More recently, network analysis of structural brain connectivity has shown a selective disturbance of pathways cross-linking regions forming the brain’s rich club,71 a collective of highly connected and densely linked nodes.69 Given its central role in brain communication, an impairment of rich club connections is likely to manifest in functional disturbances of integrative neural processing. The complexity of the check details genetic basis Inhibitors,research,lifescience,medical for most common brain and mental diseases in conjunction with their pronounced phenotypic heterogeneity greatly complicates any systematic attempts at mapping genetic risk factors

to clinical disorders, and even hinders their objective characterization on the basis of biologically Inhibitors,research,lifescience,medical based criteria. It has been suggested that the study of intermediate phenotypes, occupying positions that are intermediate between genetics and clinical phenotypes, may represent a promising way forward (Figure 7).156,157 Intermediate phenotypes may allow for an objective classification of heterogeneous phenotypes into more coherent subgroups, and thus allow a better understanding of which genetic or other biological factors participate in each subgroup’s disease mechanisms. The connectome and its endogenous and task-driven dynamics is an attractive candidate for an intermediate phenotype Inhibitors,research,lifescience,medical as it represents a point of convergence for a multitude

of genetic and environmental factors, while also offering a plethora of potential “biomarkers” or probes that have proven to be of value in characterizing disease states of the brain. As brain network approaches continue to mature, it is to be expected that much work will focus on developing network measures that can characterize healthy and abnormal variations in

brain structure Inhibitors,research,lifescience,medical and function. Inhibitors,research,lifescience,medical Such measures may help to identify factors that are associated with genetic and environmental disease mechanisms, and they may also serve as potential biomarkers for more objective diagnosis and prediction of effective treatment options. There is great potential for learning about disease states by mapping variations in network architecture in large ADAMTS5 cohorts of healthy participants, a chief goal of the Human Connectome Project. Understanding the “normal” range of variability will provide insight into how disease phenotypes differ. It has been suggested that brain and mental disorders (indeed many common human diseases) represent quantitative rather than qualitative deviations from health.158,159 Rather than being caused by the presence or absence of single genetic factors, it appears that many common diseases, including those affecting brain and mind, manifest through the accumulation of small effects contributed by numerous genetic variants160,161 and thus represent quantitative traits that form the extremes of otherwise continuous phenotypic distributions. How various measures of brain networks relate to such phenotypic traits is still largely unknown.