Abstract
In the emerging field of network neuroscience, the brain is represented as a network of discrete yet functionally and structurally interconnected areas. Mathematical and computational tools to characterize the organization of this network can provide insights into the principles subserving brain structure and function, and can pinpoint differences between healthy individuals and individuals suffering from psychiatric disease or neurological disorders. The field is now faced with the question of how to devise clinical interventions that target these network alterations. Potential solutions to this question include the combination of emerging theories of network control with cutting-edge interventions such as neurofeedback. Each of these techniques may now be mature enough to combine to obtain a theoretically-motivated framework informing viable neuropsychiatric therapies.
Introduction
The human brain is fundamentally a network, or a system of interconnected functional units [1]. Such a network can be formulated as a graph, a mathematically well-defined object that is amenable to empirical study. A brain graph represents areas or regions as network nodes, and it represents connections between those areas as network edges (Fig. 1) [2]. The graph representation of the human brain is conceptually flexible: inter-regional connections can either be structural in nature – for example, estimating the number or volume of white matter tracts between areas – or they can be functional in nature – for example, estimating the degree of functional influence that one area has on another. In the context of both structure and function, network representations have proven particularly useful in deriving organizational principles at a systems level [3].
Read the full article here from Current Opinion in Biomedical Engineering
Volume 1, March 2017, Pages 63-70 A network neuroscience of neurofeedback for clinical translation
Authors Andrew C.Murphy, Danielle S.Bassettac