Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data
processing pipelines to accurately determine brain activity; among them, the
crucial step of spatial smoothing. These pipelines are commonly suboptimal,
given the local optimisation strategy they use, treating each step in
isolation. With the advent of new tools for deep learning, recent work has
proposed to turn these pipelines into end-to-end learning networks. This change
of paradigm offers new avenues to improvement as it allows for a global
optimisation. The current work aims at benefitting from this paradigm shift by
defining a smoothing step as a layer in these networks able to adaptively
modulate the degree of smoothing required by each brain volume to better
accomplish a given data analysis task. The viability is evaluated on real fMRI
data where subjects did alternate between left and right finger tapping tasks.Comment: 4 pages, 3 figures, 1 table, IEEE 2017 International Workshop on
Pattern Recognition in Neuroimaging (PRNI
