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A review of heterogeneous data mining for brain disorder identification

Bokai Cao, Xiangnan Kong, Philip S. Yu
2015 Brain Informatics  
They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, and multi-view feature analysis.  ...  Brain disorder data poses many unique challenges for data mining research.  ...  In this paper, we review some recent data mining methods for (1) mining tensor imaging data; (2) mining brain networks; and (3) mining multi-view feature vectors.  ... 
doi:10.1007/s40708-015-0021-3 pmid:27747561 pmcid:PMC4883173 fatcat:rhvqh4vmeffnnoxts7esxwxlsq

A review of heterogeneous data mining for brain disorders [article]

Bokai Cao, Xiangnan Kong, Philip S. Yu
2015 arXiv   pre-print
They have achieved great success in various applications, such as tensor-based modeling, subgraph pattern mining, multi-view feature analysis.  ...  Brain disorder data poses many unique challenges for data mining research.  ...  In this paper, we review some recent data mining methods for (1) mining tensor imaging data; (2) mining brain networks; (3) mining multiview feature vectors.  ... 
arXiv:1508.01023v1 fatcat:e6nscurzmbc23f26p2q42ccbrm

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Li Shen, Paul M. Thompson
2019 Proceedings of the IEEE  
Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed  ...  and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.  ...  [14] reviewed regression and correlation methods for brain imaging genomics as well as set-based methods for mining high-level imaging genomics associations. Mufford et al.  ... 
doi:10.1109/jproc.2019.2947272 pmid:31902950 pmcid:PMC6941751 fatcat:rx5b44yv55d2xicdiznnwjdac4

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling

Rogers F. Silva, Sergey M. Plis, Jing Sui, Marios S. Pattichis, Tulay Adali, Vince D. Calhoun
2016 IEEE Journal on Selected Topics in Signal Processing  
In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities.  ...  Consequently, a high-level descriptive structure is exposed, ultimately helping practitioners select the right model Personal use is permitted, but republication/redistribution requires IEEE permission  ...  This view aligns with prior work coined as joint BSS [37] , and even more so with multimodal brain data analysis.  ... 
doi:10.1109/jstsp.2016.2594945 pmid:28461840 pmcid:PMC5409135 fatcat:yd3brf4lsfd5tmgzgkxqpad4t4

Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions

Andrzej Cichocki, Namgil Lee, Ivan Oseledets, Anh-Huy Phan, Qibin Zhao, Danilo P. Mandic
2016 Foundations and Trends® in Machine Learning  
(EVD), PCA/SVD, huge systems of linear equations, pseudo-inverse of very large matrices, Lasso and Canonical Correlation Analysis (CCA) (This is Part 1)  ...  It is therefore timely and valuable for the multidisciplinary research community to review tensor decompositions and tensor networks as emerging tools for large-scale data analysis and data mining.  ...  Tensor networks can be thought of as special graph structures which break down high-order tensors into a set of sparsely interconnected low-order core tensors, thus allowing for both enhanced interpretation  ... 
doi:10.1561/2200000059 fatcat:ememscddezeovamsoqrcpp33z4

Multimodal Fusion With Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia

Shile Qi, Vince D. Calhoun, Theo G. M. van Erp, Juan Bustillo, Eswar Damaraju, Jessica A. Turner, Yuhui Du, Jian Yang, Jiayu Chen, Qingbao Yu, Daniel H. Mathalon, Judith M. Ford (+8 others)
2018 IEEE Transactions on Medical Imaging  
Here we proposed a fusion with reference model, called "multi-site canonical correlation analysis with reference plus joint independent component analysis" (MCCAR+jICA), which can precisely identify co-varying  ...  Multimodal fusion is an effective approach to take advantage of cross-information among multiple imaging data to better understand brain diseases.  ...  Acknowledgments This work was supported in part by NIH via a COBRE grant P20GM103472 and grants R01EB005846 and 1R01EB006841; the "100 Talents Plan" of the Chinese Academy of Sciences, the Chinese National  ... 
doi:10.1109/tmi.2017.2725306 pmid:28708547 pmcid:PMC5750081 fatcat:37lnuob67ncbfmjuf336oedszy

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 5324-5335 High-Order Feature Learning for Multi-Atlas Based Label Fusion: Application to Brain Segmentation With MRI.  ...  ., +, TIP 2020 9032-9043 High-Order Feature Learning for Multi-Atlas Based Label Fusion: Application to Brain Segmentation With MRI.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Evolving Signal Processing for Brain–Computer Interfaces

S. Makeig, C. Kothe, T. Mullen, N. Bigdely-Shamlo, Zhilin Zhang, Kenneth Kreutz-Delgado
2012 Proceedings of the IEEE  
Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral  ...  | Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes  ...  and tensor factorization [95] , multiview clustering and canonical correlation analysis [217] - [219] , metaclassification approaches [220] , [221] , and hierarchical Bayesian models and deep learning  ... 
doi:10.1109/jproc.2012.2185009 fatcat:ebed3ribeneptnatoxaoyzn4xm

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

Xing Meng, Rongtao Jiang, Dongdong Lin, Juan Bustillo, Thomas Jones, Jiayu Chen, Qingbao Yu, Yuhui Du, Yu Zhang, Tianzi Jiang, Jing Sui, Vince D. Calhoun
2017 NeuroImage  
There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making.  ...  The ultimate goal of using brain imaging biomarkers is to perform individualized predictions.  ...  Acknowledgments This work was partially supported by the National High-Tech Development Plan (863) 2015AA020513, the "100 Talents Plan" of the Chinese Academy of Sciences (to J.  ... 
doi:10.1016/j.neuroimage.2016.05.026 pmid:27177764 pmcid:PMC5104674 fatcat:p74xh4imafah7mkncqscvatmma

2014 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 25

2014 IEEE Transactions on Neural Networks and Learning Systems  
Yuan, M., +, TNNLS Nov. 2014 2119-2126 Image coding Efficient Kernel Sparse Coding Via First-Order Smooth Optimization.  ...  Lee, J.H., +, TNNLS Dec. 2014 2250-2263 Visual databases Multilinear Sparse Principal Component Analysis. Lai, Z., +, TNNLS Oct. 2014 1942-1950 Sparse Alignment for Robust Tensor Learning.  ... 
doi:10.1109/tnnls.2015.2396731 fatcat:ztnfcozrejhhfdwg7t2f5xlype

End-to-end Training of Deep Probabilistic CCA on Paired Biomedical Observations

Gregory W. Gundersen, Bianca Dumitrascu, Jordan T. Ash, Barbara E. Engelhardt
2019 Conference on Uncertainty in Artificial Intelligence  
Our method is built around probabilistic canonical correlation analysis (PCCA), which is fit to image embeddings that are learned using convolutional neural networks and linear embeddings of paired gene  ...  To understand this relationship, we develop a multimodal modeling and inference framework that estimates shared latent structure of joint gene expression levels and medical image features.  ...  CANONICAL CORRELATION ANALYSIS Consider two paired data views, Y a ∈ R n×p a and Y b ∈ R n×p b . We assume the data are mean-centered.  ... 
dblp:conf/uai/GundersenDAE19 fatcat:zqbyocf73fep3diipyasy46eja

Visualization in Connectomics [chapter]

Hanspeter Pfister, Verena Kaynig, Charl P. Botha, Stefan Bruckner, Vincent J. Dercksen, Hans-Christian Hege, Jos B. T. M. Roerdink
2014 Mathematics and Visualization  
We also discuss data integration and neural network modeling, as well as the visualization, analysis and comparison of brain networks.  ...  Pfister et al. information from image data at macro-, meso-and microscales.  ...  As a consequence, the analysis of the connectivity between single neurons has been limited to sparse analysis of statistical properties such as average synapse densities in different brain regions [20  ... 
doi:10.1007/978-1-4471-6497-5_21 fatcat:hlsaktgoofabnaa25zpykhlgpa

Mining EEG–fMRI using independent component analysis

T EICHELE
2009 International Journal of Psychophysiology  
Independent component analysis (ICA) is a multivariate approach that has become increasingly popular for analyzing brain imaging data.  ...  An optimized method for symmetric EEG-fMRI decomposition is proposed and the outstanding challenges in multimodal integration are discussed.  ...  Other options include canonical correlation analysis (CCA) in order to treat the problem. Here, however, 2nd level inference is less well defined.  ... 
doi:10.1016/j.ijpsycho.2008.12.018 pmid:19223007 pmcid:PMC2693483 fatcat:y6a3uh7abbawpdcgmv7yzdjwka

Visualization in Connectomics [article]

Hanspeter Pfister, Verena Kaynig, Charl P. Botha, Stefan Bruckner, Vincent J. Dercksen, Hans-Christian Hege, Jos B. T. M. Roerdink
2012 arXiv   pre-print
Such a representation is believed to increase our understanding of how functional brain states emerge from their underlying anatomical structure.  ...  In this paper, we review the current state-of-the-art of visualization and image processing techniques in the field of connectomics and describe some remaining challenges.  ...  As a consequence the analysis of the connectivity between single neurons has been limited to sparse analysis of statistical properties such as average synapse densities in different brain regions [21]  ... 
arXiv:1206.1428v2 fatcat:xjayhfspffe7hloxan4drfkggy

2021 Index IEEE Signal Processing Letters Vol. 28

2021 IEEE Signal Processing Letters  
., +, LSP 2021 2073-2077 Feature Fusion for Multimodal Emotion Recognition Based on Deep Canonical Correlation Analysis.  ...  ., +, LSP 2021 2038-2042 Feature Fusion for Multimodal Emotion Recognition Based on Deep Canon-ical Correlation Analysis.  ...  + Check author entry for coauthors H Handwriting recognition Handwritten Text Generation via Disentangled Representations.  ... 
doi:10.1109/lsp.2022.3145253 fatcat:a3xqvok75vgepcckwnhh2mty74
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