Moving forward, it is useful to consider the role that aberrant connectivity between networks may play in mediating genetic liability to psychopathology. Fifth, with a few exceptions, we don’t explicitly discuss the directionality of connectivity differences in patients or risk variant carriers. There is directional heterogeneity in the literature, even between two studies using the same task in the same disorder. However, compelling directional inferences are difficult to make from functional connectivity studies, and

are model dependent in effective connectivity studies. Moreover, given the artificiality of DSM-based classification, directional comparisons between patient studies that use the same categorical diagnosis may be confounded by biological heterogeneity. One approach that addresses this issue is symptom-specific association Caspase inhibitor review (Chabernaud et al., 2011 and Shannon et al., 2011); we hope that more patient studies using biological measures will begin to adopt this approach. Finally, development of the

ideas outlined here will need to take lifespan issues and plasticity into account. There is clear evidence that connectivity patterns and plasticity vary across the life cycle, that both experience-dependent plasticity and environmental contributions may have widely different effects depending http://www.selleckchem.com/products/CP-673451.html on the time of exposure, and that critical periods, such as puberty, exist whose specific in terms of connectivity need to be elucidated fully. Synthesizing available genetic, neuroimaging and clinical data, we propose

a dimensional “common symptom, common circuit” model of psychopathology. We hope that our model will be a useful heuristic that will aid the field as it moves toward a neuroscience-based empirical classification of mental illness. A key tenet of this model is that risk factors for mental illness produce alterations in brain circuit function that induce susceptibility Vasopressin Receptor to psychopathology in a manner that is cognitive and symptom domain-specific, but disorder-general. We argue that the linkage between common symptom variance and common genetic variance is a function of the effect of that shared genetic liability on brain networks underlying symptom-relevant cognitive domains. This model would predict that variance in the function of specific connectivity circuits would be represented as distinct higher order factors that link genetic variance and circuit-appropriate symptom variance, and could be tested by confirmatory factor analyses in large, epidemiologically valid twin designs that incorporate dimensional symptom ratings and connectivity measures. We believe that the integration of brain connectivity into genetically informative and phenotypically rigorous experimental designs represents a crucial step forward toward an empirically grounded quantitative nosology of mental illness.