Circulating Levels of Brain‐Derived Neurotrophic Factor and History of Suicide Attempts in Women

A growing body of research examining biological factors associated with suicidal behaviors highlights the role of brain‐derived neurotropic factor (BDNF), involved in neurogenesis and synaptic plasticity. There is evidence suggesting that suicide attempters have lower BDNF levels than those with no history of suicide attempts. The key question addressed in the current investigation is whether differences in circulating BDNF levels persist beyond the current suicidal episode and would be observed in those with a past history of suicide attempts (SA). Plasma levels of BDNF were assessed in 73 women from the community. We found that women with a history of SA exhibited lower levels of BDNF than women with no SA history and this difference was maintained after statistically controlling for the influence of other potential psychiatric or demographic factors. These findings support and extend existing research by suggesting that circulating BDNF levels are decreased among individuals with a history of SA compared to individuals with no history of SA. This relation appeared to be specific to women's history of SA and was not explained by other potential psychiatric or demographic factors, which further highlights the role of BDNF as a promising biomarker for suicidal behavior.     

Investigating the Co‐Occurrence of Self‐Mutilation and Suicide Attempts among Opioid‐Dependent Individuals

The prevalence and risk factors associated with self‐mutilation among opioid dependent cases and controls were determined, and the co‐occurrence of self‐mutilation and attempted suicide was examined. The prevalence of self‐mutilation among cases and controls did not differ significantly (25% vs. 23%, respectively), with gender differences identified among cases only. A number of risk factors were found to be associated with self‐mutilation, including borderline personality disorder, alcohol dependence, childhood sexual abuse, and multiple suicide attempts. Not only is self‐mutilation a clinically significant problem, but when combined with a history of attempted suicide, the psychological dysfunction observed is markedly high.     

A learning algorithm for boltzmann machines

The computational power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections can allow a significant fraction of the knowledge of the system to be applied to an instance of a problem in a very short time. One kind of computation for which massively parallel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found. Second, there must be some way of choosing internal representations which allow the preexisting hardware connections to be used efficiently for encoding the constraints in the domain being searched. We describe a general parallel search method, based on statistical mechanics, and we show how it leads to a general learning rule for modifying the connection strengths so as to incorporate knowledge about a task domain in an efficient way. We describe some simple examples in which the learning algorithm creates internal representations that are demonstrably the most efficient way of using the preexisting connectivity structure.