Machine learning application to human brain network studies: a kernel approach
We consider a task of predicting normal and pathological phenotypes from macroscale human brain networks. These networks (connectomes) represent aggregated neural pathways between brain regions. We point to properties of connectomes that make them different from graphs arising in other application areas of network science. We discuss how machine learning can be organized on brain networks and focus on kernel classification methods. We describe different kernels on brain networks, including those that use information about similarity in spectral distributions of brain graphs and distances between optimal partitions of connectomes. We compare performance of the reviewed kernels in tasks of classifying autism spectrum disorder versus typical development and carriers versus non-carriers of an allele associated with an increased risk of Alzheimer’s disease.