Sparse Independent Component Analysis with an Application to Cortical Surface fMRI Data in Autism

References

• Allen, G. I., and Maletić-Savatić, M. (2011), “Sparse Non-negative Generalized PCA with Applications to Metabolomics,” Bioinformatics, 27, 3029–3035. DOI: 10.1093/bioinformatics/btr522.
   PubMed Web of Science ®Google Scholar
• Babaie-Zadeh, M., Jutten, C., and Mansour, A. (2006), “Sparse ICA via Cluster-Wise PCA,” Neurocomputing, 69, 1458–1466. DOI: 10.1016/j.neucom.2005.12.022.
   Web of Science ®Google Scholar
• Beckmann, C. F., and Smith, S. M. (2004), “Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging,” IEEE Transactions on Medical Imaging, 23, 137–152. DOI: 10.1109/TMI.2003.822821.
   PubMed Web of Science ®Google Scholar
• Bell, A. J., and Sejnowski, T. J. (1995), “An Information-Maximization Approach to Blind Separation and Blind Deconvolution,” Neural Computation, 7, 1129–1159. DOI: 10.1162/neco.1995.7.6.1129.
   PubMed Web of Science ®Google Scholar
• Boukouvalas, Z., Levin-Schwartz, Y., Calhoun, V. D., and Adal i, T. (2018), “Sparsity and Independence: Balancing Two Objectives in Optimization for Source Separation with Application to fMRI Analysis,” Journal of the Franklin Institute, 355, 1873–1887. DOI: 10.1016/j.jfranklin.2017.07.003.
   Web of Science ®Google Scholar
• Calhoun, V. D., Adali, T., Pearlson, G. D., and Pekar, J. J. (2001), “A Method for Making Group Inferences from Functional MRI Data Using Independent Component Analysis,” Human Brain Mapping, 14, 140–151. DOI: 10.1002/hbm.1048.
   PubMed Web of Science ®Google Scholar
• Calhoun, V. D., Potluru, V. K., Phlypo, R., Silva, R. F., Pearlmutter, B. A., Caprihan, A., et al. (2013), “Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence,” PloS One, 8, e73309. DOI: 10.1371/journal.pone.0073309.
   PubMed Web of Science ®Google Scholar
• Damoiseaux, J. S., Rombouts, S., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., et al. (2006), “Consistent Resting-State Networks Across Healthy Subjects,” Proceedings of the National Academy of Sciences, 103, 13848–13853. DOI: 10.1073/pnas.0601417103.
   PubMed Web of Science ®Google Scholar
• Daubechies, I., Roussos, E., Takerkart, S., Benharrosh, M., Golden, C., D’ardenne, K., et al. (2009), “Independent Component Analysis for Brain fMRI Does Not Select for Independence,” Proceedings of the National Academy of Sciences, 106, 10415–10422. DOI: 10.1073/pnas.0903525106.
   PubMed Web of Science ®Google Scholar
• Di Martino, A., Al, E., and Milham, M. P. (2013), “The Autism Brain Imaging Data Exchange: Towards a Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism,” Molecular Psychiatry, 19, 659–667. DOI: 10.1038/mp.2013.78.
   PubMed Web of Science ®Google Scholar
• Di Martino, A., O’connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., et al. (2017), “Enhancing Studies of the Connectome in Autism Using the Autism Brain Imaging Data Exchange II,” Scientific Data, 4, 1–15. DOI: 10.1038/sdata.2017.10.
   Web of Science ®Google Scholar
• Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., et al. (2020), “Neuromark: An Automated and Adaptive ICA based Pipeline to Identify Reproducible fMRI Markers of Brain Disorders,” NeuroImage: Clinical, 28, 102375. DOI: 10.1016/j.nicl.2020.102375.
   PubMed Web of Science ®Google Scholar
• Eloyan, A., and Ghosh, S. K. (2013), “A Semiparametric Approach to Source Separation Using Independent Component Analysis,” Computational Statistics & Data Analysis, 58, 383–396. DOI: 10.1016/j.csda.2012.09.012.
   PubMed Web of Science ®Google Scholar
• Erhardt, E. B., Rachakonda, S., Bedrick, E. J., Allen, E. A., Adali, T., and Calhoun, V. D. (2011), “Comparison of Multi-Subject ICA Methods for Analysis of fMRI Data,” Human Brain Mapping, 32, 2075–2095. DOI: 10.1002/hbm.21170.
   PubMed Web of Science ®Google Scholar
• Erichson, N. B., Zheng, P., Manohar, K., Brunton, S. L., Kutz, J. N., and Aravkin, A. Y. (2020), ‘Sparse Principal Component Analysis via Variable Projection,” SIAM Journal on Applied Mathematics, 80, 977–1002. DOI: 10.1137/18M1211350.
   Web of Science ®Google Scholar
• Fan, S. (2020), “Improved Algorithm for Independent Component Analysis (ICA) with the Relax and Split Approximation,” Emory Theses and Dissertations.
   Google Scholar
• Ge, R., Wang, Y., Zhang, J., Yao, L., Zhang, H., and Long, Z. (2016), “Improved Fastica Algorithm in fMRI Data Analysis Using the Sparsity Property of the Sources,” Journal of Neuroscience Methods, 263, 103–114. DOI: 10.1016/j.jneumeth.2016.02.010.
   PubMed Web of Science ®Google Scholar
• Guo, Y., and Tang, L. (2013), “A Hierarchical Model for Probabilistic Independent Component Analysis of Multi-Subject fMRI Studies,” Biometrics, 69, 970–981. DOI: 10.1111/biom.12068.
   PubMed Web of Science ®Google Scholar
• Hyvarinen, A. (1999), “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis,” IEEE Transactions on Neural Networks, 10, 626–634. DOI: 10.1109/72.761722.
   PubMed Web of Science ®Google Scholar
• Lee, H., Battle, A., Raina, R., and Ng, A. (2006), “Efficient Sparse Coding Algorithms,” in Advances in Neural Information Processing Systems (Vol. 19).
   Google Scholar
• Li, Y.-O., Adal i, T., and Calhoun, V. D. (2007), “Estimating the Number of Independent Components for Functional Magnetic Resonance Imaging Data,” Human Brain Mapping, 28, 1251–1266. DOI: 10.1002/hbm.20359.
   PubMed Web of Science ®Google Scholar
• Lidstone, D. E., Rochowiak, R., Mostofsky, S. H., and Nebel, M. B. (2021), “A Data Driven Approach Reveals that Anomalous Motor System Connectivity is Associated with the Severity of Core Autism Symptoms,” Autism Research, 1–18. DOI: 10.1002/aur.2476.
   Web of Science ®Google Scholar
• Lombardo, M. V., Eyler, L., Moore, A., Datko, M., Carter Barnes, C., Cha, D., et al. (2019), “Default Mode-Visual Network Hypoconnectivity in An Autism Subtype with Pronounced Social Visual Engagement Difficulties,” elife, 8, e47427. DOI: 10.7554/eLife.47427.
   PubMed Web of Science ®Google Scholar
• Long, Q., Bhinge, S., Levin-Schwartz, Y., Boukouvalas, Z., Calhoun, V. D., and Adal i, T. (2019), “The Role of Diversity in Data-Driven Analysis of Multi-Subject fMRI Data: Comparison of Approaches based on Independence and Sparsity Using Global Performance Metrics,” Human Brain Mapping, 40, 489–504. DOI: 10.1002/hbm.24389.
   PubMed Web of Science ®Google Scholar
• Lord, C., Brugha, T. S., Charman, T., Cusack, J., Dumas, G., Frazier, T., Jones, E. J., Jones, R. M., Pickles, A., State, M. W., et al. (2020), “Autism Spectrum Disorder,” Nature Reviews Disease Primers, 6, 1–23. DOI: 10.1038/s41572-019-0138-4.
   PubMed Web of Science ®Google Scholar
• Lord, C., Elsabbagh, M., Baird, G., and Veenstra-Vanderweele, J. (2018), “Autism Spectrum Disorder,” The Lancet, 392, 508–520. DOI: 10.1016/S0140-6736(18)31129-2.
   PubMed Web of Science ®Google Scholar
• Lukemire, J., Pagnoni, G., and Guo, Y. (2023), “Sparse Bayesian Modeling of Hierarchical Independent Component Analysis: Reliable Estimation of Individual Differences in Brain Networks,” Biometrics, 79, 3599–3611. DOI: 10.1111/biom.13867.
   PubMed Web of Science ®Google Scholar
• Mejia, A. F., Nebel, M. B., Wang, Y., Caffo, B. S., and Guo, Y. (2020), “Template Independent Component analysis: Targeted and Reliable Estimation of Subject-Level Brain Networks Using Big Data Population Priors,” Journal of the American Statistical Association, 115, 1151–1177. DOI: 10.1080/01621459.2019.1679638.
   PubMed Web of Science ®Google Scholar
• Minka, T. (2000), “Automatic Choice of Dimensionality for PCA,” in Advances in Neural Information Processing Systems (Vol. 13).
   Google Scholar
• Müller, R.-A., and Fishman, I. (2018), “Brain Connectivity and Neuroimaging of Social Networks in Autism,” Trends in Cognitive Sciences, 22, 1103–1116. DOI: 10.1016/j.tics.2018.09.008.
   PubMed Web of Science ®Google Scholar
• Nebel, M. B., Lidstone, D. E., Wang, L., Benkeser, D., Mostofsky, S. H., and Risk, B. B. (2022), “Accounting for Motion in Resting-State fMRI: What Part of the Spectrum are We Characterizing in Autism Spectrum Disorder?” NeuroImage, 257, 119296. DOI: 10.1016/j.neuroimage.2022.119296.
   PubMed Web of Science ®Google Scholar
• Padmanabhan, A., Lynch, C. J., Schaer, M., and Menon, V. (2017), “The Default Mode Network in Autism,” Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2, 476–486. DOI: 10.1016/j.bpsc.2017.04.004.
   PubMedGoogle Scholar
• Pham, D. D., Muschelli, J., and Mejia, A. F. (2022), “ciftitools: A Package for Reading, Writing, Visualizing, and Manipulating Cifti Files in r,” NeuroImage, 250, 118877. DOI: 10.1016/j.neuroimage.2022.118877.
   PubMed Web of Science ®Google Scholar
• Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., and Petersen, S. E. (2014), “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI,” NeuroImage, 84, 320–341. DOI: 10.1016/j.neuroimage.2013.08.048.
   PubMed Web of Science ®Google Scholar
• Rachakonda, S., Egolf, E., Correa, N., and Calhoun, V. (2007), “Group ICA of fMRI Toolbox (gift) Manual,” Dostupnez [cit 2011-11-5].
   Google Scholar
• Risk, B. B., and Gaynanova, I. (2021), “Simultaneous Non-Gaussian Component Analysis (sing) for Data Integration in Neuroimaging,” The Annals of Applied Statistics, 15, 1431–1454. DOI: 10.1214/21-AOAS1466.
   Web of Science ®Google Scholar
• Risk, B. B., Matteson, D. S., and Ruppert, D. (2019), “Linear Non-Gaussian Component Analysis via Maximum Likelihood,” Journal of the American Statistical Association, 114, 332–343. DOI: 10.1080/01621459.2017.1407772.
   Web of Science ®Google Scholar
• Risk, B. B., Matteson, D. S., Ruppert, D., Eloyan, A., and Caffo, B. S. (2014), “An Evaluation of Independent Component Analyses with an Application to Resting-State fMRI,” Biometrics, 70, 224–236. DOI: 10.1111/biom.12111.
   PubMed Web of Science ®Google Scholar
• Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994), “Estimation of Regression Coefficients When Some Regressors are not always Observed,” Journal of the American statistical Association, 89, 846–866. DOI: 10.1080/01621459.1994.10476818.
   Web of Science ®Google Scholar
• Sadaghiani, S., Hesselmann, G., Friston, K. J., and Kleinschmidt, A. (2010), “The Relation of Ongoing Brain Activity, Evoked Neural Responses, and Cognition,” Frontiers in Systems Neuroscience, 4, 1424. DOI: 10.3389/fnsys.2010.00020.
   Google Scholar
• Shen, H., and Huang, J. Z. (2008), ‘Sparse Principal Component Analysis via Regularized Low Rank Matrix Approximation,” Journal of Multivariate Analysis, 99, 1015–1034. DOI: 10.1016/j.jmva.2007.06.007.
   Web of Science ®Google Scholar
• Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004), “Advances in Functional and Structural MR Image Analysis and Implementation as fsl,” Neuroimage, 23, S208–S219. DOI: 10.1016/j.neuroimage.2004.07.051.
   PubMed Web of Science ®Google Scholar
• Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E., et al. (2015), “A Positive-Negative Mode of Population Covariation Links Brain Connectivity, Demographics and Behavior,” Nature Neuroscience, 18, 1565–1567. DOI: 10.1038/nn.4125.
   PubMed Web of Science ®Google Scholar
• Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., et al. (2013), “Functional Connectomics from Resting-State fMRI,” Trends in Cognitive Sciences, 17, 666–682. DOI: 10.1016/j.tics.2013.09.016.
   PubMed Web of Science ®Google Scholar
• Sobczyk, P., Bogdan, M., and Josse, J. (2017), “Bayesian Dimensionality Reduction with PCA Using Penalized Semi-integrated Likelihood,” Journal of Computational and Graphical Statistics, 26, 826–839. DOI: 10.1080/10618600.2017.1340302.
   Web of Science ®Google Scholar
• Van der Laan, M. J., Polley, E. C., and Hubbard, A. E. (2007), “Super Learner,” Statistical Applications in Genetics and Molecular Biology, 6, 1–21. DOI: 10.2202/1544-6115.1309.
   Web of Science ®Google Scholar
• Wei, T. (2015), “A Convergence and Asymptotic Analysis of the Generalized Symmetric {FastICA} Algorithm,” IEEE Transactions on Signal Processing, 63, 6445–6458. DOI: 10.1109/TSP.2015.2468686.
   Web of Science ®Google Scholar
• Welvaert, M., Durnez, J., Moerkerke, B., Verdoolaege, G., and Rosseel, Y. (2011), “neurosim: An r Package for Generating fMRI Data,” Journal of Statistical Software, 44, 1–18. DOI: 10.18637/jss.v044.i10.
   Web of Science ®Google Scholar
• Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M. et al. (2011), “The Organization of the Human Cerebral Cortex Estimated by Intrinsic Functional Connectivity,” Journal of Neurophysiology, 10, 1125–1165. DOI: 10.1152/jn.00338.2011.
   Google Scholar
• Yu, M., Linn, K. A., Cook, P. A., Phillips, M. L., McInnis, M., Fava, M. et al. (2018), “Statistical Harmonization Corrects Site Effects in Functional Connectivity Measurements from Multi-Site fMRI Data,” Human Brain Mapping, 39, 4213–4227. DOI: 10.1002/hbm.24241.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., Matteson, D. S., Mostofsky, S. H., Nebel, M. B., and Risk, B. B. (2022), “Group Linear Non-Gaussian Component Analysis with Applications to Neuroimaging,” Computational Statistics & Data Analysis, 171, 107454. DOI: 10.1016/j.csda.2022.107454.
   PubMed Web of Science ®Google Scholar
• Zheng, P., and Aravkin, A. (2020), “Relax-and-Split Method for Nonconvex Inverse Problems,” Inverse Problems, 36, 095013. DOI: 10.1088/1361-6420/aba417.
   Google Scholar
• Zou, H., Hastie, T., and Tibshirani, R. (2006), “Sparse Principal Component Analysis,” Journal of Computational and Graphical Statistics, 15, 265–286. DOI: 10.1198/106186006X113430.
   Web of Science ®Google Scholar