[1] 薛绍伟,唐一源, 李健,等.一种基于fMRI数据的脑功能网络构建方法[J].计算机应用研究,2010,27(11):4055-4057.(XUE S W, TANG Y Y, LI J, et al. Method for constructing brain functional networks based on fMRI data [J]. Application Research of Computers, 2010, 27(11): 4055-4057.) [2] SHIRER W R, JIANG H, PRICE C M, et al. Optimization of rs-fMRI pre-processing for enhanced signal-noise separation, test-retest reliability, and group discrimination [J]. Neuroimage, 2015, 117:67-79. [3] BULLMORE E, LONG C, SUCKLING J, et al. Colored noise and computational inference in fMRI time series analysis: resampling methods in time and wavelet domains [J]. Neuroimage, 2001, 13(6): 86. [4] MALDJIAN J A, LAURIENTI P J, KRAFT R A, et al. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets [J]. Neuroimage, 2003, 19(3): 1233-1239. [5] FEIS R A, SMITH S M, FILIPPINI N, et al. ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI [EB/OL]. [2015-11-15]. http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4621866&blobtype=pdf. [6] BRIGHT M G, MURPHY K. Is fMRI "noise" really noise? Resting state nuisance regressors remove variance with network structure [J]. Neuroimage, 2015, 114: 158-169. [7] KARAHANOGLU F I, CABALLERO-GAUDES C, LAZEYRAS F, et al. Total activation: fMRI deconvolution through spatio-temporal regularization [J]. Neuroimage, 2013, 73: 121-134. [8] KHALIDOV I, VAN DE VILLE D, FADILI J, et al. Activelets and sparsity: a new way to detect brain activation from fMRI data [J]. Proceedings of SPIE, 2007, 23(3): 380-388. [9] MANOKAR N V, MANOKAR V, RINESH R, et al. Wavelets based decomposition and classification of diseased fMRI brain images for inter racial disease types of Alzheimer's Vs tumors using SOFM and enhancement by LVQ neural networks [C]// Proceedings of the 2012 2nd IEEE International Conference on Parallel Distributed and Grid Computing. Piscataway, NJ: IEEE, 2012: 822-827. [10] BERGEAUD F, MALLAT S. Adaptative image decompositions in a wavelets dictionary [M]// BERGER M-O, DERICHE R, HERLIN I, et al. ICAOS '96. Berlin: Springer, 1996: 216-224. [11] CORREA N, ADALI T, CALHOUN V D. Performance of blind source separation algorithms for fMRI analysis using a group ICA method [J]. Magnetic Resonance Imaging, 2007, 25(5): 684-694. [12] AKIYAMA O, KATO C, MIYAZAWA M, et al. Numerical simulation of aeroacoustic noise generated from a polygon motor [J]. Journal of Environment and Engineering, 2009, 4(1): 162-175. [13] XU G, XU Y, WU G, et al. Task-modulation of functional synchrony between spontaneous low-frequency oscillations in the human brain detected by fMRI [J]. Magnetic Resonance in Medicine, 2006, 56(1): 41-50. [14] XIA Z M, GLAHN D, LI H T, et al. Comparison of TCA and ICA techniques in fMRI data processing [J]. Journal of Magnetic Resonance Imaging, 2004, 19(4): 397-402. [15] ZHU J F, HUANG Y. Improved threshold function of wavalet domain signal de-noising [C]// Proceedings of the 2013 International Conference on Wavelet Analysis and Pattern Recognition. Piscataway, NJ: IEEE, 2013: 190-195. [16] KHULLAR S, MICHAEL A M, CORREA N, et al. Improved 3D wavelet-based de-noising of fMRI data [C]// Proceedings of SPIE 7962. Bellingham, WA: SPIE, 2011: 215-230. [17] CHURCHILL N W, YOURGANOV G, SPRING R, et al. PHYCAA: data-driven measurement and removal of physiological noise in BOLD fMRI [J]. Neuroimage, 2012, 59(2): 1299-1314. |