Underlying Features of Inter-Individual Variability in Brain Function Using a Siamese Neural Network
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2021-08-31
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en
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Abstract
Large-scale networks are comprised of distributed brain areas that show correlated
fluctuations in their spontaneous resting state functional magnetic resonance imaging
activity, and these patterns of functional connectivity show high individual variability.
However, it is still unknown what the most important dimensions are along which
individual functional connectivity patterns can vary. In this study, we investigated
the discriminating inter-individual features of brain function. We used a deep neural
network to learn to discriminate subjects’ connectivity patterns from each other (n =
996). We found that we can reduce the high-dimensional connectivity profiles to only
three components and still identify subjects’ functional connectivity patterns with 96%
accuracy. The three identified components were mapped back onto the large-scale brain
networks, and were classified as 1) sensorimotor networks component, 2) within-networks
component, 3) higher-order networks component. Furthermore, we found correlations
between the higher-order networks component and cognitive measures as well as with
relevant brain regions’ area size. This implies that we identified three robust components
of functional connectivity that can be linked to cognition and amount of grey matter.
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Faculteit der Sociale Wetenschappen