@article{9c18a5b242484eab99b9cc98b74ac2cf,
title = "De-noising, phase ambiguity correction and visualization techniques for complex-valued ICA of group fMRI data",
abstract = "Analysis of functional magnetic resonance imaging (fMRI) data in its native, complex form has been shown to increase the sensitivity and specificity both for data-driven techniques, such as independent component analysis (ICA), and for model-driven techniques. Therefore, the possibility of increasing the usability of fMRI data in clinical and group studies provides a powerful motivation for utilizing both the phase and magnitude data. However, the unknown and noisy nature of the phase requires the introduction of new de-noising, preprocessing and visualization techniques. In addition, many complex-valued analysis algorithms, such as ICA, suffer from an inherent phase ambiguity, which introduces additional difficulty for group analysis. We present solutions for these issues, which have been among the main reasons phase information has been traditionally discarded, and show their effectiveness when used as part of a complex-valued group ICA algorithm application. The methods we present thus allow the development of new fully complex data-driven and semi-blind methods to process, analyze, and visualize fMRI data. We first introduce a physiologically motivated de-noising method that uses phase quality maps to successfully identify and eliminate noisy areas in the fMRI data so they can be used in individual and group studies. We also introduce a phase correction scheme that can be either applied subsequent to ICA of fMRI data or can be incorporated into the ICA algorithm in the form of prior information to eliminate the need for further processing for phase correction. Finally, we present a Mahalanobis distance-based thresholding method, which incorporates both magnitude and phase information into a single threshold, that can be used to increase the sensitivity in the identification of voxels of interest. This method shows particular promise for identifying voxels with significant susceptibility changes but that are located in low magnitude (i.e., activation) areas. We demonstrate the performance gain of the introduced methods on actual fMRI data.",
keywords = "De-noising, Group analysis, ICA, Phase ambiguity, Visualization, fMRI",
author = "Rodriguez, {Pedro A.} and Calhoun, {Vince D.} and T{\"u}lay Adal",
note = "Funding Information: We appreciate the insight that Dr. Arvind Caprihan, from The Mind Research Network, provided during the analysis of the visualization methods. Pedro A. Rodriguez received a Bachelor's degree in Electrical Engineering from the University of Puerto Rico, Mayaguez Campus, in 2004, Master's degrees in Applied Biomedical Engineering from Johns Hopkins University, Baltimore, in 2006, and is currently pursuing a Ph.D. degree in Electrical Engineering from the University of Maryland, Baltimore County, Baltimore. Pedro A. Rodriguez has been working full time as an image processing engineer at the Johns Hopkins Applied Physics Laboratory, Baltimore, since 2004. Much of his career has been spent on the development of image detection, tracking and classification algorithms, for various sensors and modalities. He also has experience in the development of Kalman filters, multiple hypothesis tracking and fusion algorithms for military tracking applications. Additionally, he has experience in biomedical imaging processing techniques. Pedro is a member of the Society of Hispanic Professional Engineers. He is a reviewer for many journals and conference papers. Vince D. Calhoun received a Bachelor's degree in Electrical Engineering from the University of Kansas, Lawrence, Kansas, in 1991, Master's degrees in Biomedical Engineering and Information Systems from Johns Hopkins University, Baltimore, in 1993 and 1996, respectively, and the Ph.D. degree in Electrical Engineering from the University of Maryland Baltimore County, Baltimore, in 2002. He worked as a senior research engineer at the psychiatric neuroimaging laboratory at Johns Hopkins from 1993 until 2002. He then served as the director of medical image analysis at the Olin Neuropsychiatry Research Center and as an associate professor at Yale University. Dr. Calhoun is currently Chief Technology Officer and Director of Image Analysis and MR Research at the Mind Research Network and is a Professor in the Departments of Electrical and Computer Engineering (primary), Neurosciences, Psychiatry and Computer Science at the University of New Mexico. He is the author of more than 150 full journal articles, over 250 technical reports, abstracts and conference proceedings. Much of his career has been spent on the development of data-driven approaches for the analysis of brain imaging data. He has won over $18 million in NSF and NIH grants on the incorporation of prior information into independent component analysis (ICA) for functional magnetic resonance imaging, data fusion of multimodal imaging and genetics data, and the identification of biomarkers for disease. Dr. Calhoun is a senior member of the IEEE, the Organization for Human Brain Mapping, the International Society for Magnetic Resonance in Medicine, and the American College of Neuropsychopharmacology. He is a chartered grant reviewer for NIH. He has organized workshops and special sessions at multiple conferences. He is currently serving on the IEEE Machine Learning for Signal Processing (MLSP) technical committee and previous served as the general chair of the 2005 meeting. He is a reviewer for many journals and is on the editorial board of the Human Brain Mapping and Neuroimage journals. T{\"u}lay Adal ı received the Ph.D. degree in electrical engineering from North Carolina State University, Raleigh, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, the same year. She is currently a Professor in the Department of Computer Science and Electrical Engineering at UMBC. Prof. Adalı assisted in the organization of a number of international conferences and workshops including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), the IEEE International Workshop on Neural Networks for Signal Processing (NNSP), and the IEEE International Workshop on Machine Learning for Signal Processing (MLSP). She was the General Co-Chair, NNSP (2001–2003); Technical Chair, MLSP (2004–2008); Program Co-Chair, MLSP (2008 and 2009), 2009 International Conference on Independent Component Analysis and Source Separation; Publicity Chair, ICASSP (2000 and 2005); and Publications Co-Chair, ICASSP 2008. Prof. Adalı chaired the IEEE SPS Machine Learning for Signal Processing Technical Committee (2003–2005); Member, SPS Conference Board (1998–2006); Member, Bio Imaging and Signal Processing Technical Committee (2004–2007); and Associate Editor, IEEE Transactions on Signal Processing (2003–2006), Elsevier Signal Processing Journal (2007–2010). She is currently Chair of Technical Committee 14: Signal Analysis for Machine Intelligence of the International Association for Pattern Recognition; Member, Machine Learning for Signal Processing and Signal Processing Theory and Methods technical committees; Associate Editor, IEEE Transactions on Biomedical Engineering and Journal of Signal Processing Systems for Signal, Image, and Video Technology, and Senior Editorial Board member, IEEE Journal of Selected Areas in Signal Processing. Prof. Adalı is a Fellow of the IEEE and the AIMBE and the past recipient of an NSF CAREER Award. Her research interests are in the areas of statistical signal processing, machine learning for signal processing, and biomedical data analysis. ",
year = "2012",
month = jun,
doi = "10.1016/j.patcog.2011.04.033",
language = "English (US)",
volume = "45",
pages = "2050--2063",
journal = "Pattern Recognition",
issn = "0031-3203",
publisher = "Elsevier Limited",
number = "6",
}