title: A decisional space for fMRI pattern separation using the principal component analysis--a comparative study of language networks in pediatric epilepsy.
abstract: Atypical functional magnetic resonance imaging (fMRI) language patterns may be identified by visual inspection or by region of interest (ROI)-based laterality indices (LI) but are constrained by a priori assumptions. We compared a data-driven novel application of principal component analysis (PCA) to conventional methods. We studied 122 fMRI data sets from control and localization-related epilepsy patients provided by five children's hospitals. Each subject performed an auditory description decision task. The data sets, acquired with different scanners but similar acquisition parameters, were processed through fMRIB software library to obtain 3D activation maps in standard space. A PCA analysis was applied to generate the decisional space and the data cluster into three distinct activation patterns. The classified activation maps were interpreted by (1) blinded reader rating based on predefined language patterns and (2) by language area ROI-based LI (i.e., fixed threshold vs. bootstrap approaches). The different classification results were compared through κ inter-rater agreement statistics. The unique decisional space classified activation maps into three clusters (a) lower intensity typical language representation, (b) higher intensity typical, as well as (c) higher intensity atypical representation. Inter-rater agreements among the three raters were excellent (Fleiss κ = 0.85, P = 0.05). There was substantial to excellent agreement between the conventional visual rating and LI methods (κ = 0.69-0.82, P = 0.05). The PCA-based method yielded excellent agreement with conventional methods (κ = 0.82, P = 0.05). The automated and data-driven PCA decisional space segregates language-related activation patterns in excellent agreement with current clinical rating and ROI-based methods.
authors.affiliation: Department of Biomedical Engineering, Florida International University, 10555 W. Flagler St., Miami, FL 33174, USA.
authors.forename: Xiaozhen
authors.initials: X
authors.lastname: You
authors.forename: Malek
authors.initials: M
authors.lastname: Adjouadi
authors.forename: Jin
authors.initials: J
authors.lastname: Wang
authors.forename: Magno R
authors.initials: MR
authors.lastname: Guillen
authors.forename: Byron
authors.initials: B
authors.lastname: Bernal
authors.forename: Joseph
authors.initials: J
authors.lastname: Sullivan
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Donner
authors.forename: Bruce
authors.initials: B
authors.lastname: Bjornson
authors.forename: Madison
authors.initials: M
authors.lastname: Berl
authors.forename: William D
authors.initials: WD
authors.lastname: Gaillard
mesh_terms: D000293:Adolescent; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods*; D002648:Child; D002675:Child, Preschool; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D007839:Functional Laterality / Q000502:physiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007802:Language; D008279:Magnetic Resonance Imaging; D008297:Male; D025341:Principal Component Analysis; D055815:Young Adult