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Suicide attempts (SA) are increasing in the United States, especially in veterans. Discovering individual cognitive features of the subset of suicide ideators who attempt suicide is critical. Cognitive theories attribute SA to facile schema-based negative interpretations of environmental events. Over-general autobiographical memory and facile solutions in problem solving tasks in SA survivors suggest that aversion to expending cognitive effort may be a neurobehavioral marker of SA risk. In veterans receiving care for mood disorder, we compared cognitive effort discounting and evidence-gathering in a beads task between veterans with (SAHx+; n = 26) versus without (SAHx-; n = 22) a history of SA. Groups did not differ in depressed mood or in a proxy metric of premorbid intelligence. Compared to SAHx- participants, SAHx+ participants self-reported significantly more severe cognitive problems in most domains, and also eschewed choice to earn higher monetary reward if earning it required a slightly increased working memory (WM) demand relative to an easy WM task. There was no group difference, however, in extent of evidence-gathering before declaring a conclusion in a beads task. These preliminary data suggest that aversion to expenditure of cognitive effort, potentially as a component of cognitive difficulties, may be a marker for SA risk.

We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques—biometrical genetic modeling, cluster analysis, and graph theory—to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral parcellations. Graph theoretical analysis suggested that small world, but not rich club, network properties may characterize the genetic relationships between these regional size measures. These findings suggest that cortical genetic parcellations exhibit short characteristic path lengths across a broad network of connections. This property may be protective against network failure. In contrast, previous research with structural data has observed strong rich club properties with tightly interconnected hub networks. Future studies of these genetic networks might provide powerful phenotypes for genetic studies of normal and pathological brain development, aging, and function.