Interconnection between biological abnormalities in borderline personality disorder: use of the Bayesian networks model.

There is agreement in that strengthening the sets of neurobiological data would reinforce the diagnostic objectivity of many psychiatric entities. This article attempts to use this approach in borderline personality disorder (BPD). Assuming that most of the biological findings in BPD reflect common underlying pathophysiological processes we hypothesized that most of the data involved in the findings would be statistically interconnected and interdependent, indicating biological consistency for this diagnosis. Prospectively obtained data on scalp and sleep electroencephalography (EEG), clinical neurologic soft signs, the dexamethasone suppression and thyrotropin-releasing hormone stimulation tests of 20 consecutive BPD patients were used to generate a Bayesian network model, an artificial intelligence paradigm that visually illustrates eventual associations (or inter-dependencies) between otherwise seemingly unrelated variables. The Bayesian network model identified relationships among most of the variables. EEG and TSH were the variables that influence most of the others, especially sleep parameters. Neurological soft signs were linked with EEG, TSH, and sleep parameters. The results suggest the possibility of using objective neurobiological variables to strengthen the validity of future diagnostic criteria and nosological characterization of BPD.