Abstract
How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images or neural signals. We review a wide spectrum of graph representation and graph analysis methods and their application in the study of both the genomic level and the phenotypic level of the neurological disorder. We find numerous research works that create, process and analyze graphs formed from one or a few data types to gain an understanding of specific aspects of the neurological disorders. Furthermore, with the increasing number of data of various types becoming available for neurological disorders, we find that integrative analysis approaches that combine several types of data are being recognized as a way to gain a global understanding of the diseases. Although there are still not many integrative analyses of graphs due to the complexity in analysis, multi-layer graph analysis is a promising framework that can incorporate various data types. We describe and discuss the benefits of the multi-layer graph framework for studies of neurological disease.
Highlights
The study of neurological disorders involves a wide range of specialties and experiments resulting from various data types of various levels of detail
We look at functional brain graph analysis applied to Alzheimer’s disease, Parkinson’s Disease (PD), Multiple Sclerosis (MS), Autism Spectrum Disorder (ASD), epilepsy and Attention Deficit Hyperactivity Disorder (ADHD)
In the case of brain image networks, we reviewed both structural and functional brain networks constructed from brain signals and images from experiments, including Magnetic Resonance Imaging (MRI), EEG, MEG and functional Magnetic Resonance Imaging (fMRI), and show their importance in understanding neurological disorders
Summary
The study of neurological disorders involves a wide range of specialties and experiments resulting from various data types of various levels of detail. Among the various graph analysis methods, graph clustering and subgraph similarity search are two of the most widely-used methods They have been applied to study the biological data, brain images and neural signaling data in the studies of neurological disorders. After a review of the existing work on graph analysis in neurological disorders, we find the need for an integrative analysis that incorporates various data sources in one analysis framework. For this purpose, we suggest that a multi-layer graph is the most appropriate data structure and further review studies on multi-layer graphs
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