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《工程(英文)》 >> 2019年 第5卷 第2期 doi: 10.1016/j.eng.2018.10.001

动态功能网络连接性分析框架

a The Mind Research Network, Albuquerque, NM 87106, USA
b School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
c Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA

收稿日期: 2018-01-10 修回日期: 2018-04-02 录用日期: 2018-10-16 发布日期: 2018-10-24

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参考文献

[ 1 ] Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 2014;24 (3):663–76. 链接1

[ 2 ] Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, et al. Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 2013;80(1):360–78. 链接1

[ 3 ] Hutchison RM, Womelsdorf T, Gati JS, Everling S, Menon RS. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp 2013;34(9):2154–77. 链接1

[ 4 ] Lehmann D. Past, present and future of topographic mapping. Brain Topogr 1990;3(1):191–202. 链接1

[ 5 ] Wackermann J, Lehmann D, Michel C, Strik W. Adaptive segmentation of spontaneous EEG map series into spatially defined microstates. Int J Psychophysiol 1993;14(3):269–83. 链接1

[ 6 ] Lehmann D, Strik W, Henggeler B, König T, Koukkou M. Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. visual imagery and abstract thoughts. Int J Psychophysiol 1998;29(1):1–11. 链接1

[ 7 ] Calhoun VD, Adali T, Pearlson GD, Pekar J. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 2001;14(3):140–51. 链接1

[ 8 ] Calhoun VD, Eichele T, Pearlson G. Functional brain networks in schizophrenia: a review. Front Hum Neurosci 2009;3:17. 链接1

[ 9 ] Wu L, Eichele T, Calhoun VD. Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study. NeuroImage 2010;52(4):1252–60. 链接1

[10] Yu Q, Sui J, Rachakonda S, He H, Pearlson G, Calhoun VD. Altered small-world brain networks in temporal lobe in patients with schizophrenia performing an auditory oddball task. Front Syst Neurosci 2011;5:7. 链接1

[11] Allen E, Damaraju E, Eichele T, Wu L, Calhoun V. EEG signatures of dynamic functional network connectivity states. Brain Topogr 2018;31(1):101–16. 链接1

[12] Calhoun VD, Miller R, Pearlson G, Adalı T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron 2014;84(2):262–74. 链接1

[13] Cetin MS, Houck JM, Rashid B, Agacoglu O, Stephen JM, Sui J, et al. Multimodal classification of schizophrenia patients with MEG and fMRI data using static and dynamic connectivity measures. Front Neurosci 2016;10:466. 链接1

[14] Du Y, Pearlson GD, Yu Q, He H, Lin D, Sui J, et al. Interaction among subsystems within default mode network diminished in schizophrenia patients: a dynamic connectivity approach. Schizophr Res 2016;170(1):55–65. 链接1

[15] Hutchison RM, Morton JB. Tracking the brain’s functional coupling dynamics over development. J Neurosci 2015;35(17):6849–59. 链接1

[16] Ma S, Calhoun VD, Phlypo R, Adalı T. Dynamic changes of spatial functional network connectivity in healthy individuals and schizophrenia patients using independent vector analysis. NeuroImage 2014;90:196–206. 链接1

[17] Yu Q, Erhardt EB, Sui J, Du Y, He H, Hjelm D, et al. Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia. NeuroImage 2015;107:345–55. 链接1

[18] Vidaurre D, Smith SM, Woolrich MW. Brain network dynamics are hierarchically organized in time. PNAS 2017;114(48):12827–32. 链接1

[19] Yaesoubi M, Allen EA, Miller RL, Calhoun VD. Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and timedomain information. NeuroImage 2015;120:133–42. 链接1

[20] Yaesoubi M, Calhoun VD. Adaptive windowing and windowless approaches to estimate dynamic functional brain connectivity. In: Proceedings of Wavelets and Sparsity XVII; 2017 Aug 6–9; San Diego, CA, USA. Bellingham: SPIE; 2017. 链接1

[21] Robinson LF, Wager TD, Lindquist MA. Change point estimation in multisubject fMRI studies. NeuroImage 2010;49(2):1581–92. 链接1

[22] Sakog˘lu Ü, Pearlson GD, Kiehl KA, Wang YM, Michael AM, Calhoun VD. A method for evaluating dynamic functional network connectivity and taskmodulation: application to schizophrenia. Magn Reson Mater Phys Biol Med 2010;23(5–6):351–66. 链接1

[23] Abrol A, Damaraju E, Miller RL, Stephen JM, Claus ED, Mayer AR, et al. Replicability of time-varying connectivity patterns in large resting state fMRI samples. NeuroImage 2017;163:160–76. 链接1

[24] Miller RL, Yaesoubi M, Turner JA, Mathalon D, Preda A, Pearlson G, et al. Higher dimensional meta-state analysis reveals reduced resting fMRI connectivity dynamism in schizophrenia patients. PLoS One 2016;11(3):e0149849. 链接1

[25] Du Y, Fryer SL, Fu Z, Lin D, Sui J, Chen J, et al. Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis. NeuroImage 2017;180:632–45. 链接1

[26] Chang C, Glover GH. Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 2010;50(1):81–98. 链接1

[27] 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 2016;127:242–56. 链接1

[28] Fu Z, Chan SC, Di X, Biswal B, Zhang Z. Adaptive covariance estimation of nonstationary processes and its application to infer dynamic connectivity from fMRI. IEEE Trans Biomed Circuits Syst 2014;8(2):228–39. 链接1

[29] Cribben I, Haraldsdottir R, Atlas LY, Wager TD, Lindquist MA. Dynamic connectivity regression: determining state-related changes in brain connectivity. NeuroImage 2012;61(4):907–20. 链接1

[30] Fu Z, Tu Y, Di X, Du Y, Pearlson G, Turner J, et al. Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: an application to schizophrenia. NeuroImage 2018;180:619–31. 链接1

[31] Liu X, Duyn JH. Time-varying functional network information extracted from brief instances of spontaneous brain activity. PNAS 2013;110(11): 4392–7. 链接1

[32] Fukunaga M, Horovitz SG, Van Gelderen P, De Zwart JA, Jansma JM, Ikonomidou VN, et al. Large-amplitude, spatially correlated fluctuations in BOLD fMRI signals during extended rest and early sleep stages. Magn Reson Imaging 2006;24(8):979–92. 链接1

[33] Barnes A, Bullmore ET, Suckling J. Endogenous human brain dynamics recover slowly following cognitive effort. PLoS One 2009;4(8):e6626. 链接1

[34] Damaraju E, Allen EA, Belger A, Ford J, McEwen S, Mathalon D, et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage Clin 2014;5:298–308. 链接1

[35] Tagliazucchi E, Von Wegner F, Morzelewski A, Brodbeck V, Laufs H. Dynamic BOLD functional connectivity in humans and its electrophysiological correlates. Front Hum Neurosci 2012;6:339. 链接1

[36] Chang C, Liu Z, Chen MC, Liu X, Duyn JH. EEG correlates of time-varying BOLD functional connectivity. NeuroImage 2013;72:227–36. 链接1

[37] Rashid B, Arbabshirani MR, Damaraju E, Cetin MS, Miller R, Pearlson GD, et al. Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. NeuroImage 2016;134:645–57. 链接1

[38] Calhoun VD, Adali T. Time-varying brain connectivity in fMRI data: wholebrain data-driven approaches for capturing and characterizing dynamic states. IEEE Signal Process Mag 2016;33(3):52–66. 链接1

[39] Liao W, Wu G, Xu Q, Ji G, Zhang Z, Zang Y, et al. DynamicBC: a MATLAB toolbox for dynamic brain connectome analysis. Brain Connect 2014;4(10):780–90. 链接1

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