Purpose of review: Graph theoretical analysis of neuroimaging data has emerged in the last few years as a powerful yet accessible tool to examine brain connectivity in a quantitative framework. In this review, we summarize recent advances in structural brain network research pertaining to neuropsychiatric disorders.
Recent findings: Although many neuropsychiatric disorder studies have used brain network approaches, the majority are of functional brain networks. However, seven recent studies, three on Alzheimer's disease, three on schizophrenia, and one on epilepsy, have used a structural brain network approach using either inter-regional cortical thickness, gray matter volume correlations, or diffusion tensor imaging tractography. The findings of these studies demonstrate that the structural brain network approach can be effectively used in the neuropsychiatric disorder studies to capture the abnormalities of regional and whole-brain network organizations.
Summary: By modeling the brain as a complex network, we can use graph theoretical analysis to study neuropsychiatric disorders by exploring its topological attributes. The interesting findings of the limited number of previous studies from the perspective of brain connectivity should attract more researchers to apply this method. This emerging quantitative framework may lead us to better understanding of neuropsychiatric disorders.