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Volumn 14, Issue 5, 2018, Pages

Simulations to benchmark time-varying connectivity methods for fMRI

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SOFTWARE; NEUROIMAGING; PYTHON;

EID: 85048182262     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1006196     Document Type: Article
Times cited : (24)

References (41)
  • 1
    • 84926500488 scopus 로고    scopus 로고
    • Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex
    • Hutchison RM, Hashemi N, Gati JS, Menon RS, Everling S, Electrophysiological signatures of spontaneous BOLD fluctuations in macaque prefrontal cortex. NeuroImage. Elsevier Inc. 2015;113: 257–267. doi: 10.1016/j.neuroimage.2015.03.062
    • (2015) NeuroImage. Elsevier Inc , vol.113 , pp. 257-267
    • Hutchison, R.M.1    Hashemi, N.2    Gati, J.S.3    Menon, R.S.4    Everling, S.5
  • 2
    • 84893550211 scopus 로고    scopus 로고
    • Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis
    • 24418507
    • Ma S, Calhoun VD, Phlypo R, Adali T, Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis. NeuroImage. 2014;90: 196–206. doi: 10.1016/j.neuroimage.2013.12.063 24418507
    • (2014) NeuroImage , vol.90 , pp. 196-206
    • Ma, S.1    Calhoun, V.D.2    Phlypo, R.3    Adali, T.4
  • 7
    • 85003856211 scopus 로고    scopus 로고
    • On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series
    • 27784176
    • Thompson WH, Fransson P, On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series. Brain Connectivity. 2016;6: 735–746. doi: 10.1089/brain.2016.0454 27784176
    • (2016) Brain Connectivity , vol.6 , pp. 735-746
    • Thompson, W.H.1    Fransson, P.2
  • 8
    • 85077175640 scopus 로고    scopus 로고
    • From static to temporal network theory—applications to functional brain connectivity
    • Thompson WH, Brantefors P, Fransson P, From static to temporal network theory—applications to functional brain connectivity. Network Neruoscience. 2017;1: 69–99. doi: 10.1162/NETN_a_00011
    • (2017) Network Neruoscience , vol.1 , pp. 69-99
    • Thompson, W.H.1    Brantefors, P.2    Fransson, P.3
  • 9
    • 84953307193 scopus 로고    scopus 로고
    • Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
    • 26687667
    • Betzel RF, Fukushima M, He Y, Zuo XN, Sporns O, Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks. NeuroImage. 2016;127: 287–297. doi: 10.1016/j.neuroimage.2015.12.001 26687667
    • (2016) NeuroImage , vol.127 , pp. 287-297
    • Betzel, R.F.1    Fukushima, M.2    He, Y.3    Zuo, X.N.4    Sporns, O.5
  • 10
    • 84937391519 scopus 로고    scopus 로고
    • The mean–variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI
    • Thompson WH, Fransson P, The mean–variance relationship reveals two possible strategies for dynamic brain connectivity analysis in fMRI. Frontiers in Human Neuroscience. 2015;9: 1–7. doi: 10.3389/fnhum.2015.00398
    • (2015) Frontiers in Human Neuroscience , vol.9 , pp. 1-7
    • Thompson, W.H.1    Fransson, P.2
  • 11
    • 85006333189 scopus 로고    scopus 로고
    • Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity
    • Thompson WH, Fransson P, Bursty properties revealed in large-scale brain networks with a point-based method for dynamic functional connectivity. Scientific Reports. Nature Publishing Group; 2016;6: 39156. doi: 10.1038/srep39156
    • (2016) Scientific Reports. Nature Publishing Group , vol.6 , pp. 39156
    • Thompson, W.H.1    Fransson, P.2
  • 13
    • 84929712322 scopus 로고    scopus 로고
    • Towards a statistical test for functional connectivity dynamics
    • Zalesky A, Breakspear M, Towards a statistical test for functional connectivity dynamics. NeuroImage. Elsevier Inc. 2015;114: 466–470. doi: 10.1016/j.neuroimage.2015.03.047
    • (2015) NeuroImage. Elsevier Inc , vol.114 , pp. 466-470
    • Zalesky, A.1    Breakspear, M.2
  • 14
    • 84952892201 scopus 로고    scopus 로고
    • Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?
    • Hindriks R, Adhikari MH, Murayama Y, Ganzetti M, Mantini D, Logothetis NK, et al. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?NeuroImage. The Authors; 2016;127: 242–256. doi: 10.1016/j.neuroimage.2015.11.055
    • (2016) NeuroImage. The Authors , vol.127 , pp. 242-256
    • Hindriks, R.1    Adhikari, M.H.2    Murayama, Y.3    Ganzetti, M.4    Mantini, D.5    Logothetis, N.K.6
  • 16
    • 84940528665 scopus 로고    scopus 로고
    • Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives
    • Shine JM, Koyejo O, Bell PT, Gorgolewski KJ, Gilat M, Poldrack RA, Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. NeuroImage. Elsevier Inc. 2015;122: 399–407. doi: 10.1016/j.neuroimage.2015.07.064
    • (2015) NeuroImage. Elsevier Inc , vol.122 , pp. 399-407
    • Shine, J.M.1    Koyejo, O.2    Bell, P.T.3    Gorgolewski, K.J.4    Gilat, M.5    Poldrack, R.A.6
  • 17
    • 84875045497 scopus 로고    scopus 로고
    • Time-varying functional network information extracted from brief instances of spontaneous brain activity
    • 23440216
    • Liu X, Duyn JH, Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proceedings of the National Academy of Sciences of the United States of America. 2013;110: 4392–7. doi: 10.1073/pnas.1216856110 23440216
    • (2013) Proceedings of the National Academy of Sciences of the United States of America , vol.110 , pp. 4392-4397
    • Liu, X.1    Duyn, J.H.2
  • 18
    • 84886441779 scopus 로고    scopus 로고
    • Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest
    • Leonardi N, Richiardi J, Gschwind M, Simioni S, Annoni J-M, Schluep M, et al. Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. NeuroImage. Elsevier Inc. 2013;83: 937–50. doi: 10.1016/j.neuroimage.2013.07.019
    • (2013) NeuroImage. Elsevier Inc , vol.83 , pp. 937-950
    • Leonardi, N.1    Richiardi, J.2    Gschwind, M.3    Simioni, S.4    Annoni, J.-M.5    Schluep, M.6
  • 19
    • 84866296479 scopus 로고    scopus 로고
    • Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis
    • 22347863
    • Tagliazucchi E, Balenzuela P, Fraiman D, Chialvo DR, Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis. Frontiers in physiology. 2012;3: 15. doi: 10.3389/fphys.2012.00015 22347863
    • (2012) Frontiers in physiology , vol.3 , pp. 15
    • Tagliazucchi, E.1    Balenzuela, P.2    Fraiman, D.3    Chialvo, D.R.4
  • 20
    • 84988369308 scopus 로고    scopus 로고
    • The Voxel-Wise Functional Connectome Can Be Efficiently Derived from Co-activations in a Sparse Spatio-Temporal Point-Process
    • Tagliazucchi E, Siniatchkin M, Laufs H, Chialvo DR, The Voxel-Wise Functional Connectome Can Be Efficiently Derived from Co-activations in a Sparse Spatio-Temporal Point-Process. Frontiers in Neuroscience. 2016;10: 1–13. doi: 10.3389/fnins.2016.00381
    • (2016) Frontiers in Neuroscience , vol.10 , pp. 1-13
    • Tagliazucchi, E.1    Siniatchkin, M.2    Laufs, H.3    Chialvo, D.R.4
  • 21
    • 79955476785 scopus 로고    scopus 로고
    • Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches
    • Kang J, Wang L, Yan C, Wang J, Liang X, He Y, Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches. NeuroImage. Elsevier Inc. 2011;56: 1222–1234. doi: 10.1016/j.neuroimage.2011.03.033
    • (2011) NeuroImage. Elsevier Inc , vol.56 , pp. 1222-1234
    • Kang, J.1    Wang, L.2    Yan, C.3    Wang, J.4    Liang, X.5    He, Y.6
  • 22
    • 84946893707 scopus 로고    scopus 로고
    • State space modeling of time-varying contemporaneous and lagged relations in connectivity maps
    • Molenaar PCM, Beltz AM, Gates KM, Wilson SJ, State space modeling of time-varying contemporaneous and lagged relations in connectivity maps. NeuroImage. Elsevier B.V. 2016;125: 791–802. doi: 10.1016/j.neuroimage.2015.10.088
    • (2016) NeuroImage. Elsevier B.V , vol.125 , pp. 791-802
    • Molenaar, P.C.M.1    Beltz, A.M.2    Gates, K.M.3    Wilson, S.J.4
  • 23
    • 84964698383 scopus 로고    scopus 로고
    • DynamicBC: A MATLAB Toolbox for Dynamic Brain Connectome Analysis
    • 25083734
    • Liao W, Wu G-R, Xu Q, Ji G-J, Zhang Z, Zang Y-F, et al. DynamicBC: A MATLAB Toolbox for Dynamic Brain Connectome Analysis. Brain Connectivity. 2014;4: 780–790. doi: 10.1089/brain.2014.0253 25083734
    • (2014) Brain Connectivity , vol.4 , pp. 780-790
    • Liao, W.1    Wu, G.-R.2    Xu, Q.3    Ji, G.-J.4    Zhang, Z.5    Zang, Y.-F.6
  • 25
    • 84870997831 scopus 로고    scopus 로고
    • A sliding time-window ICA reveals spatial variability of the default mode network in time
    • 22432423
    • Kiviniemi V, Vire T, Remes J, Elseoud AA, Starck T, Tervonen O, et al. A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain connectivity. 2011;1: 339–47. doi: 10.1089/brain.2011.0036 22432423
    • (2011) Brain connectivity , vol.1 , pp. 339-347
    • Kiviniemi, V.1    Vire, T.2    Remes, J.3    Elseoud, A.A.4    Starck, T.5    Tervonen, O.6
  • 26
    • 84907022675 scopus 로고    scopus 로고
    • Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach
    • Lindquist MA, Xu Y, Nebel MB, Caffo BS, Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach. NeuroImage. Elsevier Inc. 2014;101: 531–546. doi: 10.1016/j.neuroimage.2014.06.052
    • (2014) NeuroImage. Elsevier Inc , vol.101 , pp. 531-546
    • Lindquist, M.A.1    Xu, Y.2    Nebel, M.B.3    Caffo, B.S.4
  • 27
    • 85012109002 scopus 로고    scopus 로고
    • Cortical rich club regions can organize state-dependent functional network formation by engaging in oscillatory behavior
    • Senden M, Reuter N, van den Heuvel MP, Goebel R, Deco G, Cortical rich club regions can organize state-dependent functional network formation by engaging in oscillatory behavior. NeuroImage. Elsevier; 2017;146: 561–574. doi: 10.1016/j.neuroimage.2016.10.044
    • (2017) NeuroImage. Elsevier , vol.146 , pp. 561-574
    • Senden, M.1    Reuter, N.2    van den Heuvel, M.P.3    Goebel, R.4    Deco, G.5
  • 28
    • 84938742391 scopus 로고    scopus 로고
    • Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models
    • 25331991
    • Ou J, Xie L, Jin C, Li X, Zhu D, Jiang R, et al. Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models. Brain Topography. 2015;28: 666–679. doi: 10.1007/s10548-014-0406-2 25331991
    • (2015) Brain Topography , vol.28 , pp. 666-679
    • Ou, J.1    Xie, L.2    Jin, C.3    Li, X.4    Zhu, D.5    Jiang, R.6
  • 29
    • 85007574013 scopus 로고    scopus 로고
    • Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling
    • 27959921
    • Ryali S, Supekar K, Chen T, Kochalka J, Cai W, Nicholas J, et al. Temporal Dynamics and Developmental Maturation of Salience, Default and Central-Executive Network Interactions Revealed by Variational Bayes Hidden Markov Modeling. PLOS Computational Biology. 2016;12: e1005138. doi: 10.1371/journal.pcbi.1005138 27959921
    • (2016) PLOS Computational Biology , vol.12 , pp. e1005138
    • Ryali, S.1    Supekar, K.2    Chen, T.3    Kochalka, J.4    Cai, W.5    Nicholas, J.6
  • 32
  • 33
    • 34247493236 scopus 로고    scopus 로고
    • Matplotlib: A 2D graphics environment
    • Hunter JD, Matplotlib: A 2D graphics environment. Computing in Science and Engineering. 2007;9: 99–104. doi: 10.1109/MCSE.2007.55
    • (2007) Computing in Science and Engineering , vol.9 , pp. 99-104
    • Hunter, J.D.1
  • 34
    • 85006481322 scopus 로고    scopus 로고
    • Waskom M, Botvinnik O, Drewokane, Hobson P, David, Halchenko Y, et al. (June 2016). Zenodo
    • Waskom M, Botvinnik O, Drewokane, Hobson P, David, Halchenko Y, et al. seaborn: v0.7.1 (June 2016). doiorg. Zenodo;
    • (2016) seaborn: v0.7.1
  • 35
    • 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. Elsevier Inc. 2015;104: 430–436. doi: 10.1016/j.neuroimage.2014.09.007
    • (2015) NeuroImage. Elsevier Inc , vol.104 , pp. 430-436
    • Leonardi, N.1    Van De Ville, D.2
  • 36
    • 85040453272 scopus 로고    scopus 로고
    • A common framework for the problem of deriving estimates of dynamic functional brain connectivity
    • Thompson WH, Fransson P, A common framework for the problem of deriving estimates of dynamic functional brain connectivity. NeuroImage. Elsevier Ltd; 2018;172: 896–902. doi: 10.1016/j.neuroimage.2017.12.057
    • (2018) NeuroImage. Elsevier Ltd , vol.172 , pp. 896-902
    • Thompson, W.H.1    Fransson, P.2
  • 37
    • 84929702956 scopus 로고    scopus 로고
    • A jackknife approach to quantifying single-trial correlation between covariance-based metrics undefined on a single-trial basis
    • Richter CG, Thompson WH, Bosman CA, Fries P, A jackknife approach to quantifying single-trial correlation between covariance-based metrics undefined on a single-trial basis. NeuroImage. Elsevier B.V. 2015;114: 57–70. doi: 10.1016/j.neuroimage.2015.04.040
    • (2015) NeuroImage. Elsevier B.V , vol.114 , pp. 57-70
    • Richter, C.G.1    Thompson, W.H.2    Bosman, C.A.3    Fries, P.4
  • 38
    • 84901687683 scopus 로고    scopus 로고
    • The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
    • Hoffman M, Gelman A, The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research. 2014;15: 30.
    • (2014) Journal of Machine Learning Research , vol.15 , pp. 30
    • Hoffman, M.1    Gelman, A.2
  • 39
    • 84876235301 scopus 로고    scopus 로고
    • A Widely Applicable Bayesian Information Criterion
    • .;: –. Available:
    • Watanabe S, A Widely Applicable Bayesian Information Criterion. Journal of Machine Learning Research. 2013;14: 867–897. Available: http://arxiv.org/abs/1208.6338
    • (2013) Journal of Machine Learning Research , vol.14 , pp. 867-897
    • Watanabe, S.1
  • 41
    • 84961788993 scopus 로고    scopus 로고
    • Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states
    • 26952197
    • Shakil S, Lee CH, Keilholz SD, Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states. NeuroImage. 2016;133: 111–128. doi: 10.1016/j.neuroimage.2016.02.074 26952197
    • (2016) NeuroImage , vol.133 , pp. 111-128
    • Shakil, S.1    Lee, C.H.2    Keilholz, S.D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.