Anatomical Brain Networks on the Prediction of Abnormal Brain States

Abstract

Graph-based brain anatomical network analysis models the brain as a graph whose nodes represent structural/functional regions, whereas the links between them represent nervous fiber connections. Initial studies of brain anatomical networks using this approach were devoted to describe the key organizational principles of the normal brain, while current trends seem to be more focused on detecting network alterations associated to specific brain disorders. Anatomical networks reconstructed using diffusion-weighed magnetic resonance-imaging techniques can be particularly useful in predicting abnormal brain states in which the white matter structure and, subsequently, the interconnections between gray matter regions are altered (e.g., due to the presence of diseases such as schizophrenia, stroke, multiple sclerosis, and dementia). This article offers an overview from early gross connectional anatomy explorations until more recent advances on anatomical brain network reconstruction approaches, with a specific focus on how the latter move toward the prediction of abnormal brain states. While anatomical graph-based predictor approaches are still at an early stage, they bear promising implications for individualized clinical diagnosis of neurological and psychiatric disorders, as well as for neurodevelopmental evaluations and subsequent assisted creation of educational strategies related to specific cognitive disorders.