Publications

OBJECTIVE: Patients with borderline personality disorder (BPD) fare better clinically if their families are rated as being high in emotional overinvolvement, which is characterized by marked emotionality, anxious concern, and protective behavior. This is not true of patients with disorders such as schizophrenia or major depression. We used functional magnetic resonance imaging methods to explore the link between emotional overinvolvement (EOI) and better clinical outcome in BPD. Specifically, we tested the hypothesis that, unlike healthy controls or people with other psychiatric problems, people with BPD process EOI as an approach-related stimulus. METHOD: Participants with BPD (n = 13) and dysthymia (n = 10) (DSM-IV criteria for both) and healthy controls (n = 11) were imaged using a high field strength (3T) scanner while they listened to a standardized auditory stimulus consisting of either 4 neutral or 4 EOI comments. Participants also rated their mood before and after exposure to the comments. RESULTS: All participants reported increased negative mood after hearing EOI and rated the EOI comments as negative stimuli. However, after subtracting activation to neutral comments, participants with BPD showed higher activation in left prefrontal regions during EOI compared to the other groups. Increased left prefrontal activation during EOI was also correlated with clinical measures indicative of borderline pathology. Participants with dysthymia showed increased amygdala activation during EOI. This was not true for the healthy controls or participants with BPD. CONCLUSIONS: For people with BPD, EOI may be activating neural circuitry implicated in the processing of approach-related stimuli. Increased left prefrontal activation to EOI may be a vulnerability marker for BPD. These findings may also help explain why BPD patients do better clinically in high EOI family environments.

Noun classes (genders) have long played an important role in the understanding of language structure and human categorization. The current study presents and analyzes the division of nouns into classes in Tsez (Dido), an endangered Nakh-Dagestanian language of the Northeast Caucasus. Computational modeling of the Tsez system shows that noun classification in Tsez is highly predictable, with a simple semantic core and a set of highly salient formal features, that can be ranked with respect to one another. Such a system would be easily accessible to children acquiring the language, and the proposed analysis does not require additional semantic or categorical assumptions. The study serves as a proof of principle for the computational approach to the analysis of noun classification.