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Fiber Connectivity Integrated Brain Activation Detection
Burak Yoldemir1, Bernard Ng2, Todd S. Woodward3, and Rafeef Abugharbieh1
1Biomedical Signal and Image Computing Lab, The University of British Columbia, Canada
buraky@ece.ubc.ca
2Parietal Team, INRIA Saclay, France
3Department of Psychiatry, The University of British Columbia, Canada
Abstract. Inference of brain activation through the analysis of functional magnetic resonance imaging (fMRI) data is seriously confounded by the high level of noise in the observations. To mitigate the effects of noise, we propose incorporating anatomical connectivity into brain activation detection as motivated by how the functional integration of distinct brain areas is facilitated via neural fiber pathways. In this work, we formulate activation detection as a probabilistic graph-based segmentation problem with fiber networks estimated from diffusion MRI (dMRI) data serving as a prior. Our approach is reinforced with a data-driven scheme for refining the connectivity prior to reflect the fact that not all fibers are necessarily deployed during a given cognitive task as well as to account for false fiber tracts arising from limitations of dMRI tractography. Validating on real clinical data collected from 7 schizophrenia patients and 13 matched healthy controls, we show that incorporating anatomical connectivity significantly increases sensitivity in detecting task activation in controls compared to existing univariate techniques. Further, we illustrate how our model enables the detection of significant group activation differences between controls and patients that are missed with standard methods.
Keywords: activation detection, connectivity, dMRI, fMRI, random walker
LNCS 7917, p. 135 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2013