Bayesian Tensor Modeling for Image-based Classification of Alzheimer’s Disease (original) (raw)

References

• Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017). Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. Neuroimage, 145, 137–165.
  Article PubMed Google Scholar
• Armagan, A., Dunson, D. B., & Lee, J. (2013). Generalized double Pareto shrinkage. Statistica Sinica, 23(1), 119.
  PubMed PubMed Central Google Scholar
• Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis, 12(1), 26–41.
  Article CAS PubMed Google Scholar
• Becker, N., Werft, W., Toedt, G., Lichter, P., & Benner, A. (2009). penalizedSVM: a R-package for feature selection SVM classification. Bioinformatics, 25(13), 1711–1712.
  Article CAS PubMed Google Scholar
• Behler, A., Müller H. -P., Ludolph, A. C., Lulé, D., & Kassubek, J. (2022). A multivariate Bayesian classification algorithm for cerebral stage prediction by diffusion tensor imaging in amyotrophic lateral sclerosis. NeuroImage: Clinical, 35, 103094. https://doi.org/10.1016/j.nicl.2022.103094
• Ben Ahmed, O., Benois-Pineau, J., Allard, M., Ben Amar, C., Catheline, G., & Initiative, A. D. N. (2015). Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features. Multimedia Tools and Applications, 74, 1249–1266.
  Article Google Scholar
• Bettio, L. E., Rajendran, L., & Gil-Mohapel, J. (2017). The effects of aging in the hippocampus and cognitive decline. Neuroscience & Biobehavioral Reviews, 79, 66–86.
  Article Google Scholar
• Billio, M., Casarin, R., Iacopini, M., & Kaufmann, S. (2023). Bayesian dynamic tensor regression. Journal of Business & Economic Statistics, 41(2), 429–439.
  Article Google Scholar
• Bonthius, D. J., Solodkin, A., & Van Hoesen, G. W. (2005). Pathology of the insular cortex in Alzheimer disease depends on cortical architecture. Journal of Neuropathology & Experimental Neurology, 64(10), 910–922.
  Article Google Scholar
• Bradley, P. S., & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines. In ICML, 98, 82–90.
  Google Scholar
• Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189–215.
  Article Google Scholar
• Chouliaras, L., & O’Brien, J. T. (2023). The use of neuroimaging techniques in the early and differential diagnosis of dementia. Molecular Psychiatry, pages 1–14.
• de Jong, L. W., van der Hiele, K., Veer, I. M., Houwing, J., Westendorp, R., Bollen, E., de Bruin, P. W., Middelkoop, H., van Buchem, M. A., & van der Grond, J. (2008). Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study. Brain, 131(12), 3277–3285.
  Article PubMed PubMed Central Google Scholar
• Dedieu, A. (2019). Error bounds for sparse classifiers in high-dimensions. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 48–56. PMLR.
• Devika, S., Jeyaseelan, L., & Sebastian, G. (2016). Analysis of sparse data in logistic regression in medical research: A newer approach. Journal of postgraduate medicine, 62(1), 26. https://doi.org/10.4103/0022-3859.173193
  Article CAS PubMed PubMed Central Google Scholar
• Doerken, S., Avalos, M., Lagarde, E., & Schumacher, M. (2019). Penalized logistic regression with low prevalence exposures beyond high dimensional settings. PLoS One, 14(5), e0217057. https://doi.org/10.1371/journal.pone.0217057
  Article CAS PubMed PubMed Central Google Scholar
• Falahati, F., Westman, E., & Simmons, A. (2014). Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. Journal of Alzheimer’s disease, 41(3), 685–708.
  Article PubMed Google Scholar
• Fjell, A. M., Grydeland, H., Krogsrud, S. K., Amlien, I., Rohani, D. A., Ferschmann, L., Storsve, A. B., Tamnes, C. K., Sala-Llonch, R., Due-Tønnessen, P., et al. (2015). Development and aging of cortical thickness correspond to genetic organization patterns. Proceedings of the National Academy of Sciences, 112(50), 15462–15467.
  Article CAS Google Scholar
• Frenzel, S., Wittfeld, K., Habes, M., Klinger-Koenig, J., Buelow, R., Voelzke, H., & Grabe, H. J. (2020). A biomarker for Alzheimer’s disease based on patterns of regional brain atrophy. Frontiers in psychiatry, 10, 953.
  Article PubMed PubMed Central Google Scholar
• Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.
  Article PubMed PubMed Central Google Scholar
• Geweke, J. (1991). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Staff Report 148, Federal Reserve Bank of Minneapolis. https://ideas.repec.org/p/fip/fedmsr/148.html
• Griffis, J. C., Allendorfer, J. B., & Szaflarski, J. P. (2016). Voxel-based Gaussian naïve Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. Journal of neuroscience methods, 257, 97–108.
  Article PubMed Google Scholar
• Guhaniyogi, R. (2020). Bayesian methods for tensor regression (pp. 1–18). Wiley StatsRef: Statistics Reference Online.
  Google Scholar
• Guhaniyogi, R., & Spencer, D. (2021). Bayesian tensor response regression with an application to brain activation studies. Bayesian Analysis, 16(4), 1221–1249.
  Article Google Scholar
• Guhaniyogi, R., Qamar, S., & Dunson, D. B. (2017). Bayesian tensor regression. The Journal of Machine Learning Research, 18(1), 2733–2763.
  Google Scholar
• Hahn, P. R., & Carvalho, C. M. (2015). Decoupling shrinkage and selection in Bayesian linear models: a posterior summary perspective. Journal of the American Statistical Association, 110(509), 435–448.
  Article CAS Google Scholar
• Jacobs, H. I., Van Boxtel, M. P., Uylings, H. B., Gronenschild, E. H., Verhey, F. R., & Jolles, J. (2011). Atrophy of the parietal lobe in preclinical dementia. Brain and Cognition, 75(2), 154–163.
  Article PubMed Google Scholar
• Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM review, 51(3), 455–500.
  Google Scholar
• Kundu, S., Mallick, B. K., & Baladandayuthapan, V. (2019). Efficient Bayesian regularization for graphical model selection. Bayesian Analysis, 14(2), 449.
  Article PubMed Google Scholar
• Kundu, S., Reinhardt, A., Song, S., Han, J., Meadows, M. L., Crosson, B., Krishnamurthy, V. (2023). Bayesian longitudinal tensor response regression for modeling neuroplasticity. Human Brain Mapping.
• Lee, K.-J., Jones, G. L., Caffo, B. S., & Bassett, S. S. (2014). Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data. Bayesian Analysis (Online), 9(3), 699.
  PubMed Central Google Scholar
• Lock, E. F. (2018). Tensor-on-tensor regression. Journal of Computational and Graphical Statistics, 27(3), 638–647.
  Article PubMed PubMed Central Google Scholar
• Ma, X., & Kundu, S. (2022). High-dimensional measurement error models for Lipschitz loss. Preprint at https://arxiv.org/abs/2210.15008
• Madsen, S. K., Ho, A. J., Hua, X., Saharan, P. S., Toga, A. W., Jack, C. R., Jr., Weiner, M. W., Thompson, P. M., Initiative, A. D. N., et al. (2010). 3d maps localize caudate nucleus atrophy in 400 Alzheimer’s disease, mild cognitive impairment, and healthy elderly subjects. Neurobiology of aging, 31(8), 1312–1325.
  Article CAS PubMed PubMed Central Google Scholar
• Morales, D. A., Vives-Gilabert, Y., Gómez-Ansón, B., Bengoetxea, E., Larrañaga, P., Bielza, C., Pagonabarraga, J., Kulisevsky, J., Corcuera-Solano, I., & Delfino, M. (2013). Predicting dementia development in Parkinson’s disease using Bayesian network classifiers. Psychiatry Research: NeuroImaging, 213(2), 92–98.
  Article Google Scholar
• Nicholas, J. G. S., Polson, G., & Windle, J. (2013). Bayesian inference for logistic models using PÓlya-Gamma latent variables. Journal of the American Statistical Association, 108(504), 1339–1349.
  Article Google Scholar
• Pan, Y., Mai, Q., & Zhang, X. (2018). Covariate-adjusted tensor classification in high dimensions. Journal of the American statistical association.
• Peng, P., Lekadir, K., Gooya, A., Shao, L., Petersen, S. E., & Frangi, A. F. (2016). A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. Magnetic Resonance Materials in Physics, Biology and Medicine, 29, 155–195.
  Article Google Scholar
• Plant, C., Teipel, S. J., Oswald, A., Böhm, C., Meindl, T., Mourao-Miranda, J., Bokde, A. W., Hampel, H., & Ewers, M. (2010). Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage, 50(1), 162–174. https://doi.org/10.1016/j.neuroimage.2009.11.046
  Article PubMed Google Scholar
• Polson, N. G., & Scott, S. L. (2011). Data augmentation for support vector machines. Bayesian Analysis, 6, 1–24.
  Google Scholar
• Rathore, S., Habes, M., Iftikhar, M. A., Shacklett, A., & Davatzikos, C. (2017). A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. NeuroImage, 155, 530–548.
  Article PubMed Google Scholar
• Salat, D. H., Chen, J. J., Van Der Kouwe, A., Greve, D. N., Fischl, B., & Rosas, H. D. (2011). Hippocampal degeneration is associated with temporal and limbic gray matter/white matter tissue contrast in Alzheimer’s disease. Neuroimage, 54(3), 1795–1802.
  Article PubMed Google Scholar
• Sen, B., & Parhi, K. K. (2021). Predicting biological gender and intelligence from fMRI via dynamic functional connectivity. IEEE Transactions on Biomedical Engineering, 68(3), 815–825. https://doi.org/10.1109/TBME.2020.3011363
  Article PubMed Google Scholar
• Smith, M., & Fahrmeir, L. (2007). Spatial Bayesian variable selection with application to functional magnetic resonance imaging. Journal of the American Statistical Association, 102(478), 417–431.
  Article CAS Google Scholar
• Sun, W., Chang, C. Zhao, Y., & Long, Q. (2018). Knowledge-guided Bayesian support vector machine for high-dimensional data with application to analysis of genomics data. In 2018 IEEE International Conference on Big Data (Big Data), pages 1484–1493. IEEE.
• Tokdar, S. T., & Ghosh, J. K. (2007). Posterior consistency of logistic Gaussian process priors in density estimation. Journal of statistical planning and inference, 137(1), 34–42.
  Article Google Scholar
• Tustison, N. J., Avants, B. B., Cook, P. A., Zheng, Y., Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: improved N3 bias correction. IEEE transactions on medical imaging, 29(6), 1310–1320.
  Article PubMed PubMed Central Google Scholar
• Tustison, N. J., Cook, P. A., Klein, A., Song, G., Das, S. R., Duda, J. T., Kandel, B. M., van Strien, N., Stone, J. R., Gee, J. C., et al. (2014). Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. Neuroimage, 99, 166–179.
  Article PubMed Google Scholar
• Tustison, N. J., Holbrook, A. J., Avants, B. B., Roberts, J. M., Cook, P. A., Reagh, Z. M., Duda, J. T., Stone, J. R., Gillen, D. L., Yassa, M. A., et al. (2019). Longitudinal mapping of cortical thickness measurements: An Alzheimer’s Disease Neuroimaging Initiative-based evaluation study. Journal of Alzheimer’s Disease, 71(1), 165–183.
  Article PubMed Google Scholar
• Weston, P. S., Nicholas, J. M., Lehmann, M., Ryan, N. S., Liang, Y., Macpherson, K., Modat, M., Rossor, M. N., Schott, J. M., Ourselin, S., et al. (2016). Presymptomatic cortical thinning in familial Alzheimer disease: A longitudinal MRI study. Neurology, 87(19), 2050–2057.
  Article PubMed PubMed Central Google Scholar
• Wu, Y., Kundu, S., Stevens, J. S., Fani, N., & Srivastava, A. (2022). Elastic shape analysis of brain structures for predictive modeling of PTSD. Frontiers in Neuroscience, 16, 954055. https://doi.org/10.3389/fnins.2022.954055
  Article PubMed PubMed Central Google Scholar
• Xiao, Y., Hu, Y., Huang, K., Initiative, A. D. N., et al. (2023). Atrophy of hippocampal subfields relates to memory decline during the pathological progression of Alzheimer’s disease. Frontiers in Aging Neuroscience, 15.
• Yang, H., Xu, H., Li, Q., Jin, Y., Jiang, W., Wang, J., Wu, Y., Li, W., Yang, C., Li, X., et al. (2019). Study of brain morphology change in Alzheimer’s disease and amnestic mild cognitive impairment compared with normal controls. General psychiatry, 32(2).
• Yang, Z., Cummings, J. L., Cordes, D., & Initiative, A. D. N. (2023). Amyloidosis at putamen predicts vulnerability to Alzheimer’s disease. Alzheimer’s & Dementia, 19, e079343.
  Article Google Scholar
• Zhou, H., Li, L., & Zhu, H. (2013). Tensor regression with applications in neuroimaging data analysis. Journal of the American Statistical Association, 108(502), 540–552.
  Article CAS PubMed PubMed Central Google Scholar

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