A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: application to schizophrenia.
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.
Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USA. Electronic address: ecastrow@unm.edu.
Eduardo
E
Castro
Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain.
Vanessa
V
Gómez-Verdejo
Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USA; Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Madrid, Spain.
Manel
M
Martínez-Ramón
The Mind Research Network, Albuquerque, NM, USA; Department of Psychology, The University of New Mexico, Albuquerque, NM, USA.
Kent A
KA
Kiehl
Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USA; The Mind Research Network, Albuquerque, NM, USA.
Vince D
VD
Calhoun
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001931:Brain Mapping / Q000379:methods; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D012559:Schizophrenia / Q000145:classification*; D055815:Young Adult
Complex-valued fMRI data; Feature selection; Independent component analysis; Multiple kernel learning; Schizophrenia; Support vector machines