C. Chang and G. H. Glover, Time-frequency dynamics of resting-state brain connectivity measured with fMRI, Neuroimage, vol.50, pp.81-98, 2010.

M. J. Mckeown, S. Makeig, G. G. Brown, T. P. Jung, S. S. Kindermann et al., Analysis of fMRI Data by Blind Separation Into Independent Spatial Components, Hum. Brain Mapp, vol.6, pp.160-188, 1998.

V. D. Calhoun, T. Adali, G. D. Pearlson, and J. J. Pekar, A method for making group inferences from functional MRI data using independent component analysis, Hum. Brain Mapp, vol.14, pp.140-151, 2001.

N. Wang, W. Zeng, Y. Shi, and . Yan, H: Brain Functional Plasticity Driven by Career Experience: A Resting-State fMRI Study of the Seafarer, Front. Psychol, vol.8, p.1786, 2017.

N. Wang, L. Liu, W. Liu, and H. Yan, A novel automatic identification model for tracking dynamic brain functional networks at single-subject level, Proceeding of IEEE International Conference on Information and Automation (ICIA), pp.505-510, 2017.

Z. Fu, Y. Tu, X. Di, Y. Du, G. D. Pearlson et al., Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: an application to schizophrenia, Neuroimage, 2017.

Y. Du, G. D. Pearlson, D. Lin, J. Sui, J. Chen et al., Identifying dynamic functional connectivity biomarkers using GIG-ICA: Application to schizophrenia, schizoaffective disorder, and psychotic bipolar disorder, Hum. Brain Mapp, vol.38, pp.2683-2708, 2017.

N. Wang, W. Zeng, and L. Chen, A Fast-FENICA method on resting state fMRI data, J. Neurosci. Methods, vol.209, pp.1-12, 2012.

N. Wang, W. Zeng, Y. Shi, T. Ren, Y. Jing et al., WASICA: An effective wavelet-shrinkage based ICA model for brain fMRI data analysis, J. Neurosci. Methods, vol.246, pp.75-96, 2015.

N. Wang, W. Zeng, and D. Chen, A Novel Sparse Dictionary Learning Separation (SDLS) Model With Adaptive Dictionary Mutual Incoherence Constraint for fMRI Data Analysis, IEEE Trans. Biomed. Eng, vol.63, pp.2376-2389, 2016.

F. Esposito, E. Seifritz, E. Formisano, R. Morrone, T. Scarabino et al., Real-time independent component analysis of fMRI timeseries, Neuroimage, vol.20, pp.2209-2224, 2003.

V. Kiviniemi, T. Vire, J. Remes, A. A. Elseoud, T. Starck et al., A sliding time-window ICA reveals spatial variability of the default mode network in time, Brain Conn, vol.1, pp.339-347, 2011.

C. H. Moritz, B. P. Rogers, and M. E. Meyerand, Power spectrum ranked independent component analysis of a periodic fMRI complex motor paradigm. Hum. Brain Mapp, vol.18, pp.111-122, 2003.

D. Martino, F. Gentile, F. Esposito, F. Balsi, M. Di-salle et al., Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers, Neuroimage, vol.34, pp.177-194, 2007.

J. Himberg, A. Hyvärinen, and F. Esposito, Validating the independent components of neuroimaging time series via clustering and visualization, Neuroimage, vol.22, pp.1214-1222, 2004.

Z. Yang, S. Laconte, X. Weng, and X. Hu, Ranking and averaging independent component analysis by reproducibility (RAICAR). Hum, Brain Mapp, vol.29, pp.711-725, 2008.
DOI : 10.1002/hbm.20432

URL : http://www.lacontelab.org/papers/raicar.pdf

W. Zeng, A. Qiu, B. Chodkowski, and J. J. Pekar, Spatial and temporal reproducibility-based ranking of the independent components of BOLD fMRI data, Neuroimage, vol.46, pp.1041-1054, 2009.

N. Wang, C. Chang, W. Zeng, Y. Shi, and H. Yan, A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets, Front. Neurosci, vol.11, p.510, 2017.

N. Wang, W. Zeng, D. Chen, J. Yin, and L. Chen, A novel brain networks enhancement model (BNEM) for BOLD fMRI data analysis with highly spatial reproducibility, IEEE J. Biomed. Health Inform, vol.20, pp.1107-1119, 2016.

Q. H. Zou, C. Z. Zhu, Y. Yang, X. N. Zuo, X. Y. Long et al., An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF, J. Neurosci. Methods, vol.172, pp.137-141, 2008.

T. P. Minka, Automatic choice of dimensionality for PCA, Adv. Neural Inf. Process Syst, pp.598-604, 2001.