Abstract
Investigating brain connectivity networks for neurological disorder identification has attracted great interest in recent years, most of which focus on the graph representation alone. However, in addition to brain networks derived from the neuroimaging data, hundreds of clinical, immunologic, serologic, and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of subgraph selection from brain networks with side information guidance and propose a novel solution to find an optimal set of subgraph patterns for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph patterns by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view-guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.
Highlights
Modern neuroimaging techniques have enabled us to model the human brain as a brain connectivity network or a connectome
In contrast to existing subgraph mining approaches that focus on a single view of the graph representation, our method can explore multiple vector-based side views to find an optimal set of subgraph features for graph classification
Other methods that use different discrimination scores without leveraging the guidance from side views perform even worse than MSV in graph classification, because they evaluate the usefulness of subgraph patterns solely based gMSV gSSC Frequent Subgraphs (Freq) Conf Ratio Gtest HSIC MSV
Summary
Modern neuroimaging techniques have enabled us to model the human brain as a brain connectivity network or a connectome. Rather than vector-based feature representations as traditional data, brain networks are inherently in the form of graph representations which are composed of brain regions as the nodes, e.g., insula, hippocampus, thalamus, and functional/structural connectivities between the brain regions as the links. The linkage structure in these brain networks can encode tremendous information concerning the integrated activity of the human brain. In brain networks derived from functional magnetic resonance imaging (fMRI), connections/links can encode correlations between brain regions in functional activity, while structural links in diffusion tensor imaging (DTI) can capture white matter fiber pathways connecting different brain regions. The complex structures and the lack of vector representations within these graph data raise a
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
