Fiber connectivity integrated brain activation detection.

Inference of brain activation through the analysis of functional magnetic resonance imaging (fMRI) data is seriously confounded by the high level f 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.