Circuit based anti-correlation, attention orienting, and major depression

Abstract

Major depression is a multidimensional disorder producing emotional dysregulation, cognitive impairment, and neuro-vegetative symptoms. A pathophysiological model of depression needs to explain how these dimensions interact to produce specific clinical phenotypes and how these interactions may predict remission to specific treatments. It is unlikely that major depression results from discrete brain lesions. Here we propose to define major depression as a disorder of neural networks. We review evidence suggesting that the dynamics of neural networks involved in allocation of attention resources to the internal and external world contribute to cognitive impairment, increased self-focus, and dysfunctional saliency detection in depression. We describe cognitive and emotional tasks that reveal abnormal cooperation between the Central Executive Network and the Default Mode Network. Finally we suggest that depression is associated with increased social rejection sensitivity. Studies on social rejection will shed light on how attachment relates to impairment in allocation of attention resources to produce depressive symptoms such as rumination and cognitive problems.