The impact of functional connectivity changes on support vector machines mapping of fMRI data.
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.
Sato
João Ricardo
JR
Department of Statistics, Institute of Mathematics and Statistics, University of São Paulo, Brazil. jrsatobr@gmail.com
Mourão-Miranda
Janaina
J
Morais Martin
Maria da Graça
Mda G
Amaro
Edson
E
Morettin
Pedro Alberto
PA
Brammer
Michael John
MJ
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
D010100:Oxygen