title: Boost up the detection sensitivity of ASL perfusion fMRI through support vector machine.
abstract: Data analysis is challenging in arterial spin labeling (ASL) perfusion fMRI due to the intrinsic low SNR of ASL data. To boost up the detection sensitivity, this paper presented a multivariate method based group analysis approach to analyze ASL perfusion fMRI data. A spatial discriminance map (SDM) was first extracted for each subject by support vector machine learning (SVM) algorithm; a population inference about the discriminance was then given by a random effect analysis (RFX) on these individual SDMs. Evaluations were performed using 7 subjects' fingertapping ASL perfusion fMRI data, yielding similar activation patterns with enhanced sensitivity compared to the standard GLM based group analysis.
authors.affiliation: Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Ze
authors.initials: Z
authors.lastname: Wang
authors.forename: Anna R
authors.initials: AR
authors.lastname: Childress
authors.forename: John A
authors.initials: JA
authors.lastname: Detre
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010477:Perfusion / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013113:Spin Labels
chemical_list: D013113:Spin Labels