Group independent component analysis reveals consistent resting-state networks across multiple sessions
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Group independent component analysis (gICA) was performed on resting-state data from 14 healthy subjects scanned on 5 fMRI scan sessions across 16 days. The data were reduced and aggregated in 3 steps using Principal Components Analysis (PCA, within scan, within session and across session) and subjected to gICA procedures. The amount of reduction was estimated by an improved method that utilizes a first-order autoregressive fitting technique to the PCA spectrum. Analyses were performed using all sessions in order to maximize sensitivity and alleviate the problem of component identification across session. Across-session consistency was examined by three methods, all using back-reconstruction of the single-session or single-subject/session maps from the grand (5-session) maps. The gICA analysis produced 55 spatially independent maps. Obvious artifactual maps were eliminated and the remainder were grouped based upon physiological recognizability. Biologically relevant component maps were found, including sensory, motor and a 'default-mode' map. All analysis methods showed that components were remarkably consistent across session. Critically, the components with the most obvious physiological relevance were the most consistent. The consistency of these maps suggests that, at least over a period of several weeks, these networks would be useful to follow longitudinal treatment-related manipulations.
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Unsupervised dimensionality reduction performed in the combined training and test datasets prior to prediction constitutes leakage.
“Various forms of unsupervised dimensionality reduction, such as independent component analysis62,63, are popular in neuroimaging and constitute leakage if performed in the combined training and test datasets prior to performing prediction.”
From Data leakage inflates prediction performance in connectome-based machine learning models (2024) · cites this paper as [63]
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A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190 · see citing works
Co-Planar Stereotaxic Atlas of the Human Brain
Talairach, J., & Tournoux, P. (1988) · Medical Entomology and Zoology · Cited by 11,798 · see citing works
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913 · see citing works
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812 · see citing works
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986 · see citing works
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553 · see citing works
Independent component analysis, A new concept?
Comon, P. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90029-9 · Cited by 8,150 · see citing works
Co-planar stereotaxic atlas of the human brain
Laitinen, L. (1989) · Clinical Neurology and Neurosurgery · doi:10.1016/0303-8467(89)90128-5 · Cited by 7,565 · see citing works
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358 · see citing works
Fast and robust fixed-point algorithms for independent component analysis
Hyvärinen, A. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.761722 · Cited by 6,239 · see citing works
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326 · see citing works
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398 · see citing works
Searching for a baseline: Functional imaging and the resting human brain
Gusnard, D. A., & Raichle, M. E. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094500 · Cited by 3,371 · see citing works
A method for making group inferences from functional MRI data using independent component analysis
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001) · Human Brain Mapping · doi:10.1002/hbm.1048 · Cited by 2,994 · see citing works
Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations
Lowe, M. J., Mock, B. J., & Sorenson, J. A. (1998) · NeuroImage · doi:10.1006/nimg.1997.0315 · Cited by 1,394 · see citing works
A Parametric Manipulation of Factors Affecting Task-induced Deactivation in Functional Neuroimaging
McKiernan, K. A., Kaufman, J. N., Kucera-Thompson, J., & Binder, J. R. (2003) · Journal of Cognitive Neuroscience · doi:10.1162/089892903321593117 · Cited by 1,080 · see citing works
Mapping functionally related regions of brain with functional connectivity MR imaging.
Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., Quigley, M., & Meyerand, M. E. (2000) · PubMed Central · Cited by 908 · see citing works
Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity.
Sibson, N. R., Dhankhar, A., Mason, G. F., Rothman, D. L., Behar, K. L., & Shulman, R. G. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.1.316 · Cited by 897 · see citing works
Typologies of attentional networks
Raz, A., & Buhle, J. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1903 · Cited by 706 · see citing works
Energetic basis of brain activity: implications for neuroimaging
Shulman, R. G., Rothman, D. L., Behar, K. L., & Hyder, F. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.06.005 · Cited by 590 · see citing works
van de Ven, V. G., Formisano, E., Prvulovic, D., Röder, C. H., & Linden, D. E. J. (2004) · Human Brain Mapping · doi:10.1002/hbm.20022 · Cited by 538 · see citing works
Liang, M., Zhou, Y., Jiang, T., Liu, Z., Tian, L., Liu, H., & Hao, Y. (2006) · Neuroreport · doi:10.1097/01.wnr.0000198434.06518.b8 · Cited by 402 · see citing works
Intrinsic Brain Activity in Altered States of Consciousness
Boly, M., Phillips, C. L. M., Tshibanda, L., Vanhaudenhuyse, A., Schabus, M., Dang‐Vu, T. T., Moonen, G., Hustinx, R., Maquet, P., & Laureys, S. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1417.015 · Cited by 387 · see citing works
Localization of Cardiac-Induced Signal Change in fMRI
Dagli, M. S., Ingeholm, J. E., & Haxby, J. V. (1999) · NeuroImage · doi:10.1006/nimg.1998.0424 · Cited by 385 · see citing works
Fukunaga, M., Horovitz, S. G., van Gelderen, P., de Zwart, J. A., Jansma, J. M., Ikonomidou, V. N., Chu, R., Deckers, R., Leopold, D. A., & Duyn, J. H. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2006.04.018 · Cited by 365 · see citing works
Variability in fMRI: An Examination of Intersession Differences
McGonigle, D. J., Howseman, A., Athwal, B. S., Friston, K. J., Frackowiak, R. S. J., & Holmes, A. P. (2000) · NeuroImage · doi:10.1006/nimg.2000.0562 · Cited by 351 · see citing works
Functional connectivity networks are disrupted in left temporal lobe epilepsy
Waites, A. B., Briellmann, R. S., Saling, M. M., Abbott, D. F., & Jackson, G. D. (2006) · Annals of Neurology · doi:10.1002/ana.20733 · Cited by 329 · see citing works
Antidepressant Effect on Connectivity of the Mood-Regulating Circuit: An fMRI Study
Anand, A., Li, Y., Wang, Y., Wu, J., Gao, S., Bukhari, L., Mathews, V. P., Kalnin, A., & Lowe, M. J. (2005) · Neuropsychopharmacology · doi:10.1038/sj.npp.1300725 · Cited by 324 · see citing works
Alzheimer Disease: Evaluation of a Functional MR Imaging Index as a Marker
Li, S.-J., Li, Z., Wu, G., Zhang, M., Franczak, M. B., & Antuono, P. (2002) · Radiology · doi:10.1148/radiol.2251011301 · Cited by 292 · see citing works
Effect of prior cognitive state on resting state networks measured with functional connectivity
Waites, A. B., Stanislavsky, A., Abbott, D. F., & Jackson, G. D. (2004) · Human Brain Mapping · doi:10.1002/hbm.20069 · Cited by 244 · see citing works
Intrinsic brain activity sets the stage for expression of motivated behavior
Raichle, M. E., & Gusnard, D. A. (2005) · The Journal of Comparative Neurology · doi:10.1002/cne.20752 · Cited by 220 · see citing works
Long-term test–retest reliability of functional MRI in a classification learning task
Aron, A. R., Gluck, M. A., & Poldrack, R. A. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.08.010 · Cited by 193 · see citing works
Variability in fMRI: A re‐examination of inter‐session differences
Smith, S. M., Beckmann, C. F., Ramnani, N., Woolrich, M. W., Bannister, P. R., Jenkinson, M., Matthews, P. M., & McGonigle, D. J. (2005) · Human Brain Mapping · doi:10.1002/hbm.20080 · Cited by 173 · see citing works
Calhoun, V. D., Adalı, T., & Pekar, J. J. (2004) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2004.09.004 · Cited by 173 · see citing works
Schmithorst, V. J., & Holland, S. K. (2004) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.20009 · Cited by 169 · see citing works
Rombouts, S. A. R. B., Barkhof, F., Hoogenraad, F. G. C., Sprenger, M., & Scheltens, P. (1998) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(97)00253-1 · Cited by 169 · see citing works
Loubinoux, I., Carel, C., Alary, F., Boulanouar, K., Viallard, G., MANELFE, C., Rascol, O., Celsis, P., & Chollet, F. (2001) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1097/00004647-200105000-00014 · Cited by 156 · see citing works
Automaticity and Reestablishment of Executive Control—An fMRI Study
Kübler, A., Dixon, V., & Garavan, H. (2006) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2006.18.8.1331 · Cited by 147 · see citing works
Functional MRI of auditory verbal working memory: long-term reproducibility analysis
Wei, X., Yoo, S., Dickey, C. C., Zou, K. H., Guttmann, C. R. G., & Panych, L. P. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.10.039 · Cited by 113 · see citing works
Estimation of the intrinsic dimensionality of fMRI data
Cordes, D., & Nandy, R. R. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.07.054 · Cited by 65 · see citing works
Marshall, I., Simonotto, E., Deary, I. J., MacLullich, A. M. J., Ebmeier, K. P., Rose, E. J., Wardlaw, J. M., Goddard, N., & Chappell, F. M. (2004) · Radiology · doi:10.1148/radiol.2333031782 · Cited by 49 · see citing works
Functional asymmetry in human primary auditory cortex: Identified from longitudinal fMRI study
Yoo, S., O’Leary, H. M., Dickey, C. C., et al. (2005) · Neuroscience Letters · doi:10.1016/j.neulet.2005.03.025 · Cited by 18 · see citing works
Dimensionality estimation for group fMRI data reduction at multiple levels
Chen, S. C.-J., Ross, T. J., Chuang, K., et al. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.710046 · Cited by 1 · see citing works
Self-citationXiong (1999) · doi:10.1002/(sici)1097-0193(1999)8:2/3
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<i>Conn</i> : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0073 · Cited by 4,885 · see citing works
Zuo, X.-N., Kelly, C., Adelstein, J. S., Klein, D. F., Castellanos, F. X., & Milham, M. P. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.080 · Cited by 744 · see citing works
Zhu, X., Wang, X., Xiao, J., Liao, J., Zhong, M., Wang, W., & Yao, S. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.10.035 · Cited by 638 · see citing works
Kelly, R. E., Alexopoulos, G. S., Wang, Z., Gunning, F. M., Murphy, C. F., Morimoto, S. S., Kanellopoulos, D., Jia, Z., Lim, K. O., & Hoptman, M. J. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.03.028 · Cited by 345 · see citing works
Self-citationYang, C.-C., Barrós-Loscertales, A., Pinazo, D., Ventura-Campos, N., Borchardt, V., Bustamante, J.-C., Rodríguez-Pujadas, A., Fuentes-Claramonte, P., Balaguer, R., Ávila, C., et al. (2016) · Neural Plasticity · doi:10.1155/2016/9504642 · Cited by 284 · see citing works
“In the analysis of training-related RSFC changes over time, the longitudinal resting state data were grouped together when conducting group ICA following a previous report [58].”
MethodKapogiannis, D., Reiter, D. A., Willette, A. A., & Mattson, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.09.029 · Cited by 211 · see citing works
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Rosenblatt, M., Tejavibulya, L., Jiang, R., Noble, S., & Scheinost, D. (2024) · Nature Communications · doi:10.1038/s41467-024-46150-w · Cited by 236 · see citing works
Rudrauf, D. (2014) · Advances in Neuroscience · doi:10.1155/2014/462765 · Cited by 74 · see citing works
Nayak, S., Wagshul, M. E., Foley, F. W., Motl, R. W., & Holtzer, R. (2025) · Journal of Neurology · doi:10.1007/s00415-025-12955-y · Cited by 6 · see citing works
- Authors
- Chen, S., Ross, T. J., Zhan, W., Myers, C. S., Chuang, K.-S., Heishman, S. J., Stein, E. A., & Yang, Y.
- Affiliations
- National Institutes of Health (US)
- Kaohsiung Medical University (TW)
- Corresponding author
- Yang, Yihong
- Venue
- Brain Research
- Publisher
- Elsevier BV
- Published
- 2008-11
- Volume
- 1239
- Pages
- 141-151
- ISSN
- 0006-8993 (ISSN-L)
- PMC
- PMC2784277
- OpenAlex
- W2018634603
- Cited by
- 139
- References
- 45
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 5 · 2022: 4 · 2021: 18 · 2020: 4 · 2019: 6 · 2018: 9 · 2017: 9 · 2016: 7 · 2015: 7 · 2014: 10 · 2013: 13 · 2012: 12
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Brain Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99 DA999999)
- National Institute on Drug Abuse (Z01DA000469)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
- Keywords
- group independent component analysis, resting-state fMRI, functional brain networks, default mode network
- MeSH terms
- Adult, Analysis of Variance, Brain, Humans, Magnetic Resonance Imaging, Male, Neural Pathways, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Time Factors, Models, Statistical, Principal Component Analysis
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