Background Autism has previously been characterized by both structural and functional
differences in brain connectivity. However, while the literature on single-subject derivations …

Network models of anatomical connections allow for the extraction of quantitative features
describing brain organization, and their comparison across brains from different species …

We consider a task of predicting normal and pathological phenotypes from macroscale
human brain networks. These networks (connectomes) represent aggregated neural …

In this paper, we tackle a problem of predicting phenotypes from structural connectomes. We
propose that normalized Laplacian spectra can capture structural properties of brain …

The use of machine learning for whole-brain classification of magnetic resonance imaging
(MRI) data is of clear interest, both for understanding phenotypic differences in brain …

This paper considers the problem of brain disease classification based on connectome data.
A connectome is a network representation of a human brain. The typical connectome …

Brain connectivity is gaining significant attention at present given its scope to unveil brain
mechanisms and functions. Sleep, where brain undergoes cycles of distinct behaviours, is …

Autism is one of the most important neurological disorders which leads to problems in a
person's social interactions. Improvement of brain imaging technologies and techniques …

In this work, we introduce a technique of deriving symmetric connectivity matrices from
regional histograms of grey-matter volume estimated from T1-weighted MRIs. We then …

In this work, graph spectra of the normalized graph Laplacians of brain networks
(connectomes) are used for solving the task of classifying autism spectrum disorder against …