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Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity
Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs.
Connectivity-Driven Parcellation Methods for the Human Cerebral Cortex
In this thesis, we present robust and fully-automated methods for the subdivision of the entire human cerebral cortex based on connectivity information.
Exploring Heritability of Functional Brain Networks with Inexact Graph Matching
Data-driven brain parcellations aim to provide a more accurate representation of an individual's functional connectivity, since they are able to capture individual variability that arises due to development or disease.
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