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.

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 Impact of Interpretation Biases on Psychological Responses to the COVID-19 Pandemic: a Prospective Study.

BACKGROUND: This study investigates the longitudinal role of interpretation biases in the development and maintenance of health anxiety during the pandemic. Individual differences in behavioural responses to the virus outbreak and decision-making were also examined. METHODS: Two hundred seventy-nine individuals from a pre-pandemic study of interpretation bias and health anxiety completed an online survey during the third wave of the COVID-19 pandemic in Hong Kong. Participants' health anxiety, interpretation biases, and COVID-specific behaviours (i.e. practice of social distancing, adherence to preventive measures, information seeking), and health decision-making were assessed. RESULTS: Pre-pandemic tendencies to interpret ambiguous physical sensations as signals for illness did not predict health anxiety during the pandemic, b = -0.020, SE = 0.024, t = -0.843, p = .400, 99% CI [-0.082, 0.042], but were associated with a preference for risky treatment option for COVID-19, b = 0.026, SE = 0.010, Wald = 2.614, p = .009, OR = 1.026, 99% CI [1.001, 1.054]. Interpretation biases and health anxiety symptoms during the pandemic were associated with each other and were both found to be significant predictors of practice of social distancing, adherence to preventive measures, and information seeking behaviour. CONCLUSIONS: This study adds to the growing evidence of the role of interpretation biases in health anxiety and the way that people respond to the ongoing pandemic.

Towards a global partnership model in interprofessional education for cross-sector problem-solving.

OBJECTIVES: A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. METHODS: This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students' data. RESULTS: We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest-posttest differences in students' readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students' social interaction anxiety after the IPE simulation. CONCLUSIONS: The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education.

They're watching you: the impact of social evaluation and anxiety on threat-related perceptual decision-making.

In day-to-day social interactions, we frequently use cues and contextual knowledge to make perceptual decisions regarding the presence or absence of threat in facial expressions. Such perceptual decisions are often made in socially evaluative contexts. However, the influence of such contexts on perceptual discrimination of threatening and neutral expressions has not been examined empirically. Furthermore, it is unclear how individual differences in anxiety interact with socially evaluative contexts to influence threat-related perceptual decision-making. In the present study, participants completed a 2-alternative forced choice perceptual decision-making task in which they used threatening and neutral cues to discriminate between threatening and neutral faces while being socially evaluated by purported peers or not. Perceptual sensitivity and reaction time were measured. Individual differences in state anxiety were assessed immediately after the task. In the presence of social evaluation, higher state anxiety was associated with worse perceptual sensitivity, i.e., worse discrimination of threatening and neutral faces. These findings suggest that individual differences in anxiety interact with social evaluation to impair the use of threatening cues to discriminate between threatening and neutral expressions. Such impairment in perceptual decision-making may contribute to maladaptive social behavior that often accompanies evaluative social contexts.

Ultrasensitive Temperature Sensing Based on Ligand-Free Alloyed CsPbClx Br3-x Perovskite Nanocrystals Confined in Hollow Mesoporous Silica with High Density of Halide Vacancies.

Temperature sensing based on fluorescent semiconductor nanocrystals has recently received immense attention. Enhancing the trap-facilitated thermal quenching of the fluorescence should be an effective approach to achieve high sensitivity for temperature sensing. Compared with conventional semiconductor nanocrystals, the defect-tolerant feature of lead halide perovskite nanocrystals (LHP NCs) endows them with high density of defects. Here, hollow mesoporous silica (h-SiO2 ) template-assisted ligand-free synthesis and halogen manipulation (chloride-importing) are proposed to fabricate highly defective yet fluorescent CsPbCl1.2 Br1.8 NCs confined in h-SiO2 (CsPbCl1.2 Br1.8 NCs@h-SiO2 ) for ultrasensitive temperature sensing. The trap barrier heights, exciton-phonon scattering, and trap state filling process in the CsPbCl1.2 Br1.8 NCs@h-SiO2 and CsPbBr3 NCs@h-SiO2 are studied to illustrate the higher temperature sensitivity of CsPbCl1.2 Br1.8 NCs@h-SiO2 at physiological temperature range. By integrating the thermal-sensitive CsPbCl1.2 Br1.8 NCs@h-SiO2 and thermal-insensitive K2 SiF6 :Mn4+ phosphor into the flexible ethylene-vinyl acetate polymer matrix, ratiometric temperature sensing from 30.0 °C to 45.0 °C is demonstrated with a relative temperature sensitivity up to 13.44% °C-1 at 37.0 °C. The composite film shows high potential as a thermometer for monitoring the body temperature. This work demonstrates the unparalleled temperature sensing performance of LHP NCs and provides new inspiration on switching the defects into advantages in sensing applications.

Dual-Mode of Fluorescence Turn-On and Wavelength-Shift for Methylamine Gas Sensing Based on Space-Confined Growth of Methylammonium Lead Tribromide Perovskite Nanocrystals.

The defect-tolerant nature of lead halide perovskites renders outstanding luminescence by simple space-confined growth in nanopores. The fluorescence turn-on and wavelength-shift phenomena could be found in the formation of methylammonium lead tribromide (MAPbBr3) nanocrystals in hollow SiO2 nanospheres triggered by the reaction between methylamine (MA) gas and HPbBr3/PbBr2@SiO2 nanospheres. The enhanced fluorescence intensity is linear with the MA concentration in the range of 1.0-95 ppm with a limit of detection (LOD) of 70 ppb (S/N = 3). In addition, the maximum emission wavelength is consistently red-shifted from 478.7 to 510.6 nm as the MA concentration increases from 1.0 to 95 ppm, imparting the potential for colorimetric sensing. By combining the fluorescence turn-on and colorimetric sensing modes, the flexible method meets the demands for visual discrimination and point-of-care determination with portable devices.

Intrinsic neural circuitry of depression in adolescent females.

BACKGROUND: Adolescence is characterized by affective and cognitive changes that increase vulnerability to depression, especially in females. Neurodevelopmental models attribute adolescent depression to abnormal responses in amygdala, striatum, and prefrontal cortex (PFC). We examined whether the strength of functional brain networks involving these regions predicts depression symptoms in adolescent females. METHODS: In this longitudinal study, we recorded resting-state functional connectivity (RSFC) in 174 adolescent females. Using a cross-validation strategy, we related RSFC profiles that included (a) a network consisting of amygdala, striatum, and PFC (within-circuit model), (b) connectivity of this network to the whole brain (extended-circuit model), and (c) a network consisting of the entire brain (whole-brain model) to depression symptoms assessed concurrently and 18 months later. RESULTS: In testing subsets, the within-circuit RSFC profiles were associated with depression symptoms concurrently and 18 months later, while the extended-circuit and whole-brain model did not explain any additional variance in depression symptoms. Connectivity related to anterior cingulate and ventromedial prefrontal cortex contributed most to the association. CONCLUSIONS: Our results demonstrate that RSFC-based brain networks that include amygdala, striatum, and PFC are stable neural signatures of concurrent and future depression symptoms, representing a significant step toward identifying the neural mechanism of depression in adolescence.

Human Amygdala Represents the Complete Spectrum of Subjective Valence.

Although the amygdala is a major locus for hedonic processing, how it encodes valence information is poorly understood. Given the hedonic potency of odor stimuli and the amygdala's anatomical proximity to the peripheral olfactory system, we combined high-resolution fMRI with pattern-based multivariate techniques to examine how valence information is encoded in the amygdala. Ten human subjects underwent fMRI scanning while smelling 9 odorants that systematically varied in perceived valence. Representational similarity analyses showed that amygdala codes the entire dimension of valence, ranging from pleasantness to unpleasantness. This unidimensional representation significantly correlated with self-reported valence ratings but not with intensity ratings. Furthermore, within-trial valence representations evolved over time, prioritizing earlier differentiation of unpleasant stimuli. Together, these findings underscore the idea that both spatial and temporal features uniquely encode pleasant and unpleasant odor valence in the amygdala. The availability of a unidimensional valence code in the amygdala, distributed in both space and time, would create greater flexibility in determining the pleasantness or unpleasantness of stimuli, providing a mechanism by which expectation, context, attention, and learning could influence affective boundaries for guiding behavior. SIGNIFICANCE STATEMENT: Our findings elucidate the mechanisms of affective processing in the amygdala by demonstrating that this brain region represents the entire valence dimension from pleasant to unpleasant. An important implication of this unidimensional valence code is that pleasant and unpleasant valence cannot coexist in the amygdale because overlap of fMRI ensemble patterns for these two valence extremes obscures their unique content. This functional architecture, whereby subjective valence maps onto a pattern continuum between pleasant and unpleasant poles, offers a robust mechanism by which context, expectation, and experience could alter the set-point for valence-based behavior. Finally, identification of spatial and temporal differentiation of valence in amygdala may shed new insights into individual differences in emotional responding, with potential relevance for affective disorders.

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.

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

BACKGROUND: The mechanisms linking 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 (DDM) on behavioral data have delineated computational indices of reward sensitivity. Therefore, the present study examined whether RewP is a neural mediator of DDM-based indices of reward processing in predicting pediatric depression across varying levels of symptom severity. METHODS: A community sample of 166 girls, aged 8 to 14 years, completed two 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, DDM analysis was applied to behavioral data to quantify the efficiency of accumulating reward-related evidence (drift rate) and potential baseline bias (starting point) towards the differently rewarded choices. Depression severity was measured using the self-report Children's Depression Inventory (CDI). RESULTS: RewP was correlated with drift rate, but not starting point bias, towards the more rewarded choice. Furthermore, RewP completely mediated the association between a slower drift rate towards the more rewarded option and higher depression symptom severity. CONCLUSION: Our findings suggest that reduced neural sensitivity to reward feedback might be a neural mechanism underscoring 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.

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.

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.

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.

Quantitative fluorescence spectroscopy in turbid media: a practical solution to the problem of scattering and absorption.

The presence of practically unavoidable scatterers and background absorbers in turbid media such as biological tissue or cell suspensions can significantly distort the shape and intensity of fluorescence spectra of fluorophores and, hence, greatly hinder the in situ quantitative determination of fluorophores in turbid media. In this contribution, a quantitative fluorescence model (QFM) was proposed to explicitly model the effects of the scattering and absorption on fluorescence measurements. On the basis of the proposed model, a calibration strategy was developed to remove the detrimental effects of scattering and absorption and, hence, realize accurate quantitative analysis of fluorophores in turbid media. A proof-of-concept model system, the determination of free Ca(2+) in turbid media using Fura-2, was utilized to evaluate the performance of the proposed method. Experimental results showed that QFM can provide quite precise concentration predictions for free Ca(2+) in turbid media with an average relative error of about 7%, probably the best results ever achieved for turbid media without the use of advanced optical technologies. QFM has not only good performance but also simplicity of implementation. It does not require characterization of the light scattering properties of turbid media, provided that the light scattering and absorption properties of the test samples are reasonably close to those of the calibration samples. QFM can be developed and extended in many application areas such as ratiometric fluorescent sensors for quantitative live cell imaging.

Linking the past to the future by predictive processing: Implications for psychopathology.

Most theories of psychopathology have focused on etiology at a specific level (e.g., genetic, neurobiological, psychological, or environmental) to explain specific symptoms or disorders. A few biopsychosocial theories have provided explanations that attempt to integrate different levels and disorders to some extent. However, these theories lack a framework in which different levels of analysis are integrated and thus do not explain the mechanism by which etiological factors interact and perturb neurobiology which in turn leads to psychopathology. We propose that predictive processing (PP), which originated in theoretical neurobiology literature, may provide a conceptually parsimonious and biologically plausible framework to achieve such integration. In PP, the human brain can be cast as implementing a generative model whose task is to minimize the surprise of sensory evidence by inferring its causes and actively controlling future sensory signals via action. This account offers a unifying model of perception, action, and emotion implicated in psychopathology. Furthermore, we show that PP can explain how different factors or levels result in psychopathology via updates of the generative model (the depth of the PP framework). Finally, we demonstrate the transdiagnostic appeal of PP by showing how perturbations within this framework can explain a broad range of psychopathology (the breadth of the PP framework), with a focus on bridging well-established psychosocial theories of psychopathology and PP. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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.

Inferring Trajectories of Psychotic Disorders Using Dynamic Causal Modeling.

Introduction: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data. Methods: A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation. Results: Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values. Conclusion: DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment.