A neural network model of Borderline Personality Disorder.
The etiology of Borderline Personality Disorder (BPD) is unknown. This paper develops an etiological hypothesis by constructing a neural network with constraints from neuroanatomy, neurophysiology, and behavior. The neural network ascribes roles to the brainstem's periaqueductal gray, the amygdala, and the anterior cingulate/ventromedial prefrontal cortex (ACC/vmPFC). Neural network simulations show how these brain structures might interact during BPD behavior. The simulations suggest that long term depression (LTD) in ACC/vmPFC may explain several BPD symptoms. The network makes testable suggestions. The current work is the first-ever neural network simulation of BPD.
Berdahl
Carl H
CH
Carl.Berdahl@verizon.net
D000465:Algorithms; D001883:Borderline Personality Disorder / Q000503:physiopathology*; D001921:Brain / Q000503:physiopathology*; D003198:Computer Simulation; D005239:Fear / Q000502:physiology; D006801:Humans; D036881:Long-Term Synaptic Depression / Q000502:physiology; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000503:physiopathology