Robust coupled tensor decomposition and feature extraction for multimodal medical data

References

• Acar, E., Levin-Schwartz, Y., Calhoun, V. D., & Adali, T. (2017). ACMTF for fusion of multi-modal neuroimaging data and identification of biomarkers [Paper presentation]. 2017 25th European Signal Processing Conference (EUSIPCO), (pp. 643–647). https://doi.org/10.23919/EUSIPCO.2017.8081286
   Google Scholar
• Acar, E., Kolda, T. G., & Dunlavy, D. M. (2011). All-at-once optimization for coupled matrix and tensor factorizations. arXiv preprint arXiv:1105.3422
   Google Scholar
• Acar, E., Papalexakis, E. E., Gürdeniz, G., Rasmussen, M. A., Lawaetz, A. J., Nilsson, M., & Bro, R. (2014). Structure-revealing data fusion. BMC Bioinformatics, 15(1), 239. https://doi.org/10.1186/1471-2105-15-239
   PubMedGoogle Scholar
• Anandkumar, A., Jain, P., Shi, Y., & Niranjan, U. N. (2016). Tensor vs. matrix methods: Robust tensor decomposition under block sparse perturbations. Artificial Intelligence and Statistics, 51, 268–276.
   Google Scholar
• Ashburner, J., Barnes, G., Chen, C.-C., Daunizeau, J., Flandin, G., & Friston, K. (2014). Spm12 manual. Wellcome Trust Centre for Neuroimaging.
   Google Scholar
• Badea, L. (2008). Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization. In Biocomputing 2008 (pp. 267–278). World Scientific.
   Google Scholar
• Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., Friend, S. H., & Trister, A. D. (2016). The mpower study, Parkinson disease mobile data collected using researchkit. Scientific Data, 3(1), 1–9. https://doi.org/10.1038/sdata.2016.11
   Google Scholar
• Bridwell, D., & Calhoun, V. (2019). Fusing concurrent EEG and fMRI intrinsic networks. In Magnetoencephalography: From signals to dynamic cortical networks, 293–315. Springer.
   Google Scholar
• Bullock, M., Jackson, G. D., & Abbott, D. F. (2021). Artifact reduction in simultaneous EEG-fMRI: A systematic review of methods and contemporary usage. Frontiers in Neurology, 12, 193. https://doi.org/10.3389/fneur.2021.622719
   Web of Science ®Google Scholar
• Chaibub Neto, E., Bot, B. M., Perumal, T., Omberg, L., Guinney, J., & Kellen, M. (2016). Personalized hypothesis tests for detecting medication response in Parkinson disease patients using iphone sensor data. In Biocomputing 2016: Proceedings of the Pacific Symposium (pp. 273––284). .
   Google Scholar
• Chang, C.-C., & Lin, C.-J. (2011). Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 1–27. [Database] https://doi.org/10.1145/1961189.1961199
   Web of Science ®Google Scholar
• Chatzichristos, C., Davies, M., Escudero, J., Kofidis, E., & Theodoridis, S. (2018). Fusion of EEG and fMRI via soft coupled tensor decompositions [Paper presentation]. 2018 26th European Signal Processing Conference (EUSIPCO), (pp. 56–60). https://doi.org/10.23919/EUSIPCO.2018.8553077
   Google Scholar
• Chatzichristos, C., Kofidis, E., Van Paesschen, W., De Lathauwer, L., Theodoridis, S., & Van Huffel, S. (2022). Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis. Human Brain Mapping, 43(4), 1231–1255. https://doi.org/10.1002/hbm.25717
   PubMed Web of Science ®Google Scholar
• Dizaji, A. S., & Soltanian-Zadeh, H. (2017). A change-point analysis method for single-trial study of simultaneous EEG-fMRI of auditory/visual oddball task. Biorxiv, 100487.
   Google Scholar
• Fang, X., Paynabar, K., & Gebraeel, N. (2019). Image-based prognostics using penalized tensor regression. Technometrics, 61(3), 369–384. https://doi.org/10.1080/00401706.2018.1527727
   Web of Science ®Google Scholar
• Farias, R. C., Cohen, J. E., & Comon, P. (2016). Exploring multimodal data fusion through joint decompositions with flexible couplings. IEEE Transactions on Signal Processing, 64(18), 4830–4844. https://doi.org/10.1109/TSP.2016.2576425
   Web of Science ®Google Scholar
• Gaw, N., Hawkins-Daarud, A., Hu, L. S., Yoon, H., Wang, L., Xu, Y., Jackson, P. R., Singleton, K. W., Baxter, L. C., Eschbacher, J., Gonzales, A., Nespodzany, A., Smith, K., Nakaji, P., Mitchell, J. R., Wu, T., Swanson, K. R., & Li, J. (2019). Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI. Scientific Reports, 9(1), 1–9. https://doi.org/10.1038/s41598-019-46296-4
   PubMedGoogle Scholar
• Gaw, N., Schwedt, T. J., Chong, C. D., Wu, T., & Li, J. (2018). A clinical decision support system using multi-modality imaging data for disease diagnosis. IISE Transactions on Healthcare Systems Engineering, 8(1), 36–46. https://doi.org/10.1080/24725579.2017.1403520
   Google Scholar
• Gaw, N., Yousefi, S., & Reisi Gahrooei, M. (2022). Multimodal data fusion for systems improvement: A review. IISE Transactions, 54(11), 1098–1116 .
   Web of Science ®Google Scholar
• Goetz, C. G., Tilley, B. C., Shaftman, S. R., Stebbins, G. T., Fahn, S., Martinez-Martin, P., Poewe, W., Sampaio, C., Stern, M. B., Dodel, R., Dubois, B., Holloway, R., Jankovic, J., Kulisevsky, J., Lang, A. E., Lees, A., Leurgans, S., LeWitt, P. A., Nyenhuis, D., … LaPelle, N. (2008). Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (mds-updrs): scale presentation and clinimetric testing results. Movement Disorders, 23(15), 2129–2170. https://doi.org/10.1002/mds.22340
   PubMed Web of Science ®Google Scholar
• Gu, Q., Gui, H., & Han, J. (2014). Robust tensor decomposition with gross corruption. Advances in Neural Information Processing Systems, 27, 1422–1430.
   Google Scholar
• Harshman, R. A., & Lundy, M. E. (1994). Parafac: Parallel factor analysis. Computational Statistics & Data Analysis, 18(1), 39–72. https://doi.org/10.1016/0167-9473(94)90132-5
   Web of Science ®Google Scholar
• He, H., Henderson, J., & Ho, J. C. (2019). Distributed tensor decomposition for large scale health analytics [Paper presentation]. The World Wide Web Conference, (pp. 659–669).
   Google Scholar
• Hotelling, H. (1992). Relations between two sets of variates. In Breakthroughs in statistics (pp. 162–190). Springer.
   Google Scholar
• Hu, L. S., Ning, S., Eschbacher, J. M., Baxter, L. C., Gaw, N., Ranjbar, S., Plasencia, J., Dueck, A. C., Peng, S., Smith, K. A., Nakaji, P., Karis, J. P., Quarles, C. C., Wu, T., Loftus, J. C., Jenkins, R. B., Sicotte, H., Kollmeyer, T. M., O'Neill, B. P., … Mitchell, J. R. (2017). Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro-oncology, 19(1), 128–137. https://doi.org/10.1093/neuonc/now135
   PubMed Web of Science ®Google Scholar
• Jonmohamadi, Y., Muthukumaraswamy, S., Chen, J., Roberts, J., Crawford, R., & Pandey, A. (2020). Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition. Brain Topography, 33(5), 636–650.
   PubMed Web of Science ®Google Scholar
• Khanzadeh, M., Tian, W., Yadollahi, A., Doude, H. R., Tschopp, M. A., & Bian, L. (2018). Dual process monitoring of metal-based additive manufacturing using tensor decomposition of thermal image streams. Additive Manufacturing, 23, 443–456.
   Web of Science ®Google Scholar
• Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM Review, 51(3), 455–500. https://doi.org/10.1137/07070111X
   Web of Science ®Google Scholar
• Liu, X., Chen, K., Weidman, D., Wu, T., Lure, F., & Li, J., for the Alzheimer’s Disease Neuroimaging Initiative. (2021). A novel transfer learning model for predictive analytics using incomplete multimodality data. IISE Transactions, 53(9), 1010–1022. https://doi.org/10.1080/24725854.2020.1798569
   PubMed Web of Science ®Google Scholar
• Miao, H., Wang, A., Li, B., & Shi, J. (2022). Structural tensor-on-tensor regression with interaction effects and its application to a hot rolling process. Journal of Quality Technology, 54(5), 547–560.
   Web of Science ®Google Scholar
• Mosayebi, R., & Hossein-Zadeh, G.-A. (2020). Correlated coupled matrix tensor factorization method for simultaneous EEG-fMRI data fusion. Biomedical Signal Processing and Control, 62, 102071. https://doi.org/10.1016/j.bspc.2020.102071
   Web of Science ®Google Scholar
• Naskovska, K., & Haardt, M. (2016). Extension of the semi-algebraic framework for approximate CP decompositions via simultaneous matrix diagonalization to the efficient calculation of coupled CP decompositions [Paper presentation]. 2016 50th Asilomar Conference on Signals, Systems and Computers, (pp. 1728–1732). https://doi.org/10.1109/ACSSC.2016.7869678
   Google Scholar
• Naskovska, K., Korobkov, A. A., Haardt, M., & Haueisen, J. (2017). Analysis of the photic driving effect via joint EEG and MEG data processing based on the coupled CP decomposition [Paper presentation]. 2017 25th European Signal Processing Conference (EUSIPCO), (pp. 1285–1289). https://doi.org/10.23919/EUSIPCO.2017.8081415
   Google Scholar
• Parkinsons News Today. (2020). Parkinson’s disease statistics. Retrieved February 14, 2022, from https://parkinsonsnewstoday.com/parkinsons-disease-statistics/.
   Google Scholar
• Pew Research Center. (2021). Mobile fact sheet. Retrieved February 14, 2022, from https://www.pewresearch.org/internet/fact-sheet/mobile/.
   Google Scholar
• Roemer, F., & Haardt, M. (2013). A semi-algebraic framework for approximate CP decompositions via simultaneous matrix diagonalizations (SECSI). Signal Processing, 93(9), 2722–2738. https://doi.org/10.1016/j.sigpro.2013.02.016
   Web of Science ®Google Scholar
• Singh, A. P., & Gordon, G. J. (2008). Relational learning via collective matrix factorization [Paper presentation]. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 650–658). https://doi.org/10.1145/1401890.1401969
   Google Scholar
• Smilde, A. K., Westerhuis, J. A., & Boque, R. (2000). Multiway multiblock component and covariates regression models. Journal of Chemometrics, 14(3), 301–331. https://doi.org/10.1002/1099-128X(200005/06)14:3<301::AID-CEM594>3.0.CO;2-H
   Web of Science ®Google Scholar
• Soon, C. S., Vinogradova, K., Ong, J. L., Calhoun, V. D., Liu, T., Zhou, J. H., Ng, K. K., & Chee, M. W. L. (2021). Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes. NeuroImage, 237, 118129. https://doi.org/10.1016/j.neuroimage.2021.118129
   PubMed Web of Science ®Google Scholar
• Sørensen, M., & De Lathauwer, L. (2013). Coupled tensor decompositions for applications in array signal processing [Paper presentation]. 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), (pp. 228–231).
   Google Scholar
• Tsanas, A. (2012). Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning [Unpublished doctoral dissertation]. Oxford University.
   Google Scholar
• Tsanas, A., Little, M. A., McSharry, P. E., & Ramig, L. O. (2011). Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity. Journal of the Royal Society, Interface, 8(59), 842–855.
   PubMed Web of Science ®Google Scholar
• Van Eyndhoven, S., Hunyadi, B., De Lathauwer, L., & Van Huffel, S. (2017). Flexible fusion of electroencephalography and functional magnetic resonance imaging: Revealing neural-hemodynamic coupling through structured matrix-tensor factorization [Paper presentation]. 2017 25th European Signal Processing Conference (EUSIPCO), (pp. 26–30). https://doi.org/10.23919/EUSIPCO.2017.8081162
   Google Scholar
• Vervliet, N., Debals, O., & De Lathauwer, L. (2016). Tensorlab 3.0—numerical optimization strategies for large-scale constrained and coupled matrix/tensor factorization. 2016 50th Asilomar Conference on Signals, Systems and Computers, (pp. 1733–1738). https://doi.org/10.1109/ACSSC.2016.7869679.
   Google Scholar
• Walz, J. M., Goldman, R. I., Muraskin, J., Conroy, B., Brown, T. R., & Sajda, P. (2018). Auditory and Visual Oddball EEG-fMRI. OpenNeuro.
   Google Scholar
• Xue, N., Papamakarios, G., Bahri, M., Panagakis, Y., & Zafeiriou, S. (2017). Robust low-rank tensor modelling using tucker and CP decomposition [Paper presentation]. 2017 25th European Signal Processing Conference (EUSIPCO), (pp. 1185–1189). https://doi.org/10.23919/EUSIPCO.2017.8081395
   Google Scholar
• Yan, C.-G., Wang, X.-D., Zuo, X.-N., & Zang, Y.-F. (2016). Dpabi: data processing & analysis for (resting-state) brain imaging. Neuroinformatics, 14(3), 339–351. https://doi.org/10.1007/s12021-016-9299-4
   PubMed Web of Science ®Google Scholar
• Yan, H., Paynabar, K., & Pacella, M. (2019). Structured point cloud data analysis via regularized tensor regression for process modeling and optimization. Technometrics, 61(3), 385–395. https://doi.org/10.1080/00401706.2018.1529628
   Web of Science ®Google Scholar
• Yoon, H., & Gaw, N. (2021). A novel multi-task linear mixed model for smartphone-based telemonitoring. Expert Systems with Applications, 164, 113809. https://doi.org/10.1016/j.eswa.2020.113809
   Web of Science ®Google Scholar
• Zhao, Q., Caiafa, C. F., Mandic, D. P., Chao, Z. C., Nagasaka, Y., Fujii, N., Zhang, L., & Cichocki, A. (2013). Higher order partial least squares (HOPLS): a generalized multilinear regression method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(7), 1660–1673. https://doi.org/10.1109/TPAMI.2012.254
   PubMed Web of Science ®Google Scholar