Predictive processing in depression: Increased prediction error following negative valence contexts and influence of recent mood-congruent yet irrelevant experiences

Depressió psíquica; Emocions; Expressió facialDepression; Emotions; Facial ExpressionDepresión; Emociones; Expresión facialBackground: Novel theoretical models of depression have recently emerged based on an influential new perspective in neuroscience known as predictive processing. In these models, depression may be understood as an imbalance of predictive signals in the brain; more specifically, a dominance of predictions leading to a relative insensitivity to prediction error. Despite these important theoretical advances, empirical evidence remains limited, and how expectations are generated and used dynamically in individuals with depression remains largely unexplored. Methods: In this study, we induced facial expression predictions using emotion contexts in 34 individuals with depression and 34 healthy controls. Results: Compared to controls, individuals with depression perceived displayed facial expressions as less similar to their expectations (i.e., increased difference between expectations and actual sensory input) following contexts evoking negative valence emotions, indicating that depressed individuals have increased prediction error in such contexts. This effect was amplified by recent mood-congruent yet irrelevant experiences. Limitations: The clinical sample included participants with comorbid psychopathology and taking medication. Additionally, the two groups were not evaluated in the same setting, and only three emotion categories (fear, sadness, and happiness) were explored. Conclusions: Our results shed light on potential mechanisms underlying processing abnormalities regarding negative information, which has been consistently reported in depression, and may be a relevant point of departure for exploring transdiagnostic vulnerability to mental illness. Our data also has the potential to improve clinical practice through the implementation of novel diagnostic and therapeutic tools based on the assessment and modulation of predictive signals

Predictive processing in depression : Increased prediction error following negative valence contexts and influence of recent mood-congruent yet irrelevant experiences

Acord transformatiu CRUE-CSICBackground: Novel theoretical models of depression have recently emerged based on an influential new perspective in neuroscience known as predictive processing. In these models, depression may be understood as an imbalance of predictive signals in the brain; more specifically, a dominance of predictions leading to a relative insensitivity to prediction error. Despite these important theoretical advances, empirical evidence remains limited, and how expectations are generated and used dynamically in individuals with depression remains largely unexplored. Methods: In this study, we induced facial expression predictions using emotion contexts in 34 individuals with depression and 34 healthy controls. Results: Compared to controls, individuals with depression perceived displayed facial expressions as less similar to their expectations (i.e., increased difference between expectations and actual sensory input) following contexts evoking negative valence emotions, indicating that depressed individuals have increased prediction error in such contexts. This effect was amplified by recent mood-congruent yet irrelevant experiences. Limitations: The clinical sample included participants with comorbid psychopathology and taking medication. Additionally, the two groups were not evaluated in the same setting, and only three emotion categories (fear, sadness, and happiness) were explored. Conclusions: Our results shed light on potential mechanisms underlying processing abnormalities regarding negative information, which has been consistently reported in depression, and may be a relevant point of departure for exploring transdiagnostic vulnerability to mental illness. Our data also has the potential to improve clinical practice through the implementation of novel diagnostic and therapeutic tools based on the assessment and modulation of predictive signals

Use of 16S ribosomal RNA gene analyses to characterize the bacterial signature associated with poor oral health in West Virginia

<p>Abstract</p> <p>Background</p> <p>West Virginia has the worst oral health in the United States, but the reasons for this are unclear. This pilot study explored the etiology of this disparity using culture-independent analyses to identify bacterial species associated with oral disease.</p> <p>Methods</p> <p>Bacteria in subgingival plaque samples from twelve participants in two independent West Virginia dental-related studies were characterized using 16S rRNA gene sequencing and Human Oral Microbe Identification Microarray (HOMIM) analysis. Unifrac analysis was used to characterize phylogenetic differences between bacterial communities obtained from plaque of participants with low or high oral disease, which was further evaluated using clustering and Principal Coordinate Analysis.</p> <p>Results</p> <p>Statistically different bacterial signatures (<it>P </it>< 0.001) were identified in subgingival plaque of individuals with low or high oral disease in West Virginia based on 16S rRNA gene sequencing. Low disease contained a high frequency of <it>Veillonella </it>and <it>Streptococcus</it>, with a moderate number of <it>Capnocytophaga</it>. High disease exhibited substantially increased bacterial diversity and included a large proportion of Clostridiales cluster bacteria (<it>Selenomonas</it>, <it>Eubacterium, Dialister</it>). Phylogenetic trees constructed using 16S rRNA gene sequencing revealed that Clostridiales were repeated colonizers in plaque associated with high oral disease, providing evidence that the oral environment is somehow influencing the bacterial signature linked to disease.</p> <p>Conclusions</p> <p>Culture-independent analyses identified an atypical bacterial signature associated with high oral disease in West Virginians and provided evidence that the oral environment influenced this signature. Both findings provide insight into the etiology of the oral disparity in West Virginia.</p