Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis

• Published in: Human brain mapping Vol. 27; no. 5; pp. 392 - 401
• Main Authors: Goebel, Rainer, Esposito, Fabrizio, Formisano, Elia
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.05.2006
• Subjects: Algorithms; Brain Mapping - methods; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; Cohort Studies; cortex-based analysis; cortical alignment; fMRI data analysis; Frontal Lobe - anatomy & histology; Frontal Lobe - physiology; functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - trends; independent component analysis; Language; Language Tests; Linear Models; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - trends; Nerve Net - anatomy & histology; Nerve Net - physiology; Principal Component Analysis - methods; Principal Component Analysis - standards; Reproducibility of Results; Software - standards; Software - trends; Speech Perception - physiology; Temporal Lobe - anatomy & histology; Temporal Lobe - physiology; Verbal Behavior - physiology
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.20249

The Functional Image Analysis Contest (FIAC) 2005 dataset was analyzed using BrainVoyager QX. First, we performed a standard analysis of the functional and anatomical data that includes preprocessing, spatial normalization into Talairach space, hypothesis‐driven statistics (one‐ and two‐factorial, single‐subject and group‐level random effects, General Linear Model [GLM]) of the block‐ and event‐related paradigms. Strong sentence and weak speaker group‐level effects were detected in temporal and frontal regions. Following this standard analysis, we performed single‐subject and group‐level (Talairach‐based) Independent Component Analysis (ICA) that highlights the presence of functionally connected clusters in temporal and frontal regions for sentence processing, besides revealing other networks related to auditory stimulation or to the default state of the brain. Finally, we applied a high‐resolution cortical alignment method to improve the spatial correspondence across brains and re‐run the random effects group GLM as well as the group‐level ICA in this space. Using spatially and temporally unsmoothed data, this cortex‐based analysis revealed comparable results but with a set of spatially more confined group clusters and more differential group region of interest time courses. Hum. Brain Mapp, 2006. © 2006 Wiley‐Liss, Inc.