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University
✱ These authors have contributed equally to this work.
Language :
English
Title :
Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness.
Abdelnour F, Dayan M, Devinsky O, Thesen T, Raj A. 2018. Functional brain connectivity is predictable from anatomic network’s Laplacian eigen-structure. NeuroImage 172:728–739. DOI: https://doi.org/10.1016/j. neuroimage.2018.02.016, PMID: 29454104
Amico E, Marinazzo D, Di Perri C, Heine L, Annen J, Martial C, Dzemidzic M, Kirsch M, Bonhomme V, Laureys S, Goñi J. 2017. Mapping the functional connectome traits of levels of consciousness. NeuroImage 148:201–211. DOI: https://doi.org/10.1016/j.neuroimage.2017.01.020, PMID: 28093358
Andersson CA, Bro R. 2000. The n-way toolbox for matlab. Chemometrics and Intelligent Laboratory Systems 52:1–4. DOI: https://doi.org/10.1016/S0169-7439(00)00071-X
Andersson JLR, Jenkinson M, Smith S. 2007. Non-linear registrationakaSpatial normalisation. FMRIB Analysis Group. https://www.fmrib.ox.ac.uk/datasets/techrep/tr07ja2/tr07ja2.pdf
Andersson JLR, Sotiropoulos SN. 2016. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125:1063–1078. DOI: https://doi.org/10.1016/j. neuroimage.2015.10.019, PMID: 26481672
Annen J, Heine L, Ziegler E, Frasso G, Bahri M, Di Perri C, Stender J, Martial C, Wannez S, D’ostilio K, Amico E, Antonopoulos G, Bernard C, Tshibanda F, Hustinx R, Laureys S. 2016. Function-structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET. Human Brain Mapping 37:3707–3720. DOI: https://doi.org/10.1002/hbm.23269, PMID: 27273334
Aqil M, Atasoy S, Kringelbach ML, Hindriks R. 2021. Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome. PLOS Computational Biology 17:e1008310. DOI: https://doi. org/10.1371/journal.pcbi.1008310, PMID: 33507899
Aru J, Suzuki M, Larkum ME. 2020. Cellular mechanisms of conscious processing. Trends in Cognitive Sciences 24:814–825. DOI: https://doi.org/10.1016/j.tics.2020.07.006, PMID: 32855048
Atasoy S, Donnelly I, Pearson J. 2016. Human brain networks function in connectome-specific harmonic waves. Nature Communications 7:10340. DOI: https://doi.org/10.1038/ncomms10340, PMID: 26792267
Atasoy S, Deco G, Kringelbach ML, Pearson J. 2018. Harmonic brain modes: a unifying framework for linking space and time in brain dynamics. The Neuroscientist 24:277–293. DOI: https://doi.org/10.1177/1073858417728032, PMID: 28863720
Avena-Koenigsberger A, Misic B, Sporns O. 2018. Communication dynamics in complex brain networks. Nature Reviews. Neuroscience 19:17–33. DOI: https://doi.org/10.1038/nrn.2017.149, PMID: 29238085
Baars BJ. 2002. The conscious access hypothesis: origins and recent evidence. Trends in Cognitive Sciences 6:47–52. DOI: https://doi.org/10.1016/s1364-6613(00)01819-2, PMID: 11849615
Barttfeld P, Uhrig L, Sitt JD, Sigman M, Jarraya B, Dehaene S. 2014. Signature of consciousness in the dynamics of resting-state brain activity. PNAS 112:887–892. DOI: https://doi.org/10.1073/pnas.1418031112, PMID: 25561541
Behrens TEJ, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG, Clare S, Matthews PM, Brady JM, Smith SM. 2003. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine 50:1077–1088. DOI: https://doi.org/10.1002/mrm.10609, PMID: 14587019
Behrens TEJ, Berg HJ, Jbabdi S, Rushworth MFS, Woolrich MW. 2007. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34:144–155. DOI: https://doi.org/10.1016/j. neuroimage.2006.09.018, PMID: 17070705
Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society 57:289–300. DOI: https://doi.org/10.1111/j.2517-6161. 1995.tb02031.x
Blumenfeld H. 2021. Brain mechanisms of conscious awareness: detect, pulse, switch, and wave. The Neuroscientist:378, 1. DOI: https://doi.org/10.1177/10738584211049378, PMID: 34632846
Bro R. 1997. PARAFAC: Tutorial and applications. Chemometrics and Intelligent Laboratory Systems 38:149–171. DOI: https://doi.org/10.1016/S0169-7439(97)00032-4
Cao B, Chen Y, Yu R, Chen L, Chen P, Weng Y, Chen Q, Song J, Xie Q, Huang R. 2019. Abnormal dynamic properties of functional connectivity in disorders of consciousness. NeuroImage. Clinical 24:102071. DOI: https://doi.org/10.1016/j.nicl.2019.102071, PMID: 31795053
Cavanna F, Vilas MG, Palmucci M, Tagliazucchi E. 2018. Dynamic functional connectivity and brain metastability during altered states of consciousness. NeuroImage 180:383–395. DOI: https://doi.org/10.1016/j.neuroimage. 2017.09.065, PMID: 28986208
Crone JS, Bio BJ, Vespa PM, Lutkenhoff ES, Monti MM. 2018. Restoration of thalamo-cortical connectivity after brain injury: recovery of consciousness, complex behavior, or passage of time? Journal of Neuroscience Research 96:671–687. DOI: https://doi.org/10.1002/jnr.24115, PMID: 28801920
Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M. 2013. Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. The Journal of Neuroscience 33:11239–11252. DOI: https://doi.org/10.1523/JNEUROSCI.1091-13.2013, PMID: 23825427
Deco G, Kringelbach ML. 2016. Metastability and coherence: extending the communication through coherence hypothesis using a whole-brain computational perspective. Trends in Neurosciences 39:125–135. DOI: https://doi.org/10.1016/j.tins.2016.01.001, PMID: 26833259
Deco G, Kringelbach ML, Jirsa VK, Ritter P. 2017. The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Scientific Reports 7:3095. DOI: https://doi.org/10.1038/s41598-017-03073-5, PMID: 28596608
Dehaene S, Changeux JP. 2011. Experimental and theoretical approaches to conscious processing. Neuron 70:200–227. DOI: https://doi.org/10.1016/j.neuron.2011.03.018, PMID: 21521609
Dehaene S, Changeux JP, Naccache L. 2011. The global neuronal workspace model of conscious access: from neuronal architectures to clinical applications. Charact Conscious From Cogn to Clin 1:55–84. DOI: https://doi. org/10.1007/978-3-642-18015-6_4
Demertzi A, Soddu A, Laureys S. 2013. Consciousness supporting networks. Current Opinion in Neurobiology 23:239–244. DOI: https://doi.org/10.1016/j.conb.2012.12.003, PMID: 23273731
Demertzi A, Antonopoulos G, Heine L, Voss HU, Crone JS, de Los Angeles C, Bahri MA, Di Perri C, Vanhaudenhuyse A, Charland-Verville V, Kronbichler M, Trinka E, Phillips C, Gomez F, Tshibanda L, Soddu A, Schiff ND, Whitfield-Gabrieli S, Laureys S. 2015. Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain: A Journal of Neurology 138:2619–2631. DOI: https://doi.org/10. 1093/brain/awv169, PMID: 26117367
Demertzi A, Tagliazucchi E, Dehaene S, Deco G, Barttfeld P, Raimondo F, Martial C, Fernández-Espejo D, Rohaut B, Voss HU, Schiff ND, Owen AM, Laureys S, Naccache L, Sitt JD. 2019. Human consciousness is supported by dynamic complex patterns of brain signal coordination. Science Advances 5:eaat7603. DOI: https://doi.org/10.1126/sciadv.aat7603, PMID: 30775433
Edlow BL, Claassen J, Schiff ND, Greer DM. 2021. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nature Reviews. Neurology 17:135–156. DOI: https://doi.org/10.1038/s41582-020-00428-x, PMID: 33318675
Enciso-Olivera CO, Ordóñez-Rubiano EG, Casanova-Libreros R, Rivera D, Zarate-Ardila CJ, Rudas J, Pulido C, Gómez F, Martínez D, Guerrero N, Hurtado MA, Aguilera-Bustos N, Hernández-Torres CP, Hernandez J, Marín-Muñoz JH. 2021. Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness. Scientific Reports 11:22952. DOI: https://doi. org/10.1038/s41598-021-98506-7, PMID: 34824383
Escrichs A, Perl YS, Uribe C, Camara E, Türker B, Pyatigorskaya N, López-González A, Pallavicini C, Panda R, Annen J, Grosseries O, Laureys S, Naccache L, Sitt JD, Laufs H, Tagliazucchi E, Kringelbach ML, Deco G. 2021. Unifying turbulent dynamics framework distinguishes different brain states. [bioRxiv]. DOI: https://doi.org/10. 1101/2021.10.14.464380
Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, Papademetris X, Constable RT. 2015. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience 18:1664–1671. DOI: https://doi.org/10.1038/nn.4135, PMID: 26457551
Fridman EA, Beattie BJ, Broft A, Laureys S, Schiff ND. 2014. Regional cerebral metabolic patterns demonstrate the role of anterior forebrain mesocircuit dysfunction in the severely injured brain. PNAS 111:6473–6478. DOI: https://doi.org/10.1073/pnas.1320969111, PMID: 24733913
Giacino JT, Ashwal S, Childs N, Cranford R, Jennett B, Katz DI, Kelly JP, Rosenberg JH, Whyte J, Zafonte RD, Zasler ND. 2002. The minimally conscious state: definition and diagnostic criteria. Neurology 58:349–353. DOI: https://doi.org/10.1212/wnl.58.3.349, PMID: 11839831
Giacino JT, Kalmar K, Whyte J. 2004. The jfk coma recovery scale-revised: measurement characteristics and diagnostic utility. Archives of Physical Medicine and Rehabilitation 85:2020–2029. DOI: https://doi.org/10. 1016/j.apmr.2004.02.033, PMID: 15605342
Giacino JT, Fins JJ, Laureys S, Schiff ND. 2014. Disorders of consciousness after acquired brain injury: the state of the science. Nature Reviews. Neurology 10:99–114. DOI: https://doi.org/10.1038/nrneurol.2013.279, PMID: 24468878
Glerean E, Salmi J, Lahnakoski JM, Jääskeläinen IP, Sams M. 2012. Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity. Brain Connectivity 2:91–101. DOI: https://doi.org/10.1089/brain.2011.0068, PMID: 22559794
Golkowski D, Willnecker R, Rösler J, Ranft A, Schneider G, Jordan D, Ilg R. 2021. Dynamic patterns of global brain communication differentiate conscious from unconscious patients after severe brain injury. Frontiers in Systems Neuroscience 15:e919. DOI: https://doi.org/10.3389/fnsys.2021.625919, PMID: 34566586
Griffanti L, Salimi-Khorshidi G, Beckmann CF, Auerbach EJ, Douaud G, Sexton CE, Zsoldos E, Ebmeier KP, Filippini N, Mackay CE, Moeller S, Xu J, Yacoub E, Baselli G, Ugurbil K, Miller KL, Smith SM. 2014. ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage 95:232–247. DOI: https://doi.org/10.1016/j.neuroimage.2014.03.034, PMID: 24657355
Heine L, Soddu A, Gómez F, Vanhaudenhuyse A, Tshibanda L, Thonnard M, Charland-Verville V, Kirsch M, Laureys S, Demertzi A. 2012. Resting state networks and consciousness. Frontiers in Psychology 3:295. DOI: https://doi.org/10.3389/fpsyg.2012.00295
Hindriks R, Adhikari MH, Murayama Y, Ganzetti M, Mantini D, Logothetis NK, Deco G. 2016. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage 127:242–256. DOI: https://doi.org/10.1016/j.neuroimage.2015.11.055, PMID: 26631813 Jenkinson M, Smith S. 2001. A global optimisation method for robust affine registration of brain images. Medical Image Analysis 5:143–156. DOI: https://doi.org/10.1016/s1361-8415(01)00036-6, PMID: 11516708
Kuceyeski A, Shah S, Dyke JP, Bickel S, Abdelnour F, Schiff ND, Voss HU, Raj A. 2016. The application of a mathematical model linking structural and functional connectomes in severe brain injury. NeuroImage. Clinical 11:635–647. DOI: https://doi.org/10.1016/j.nicl.2016.04.006, PMID: 27200264
Lant ND, Gonzalez-Lara LE, Owen AM, Fernández-Espejo D. 2016. Relationship between the anterior forebrain mesocircuit and the default mode network in the structural bases of disorders of consciousness. NeuroImage. Clinical 10:27–35. DOI: https://doi.org/10.1016/j.nicl.2015.11.004, PMID: 26693399
Laureys S, Faymonville ME, Luxen A, Lamy M, Franck G, Maquet P. 2000. Restoration of thalamocortical connectivity after recovery from persistent vegetative state. The Lancet 355:1790–1791. DOI: https://doi.org/10.1016/S0140-6736(00)02271-6, PMID: 10832859
Laureys S, Celesia GG, Cohadon F, Lavrijsen J, León-Carrión J, Sannita WG, Sazbon L, Schmutzhard E, von Wild KR, Zeman A, Dolce G, European Task Force on Disorders of Consciousness. 2010. Unresponsive wakefulness syndrome: A new name for the vegetative state or apallic syndrome. BMC Medicine 8:68. DOI: https://doi.org/10.1186/1741-7015-8-68, PMID: 21040571
López-González A, Panda R, Ponce-Alvarez A, Zamora-López G, Escrichs A, Martial C, Thibaut A, Gosseries O, Kringelbach ML, Annen J, Laureys S, Deco G. 2021. Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics. Communications Biology 4:1037. DOI: https://doi.org/10.1038/s42003-021-02537-9, PMID: 34489535
Luppi AI, Craig MM, Pappas I, Finoia P, Williams GB, Allanson J, Pickard JD, Owen AM, Naci L, Menon DK, Stamatakis EA. 2019. Consciousness-specific dynamic interactions of brain integration and functional diversity. Nature Communications 10:4616. DOI: https://doi.org/10.1038/s41467-019-12658-9, PMID: 31601811
Mashour GA, Roelfsema P, Changeux JP, Dehaene S. 2020. Conscious processing and the global neuronal workspace hypothesis. Neuron 105:776–798. DOI: https://doi.org/10.1016/j.neuron.2020.01.026, PMID: 32135090
Mashour GA, Palanca BJA, Basner M, Li D, Wang W, Blain-Moraes S, Lin N, Maier K, Muench M, Tarnal V, Vanini G, Ochroch EA, Hogg R, Schwartz M, Maybrier H, Hardie R, Janke E, Golmirzaie G, Picton P, McKinstry-Wu AR, et al. 2021. Recovery of consciousness and cognition after general anesthesia in humans. eLife 10:e59525. DOI: https://doi.org/10.7554/eLife.59525, PMID: 33970101
Monti MM, Rosenberg M, Finoia P, Kamau E, Pickard JD, Owen AM. 2015. Thalamo-frontal connectivity mediates top-down cognitive functions in disorders of consciousness. Neurology 84:167–173. DOI: https://doi. org/10.1212/WNL.0000000000001123, PMID: 25480912
Naccache L. 2018. Minimally conscious state or cortically mediated state? Brain 141:949–960. DOI: https://doi. org/10.1093/brain/awx324, PMID: 29206895
Naze S, Proix T, Atasoy S, Kozloski JR. 2021. Robustness of connectome harmonics to local gray matter and long-range white matter connectivity changes. NeuroImage 224:117364. DOI: https://doi.org/10.1016/j. neuroimage.2020.117364, PMID: 32947015
Panda R, López-González A, Gilson M, Gosseries O, Thibaut A, Frasso G, Cecconi B, Escrichs A, Deco G, Laureys S, Zamora-López G, Annen J. 2021. Posterior integration and thalamo-frontotemporal broadcasting are impaired in disorders of consciousness. [bioRxiv]. DOI: https://doi.org/10.1101/2021.11.08.467694
Ponce-Alvarez A, Deco G, Hagmann P, Romani GL, Mantini D, Corbetta M. 2015. Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity. PLOS Computational Biology 11:e1004100. DOI: https://doi.org/10.1371/journal.pcbi.1004100, PMID: 25692996
Preti MG, Van De Ville D. 2019. Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nature Communications 10:1–7. DOI: https://doi.org/10.1038/s41467-019-12765-7, PMID: 31628329
Quian Quiroga R, Kraskov A, Kreuz T, Grassberger P. 2002. Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 65:041903. DOI: https://doi.org/10.1103/PhysRevE.65.041903, PMID: 12005869
Rizkallah J, Annen J, Modolo J, Gosseries O, Benquet P, Mortaheb S, Amoud H, Cassol H, Mheich A, Thibaut A, Chatelle C, Hassan M, Panda R, Wendling F, Laureys S. 2019. Decreased integration of EEG source-space networks in disorders of consciousness. NeuroImage. Clinical 23:101841. DOI: https://doi.org/10.1016/j.nicl. 2019.101841, PMID: 31063944
Robinson PA, Zhao X, Aquino KM, Griffiths JD, Sarkar S, Mehta-Pandejee G. 2016. Eigenmodes of brain activity: Neural field theory predictions and comparison with experiment. NeuroImage 142:79–98. DOI: https://doi.org/10.1016/j.neuroimage.2016.04.050, PMID: 27157788
Sanz Perl Y, Pallavicini C, Pérez Ipiña I, Demertzi A, Bonhomme V, Martial C, Panda R, Annen J, Ibañez A, Kringelbach M, Deco G, Laufs H, Sitt J, Laureys S, Tagliazucchi E. 2021. Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness. PLOS Computational Biology 17:e1009139. DOI: https://doi.org/10.1371/journal.pcbi.1009139, PMID: 34314430
Schiff ND. 2010. Recovery of consciousness after brain injury: a mesocircuit hypothesis. Trends in Neurosciences 33:1–9. DOI: https://doi.org/10.1016/j.tins.2009.11.002, PMID: 19954851
Schnakers C, Chatelle C, Vanhaudenhuyse A, Majerus S, Ledoux D, Boly M, Bruno M-A, Boveroux P, Demertzi A, Moonen G, Laureys S. 2010. The Nociception Coma Scale: a new tool to assess nociception in disorders of consciousness. Pain 148:215–219. DOI: https://doi.org/10.1016/j.pain.2009.09.028, PMID: 19854576
Shen X, Tokoglu F, Papademetris X, Constable RT. 2013. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage 82:403–415. DOI: https://doi.org/10.1016/j. neuroimage.2013.05.081, PMID: 23747961
Stender J, Gosseries O, Bruno MA, Charland-Verville V, Vanhaudenhuyse A, Demertzi A, Chatelle C, Thonnard M, Thibaut A, Heine L, Soddu A, Boly M, Schnakers C, Gjedde A, Laureys S. 2014. Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study. Lancet384:514–522. DOI: https://doi.org/10.1016/S0140-6736(14)60042-8, PMID: 24746174
Suárez LE, Markello RD, Betzel RF, Misic B. 2020. Linking structure and function in macroscale brain networks. Trends in Cognitive Sciences 24:302–315. DOI: https://doi.org/10.1016/j.tics.2020.01.008, PMID: 32160567
Tewarie P, Abeysuriya R, Byrne Á, O’Neill GC, Sotiropoulos SN, Brookes MJ, Coombes S. 2019. How do spatially distinct frequency specific MEG networks emerge from one underlying structural connectome? The role of the structural eigenmodes. NeuroImage 186:211–220. DOI: https://doi.org/10.1016/j.neuroimage.2018.10.079, PMID: 30399418
Tewarie P, Prasse B, Meier JM, Santos FAN, Douw L, Schoonheim MM, Stam CJ, Van Mieghem P, Hillebrand A. 2020. Mapping functional brain networks from the structural connectome: Relating the series expansion and eigenmode approaches. NeuroImage 216:116805. DOI: https://doi.org/10.1016/j.neuroimage.2020.116805, PMID: 32335264
Tewarie P, Prasse B, Meier J, Mandke K, Warrington S, Stam CJ, Brookes MJ, Van Mieghem P, Sotiropoulos SN, Hillebrand A. 2022. Predicting time-resolved electrophysiological brain networks from structural eigenmodes. Human Brain Mapping 1:e5967. DOI: https://doi.org/10.1002/hbm.25967, PMID: 35642600
Timmerman ME, Kiers HAL. 2000. Three-mode principal components analysis: choosing the numbers of components and sensitivity to local optima. The British Journal of Mathematical and Statistical Psychology 53 (Pt 1):1–16. DOI: https://doi.org/10.1348/000711000159132, PMID: 10895519
Uhrig L, Sitt JD, Jacob A, Tasserie J, Barttfeld P, Dupont M, Dehaene S, Jarraya B. 2018. Resting-state dynamics as a cortical signature of anesthesia in monkeys. Anesthesiology 129:942–958. DOI: https://doi.org/10.1097/ALN.0000000000002336, PMID: 30028727
Weng L, Xie Q, Zhao L, Zhang R, Ma Q, Wang J, Jiang W, He Y, Chen Y, Li C, Ni X, Xu Q, Yu R, Huang R. 2017. Abnormal structural connectivity between the basal ganglia, thalamus, and frontal cortex in patients with disorders of consciousness. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 90:71–87. DOI: https://doi.org/10.1016/j.cortex.2017.02.011, PMID: 28365490
Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. 2014. Time-resolved resting-state brain networks. PNAS 111:10341–10346. DOI: https://doi.org/10.1073/pnas.1400181111, PMID: 24982140
Zamani Esfahlani F, Jo Y, Faskowitz J, Byrge L, Kennedy DP, Sporns O, Betzel RF. 2020. High-amplitude cofluctuations in cortical activity drive functional connectivity. PNAS 117:28393–28401. DOI: https://doi.org/10. 1073/pnas.2005531117, PMID: 33093200
