Abstract
BackgroundThe significant proportion of schizophrenia patients refractory to treatment targeting the dopamine system suggests that more than one mechanism may cause psychotic symptoms. Reinforcement learning tasks have frequently been employed in schizophrenia to assess dopaminergic functioning and reward processing, but studies have not directly compared groups of treatment-refractory and non-refractory patients.MethodsIn the current functional magnetic resonance imaging study 21 patients with treatment resistant schizophrenia (TRS), 21 patients with non-treatment resistant schizophrenia (NTR), and 24 healthy controls (HC) performed a probabilistic reinforcement learning task, utilising emotionally valenced face stimuli which elicit a social bias toward happy faces. Behavior was characterized with a reinforcement learning model. Trial-wise reward prediction error (RPE) signaling and the differential impact of emotional bias on these reward signals were compared between groups.ResultsPatients showed impaired reinforcement learning relative to controls, while all groups demonstrated an emotional bias favouring selection of the happy faces. The pattern of RPE signaling was similar in HC and TRS groups, whereas NTR patients showed significant attenuation of RPE-related activation. The TRS patients differed from the NTR patients in the relationship between emotional bias and subcortical RPE signal during negative feedback.DiscussionTRS can be dissociated from NTR on the basis of a different neural mechanism underlying their symptoms. The data support the hypothesis that a favourable response to antipsychotic treatment may be contingent on dopaminergic dysfunction, characterized by aberrant RPE signaling, whereas treatment resistance may be characterized by an abnormality in distinct cognitive mechanisms interacting with this response.
Highlights
The significant proportion of schizophrenia patients refractory to treatment targeting the dopamine system suggests that more than one mechanism may cause psychotic symptoms
We found that the clustering coefficient of brain functional network in schizophrenia increased (p = 0.032, beta = 0.000194 at the minimum network sparsity 35% in which all the nodes were fully connected), whereas the global efficiency decreased (p = 0.005, beta = -0.000022), as the progression of schizophrenia
Recent research suggests that individuals at ultra-high risk for psychosis (UHR) show altered resting cerebral blood flow in key regions linked to psychosis pathophysiology: the hippocampus, midbrain, and basal ganglia
Summary
We found that the clustering coefficient of brain functional network in schizophrenia increased (p = 0.032, beta = 0.000194 at the minimum network sparsity 35% in which all the nodes were fully connected), whereas the global efficiency decreased (p = 0.005, beta = -0.000022), as the progression of schizophrenia. The main effect of social anhedonia in predicting both the clustering efficient and the global efficiency were not significant, its interaction with the duration of illness was significant (p = 0.021, beta = 0.000038 for the clustering coefficient; p = 0.023, beta = -0.000003 for the global efficiency). Discussion: With the development of schizophrenia, the increase of clustering coefficient and decrease of global efficiency of functional brain network may reflect the pathophysiology of schizophrenia since the onset of illness.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
