DEGENERATION AND REPAIRFrom brain topography to brain topology relevance of graph theory to functional neuroscienceMinati, Ludovicoa,b; Varotto, Giuliac; D’Incerti, Ludovicod; Panzica, Ferruccioc; Chan, Dennisa Author Information aDepartment of Neurology, Brighton & Sussex Medical School (BSMS), Clinical Imaging Science Centre (CISC), University of Sussex, Falmer, UK bScientific Department cDivision of Epileptology and Clinical Neurophysiology dNeuroradiology Unit, Fondazione IRCCS Istituto Neurologico ‘Carlo Besta’, Milano, Italy Correspondence to Ludovico Minati, PhD, CEng, CPhys, CSci, Department of Neurology, Brighton & Sussex Medical School (BSMS), Clinical Imaging Science Centre (CISC), University of Sussex, Southern Ring Road, BN1 9RR, Falmer, UK Tel: +44 784 960 3287; fax: +44 127 387 6721; e-mails: [email protected], [email protected] Received March 11, 2013 Accepted April 5, 2013 NeuroReport: July 10, 2013 - Volume 24 - Issue 10 - p 536-543 doi: 10.1097/WNR.0b013e3283621234 Buy Metrics Abstract Although several brain regions show significant specialization, higher functions such as cross-modal information integration, abstract reasoning and conscious awareness are viewed as emerging from interactions across distributed functional networks. Analytical approaches capable of capturing the properties of such networks can therefore enhance our ability to make inferences from functional MRI, electroencephalography and magnetoencephalography data. Graph theory is a branch of mathematics that focuses on the formal modelling of networks and offers a wide range of theoretical tools to quantify specific features of network architecture (topology) that can provide information complementing the anatomical localization of areas responding to given stimuli or tasks (topography). Explicit modelling of the architecture of axonal connections and interactions among areas can furthermore reveal peculiar topological properties that are conserved across diverse biological networks, and highly sensitive to disease states. The field is evolving rapidly, partly fuelled by computational developments that enable the study of connectivity at fine anatomical detail and the simultaneous interactions among multiple regions. Recent publications in this area have shown that graph-based modelling can enhance our ability to draw causal inferences from functional MRI experiments, and support the early detection of disconnection and the modelling of pathology spread in neurodegenerative disease, particularly Alzheimer’s disease. Furthermore, neurophysiological studies have shown that network topology has a profound link to epileptogenesis and that connectivity indices derived from graph models aid in modelling the onset and spread of seizures. Graph-based analyses may therefore significantly help understand the bases of a range of neurological conditions. This review is designed to provide an overview of graph-based analyses of brain connectivity and their relevance to disease aimed principally at general neuroscientists and clinicians. © 2013 Lippincott Williams & Wilkins, Inc.