TY - JOUR

T1 - Concepts and principles in the analysis of brain networks

AU - Wig, Gagan S.

AU - Schlaggar, Bradley L.

AU - Petersen, Steven E.

PY - 2011/4

Y1 - 2011/4

N2 - The brain is a large-scale network, operating at multiple levels of information processing ranging from neurons, to local circuits, to systems of brain areas. Recent advances in the mathematics of graph theory have provided tools with which to study networks. These tools can be employed to understand how the brain's behavioral repertoire is mediated by the interactions of objects of information processing. Within the graph-theoretic framework, networks are defined by independent objects (nodes) and the relationships shared between them (edges). Importantly, the accurate incorporation of graph theory into the study of brain networks mandates careful consideration of the assumptions, constraints, and principles of both the mathematics and the underlying neurobiology. This review focuses on understanding these principles and how they guide what constitutes a brain network and its elements, specifically focusing on resting-state correlations in humans. We argue that approaches that fail to take the principles of graph theory into consideration and do not reflect the underlying neurobiological properties of the brain will likely mischaracterize brain network structure and function.

AB - The brain is a large-scale network, operating at multiple levels of information processing ranging from neurons, to local circuits, to systems of brain areas. Recent advances in the mathematics of graph theory have provided tools with which to study networks. These tools can be employed to understand how the brain's behavioral repertoire is mediated by the interactions of objects of information processing. Within the graph-theoretic framework, networks are defined by independent objects (nodes) and the relationships shared between them (edges). Importantly, the accurate incorporation of graph theory into the study of brain networks mandates careful consideration of the assumptions, constraints, and principles of both the mathematics and the underlying neurobiology. This review focuses on understanding these principles and how they guide what constitutes a brain network and its elements, specifically focusing on resting-state correlations in humans. We argue that approaches that fail to take the principles of graph theory into consideration and do not reflect the underlying neurobiological properties of the brain will likely mischaracterize brain network structure and function.

KW - Brain networks

KW - Graph theory

KW - Resting state functional connectivity

UR - http://www.scopus.com/inward/record.url?scp=79954455075&partnerID=8YFLogxK

U2 - 10.1111/j.1749-6632.2010.05947.x

DO - 10.1111/j.1749-6632.2010.05947.x

M3 - Review article

C2 - 21486299

AN - SCOPUS:79954455075

SN - 0077-8923

VL - 1224

SP - 126

EP - 146

JO - Annals of the New York Academy of Sciences

JF - Annals of the New York Academy of Sciences

IS - 1

ER -