Neural response to monetary and social rewards in adolescent girls and their parents.

Functional magnetic resonance imaging (fMRI) studies have indicated that the mesocorticolimbic dopamine system is heavily involved in all stages of reward processing. However, the majority of research has been conducted using monetary rewards and it is unclear to what extent other types of rewards, such as social rewards, evoke similar or different neural activation. There have also been few investigations into potential differences or similarities between reward processing in parents and offspring. The present study examined fMRI neural activation in response to monetary and social reward in a sample of 14-22-year-old adolescent girls (N = 145) and a biological parent (N = 124) and compared activation across adolescent-parent dyads (N = 82). Across all participants, both monetary and social reward elicited bilateral striatal activation, which did not differ between reward types or between adolescents and their parents. Neural activation in response to the different reward types were positively correlated in the striatum among adolescents and in the mPFC and OFC among parents. Overall, the present study suggests that both monetary and social reward elicit striatal activation regardless of age and provides evidence that neural mechanisms underlying reward processing may converge differentially among youth and adults.

Reward-Related Brain Activity Mediates the Relationship Between Decision-Making Deficits and Pediatric Depression Symptom Severity.

BACKGROUND: The mechanisms that link neural and behavioral indices of reduced reward sensitivity in depression, particularly in children, remain unclear. Reward positivity (RewP), a neural index of reward processing, has been consistently associated with depression. Separately, recent studies using the drift-diffusion model on behavioral data have delineated computational indices of reward sensitivity. Therefore, in the current study, we examined whether RewP is a neural mediator of drift-diffusion model-based indices of reward processing in predicting pediatric depression across varying levels of symptom severity. METHODS: A community sample of 166 girls, ages 8 to 14 years, completed 2 tasks. The first was a reward guessing task from which RewP was computed using electroencephalography; the second was a probabilistic reward-based decision-making task. On this second task, drift-diffusion model analysis was applied to behavioral data to quantify the efficiency of accumulating reward-related evidence (drift rate) and potential baseline bias (starting point) toward the differently rewarded choices. Depression severity was measured using the self-report Children's Depression Inventory. RESULTS: RewP was correlated with drift rate, but not starting point bias, toward the more rewarded choice. Furthermore, RewP completely mediated the association between a slower drift rate toward the more rewarded option and higher depression symptom severity. CONCLUSIONS: Our findings suggest that reduced neural sensitivity to reward feedback may be a neural mechanism that underlies behavioral insensitivity to reward in children and adolescents with higher depression symptom severity, offering novel insights into the relationship between neural and computational indices of reward processing in this context.

The Prospective Predictive Power of Parent-Reported Personality Traits and Facets in First-Onset Depression in Adolescent Girls.

Certain personality traits and facets are well-known risk factors that predict first-onset depression during adolescence. However, prior research predominantly relied on self-reported data, which has limitations as a source of personality information. Reports from close informants have the potential to increase the predictive power of personality on first-onsets of depression in adolescents. With easy access to adolescents' behaviors across settings and time, parents may provide important additional information about their children's personality. The same personality trait(s) and facet(s) rated by selves (mean age 14.4 years old) and biological parents at baseline were used to prospectively predict depression onsets among 442 adolescent girls during a 72-month follow-up. First, bivariate logistic regression was used to examine whether parent-reported personality measures predicted adolescent girls' depression onsets; then multivariate logistic regression was used to test whether parent reports provided additional predictive power above and beyond self-reports of same trait or facet. Parent-reported personality traits and facets predicted adolescents' depression onsets, similar to findings using self-reported data. After controlling for the corresponding self-report measures, parent-reported higher openness (at the trait level) and higher depressivity (at the facet-level) incrementally predicted first-onset of depression in the sample. Findings demonstrated additional variance contributed by parent-reported personality measures and validated a multi-informant approach in using personality to prospectively predict onsets of depression in adolescent girls.

Knowledge of Threat Biases Perceptual Decision Making in Anxiety: Evidence From Signal Detection Theory and Drift Diffusion Modeling.

Background: Threat biases are considered key factors in the development and maintenance of anxiety. However, these biases are poorly operationalized and remain unquantified. Furthermore, it is unclear whether and how prior knowledge of threat and its uncertainty induce these biases and how they manifest in anxiety. Method: Participants (n = 55) used prestimulus cues to decide whether the subsequently presented stimuli were threatening or neutral. The cues either provided no information about the probability (high uncertainty) or indicated high probability (low uncertainty) of encountering threatening or neutral targets. We used signal detection theory and hierarchical drift diffusion modeling to quantify bias. Results: High-uncertainty threat cues improved discrimination of subsequent threatening and neutral stimuli more than neutral cues. However, anxiety was associated with worse discrimination of threatening versus neutral stimuli following high-uncertainty threat cues. Using hierarchical drift diffusion modeling, we found that threat cues biased decision making not only by shifting the starting point of evidence accumulation toward the threat decision but also by increasing the efficiency with which sensory evidence was accumulated for both threat-related and neutral decisions. However, higher anxiety was associated with a greater shift of starting point toward the threat decision but not with the efficiency of evidence accumulation. Conclusions: Using computational modeling, these results highlight the biases by which knowledge regarding uncertain threat improves perceptual decision making but impairs it in case of anxiety.

Metal-assisted core-shell plasmonic nanoparticles for small molecule biothiol analysis and enantioselective recognition.

Cysteine (Cys) enantiomorphs, important small-molecule biothiols, participate in various antioxidative, flavoring, and poison-removing processes in the food industry. Current cysteine enantiomorph analysis methods require effective strategies for distinguishing them due to their similar structures and reactivity. Herein, we present a metal ion-assisted enantiomorph-selective surface-enhanced Raman scattering (SERS) biosensor based on an amphiphilic polymer matrix (APM), which can promote cysteine enantiomorph (L/D-Cys) identification. The highly selective molecular orientation is perhaps caused by the intermolecular hydrogen bonding with chiral isomers (metal centers). The experimental results show that the SERS biosensor has a sensitivity-distincting factor toward L-Cys and D-Cys. The linear range is from 1 mmol L-1 to 1 nmol L-1, along with a low limit of detection of 0.77 pmol L-1. Moreover, the fabricated Cu-APM biosensor exhibits remarkable stability and high repeatability, with an RSD of 3.7%. Real food cysteine enantiomorph detection was performed with L-Cys-containing samples of onion, cauliflower, garlic, and apple, and D-Cys-containing samples of vinegar, black garlic, cheese, and beer. The results show that the Cu-APM biosensor can be utilized as a powerful tool for real-time determination of Cys enantiomorphs in different food samples. Thus, the metal-ion-assisted enantiomorph-selective SERS biosensor has potential as an adaptable tool for enantiomorph detection and food sample analysis.

Understanding anxiety through uncertainty quantification.

Uncertainty has been a central concept in psychological theories of anxiety. However, this concept has been plagued by divergent connotations and operationalizations. The lack of consensus hinders the current search for cognitive and biological mechanisms of anxiety, jeopardizes theory creation and comparison, and restrains translation of basic research into improved diagnoses and interventions. Drawing upon uncertainty decomposition in Bayesian Decision Theory, we propose a well-defined conceptual structure of uncertainty in cognitive and clinical sciences, with a focus on anxiety. We discuss how this conceptual structure provides clarity and can be naturally applied to existing frameworks of psychopathology research. Furthermore, it allows formal quantification of various types of uncertainty that can benefit both research and clinical practice in the era of computational psychiatry.

Anti-galvanic reaction induced interfacial engineering to reconstruct ternary colloid satellite platform for exceptionally high-performance redox-responsive sensor.

The anti-galvanic reaction (AGR), which is a classic galvanic reaction (GR) with an opposite effect, is a unique phenomenon associated with the quantum size effect. This reaction involves the interaction between metal ions and nanoclusters, offering opportunities to create well-defined nanomaterials and diverse reductive behavior. In hence, in our work, we utilize the AGR to generate gold (Au), silver (Ag), and copper (Cu) satellite nanoclusters which have superior electromagnetic properties for Surface-enhanced Raman spectroscopy (SERS) sensor. As the AGR process, weak oxidant Cu2+ is selected to etched matrix Au@Ag NPs, reduced to Cu(0) or Cu(1) and generated the ultrasmall metal nanoparticles (Ag). To facilitate the AGR, we introduce the nucleophilic thiol 4-mercaptopyridine (4-Mpy) to bridge the metal ions or ultrasmall metal nanoparticles to reconstruct the satellite nanoclusters. These experimental displays that the AGR based biosensors has highly sensitivity for reductive molecule glucose. The liner ranges from 1 mmol/L to 1 nmol/L and alongs with a correlation coefficient and detection limit (LOD) of 0.999 and 0.14 nmol/L. Moreover, the AGR based biosensors exhibits remarkable stability and high repeatability with RSD 1.3 %. The food samples are tested to further investigate the accuracy and reliability of the method, which provides a novel and effective SERS method for the reduction molecules detection.

Hallucination-Proneness is Associated With a Decrease in Robust Averaging of Perceptual Evidence.

BACKGROUND AND HYPOTHESIS: Hallucinations are characterized by disturbances in perceptual decision-making about environmental stimuli. When integrating across multiple stimuli to form a perceptual decision, typical observers engage in "robust averaging" by down-weighting extreme perceptual evidence, akin to a statistician excluding outlying data. Furthermore, observers adapt to contexts with more unreliable evidence by increasing this down-weighting strategy. Here, we test the hypothesis that hallucination-prone individuals (n = 38 high vs n = 91 low) would show a decrease in this robust averaging and diminished sensitivity to changes in evidence variance. STUDY DESIGN: We used a multielement perceptual averaging task to elicit dichotomous judgments about the "average color" (red/blue) of an array of stimuli in trials with varied strength (mean) and reliability (variance) of decision-relevant perceptual evidence. We fitted computational models to task behavior, with a focus on a log-posterior-ratio (LPR) model which integrates evidence as a function of the log odds of each perceptual option and produces a robust averaging effect. STUDY RESULTS: Hallucination-prone individuals demonstrated less robust averaging, seeming to weigh inlying and outlying extreme or untrustworthy evidence more equally. Furthermore, the model that integrated evidence as a function of the LPR of the two perceptual options and produced robust averaging showed poorer fit for the group prone to hallucinations. Finally, the weighting strategy in hallucination-prone individuals remained insensitive to evidence variance. CONCLUSIONS: Our findings provide empirical support for theoretical proposals regarding evidence integration aberrations in psychosis and alterations in the perceptual systems that track statistical regularities in environmental stimuli.

Virus-Like Magnetic Heterostructure: an Outstanding Metal-Complex Active Platform Enables High-Efficiency Separation and Catalysis.

Assembled heterostructure systems, as emerging functional materials, have broad applications ranging from enzyme and drug payload to catalysis and purification. However, these require trial- and -error design process and complex experimental environment to generate heterostructure materials. Here, this study describes an easy-to-execute strategy to fabricate magnetic heterostructure as multifunctional delivery system. We utilize first-row transition metal copper and nitroso/amino ligand as modules to assemble around Fe3 O4 magnetic nanoparticles by excessed mild stimuli and fabricate the magnetic heterostructure materials (Fe3 O4 @ TACN NPs (tetraamminecopper (II) nitrate)). Notably, the Fe3 O4 @ TACN NPs present with cat's-whisker structure containing ligand and metal center. The nitroso-group ligands exhibit strong binding affinity to heme-structure enzyme, ensuring effective capture and isolate of cytochrome C (Cyt-c), resulting in their excellent isolation property. The copper complex-powered magnetic heterostructure materials can effectively isolation Cyt-c from complex biological sample (pork heart). Importantly, the Fe3 O4 @ TACN NPs coordinated with heme-structure, induced methionine 80 (Met80) disassociates from heme prosthetic group, and contributed to peroxidase-like (POD-like) activities increasing. These results exhibit that copper complex-powered magnetic heterostructure materials can not only satisfy the Cyt-c isolation and immobilization in an alkaline medium, but also be of the potential for improving the immobilization enzyme reactor performance.

The interpersonal computational psychiatry of social coordination in schizophrenia.

Impairments in social coordination form a core dimension of various psychiatric disorders, including schizophrenia. Advances in interpersonal and computational psychiatry support a major change in studying social coordination in schizophrenia. Although these developments provided novel perspectives to study how interpersonal activities shape coordination and to examine computational mechanisms, direct attempts to integrate the two methodologies have been sparse. Here, we propose an interpersonal computational framework that (1) leverages the active inference framework to model aberrant social coordination processes in schizophrenia and (2) incorporates dynamical system models to dissect intrapersonal and interpersonal synchronisation to inform a statistical model based on active inference. We discuss how this interpersonal computational psychiatry framework can elucidate the aberrant processes leading to psychopathology, with schizophrenia as an example, and highlight how it might aid clinical intervention and practice. Finally, we discuss challenges and opportunities for using the framework in studying social coordination impairments.