TY - JOUR
T1 - Canonical correlation analysis for data fusion and group inferences
AU - Correa, Nicolle
AU - Adali, Tulay
AU - Li, Yi Ou
AU - Calhoun, Vince D.
N1 - Funding Information:
Tülay Adalı (adali@umbc.edu) received the Ph.D. degree in electrical engineering from North Carolina State University, Raleigh, in 1992 and joined the faculty at UMBC as a professor that same year. She was the general cochair of the IEEE International Workshop on Neural Networks for Signal Processing (2001–2003); technical chair of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (2004–2008); publicity chair of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2000 and 2005); and publications cochair of ICASSP 2008. She chaired the IEEE Signal Processing Society (SPS) MLSP Technical Committee (2003– 2005) and serves on the IEEE SPS MLSP and the Signal Processing Theory and Methods Technical Committees. She was an associate editor for both IEEE Transactions on Signal Processing and Signal Processing and is currently an associate editor for IEEE Transactions on Biomedical Engineering, IEEE Journal of Selected Topics in Signal Processing, and Journal of Signal Processing Systems. She is a Fellow of the IEEE and the AIMBE and a recipient of an NSF CAREER Award. Her research interests are in the areas of statistical signal processing, MLSP, and biomedical data analysis. Yi-Ou Li (liyiou1@umbc.edu) received his B.S. degree in electrical engineering from Beijing University of Posts and Telecommunications. He graduated from UMBC with a Ph.D. degree in electrical engineering. He joined the Neural Connectivity Lab in the Department of Radiology and Biomedical Imaging at the University of California, San Francisco, in 2009. His research interests include statistical signal processing, multivariate analysis, and quantitative evaluation of data-driven methods and their applications to functional MRI data analysis.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2010/7
Y1 - 2010/7
N2 - Data-driven analysis methods, such as blind source separation (BSS) based on independent component analysis (ICA), have proven very useful in the study of brain function, in particular when the dynamics are hard to model and underlying assumptions about the data have to be minimized. Many problems in medical data analysis involve the analysis of multiple data sets, either of the same type as in a group study where inferences are based on the same modality, e.g., group inferences from functional magnetic resonance imaging (fMRI) data collected from multiple subjects, or from different modalities as in the case of data fusion where inferences have to be drawn from data collected from multiple modalities such as fMRI, electroencephalography (EEG), and structural MRI (sMRI), for the same group of subjects. Canonical correlation analysis (CCA) [1], another data-driven approach, and its extension to multiple data setsmultiset CCA (M-CCA) [2]provide a natural framework for both types of study. In this article, we show how CCA and M-CCA can be used for the analysis of data from a single modality for group inferences as well as fusion of data from multiple modalities using a feature-based approach, discuss the advantages of the CCA-based approach, and compare its performance to ICA that has been successfully applied to both types of study.
AB - Data-driven analysis methods, such as blind source separation (BSS) based on independent component analysis (ICA), have proven very useful in the study of brain function, in particular when the dynamics are hard to model and underlying assumptions about the data have to be minimized. Many problems in medical data analysis involve the analysis of multiple data sets, either of the same type as in a group study where inferences are based on the same modality, e.g., group inferences from functional magnetic resonance imaging (fMRI) data collected from multiple subjects, or from different modalities as in the case of data fusion where inferences have to be drawn from data collected from multiple modalities such as fMRI, electroencephalography (EEG), and structural MRI (sMRI), for the same group of subjects. Canonical correlation analysis (CCA) [1], another data-driven approach, and its extension to multiple data setsmultiset CCA (M-CCA) [2]provide a natural framework for both types of study. In this article, we show how CCA and M-CCA can be used for the analysis of data from a single modality for group inferences as well as fusion of data from multiple modalities using a feature-based approach, discuss the advantages of the CCA-based approach, and compare its performance to ICA that has been successfully applied to both types of study.
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U2 - 10.1109/MSP.2010.936725
DO - 10.1109/MSP.2010.936725
M3 - Article
AN - SCOPUS:85032751181
VL - 27
SP - 39
EP - 50
JO - IEEE Acoustics, Speech, and Signal Processing Newsletter
JF - IEEE Acoustics, Speech, and Signal Processing Newsletter
SN - 1053-5888
IS - 4
M1 - 5484191
ER -