title: The impact of functional connectivity changes on support vector machines mapping of fMRI data.
abstract: Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called "mass-univariate" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM's power to detect discriminative voxels.
authors.affiliation: Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, Brazil. jrsatobr@gmail.com
authors.forename: João Ricardo
authors.initials: JR
authors.lastname: Sato
authors.forename: Janaina
authors.initials: J
authors.lastname: Mourão-Miranda
authors.forename: Maria da Graça
authors.initials: Mda G
authors.lastname: Morais Martin
authors.forename: Edson
authors.initials: E
authors.lastname: Amaro
authors.forename: Pedro Alberto
authors.initials: PA
authors.lastname: Morettin
authors.forename: Michael John
authors.initials: MJ
authors.lastname: Brammer
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D001921:Brain / Q000098:blood supply* / Q000502:physiology; D001931:Brain Mapping; D003198:Computer Simulation; D005260:Female; D007839:Functional Laterality; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008568:Memory / Q000502:physiology; D009415:Nerve Net / Q000098:blood supply / Q000502:physiology*; D010100:Oxygen / Q000097:blood; D011597:Psychomotor Performance / Q000502:physiology; D012684:Sensory Thresholds / Q000502:physiology
chemical_list: D010100:Oxygen