Generalized Coupled Matrix Tensor Factorization Method Based on Normalized Mutual Information for Simultaneous EEG-fMRI Data Analysis

Acar, E., Kolda, T. G., & Dunlavy, D. M. (2011). All-at-once optimization for coupled matrix and tensor factorizations. arXiv preprint arXiv:1105.3422.

Acar, E., Gurdeniz, G. Z., Rasmussen, M. A., Rago, D., Dragsted, L. O. (2012). Coupled matrix factorization with sparse factors to identify potential biomarkers in metabolomics. IEEE 12th International Conference on Data Mining Workshops. IEEE.

Acar, E., Rasmussen, M. A., Savorani, F., Naes, T., & Bro, R. (2013). Understanding data fusion within the framework of coupled matrix and tensor factorizations. Chemometrics and Intelligent Laboratory Systems, 129, 53–63.

Article  CAS  Google Scholar 

Acar, E., Papalexakis, E. E., Gurdeniz, G. Z., Rasmussen, M. A., Lawaetz, A. J., et al. (2014). Structure-revealing data fusion. BMC Bioinformatics, 15, 1–17.

Article  Google Scholar 

AdaliTL, Levin-Schwartz, Y., & CalhounVD (2015). Multimodal data fusion using source separation: Two effective models based on ICA and IVA and their properties. Proceedings of the IEEE, 103, 1478–1493.

Article  PubMed  Google Scholar 

Alanazi, F. I., Kalia, S. K., Hodaie, M., Lopez Rios, A. L., Lozano, A. M., Milosevic, L., et al. (2023). Top-down control of human motor thalamic neuronal activity during the auditory oddball task. Npj Parkinson’s Disease, 9(1), 46.

Article  PubMed Central  PubMed  Google Scholar 

Babaie-Zadeh, M., & Jutten, C. (2005). A general approach for mutual information minimization and its application to blind source separation. Signal Processing, 85, 975–995.

Article  Google Scholar 

Babaie-Zadeh, M., Jutten, C., & Nayebi, K. (2004). Differential of the mutual information. IEEE Signal Processing Letters, 11, 48–51.

Article  Google Scholar 

Bader, B. W., & Kolda, T. G. (2012). MATLAB Tensor Toolbox Version 2.5, available online, January.

Chatzichristos, C., Kofidis, E., De Lathauwer, L., Theodoridid, S., & Van Huffel, S. (2020). Early soft and flexible fusion of EEG and fMRI via tensor decompositions. arXiv preprint arXiv:2005.07134.

Chatzichristos, C., Van Eyndhoven, S., Kofidis, E., & Van Huffel, S. (2023). Coupled tensor decompositions for data fusion. Tensors for data processing. Elsevier.

Google Scholar 

Correa, N. M., Li, Y. O., Adali, T., & Calhoun, V. D. (2008). Canonical correlation analysis for feature-based fusion of biomedical imaging modalities and its application to detection of associative networks in schizophrenia. IEEE Journal of Selected Topics in Signal Processing, 2, 998–1007.

Article  PubMed Central  PubMed  Google Scholar 

Correa, N. M., Adali, T., Li, Y. O., & Calhoun, V. D. (2010a). Canonical correlation analysis for data fusion and group inferences. IEEE Signal Processing Magazine, 27, 39–50.

Article  PubMed Central  PubMed  Google Scholar 

Correa, N. M., Eichele, T., Adali, T. L., Li, Y. O., & Calhoun, V. D. (2010b). Multi-set canonical correlation analysis for the fusion of concurrent single trial ERP and functional MRI. Neuroimage, 50, 1438–1445.

Article  PubMed  Google Scholar 

Dunlavy, D. M., Acar, E., & Kolda, T. G. (2010). Poblano v1. 0: A Matlab toolbox for gradient-based optimization. Sandia National Laboratories (SNL).

Book  Google Scholar 

Ferdowsi, S., Abolghasemi, V., & Sanei, S. (2015). A new informed tensor factorization approach to EEG-fMRI fusion. Journal of Neuroscience Methods, 254, 27–35.

Article  PubMed  Google Scholar 

Functional Imaging Laboratory (2020). https://www.fil.ion.ucl.ac.uk/spm/software/spm12/

Goldman, R. I., Wei, C. Y., Philiastides, M. G., Gerson, A. D., Friedman, D., et al. (2009). Single-trial discrimination for integrating simultaneous EEG and fMRI: Identifying cortical areas contributing to trial-to-trial variability in the auditory oddball task. Neuroimage, 47, 136–147.

Article  PubMed  Google Scholar 

Gottwald, B., Mihajlovic, Z., Wilde, B., & Mehdorn, H. M. (2003). Does the cerebellum contribute to specific aspects of attention? Neuropsychologia, 41, 1452–1460.

Article  PubMed  Google Scholar 

Jain, N., & Murthy, C. A. (2016). New estimate of mutual information based measure of dependence between two variables: Properties and fast implementation. International Journal of Machine Learning and Cybernetics, 7, 857–875.

Article  Google Scholar 

Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl NeuroImage, 62, 782–790.

Article  PubMed  Google Scholar 

Jiang, Z., Liu, Y., Li, W. D. A. I., & Zou, Y., L (2023). Integration of simultaneous fMRI and EEG source localization in emotional decision problems. Behavioral Brain Research, 448, 114445.

Article  Google Scholar 

Jimmy, S. (2023). Tools for NIfTI and ANALYZE image. https://www.mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-image

Jonmohamadi, Y., Muthukumaraswamy, S., Chen, J., Roberts, J., Crawford, R., et al. (2020). Extraction of common task features in EEG-fMRI data using coupled tensor-tensor decomposition. Brain Topography, 33, 636–650.

Article  PubMed  Google Scholar 

Justen, C., & Herbert, C. (2018). The spatio-temporal dynamics of deviance and target detection in the passive and active auditory oddball paradigm: A sLORETA study. BMC Neuroscience, 19, 1–18.

Article  Google Scholar 

Karahan, E., Rojas-Lopez, P. A., Bringas-Vega, M. L., Valdes-Hernazdez, P. A., & Valdes, P. A. (2015). Tensor analysis and fusion of multimodal brain images. Proceedings of the IEEE, 2015(103), 1531–1559.

Article  Google Scholar 

Kieh, K. A., Laurens, K. R., Duty, T. L., Forster, B. B., & Liddle, P. F. (2001). Neural sources involved in auditory target detection and novelty processing: An event-related fMRI study. Psychophysiology, 38, 133–142.

Google Scholar 

Kvalseth, T. O. (2017). On normalized mutual information: Measure derivations and properties. Entropy, 19, 631.

Article  Google Scholar 

Lahat, D., Adali, T. L., & Jutten, C. (2015). Multimodal data fusion: an overview of methods, challenges, and prospects. Proceedings of the IEEE, 103, 1449–1477.

Lei, X., Valdes-Sosa, P. A., & Yao, D. (2012). EEG/fMRI fusion based on independent component analysis: Integration of data-driven and model-driven methods. Journal of Integrative Neuroscience, 11, 313–337.

Article  PubMed  Google Scholar 

Lima, C. S. F., Krishnan, S., & Scott, S. K. (2016). Roles of supplementary motor areas in auditory processing and auditory imagery. Trends in Neurosciences, 39, 527–542.

Article  CAS  PubMed Central  PubMed  Google Scholar 

Liu, K., So, H. C., Da Costa, J. P. C., & Huang, L. (2013). Core consistency diagnostic aided by reconstruction error for accurate enumeration of the number of components in PARAFAC models. IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE.

Manganas, S., Bourbakis, N. A comparative survey on simultaneous EEG-fMRI methodologies. IEEE 17th International Conference on Bioinformatics and, & Bioengineering (2017). (BIBE). IEEE.

Martnez-Montes, E., Valdes-Sosa, P. A., Miwakeichi, F., Goldman, R. I., & Cohen, M. S. (2004). Concurrent EEG/fMRI analysis by multiway partial least squares. Neuroimage, 22, 1023–1034.

Article  Google Scholar 

McDaid, A. F., Greene, D., & Hurley, N. (2011). Normalized mutual information to evaluate overlapping community finding algorithms. arXiv preprint arXiv:1110.2515.

Michalopoulos, K., Zervakis, M., & Bourbakis, N. (2015). Current trends in ERP analysis using EEG and EEG/fMRI synergistic methods. Modern Electroencephalographic Assessment Techniques: Theory and Applications, 323–350.

Mihai, M. D., Donos, C., Barborica, A., Popa, I., Ciurea, J., et al. (2018). Functional mapping and effective connectivity of the human operculum. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 109, 303–321.

Article  Google Scholar 

Mohimani, H., Babaie-Zadeh, M., & Jutten, C. A. (2008). fast approach for overcomplete sparse decomposition based on smoothed \(\:_\)-norm. IEEE Transactions on Signal Processing, 57, 289–301.

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.

Article  Google Scholar 

Mugruza-Vassallo, C. A., Potter, D. D., Tsiora, S., Macfarlane, J. A., & Maxwell, A. (2021). Prior context influences motor brain areas in an auditory oddball task and prefrontal cortex multitasking modelling. Brain Informatics, 8, 1–28.

Article  Google Scholar 

PoldrackRA, & GorgolewskiKJ (2014). Making big data open: Data sharing in neuroimaging. Nature Neuroscience, 17, 1510–1517. www.openfmri.org

Article  CAS  PubMed  Google Scholar 

Poudel, G. R., & Jones, R. D. (2021). Multimodal Neuroimaging with simultaneous fMRI and EEG. Handbook of Neuroengineering. Springer.

Google Scholar 

Ritter, P., & Villringer, A. (2006). Simultaneous EEG-fMRI. Neuroscience & Biobehavioral Reviews, 30, 823–838.

Article  Google Scholar 

Rivet, B., Duda, M., Guerin-Dugue, A., Jutten, C., & Comon, P. (2015). Multimodal approach to estimate the ocular movements during EEG recordings: a coupled tensor factorization method, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Seichepine, N., Essid, S., Fevotte, C. D., & Cappe, O. (2014). Soft nonnegative matrix co-factorization. IEEE Transactions on Signal Processing, 62, 5940–5949.

Article  Google Scholar 

Song, L., Langfelder, P., & Horvath, S. (2012). Comparison of co-expression measures: Mutual information, correlation, and model based indices. BMC Bioinformatics, 13, 1–21.

Article  Google Scholar 

Stevens, M. C., Laurens, K. R., Liddle, P. F., & Kiehl, K. A. (2006). The hemodynamics of oddball processing during single-tone and two-tone target detection tasks. International Journal of Psychophysiology, 60, 292–303.

Article  PubMed  Google Scholar 

Swartz Center for Computational Neuroscience (2021). https://sccn.ucsd.edu/eeglab/downloadtoolbox.php/

Van Eyndhoven, S., Hunyadi, B. L., 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. 25th European Signal Processing Conference (EUSIPCO). IEEE.

Van Eyndhoven, S., Dupont, P., Tousseyn, S., Vervliet, N., Van Paesschen, W. (2021). Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data. NeuroImage.

Vertes, R. P., Hoover, W. B., & Diprisco, G. V. (2004). Theta rhythm of the hippocampus: Subcortical control and functional significance. Behavioral and Cognitive Neuroscience Reviews, 3, 173–200.

Article  PubMed  Google Scholar 

Walz, J. M., Goldman, R. I., Carapezza, M., Muraskin, J., Brown, T. R., et al. (2014). Simultaneous EEG-fMRI reveals a temporal cascade of task-related and default-mode activations during a simple target detection task. Neuroimage, 102, 229–239.

Article 

Comments (0)

No login
gif