Replication study on the role of dopamine-dependent prefrontal reactivations in human extinction memory retrieval.

Even after successful extinction, conditioned fear can return. Strengthening the consolidation of the fear-inhibitory safety memory formed during extinction is one way to counteract return of fear. In a previous study, we found that post-extinction L-DOPA administration improved extinction memory retrieval 24 h later. Furthermore, spontaneous post-extinction reactivations of a neural activation pattern evoked in the ventromedial prefrontal cortex (vmPFC) during extinction predicted extinction memory retrieval, L-DOPA increased the number of these reactivations, and this mediated the effect of L-DOPA on extinction memory retrieval. Here, we conducted a preregistered replication study of this work in healthy male participants. We confirm that spontaneous post-extinction vmPFC reactivations predict extinction memory retrieval. This predictive effect, however, was only observed 90 min after extinction, and was not statistically significant at 45 min as in the discovery study. In contrast to our previous study, we find no evidence that L-DOPA administration significantly enhances retrieval and that this is mediated by enhancement of the number of vmPFC reactivations. However, additional non-preregistered analyses reveal a beneficial effect of L-DOPA on extinction retrieval when controlling for the trait-like stable baseline levels of salivary alpha-amylase enzymatic activity. Further, trait salivary alpha-amylase negatively predicts retrieval, and this effect is reduced by L-DOPA treatment. Importantly, the latter findings result from non-preregistered analyses and thus further investigation is needed.

Dataset on the effects of psychological care on depression and suicide ideation in underrepresented children.

Massive increases in the risks of depressive disorders and the ensuing suicide have become the overarching menace for children/adolescents. Despite global consensus to instigate psychological healthcare policy for these children/adolescents, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in underprivileged children/adolescents in lower-middle-economic-status countries/areas, the data collection may not be as equally accessible as in developed countries/areas, thus resulting in underrepresented observations. To address these challenges, we released a large-scale and multi-center cohort dataset (n = 249,772) showing the effects of primary psychological healthcare on decreasing depression and suicidal ideation in these children/adolescents who were underrepresented in previous studies or current healthcare systems, including unattended children/adolescents, orphans, children/adolescents in especially difficult circumstances, and "left-behind" and "single-parenting" children/adolescents. We provided all individual data recording the depressive symptoms and suicide ideation that had been collected at baseline (Oct 2022) and half-year follow-up (May 2023) from practicing this psychological healthcare system.

A unified approach to demographic data collection for research with young children across diverse cultures.

Culture is a key determinant of children's development both in its own right and as a measure of generalizability of developmental phenomena. Studying the role of culture in development requires information about participants' demographic backgrounds. However, both reporting and treatment of demographic data are limited and inconsistent in child development research. A barrier to reporting demographic data in a consistent fashion is that no standardized tool currently exists to collect these data. Variation in cultural expectations, family structures, and life circumstances across communities make the creation of a unifying instrument challenging. Here, we present a framework to standardize demographic reporting for early child development (birth to 3 years of age), focusing on six core sociodemographic construct categories: biological information, gestational status, health status, community of descent, caregiving environment, and socioeconomic status. For each category, we discuss potential constructs and measurement items and provide guidance for their use and adaptation to diverse contexts. These items are stored in an open repository of context-adapted questionnaires that provide a consistent approach to obtaining and reporting demographic information so that these data can be archived and shared in a more standardized format. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.

BACKGROUND: The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS: Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS: A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (ß = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS: Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.

Importance: Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. Objective: To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. Evidence Review: PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. Findings: A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). Conclusions and Relevance: This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Obliviate! Reviewing Neural Fundamentals of Intentional Forgetting from a Meta-Analytic Perspective.

Intentional forgetting (IF) is an important adaptive mechanism necessary for correct memory functioning, optimal psychological wellbeing, and appropriate daily performance. Due to its complexity, the neuropsychological processes that give birth to successful intentional forgetting are not yet clearly known. In this study, we used two different meta-analytic algorithms, Activation Likelihood Estimation (ALE) & Latent Dirichlet Allocation (LDA) to quantitatively assess the neural correlates of IF and to evaluate the degree of compatibility between the proposed neurobiological models and the existing brain imaging data. We found that IF involves the interaction of two networks, the main "core regions" consisting of a primarily right-lateralized frontal-parietal circuit that is activated irrespective of the paradigm used and sample characteristics and a second less constrained "supportive network" that involves frontal-hippocampal interactions when IF takes place. Additionally, our results support the validity of the inhibitory or thought suppression hypothesis. The presence of a neural signature of IF that is stable regardless of experimental paradigms is a promising finding that may open new venues for the development of effective clinical interventions.

Situational factors shape moral judgements in the trolley dilemma in Eastern, Southern and Western countries in a culturally diverse sample.

The study of moral judgements often centres on moral dilemmas in which options consistent with deontological perspectives (that is, emphasizing rules, individual rights and duties) are in conflict with options consistent with utilitarian judgements (that is, following the greater good based on consequences). Greene et al. (2009) showed that psychological and situational factors (for example, the intent of the agent or the presence of physical contact between the agent and the victim) can play an important role in moral dilemma judgements (for example, the trolley problem). Our knowledge is limited concerning both the universality of these effects outside the United States and the impact of culture on the situational and psychological factors affecting moral judgements. Thus, we empirically tested the universality of the effects of intent and personal force on moral dilemma judgements by replicating the experiments of Greene et al. in 45 countries from all inhabited continents. We found that personal force and its interaction with intention exert influence on moral judgements in the US and Western cultural clusters, replicating and expanding the original findings. Moreover, the personal force effect was present in all cultural clusters, suggesting it is culturally universal. The evidence for the cultural universality of the interaction effect was inconclusive in the Eastern and Southern cultural clusters (depending on exclusion criteria). We found no strong association between collectivism/individualism and moral dilemma judgements.

Engaging the open science framework in quantifying and tracing scientists' research credits.

Preface illustration. The "first-last-author-credit" hierarchy has long been dominated in the scientific incentive system despite intensive calling for contribution-based credits (author contribution statement). In the scientific communities, senior researchers would still make a decision to recommend one's promotion based on first and last positions in authorship rather than their contributions. Similarly, in the job market, institutions would acknowledge one's credit by positions in authorship in a study for faculty recruitment, while overlooking the author contribution statement at the end of studies. Thus, the current authorship system has brought on the risks underlying authorship disputes and race/gender inequalities in credit allocation heavily, especially for early career researchers and female scientists. In addition, this is one of the major barriers to extend teamwork and academic collaboration. On the contrary, scrambling for first and last positions leads to prominent credit inflation-that is to be observed-the number of co-first and co-corresponding authors has been increasing dramatically. Thus, we shall propose a new contributionship to acknowledge the author's credit for an open science and quantitative framework to tackle these issues. Credit: ZC and XRL.

Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective.

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

The symptom structure of posttraumatic stress disorder and co-morbid depression among college students with childhood abuse experience: A network analysis.

BACKGROUND: Posttraumatic stress disorder (PTSD) and depression are highly co-morbid among individuals with childhood abuse history, while the mechanism of the co-morbidity is highly debated. This study sought to extent the work among college students with network analysis, which is a novel method that sees the co-morbidity from a symptom interacting perspective. METHODS: Data was collected from 476 college students who were assessed to have childhood abuse history, PTSD and depression at the same time, using Childhood Trauma Questionnaire- Short Form, PTSD Checklist for DSM-5 and The Center for Epidemiological Studies Depression. We created a Graphical Gaussian Model (GGM) network to show associations between symptom pairs and a Directed Acyclic Graph (DAG) to estimate potential casual relationships among symptoms. RESULTS: The GGM network was reliably stable, feeling sad (Depression) and trouble experiencing positive feelings (PTSD) were the most central nodes. Trouble experiencing positive feelings and several negative affect symptoms, sleep problems and difficulty in concentrating were acting as important bridging nodes. The DAG network suggested the key triggering roles of exaggerated startle (PTSD) and several re-experiencing symptoms. LIMITATIONS: The study used cross-sectional data and self-reported measures. Results from network analysis could be affected by scale factors and contain spurious correlations. CONCLUSIONS: In the childhood-abuse-related co-morbid structure, several negative affect symptoms both in PTSD and depression have pivotal roles, hyper-arousal symptoms and re-experiencing symptoms could trigger the co-morbid structure. Illustrating the strength and limitations of network analysis, this study help target the potentially influential symptoms for better clinical intervention.

Seeking the "Beauty Center" in the Brain: A Meta-Analysis of fMRI Studies of Beautiful Human Faces and Visual Art.

During the past two decades, cognitive neuroscientists have sought to elucidate the common neural basis of the experience of beauty. Still, empirical evidence for such common neural basis of different forms of beauty is not conclusive. To address this question, we performed an activation likelihood estimation (ALE) meta-analysis on the existing neuroimaging studies of beauty appreciation of faces and visual art by nonexpert adults (49 studies, 982 participants, meta-data are available at https://osf.io/s9xds/ ). We observed that perceiving these two forms of beauty activated distinct brain regions: While the beauty of faces convergently activated the left ventral striatum, the beauty of visual art convergently activated the anterior medial prefrontal cortex (aMPFC). However, a conjunction analysis failed to reveal any common brain regions for the beauty of visual art and faces. The implications of these results are discussed.