Functional Magnetic Resonance Imaging (fMRI) is a key neuroimaging technique in large cohort studies, allowing the analysis of healthy and pathological networks of spontaneous brain function. However, resting stage fMRI analysis is often limited by the requirement for image registration and the resulting spatial smoothing used to ensure spatial consistency between subjects. We propose an analysis strategy to overcome these limitations using a novel non-linear Sparse Autoencoder to produce functional network decompositions in each subject without the need for spatial smoothing nor registration. This technique applied at both the group and individual level retains the capability to obtain unique single subject functional network representations. We use this technique to reveal consistent individual-level network differences between a group of healthy controls and individuals diagnosed with young-onset dementia; most strikingly in areas representing a working-memory network.
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