title: Multivariate examination of brain abnormality using both structural and functional MRI.
abstract: A multivariate classification approach has been presented to examine the brain abnormalities, i.e., due to prenatal cocaine exposure, using both structural and functional brain images. First, a regional statistical feature extraction scheme was adopted to capture discriminative features from voxel-wise morphometric and functional representations of brain images, in order to reduce the dimensionality of the features used for classification, as well as to achieve the robustness to registration error and inter-subject variations. Then, this feature extraction method was used in conjunction with a hybrid feature selection method and a nonlinear support vector machine for the classification of brain abnormalities. This brain classification approach has been applied to detecting the brain abnormality associated with prenatal cocaine exposure in adolescents. A promising classification performance was achieved on a data set of 49 subjects (24 normal and 25 prenatally cocaine-exposed teenagers), with a leave-one-out cross-validation. Experimental results demonstrated the efficacy of our method, as well as the importance of incorporating both structural and functional images for brain classification. Moreover, spatial patterns of group difference derived from the constructed classifier were mostly consistent with the results of the conventional statistical analysis method. Therefore, the proposed approach provided not only a multivariate classification method for detecting brain abnormalities, but also an alternative way for group analysis of multimodality images.
authors.affiliation: Department of Radiology, University of Pennsylvania, PA 19104, USA. yong.fan@uphs.upenn.edu
authors.forename: Yong
authors.initials: Y
authors.lastname: Fan
authors.forename: Hengyi
authors.initials: H
authors.lastname: Rao
authors.forename: Hallam
authors.initials: H
authors.lastname: Hurt
authors.forename: Joan
authors.initials: J
authors.lastname: Giannetta
authors.forename: Marc
authors.initials: M
authors.lastname: Korczykowski
authors.forename: David
authors.initials: D
authors.lastname: Shera
authors.forename: Brian B
authors.initials: BB
authors.lastname: Avants
authors.forename: James C
authors.initials: JC
authors.lastname: Gee
authors.forename: Jiongjiong
authors.initials: J
authors.lastname: Wang
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000187:drug effects* / Q000473:pathology / Q000503:physiopathology; D003042:Cocaine / Q000633:toxicity*; D005260:Female; D006801:Humans; D013287:Illicit Drugs / Q000633:toxicity*; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015999:Multivariate Analysis; D017711:Nonlinear Dynamics; D011247:Pregnancy; D011297:Prenatal Exposure Delayed Effects; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D013287:Illicit Drugs; D003042:Cocaine