Open AccessA decisional space for fMRI pattern separation using the principal component analysis—a comparative study of language networks in pediatric epilepsy
Author(s) -
You Xiaozhen,
Adjouadi Malek,
Wang Jin,
Guillen Magno R.,
Bernal Byron,
Sullivan Joseph,
Donner Elizabeth,
Bjornson Bruce,
Berl Madison,
Gaillard William D.
Publication year - 2013
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22069
Subject(s) - principal component analysis , functional magnetic resonance imaging , pattern recognition (psychology) , artificial intelligence , region of interest , computer science , a priori and a posteriori , scatter plot , psychology , natural language processing , machine learning , neuroscience , philosophy , epistemology
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. Hum Brain Mapp 34:2330–2342, 2013 . © 2012 Wiley Periodicals, Inc.