Human brain networks in health and disease

Abstract

Purpose of review: Recent developments in the statistical physics of complex networks have been translated to neuroimaging data in an effort to enhance our understanding of human brain structural and functional networks. This review focuses on studies using graph theoretical measures applied to structural MRI, diffusion MRI, functional MRI, electroencephalography, and magnetoencephalography data.

Recent findings: Complex network properties have been identified with some consistency in all modalities of neuroimaging data and over a range of spatial and time scales. Conserved properties include small worldness, high efficiency of information transfer for low wiring cost, modularity, and the existence of network hubs. Structural and functional network metrics have been found to be heritable and to change with normal aging. Clinical studies, principally in Alzheimer's disease and schizophrenia, have identified abnormalities of network configuration in patients. Future work will likely involve efforts to synthesize structural and functional networks in integrated models and to explore the interdependence of network configuration and cognitive performance.

Summary: Graph theoretical analysis of neuroimaging data is growing rapidly and could potentially provide a relatively simple but powerful quantitative framework to describe and compare whole human brain structural and functional networks under diverse experimental and clinical conditions.

Figure 1

Tutorial of basic network concepts. Top Plaque Definitions for a node, an edge, a triangle, and a connected triple. Second Plaque The clustering coefficient, C, is given by the ratio of the number of connected triangles to the number of connected triples. Third Plaque The path length, L, is given by the fewest number of edges linking one node, i, to another node, j. Bottom Plaque A modular network structure occurs when there are more connections within a module than between modules. In this schematic, modules are given by distinct colors, e.g., blue, green, and yellow.

Figure 2

Workflow of human brain network construction. To construct a brain network, one can begin with either structural (including either gray or white matter measurements) or functional data (including low frequency fMRI data and high frequency EEG or MEG data). Raw data is conventionally put into a parcellation scheme whereby the brain is subdivided into on the order of 100 regions of interest. For EEG and MEG data, this parcellation is already performed by the sensors. The pairwise association between brain regions is then computed, and usually thresholded to create a binary matrix. A brain network is then constructed from nodes (brain regions) and edges (pairwise associations which were larger than the chosen threshold).

Figure 3

Network hubs have increased amyloid-β deposition in Alzheimer’s disease. Left Location of cortical hubs, i.e., nodes with a high number of connections or degree, in healthy resting state fMRI networks. Right Location of greatest amyloid-β deposition in people with Alzheimer’s disease as measured in a PET study. Reproduced with permission from [28].