31 October 2018: Deficient belief updating as a convergent computational mechanism of psychosis

Guillermo Horga
Department of Psychiatry
Columbia University

Predictive coding and related models of perceptual inference provide a concrete framework to understand how expectations are integrated into subjective perceptual experiences and how sensory information affects the formation and updating of beliefs about hidden states. Recent work has begun to link specific computations underlying these processes to psychotic phenomena such as hallucinations and delusions. However, it remains unclear how the computations underlying perceptual inference relate to the well-established increase in nigro-striatal dopamine function in psychosis, what is the specific nature of abnormalities in these computations, and whether they can explain the concurrence of hallucinations and delusions that characterizes the psychotic syndrome of schizophrenia. During my presentation, I will discuss recent work from our group that capitalizes on computational models of inference and which suggests that deficient belief updating is a core mechanism underlying both hallucinations and delusions in schizophrenia, a mechanism that depends on nigro-striatal dopamine. Our findings further suggest the existence of symptom-specific pathways consistent with a hierarchical belief-updating model whereby lower-level deficits relate to hallucinations and higher-level deficits relate to delusions. I will also discuss the implications of this work for generating novel downstream targets that are more proximal to specific symptoms of psychosis relative to upstream abnormalities in dopaminergic nigro-striatal pathways.

17 October 2018: Connectome-based prediction of substance-use

Sarah W. Yip
Department of Psychiatry
Yale School of Medicine

Despite advances, the effectiveness of most substance-use interventions remains highly variable across individuals and multiple quit-attempts are standard. This talk will present recent findings demonstrating the ability of connectome-based predictive modeling (CPM)¬—a data-driven method of identifying neural networks subserving specific behaviors—to predict abstinence during substance-use treatment. Evidence for largely dissociable neural substrates of cocaine versus opioid use in poly-addicted individuals will also be presented and clinical implications will be discussed.