Major depressive disorder (MDD), bipolar disorder, and schizophrenia involve disruptions in processing rewarding stimuli. In this review, we propose that distinct mechanistic pathways underlie these disruptions in mood disorders versus schizophrenia, and we highlight the importance of understanding these differences for developing personalized treatments. We summarize evidence suggesting that reward processing abnormalities in mood disorders are driven by dysregulated motivational systems; MDD is characterized by blunted responses to reward cues, and bipolar disorder is characterized by heightened responses. In contrast, we argue that reward processing disruptions in schizophrenia do not reflect abnormalities in motivation or hedonic experience; rather, they reflect impairments in the cognitive representation of past and future rewards as well as misdirected attention to irrelevant stimuli. To integrate these findings, we present a neurodevelopmental framework for the onset of mood and psychotic disorders and explore how disruptions in normative brain development contribute to their pathophysiology, timing, and onset. Additionally, we move beyond viewing these conditions as homogeneous disorders and discuss how reward processing profiles may align with specific symptom dimensions.
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http://dx.doi.org/10.1146/annurev-clinpsy-080822-041621 | DOI Listing |
Neuropsychologia
March 2025
Department of Psychology, Institute of Education, China West Normal University, Nanchong 637002.
Reward prediction-error carries significant implications for learning, facilitating the process by influencing prior knowledge and shaping future expectations and decisions. However, the electrophysiological mechanism through which reward prediction-error impacts learning remains incompletely understood. This study aimed to investigate the neural characteristics of reward prediction-error and its effect on recognition memory using Event-Related Potentials (ERPs).
View Article and Find Full Text PDFSci Rep
March 2025
Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi, Japan.
Elucidating how ancestral behavioural traits have been repurposed for psychological and social functions is critical to advancing our understanding of human behavioural evolution. Self-scratching, originally a hygienic response and known to exhibit social contagion, serves as a model for this process. Although human scratching behaviour is traditionally linked to negative emotions, evidence from non-human animals has produced inconsistent results, casting doubt on its association with negative emotions.
View Article and Find Full Text PDFJ Neurosci
March 2025
Department of Psychology and Djavad Mowafaghian Centre for Brain Health, University of British Columbia
The medial orbitofrontal cortex (mOFC) has been implicated in shaping decisions involving reward uncertainty, in part by using memories to infer future outcomes. This region is interconnected with other key systems that mediated these decisions, including the basolateral amygdala (BLA) and prelimbic (PL) region of the medial prefrontal cortex, yet the functional importance of these circuits remains unclear. The present study used chemogenetic silencing to examine the contribution of different input and output pathways of the mOFC to risk/reward decision making.
View Article and Find Full Text PDFSLAS Technol
March 2025
Business School, University of Shanghai for Science and Technology, 200093, Shanghai, China. Electronic address:
In the face of a sudden major epidemic, people's panic may likely lead to the disruption of the public opinion ecosystem and the disorder of public opinion order. Therefore, clarifying the key main bodies and mechanisms in governing online public opinion is of crucial significance for effectively managing and guiding it. Firstly, based on the sentiment analysis of opinion leaders, an evolutionary game model involving the government, netizens, and opinion leaders was constructed.
View Article and Find Full Text PDFJ Chromatogr A
March 2025
Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium. Electronic address:
Chromatographic problem solving, commonly referred to as method development (MD), is hugely complex, given the many operational parameters that must be optimized and their large effect on the elution times of individual sample compounds. Recently, the use of reinforcement learning has been proposed to automate and expedite this process for liquid chromatography (LC). This study further explores deep reinforcement learning (RL) for LC method development.
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