Homotopic Group ICA for Multi-Subject Brain Imaging Data

• Activity Number: 493
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Juemin Yang*+ and Ani Eloyan and Anita Barber and Mary Beth Nebel and Stewart Mostofsky and James Pekar and Ciprian M. Crainiceanu and Brian Caffo
• Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Kennedy Krieger Institute and Kennedy Krieger Institute and Kennedy Krieger Institute and Kennedy Krieger Institute and The Johns Hopkins University and Johns Hopkins University
• Keywords: Brain Functional Homotopy ; Functional MRI ; Independent Component Analysis

Abstract

Independent Component Analysis (ICA) is a computational technique for revealing hidden factors that underlie sets of measurements or signals. This paper reports on the development of a new group ICA approach, Homotopic Group ICA ('H-gICA'), for use on resting state functional magnetic resonance imaging (fMRI) data. This approach allows attainment of improved network estimates via brain functional homotopy, based upon the high degree of synchrony in spontaneous activity between geometrically corresponding inter-hemispheric (i.e., homotopic) regions. H-gICA increases the potential for network discovery. Moreover, compared to commonly applied group ICA algorithms, the structure of the H-gICA input data leads to significant improvement in computational efficiency. A simulation study comfirms its effectiveness in homotopic, non-homotopic and mixed settings as well as on the ADHD-200 dataset. From the fifteen components postulated by H-gICA, several brain networks were found including: visual networks, the default mode network, the auditory network, and others. In addition to improving network estimation, H-gICA facilitates the investigation of functional homotopy via ICA based networks.