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Volumn 11, Issue 8, 2016, Pages

Analytical operations relate structural and functional connectivity in the brain

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONAL CONNECTIVITY; NOISE; REST; STATISTICAL MODEL; STRUCTURE ACTIVITY RELATION; TIME SERIES ANALYSIS; TRACTOGRAPHY; WHITE MATTER; ANATOMY AND HISTOLOGY; BIOLOGICAL MODEL; BRAIN; DIAGNOSTIC IMAGING; FUNCTIONAL NEUROIMAGING; HUMAN; NERVE TRACT; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY;

EID: 84984868975     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0157292     Document Type: Article
Times cited : (36)

References (62)
  • 1
    • 34547219105 scopus 로고    scopus 로고
    • Network structure of cerebral cortex shapes functional connectivity on multiple time scales
    • Honey CJ, Kötter R, Breakspear M, Sporns O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences. 2007; 104 (24):10240-10245. doi: 10.1073/pnas.0701519104
    • (2007) Proceedings of the National Academy of Sciences , vol.104 , Issue.24 , pp. 10240-10245
    • Honey, C.J.1    Kötter, R.2    Breakspear, M.3    Sporns, O.4
  • 3
    • 84994000511 scopus 로고    scopus 로고
    • On the influence of amplitude on the connectivity between phases
    • Daffertshofer A, van Wijk BC. On the influence of amplitude on the connectivity between phases. Frontiers in neuroinformatics. 2011; 5. doi: 10.3389/fninf.2011.00006
    • (2011) Frontiers in Neuroinformatics , vol.5
    • Daffertshofer, A.1    Van Wijk, B.C.2
  • 4
    • 84874533008 scopus 로고    scopus 로고
    • Synchrony of two brain regions predicts the blood oxygen level dependent activity of a third
    • Rho YA, McIntosh RA, Jirsa VK. Synchrony of two brain regions predicts the blood oxygen level dependent activity of a third. Brain Connectivity. 2011; 1(1):73-80. doi: 10.1089/brain.2011.0009
    • (2011) Brain Connectivity , vol.1 , Issue.1 , pp. 73-80
    • Rho, Y.A.1    McIntosh, R.A.2    Jirsa, V.K.3
  • 5
    • 1542400571 scopus 로고    scopus 로고
    • Enhancement of neural synchrony by time delay
    • Dhamala M, Jirsa VK, Ding M. Enhancement of neural synchrony by time delay. Physical Review Letters. 2004; 92(7):074104. doi: 10.1103/PhysRevLett.92.074104
    • (2004) Physical Review Letters , vol.92 , Issue.7 , pp. 074104
    • Dhamala, M.1    Jirsa, V.K.2    Ding, M.3
  • 6
    • 33645684557 scopus 로고    scopus 로고
    • Synchronization in networks with random interactions: Theory and applications. Chaos
    • Feng J, Jirsa VK, Ding M. Synchronization in networks with random interactions: Theory and applications. Chaos: An Interdisciplinary Journal of Nonlinear Science. 2006; 16(1):015109. doi: 10.1063/1. 2180690
    • (2006) An Interdisciplinary Journal of Nonlinear Science , vol.16 , Issue.1 , pp. 015109
    • Feng, J.1    Jirsa, V.K.2    Ding, M.3
  • 7
    • 38849087435 scopus 로고    scopus 로고
    • Dispersion and time delay effects in synchronized spike-burst networks
    • Jirsa VK. Dispersion and time delay effects in synchronized spike-burst networks. Cognitive neurodynamics. 2008; 2(1):29-38. doi: 10.1007/s11571-007-9030-0
    • (2008) Cognitive Neurodynamics , vol.2 , Issue.1 , pp. 29-38
    • Jirsa, V.K.1
  • 8
    • 55449107481 scopus 로고    scopus 로고
    • Noise during rest enables the exploration of the brain's dynamic repertoire
    • Ghosh A, Rho Y, McIntosh AR, Kötter R, Jirsa VK. Noise during rest enables the exploration of the brain's dynamic repertoire. PLoS Computational Biology. 2008; 4(10):e1000196. doi: 10.1371/journal. pcbi.1000196
    • (2008) PLoS Computational Biology , vol.4 , Issue.10 , pp. e1000196
    • Ghosh, A.1    Rho, Y.2    McIntosh, A.R.3    Kötter, R.4    Jirsa, V.K.5
  • 9
    • 78650339790 scopus 로고    scopus 로고
    • Emerging concepts for the dynamical organization of resting-state activity in the brain
    • Deco G, Jirsa VK, McIntosh AR. Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews Neuroscience. 2011; 12(1):43-56. doi: 10.1038/nrn2961
    • (2011) Nature Reviews Neuroscience , vol.12 , Issue.1 , pp. 43-56
    • Deco, G.1    Jirsa, V.K.2    McIntosh, A.R.3
  • 11
    • 84923086490 scopus 로고    scopus 로고
    • A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks
    • Messé A, Hött MT, König P, Hilgetag CC. A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks. Scientific reports. 2015; 5. doi: 10.1038/srep07870
    • (2015) Scientific Reports , vol.5
    • Messé, A.1    Hött, M.T.2    König, P.3    Hilgetag, C.C.4
  • 14
    • 84880452652 scopus 로고    scopus 로고
    • Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations
    • Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M. Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. The Journal of Neuroscience. 2013; 33(27):11239-11252. doi: 10.1523/JNEUROSCI.1091-13.2013
    • (2013) The Journal of Neuroscience , vol.33 , Issue.27 , pp. 11239-11252
    • Deco, G.1    Ponce-Alvarez, A.2    Mantini, D.3    Romani, G.L.4    Hagmann, P.5    Corbetta, M.6
  • 15
    • 84864011851 scopus 로고    scopus 로고
    • Using computational models to relate structural and functional brain connectivity
    • Hlinka J, Coombes S. Using computational models to relate structural and functional brain connectivity. European Journal of Neuroscience. 2012; 36(2):2137-2145. doi: 10.1111/j.1460-9568.2012.08081.x
    • (2012) European Journal of Neuroscience , vol.36 , Issue.2 , pp. 2137-2145
    • Hlinka, J.1    Coombes, S.2
  • 16
    • 84857802687 scopus 로고    scopus 로고
    • Ongoing cortical activity at rest: Criticality, multistability, and ghost attractors
    • Deco G, Jirsa VK. Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. The Journal of Neuroscience. 2012; 32(10):3366-3375. doi: 10.1523/JNEUROSCI.2523-11.2012
    • (2012) The Journal of Neuroscience , vol.32 , Issue.10 , pp. 3366-3375
    • Deco, G.1    Jirsa, V.K.2
  • 17
    • 19444371087 scopus 로고    scopus 로고
    • Will a large complex system with time delays be stable?
    • Jirsa VK, Ding M. Will a large complex system with time delays be stable? Physical Review Letters. 2004; 93(7):070602. doi: 10.1103/PhysRevLett.93.070602
    • (2004) Physical Review Letters , vol.93 , Issue.7 , pp. 070602
    • Jirsa, V.K.1    Ding, M.2
  • 19
    • 34547270480 scopus 로고    scopus 로고
    • Neural field dynamics with heterogeneous connection topology
    • Qubbaj MR, Jirsa VK. Neural field dynamics with heterogeneous connection topology. Physical Review Letters. 2007; 98(23):238102. doi: 10.1103/PhysRevLett.98.238102
    • (2007) Physical Review Letters , vol.98 , Issue.23 , pp. 238102
    • Qubbaj, M.R.1    Jirsa, V.K.2
  • 20
    • 81355137473 scopus 로고    scopus 로고
    • Functional and effective connectivity: A review
    • Friston KJ. Functional and effective connectivity: A review. Brain connectivity. 2011; 1(1):13-36. doi: 10.1089/brain.2011.0008
    • (2011) Brain Connectivity , vol.1 , Issue.1 , pp. 13-36
    • Friston, K.J.1
  • 21
    • 78650180896 scopus 로고    scopus 로고
    • Functional connectivity in resting-state fMRI: Is linear correlation sufficient?
    • Hlinka J, Paluš M, Vejmelka M, Mantini D, Corbetta M. Functional connectivity in resting-state fMRI: is linear correlation sufficient? Neuroimage. 2011; 54(3):2218-2225. doi: 10.1016/j.neuroimage.2010.08. 042
    • (2011) Neuroimage , vol.54 , Issue.3 , pp. 2218-2225
    • Hlinka, J.1    Paluš, M.2    Vejmelka, M.3    Mantini, D.4    Corbetta, M.5
  • 22
    • 84897460560 scopus 로고    scopus 로고
    • Relating structure and function in the human brain: Relative contributions of anatomy stationary dynamics and non-stationarities
    • Messé A, Rudrauf D, Benali H, Marrelec G. Relating Structure and Function in the Human Brain: Relative Contributions of Anatomy, Stationary Dynamics, and Non-stationarities. PLoS Computational Biology. 2014; 10(3):e1003530. doi: 10.1371/journal.pcbi.1003530
    • (2014) PLoS Computational Biology , vol.10 , Issue.3 , pp. e1003530
    • Messé, A.1    Rudrauf, D.2    Benali, H.3    Marrelec, G.4
  • 23
    • 75249093217 scopus 로고    scopus 로고
    • Time-frequency dynamics of resting-state brain connectivity measured with fMRI
    • Chang C, Glover GH. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage. 2010; 50(1):81-98. doi: 10.1016/j.neuroimage.2009.12.011
    • (2010) Neuroimage , vol.50 , Issue.1 , pp. 81-98
    • Chang, C.1    Glover, G.H.2
  • 24
    • 84924203679 scopus 로고    scopus 로고
    • Using the virtual brain to reveal the role of oscillations and plasticity in shaping brain's dynamical landscape
    • Roy D, Sigala R, Breakspear M, McIntosh AR, Jirsa VK, Deco G, et al. Using the Virtual Brain to Reveal the Role of Oscillations and Plasticity in Shaping Brain's Dynamical Landscape. Brain Connectivity. 2014; 4(10):791-811. doi: 10.1089/brain.2014.0252
    • (2014) Brain Connectivity , vol.4 , Issue.10 , pp. 791-811
    • Roy, D.1    Sigala, R.2    Breakspear, M.3    McIntosh, A.R.4    Jirsa, V.K.5    Deco, G.6
  • 25
    • 84893558069 scopus 로고    scopus 로고
    • Network diffusion accurately models the relationship between structural and functional brain connectivity networks
    • Abdelnour F, Voss HU, Raj A. Network diffusion accurately models the relationship between structural and functional brain connectivity networks. Neuroimage. 2014; 90:335-347. doi: 10.1016/j. neuroimage.2013.12.039
    • (2014) Neuroimage , vol.90 , pp. 335-347
    • Abdelnour, F.1    Voss, H.U.2    Raj, A.3
  • 26
    • 84914820204 scopus 로고    scopus 로고
    • Functional connectivity dynamics: Modeling the switching behavior of the resting state
    • Hansen EC, Battaglia D, Spiegler A, Deco G, Jirsa VK. Functional connectivity dynamics: Modeling the switching behavior of the resting state. NeuroImage. 2015; 105:525-535. doi: 10.1016/j.neuroimage. 2014.11.001
    • (2015) NeuroImage , vol.105 , pp. 525-535
    • Hansen, E.C.1    Battaglia, D.2    Spiegler, A.3    Deco, G.4    Jirsa, V.K.5
  • 28
    • 80054696597 scopus 로고    scopus 로고
    • Resting-state brain networks: Literature review and clinical applications
    • Rosazza C, Minati L. Resting-state brain networks: literature review and clinical applications. Neurological Sciences. 2011; 32(5):773-785. doi: 10.1007/s10072-011-0636-y
    • (2011) Neurological Sciences , vol.32 , Issue.5 , pp. 773-785
    • Rosazza, C.1    Minati, L.2
  • 30
    • 84876245128 scopus 로고    scopus 로고
    • A model of functional brain connectivity and background noise as a biomarker for cognitive phenotypes: Application to autism
    • Domínguez LG, Velázquez JLP, Galán RF. A model of functional brain connectivity and background noise as a biomarker for cognitive phenotypes: Application to autism. PLoS One. 2013; 8(4):e61493. doi: 10.1371/journal.pone.0061493
    • (2013) PLoS One , vol.8 , Issue.4 , pp. e61493
    • Domínguez, L.G.1    Jlp, V.2    Galán, R.F.3
  • 31
    • 84873283933 scopus 로고    scopus 로고
    • Individual variability in functional connectivity architecture of the human brain
    • Mueller S, Wang D, Fox MD, Yeo B, Sepulcre J, Sabuncu MR, et al. Individual variability in functional connectivity architecture of the human brain. Neuron. 2013; 77(3):586-595. doi: 10.1016/j.neuron. 2012.12.028
    • (2013) Neuron , vol.77 , Issue.3 , pp. 586-595
    • Mueller, S.1    Wang, D.2    Fox, M.D.3    Yeo, B.4    Sepulcre, J.5    Sabuncu, M.R.6
  • 32
    • 60549103853 scopus 로고    scopus 로고
    • Complex brain networks: Graph theoretical analysis of structural and functional systems
    • Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. 2009; 10(3):186-198. doi: 10.1038/nrn2575
    • (2009) Nature Reviews Neuroscience , vol.10 , Issue.3 , pp. 186-198
    • Bullmore, E.1    Sporns, O.2
  • 33
    • 0028245445 scopus 로고
    • A measure for brain complexity: Relating functional segregation and integration in the nervous system
    • Tononi G, Sporns O, Edelman GM. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences. 1994; 91 (11):5033-5037. doi: 10.1073/pnas.91.11.5033
    • (1994) Proceedings of the National Academy of Sciences , vol.91 , Issue.11 , pp. 5033-5037
    • Tononi, G.1    Sporns, O.2    Edelman, G.M.3
  • 34
    • 67549119293 scopus 로고    scopus 로고
    • Neural complexity and structural connectivity
    • Barnett L, Buckley CL, Bullock S. Neural complexity and structural connectivity. Physical Review E. 2009; 79(5):051914. doi: 10.1103/PhysRevE.79.051914
    • (2009) Physical Review e , vol.79 , Issue.5 , pp. 051914
    • Barnett, L.1    Buckley, C.L.2    Bullock, S.3
  • 35
    • 47749114203 scopus 로고    scopus 로고
    • On how network architecture determines the dominant patterns of spontaneous neural activity
    • Galán RF. On how network architecture determines the dominant patterns of spontaneous neural activity. PLoS One. 2008; 3(5):e2148. doi: 10.1371/journal.pone.0002148
    • (2008) PLoS One , vol.3 , Issue.5 , pp. e2148
    • Galán, R.F.1
  • 36
    • 84856660135 scopus 로고    scopus 로고
    • Interrelating anatomical, effective, and functional brain connectivity using propagators and neural field theory
    • Robinson P. Interrelating anatomical, effective, and functional brain connectivity using propagators and neural field theory. Physical Review E. 2012; 85(1):011912. doi: 10.1103/PhysRevE.85.011912
    • (2012) Physical Review e , vol.85 , Issue.1 , pp. 011912
    • Robinson, P.1
  • 37
    • 36149005118 scopus 로고
    • On the theory of the Brownian motion
    • Uhlenbeck GE, Ornstein LS. On the theory of the Brownian motion. Physical review. 1930; 36(5):823. doi: 10.1103/PhysRev.36.823
    • (1930) Physical Review , vol.36 , Issue.5 , pp. 823
    • Uhlenbeck, G.E.1    Ornstein, L.S.2
  • 39
    • 84901790981 scopus 로고    scopus 로고
    • Identification of optimal structural connectivity using functional connectivity and neural modeling
    • Deco G, McIntosh AR, Shen K, Hutchison RM, Menon RS, Everling S, et al. Identification of Optimal Structural Connectivity Using Functional Connectivity and Neural Modeling. The Journal of Neuroscience. 2014; 34(23):7910-7916. doi: 10.1523/JNEUROSCI.4423-13.2014
    • (2014) The Journal of Neuroscience , vol.34 , Issue.23 , pp. 7910-7916
    • Deco, G.1    McIntosh, A.R.2    Shen, K.3    Hutchison, R.M.4    Menon, R.S.5    Everling, S.6
  • 40
    • 84905453929 scopus 로고    scopus 로고
    • Determination of effective brain connectivity from functional connectivity with application to resting state connectivities
    • Robinson P, Sarkar S, Pandejee GM, Henderson J. Determination of effective brain connectivity from functional connectivity with application to resting state connectivities. Physical Review E. 2014; 90 (1):012707. doi: 10.1103/PhysRevE.90.012707
    • (2014) Physical Review e , vol.90 , Issue.1 , pp. 012707
    • Robinson, P.1    Sarkar, S.2    Pandejee, G.M.3    Henderson, J.4
  • 41
    • 61449192343 scopus 로고    scopus 로고
    • Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI
    • Marrelec G, Kim J, Doyon J, Horwitz B. Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI. Human Brain Mapping. 2009; 30(3):941-950. doi: 10.1002/hbm.20555
    • (2009) Human Brain Mapping , vol.30 , Issue.3 , pp. 941-950
    • Marrelec, G.1    Kim, J.2    Doyon, J.3    Horwitz, B.4
  • 43
    • 83455199059 scopus 로고    scopus 로고
    • Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development
    • Uddin LQ, Supekar KS, Ryali S, Menon V. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. The Journal of Neuroscience. 2011; 31(50):18578-18589. doi: 10.1523/JNEUROSCI.4465-11.2011
    • (2011) The Journal of Neuroscience , vol.31 , Issue.50 , pp. 18578-18589
    • Uddin, L.Q.1    Supekar, K.S.2    Ryali, S.3    Menon, V.4
  • 45
    • 85018936667 scopus 로고    scopus 로고
    • The virtual brain integrates computational modeling and multimodal neuroimaging
    • Ritter P, Schirner M, McIntosh AR, Jirsa VK. The virtual brain integrates computational modeling and multimodal neuroimaging. Brain Connectivity. 2013; 3(2):121-145. doi: 10.1089/brain.2012.0120
    • (2013) Brain Connectivity , vol.3 , Issue.2 , pp. 121-145
    • Ritter, P.1    Schirner, M.2    McIntosh, A.R.3    Jirsa, V.K.4
  • 46
    • 84936749511 scopus 로고    scopus 로고
    • An automated pipeline for constructing personalised virtual brains from multimodal neuroimaging data
    • Schirner M, Rothmeier S, Jirsa VK, McIntosh AR, Ritter P. An automated pipeline for constructing personalised virtual brains from multimodal neuroimaging data. NeuroImage. 2015;. doi: 10.1016/j. neuroimage.2015.03.055
    • (2015) NeuroImage
    • Schirner, M.1    Rothmeier, S.2    Jirsa, V.K.3    McIntosh, A.R.4    Ritter, P.5
  • 47
    • 84861330561 scopus 로고    scopus 로고
    • Quantitative assessment of a framework for creating anatomical brain networks via global tractography
    • Li L, Rilling JK, Preuss TM, Glasser MF, Damen FW, Hu X. Quantitative Assessment of a Framework for Creating Anatomical Brain Networks via Global Tractography. NeuroImage. 2012; 61(4):1017. doi: 10.1016/j.neuroimage.2012.03.071
    • (2012) NeuroImage , vol.61 , Issue.4 , pp. 1017
    • Li, L.1    Rilling, J.K.2    Preuss, T.M.3    Glasser, M.F.4    Damen, F.W.5    Hu, X.6
  • 48
    • 84926317380 scopus 로고    scopus 로고
    • On spurious and real fluctuations of dynamic functional connectivity during rest
    • Leonardi N, Van De Ville D. On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage. 2015; 104:430-436. doi: 10.1016/j.neuroimage.2014.09.007
    • (2015) Neuroimage , vol.104 , pp. 430-436
    • Leonardi, N.1    Van De Ville, D.2
  • 49
    • 47949127882 scopus 로고    scopus 로고
    • Mode level cognitive subtraction (MLCS) quantifies spatiotemporal reorganization in large-scale brain topographies
    • Banerjee A, Tognoli E, Assisi CG, Kelso J, Jirsa VK. Mode level cognitive subtraction (MLCS) quantifies spatiotemporal reorganization in large-scale brain topographies. NeuroImage. 2008; 42(2):663-674. doi: 10.1016/j.neuroimage.2008.04.260
    • (2008) NeuroImage , vol.42 , Issue.2 , pp. 663-674
    • Banerjee, A.1    Tognoli, E.2    Assisi, C.G.3    Kelso, J.4    Jirsa, V.K.5
  • 50
    • 80055066621 scopus 로고    scopus 로고
    • Brain rhythms reveal a hierarchical network organization
    • Steinke GK, Galán RF. Brain rhythms reveal a hierarchical network organization. PLoS Comput Biol. 2011; 7(10):e1002207. doi: 10.1371/journal.pcbi.1002207
    • (2011) PLoS Comput Biol , vol.7 , Issue.10 , pp. e1002207
    • Steinke, G.K.1    Galán, R.F.2
  • 52
    • 84887291519 scopus 로고    scopus 로고
    • Structural and functional brain networks: From connections to cognition
    • Park HJ, Friston K. Structural and functional brain networks: from connections to cognition. Science. 2013; 342(6158):1238411. doi: 10.1126/science.1238411
    • (2013) Science , vol.342 , Issue.6158 , pp. 1238411
    • Park, H.J.1    Friston, K.2
  • 53
    • 84879155057 scopus 로고    scopus 로고
    • A pairwise maximum entropy model accurately describes resting-state human brain networks
    • Watanabe T, Hirose S, Wada H, Imai Y, Machida T, Shirouzu I, et al. A pairwise maximum entropy model accurately describes resting-state human brain networks. Nature communications. 2013; 4:1370. doi: 10.1038/ncomms2388
    • (2013) Nature Communications , vol.4 , pp. 1370
    • Watanabe, T.1    Hirose, S.2    Wada, H.3    Imai, Y.4    Machida, T.5    Shirouzu, I.6
  • 55
    • 79960956233 scopus 로고    scopus 로고
    • EEG quality: The image acquisition artifact
    • Springer
    • Ritter P, Becker R, Freyer F, Villringer A. EEG quality: The image acquisition artefact. In: EEG-fMRI. Springer; 2010. p. 153-171.
    • (2010) EEG-fMRI , pp. 153-171
    • Ritter, P.1    Becker, R.2    Freyer, F.3    Villringer, A.4
  • 56
    • 33746932485 scopus 로고    scopus 로고
    • An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
    • Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006; 31(3):968-980. doi: 10.1016/j.neuroimage.2006.01.021
    • (2006) Neuroimage , vol.31 , Issue.3 , pp. 968-980
    • Desikan, R.S.1    Ségonne, F.2    Fischl, B.3    Quinn, B.T.4    Dickerson, B.C.5    Blacker, D.6
  • 57
    • 7444257398 scopus 로고    scopus 로고
    • Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution
    • Tournier J, Calamante F, Gadian DG, Connelly A, et al. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage. 2004; 23 (3):1176-1185. doi: 10.1016/j.neuroimage.2004.07.037
    • (2004) NeuroImage , vol.23 , Issue.3 , pp. 1176-1185
    • Tournier, J.1    Calamante, F.2    Gadian, D.G.3    Connelly, A.4
  • 58
    • 34147121196 scopus 로고    scopus 로고
    • Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution
    • Tournier J, Calamante F, Connelly A, et al. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage. 2007; 35(4):1459-1472. doi: 10.1016/j.neuroimage.2007.02.016
    • (2007) NeuroImage , vol.35 , Issue.4 , pp. 1459-1472
    • Tournier, J.1    Calamante, F.2    Connelly, A.3
  • 59
    • 84900445033 scopus 로고    scopus 로고
    • Addressing the path-length-dependency confound in white matter Tract Segmentation
    • Liptrot MG, Sidaros K, Dyrby TB. Addressing the Path-Length-Dependency Confound in White Matter Tract Segmentation. PloS one. 2014; 9(5):e96247. doi: 10.1371/journal.pone.0096247
    • (2014) PloS One , vol.9 , Issue.5 , pp. e96247
    • Liptrot, M.G.1    Sidaros, K.2    Dyrby, T.B.3
  • 60
    • 57649158932 scopus 로고    scopus 로고
    • The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?
    • Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA. The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? Neuroimage. 2009; 44(3):893-905. doi: 10.1016/j.neuroimage.2008.09.036
    • (2009) Neuroimage , vol.44 , Issue.3 , pp. 893-905
    • Murphy, K.1    Birn, R.M.2    Handwerker, D.A.3    Jones, T.B.4    Bandettini, P.A.5
  • 61
    • 18244374693 scopus 로고    scopus 로고
    • On multidimensional Ornstein-Uhlenbeck processes driven by a general Lévy process
    • Masuda H, et al. On multidimensional Ornstein-Uhlenbeck processes driven by a general Lévy process. Bernoulli. 2004; 10(1):97-120. doi: 10.3150/bj/1077544605
    • (2004) Bernoulli , vol.10 , Issue.1 , pp. 97-120
    • Masuda, H.1
  • 62
    • 33748653805 scopus 로고    scopus 로고
    • Partial correlation for functional brain interactivity investigation in functional MRI
    • Marrelec G, Krainik A, Duffau H, Pélégrini-Issac M, Lehéricy S, Doyon J, et al. Partial correlation for functional brain interactivity investigation in functional MRI. Neuroimage. 2006; 32(1):228-237. doi: 10. 1016/j.neuroimage.2005.12.057
    • (2006) Neuroimage , vol.32 , Issue.1 , pp. 228-237
    • Marrelec, G.1    Krainik, A.2    Duffau, H.3    Pélégrini-Issac, M.4    Lehéricy, S.5    Doyon, J.6


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