TY - JOUR
T1 - A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis
T2 - Application to schizophrenia
AU - Castro, Eduardo
AU - Gómez-Verdejo, Vanessa
AU - Martínez-Ramón, Manel
AU - Kiehl, Kent A.
AU - Calhoun, Vince D.
N1 - Funding Information:
We would like to thank the Mind Research Network for providing the data that was used by the approach proposed in this paper. This work has been supported by the following grants: NSF 0715022 , NIH 1R01EB006841 , and NIH 5P20RR021938 . Appendix A
PY - 2014/2/15
Y1 - 2014/2/15
N2 - FMRI data are acquired as complex-valued spatiotemporal images. Despite the fact that several studies have identified the presence of novel information in the phase images, they are usually discarded due to their noisy nature. Several approaches have been devised to incorporate magnitude and phase data, but none of them has performed between-group inference or classification. Multiple kernel learning (MKL) is a powerful field of machine learning that finds an automatic combination of kernel functions that can be applied to multiple data sources. By analyzing this combination of kernels, the most informative data sources can be found, hence providing a better understanding of the analyzed learning task. This paper presents a methodology based on a new MKL algorithm (ν-MKL) capable of achieving a tunable sparse selection of features' sets (brain regions' patterns) that improves the classification accuracy rate of healthy controls and schizophrenia patients by 5% when phase data is included. In addition, the proposed method achieves accuracy rates that are equivalent to those obtained by the state of the art lp-norm MKL algorithm on the schizophrenia dataset and we argue that it better identifies the brain regions that show discriminative activation between groups. This claim is supported by the more accurate detection achieved by ν-MKL of the degree of information present on regions of spatial maps extracted from a simulated fMRI dataset. In summary, we present an MKL-based methodology that improves schizophrenia characterization by using both magnitude and phase fMRI data and is also capable of detecting the brain regions that convey most of the discriminative information between patients and controls.
AB - FMRI data are acquired as complex-valued spatiotemporal images. Despite the fact that several studies have identified the presence of novel information in the phase images, they are usually discarded due to their noisy nature. Several approaches have been devised to incorporate magnitude and phase data, but none of them has performed between-group inference or classification. Multiple kernel learning (MKL) is a powerful field of machine learning that finds an automatic combination of kernel functions that can be applied to multiple data sources. By analyzing this combination of kernels, the most informative data sources can be found, hence providing a better understanding of the analyzed learning task. This paper presents a methodology based on a new MKL algorithm (ν-MKL) capable of achieving a tunable sparse selection of features' sets (brain regions' patterns) that improves the classification accuracy rate of healthy controls and schizophrenia patients by 5% when phase data is included. In addition, the proposed method achieves accuracy rates that are equivalent to those obtained by the state of the art lp-norm MKL algorithm on the schizophrenia dataset and we argue that it better identifies the brain regions that show discriminative activation between groups. This claim is supported by the more accurate detection achieved by ν-MKL of the degree of information present on regions of spatial maps extracted from a simulated fMRI dataset. In summary, we present an MKL-based methodology that improves schizophrenia characterization by using both magnitude and phase fMRI data and is also capable of detecting the brain regions that convey most of the discriminative information between patients and controls.
KW - Complex-valued fMRI data
KW - Feature selection
KW - Independent component analysis
KW - Multiple kernel learning
KW - Schizophrenia
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=84890888665&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890888665&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2013.10.065
DO - 10.1016/j.neuroimage.2013.10.065
M3 - Article
C2 - 24225489
AN - SCOPUS:84890888665
SN - 1053-8119
VL - 87
SP - 1
EP - 17
JO - NeuroImage
JF - NeuroImage
ER -