ImportanceNeuroimaging-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.ObjectiveTo systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.Evidence ReviewPubMed 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.FindingsA 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 RelevanceThis 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.

The COVID-19 pandemic prominently hit almost all the aspects of our life, especially in routine education. For public health security, online learning has to be enforced to replace classroom learning. Thus, it is a priority to clarify how these changes impacted students. We built a random-effect model of a meta-analysis to pool individual effect sizes for published articles concerning the attitudes and performance towards online learning. Databases included Google Scholar, PubMed and (Chinese) CNKI repository. Further, a moderated analysis and meta-regression were further used to clarify potential heterogenous factors impacting this pooled effect. Forty published papers (n = 98,558) were screened that were eligible for formal analysis. Meta-analytic results demonstrated that 13.3% (95% CI: 10.0–17.5) of students possessed negative attitudes towards online learning during the COVID-19 pandemic. A total of 12.7% (95% CI: 9.6–16.8) students were found to report poor performance in online learning. Moderated analysis revealed poor performance in online learning in the early pandemic (p = 0.006). Results for the meta-regression analysis showed that negative attitudes could predict poor learning performance significantly (p = 0.026). In conclusion, online learning that is caused by COVID-19 pandemic may have brought about negative learning attitudes and poorer learning performance compared to classroom learning, especially in the early pandemic.

Neurofunctional dysregulations in spatially discrete areas or isolated pathways have been suggested as neural markers for attention deficit hyperactivity disorder (ADHD). However, multiscale perspectives into the neurobiological underpins of ADHD spanning multiple biological systems remain sparse. This points to the need of multi-levels of analysis encompassing brain functional organization and its correlation with molecular and cell-specific transcriptional signatures are stressed. Here, we capitalized on diffusion mapping embedding model to derive the functional connectome gradient, and deployed multivariate partial least square (PLS) method to uncover the enrichment of neurotransmitomic, cellular and chromosomal connectome-transcriptional signatures of ADHD. Compared to typical control, ADHD children presented connectopic cortical perturbations in lateral orbito-frontal and superior temporal regions, which had also been validated in another independent sample. This gradient-derived variants in ADHD further aligned spatially with distributions of GABAA/BZ and 5-HT2A receptors and co-varied with genetic transcriptional expression. Cognitive decoding and gene-expression annotation showed the correlates of these variants in memory, emotional regulation and spatial attention. Moreover, the gradient-derived transcriptional signatures of ADHD exhibited enriched expression of oligodendrocyte precursors and endothelial cells, and were mainly involved as variants of chromosome 18, 19 and X. In conclusion, our findings bridged in-vivo neuroimging assessed functional brain organization patterns to a multi-level molecular pathway in ADHD, possibly shedding light on the interrelation of biological systems that may coalesce to the emergence of this disorder.

Creativity serves as a fountain for social and scientific development. As one of the most crucial human capabilities, creativity has been believed to be supported by the core component of higher cognitive functions—working memory capacity (WMC). However, the evidence supporting the association between WMC and creativity remains contradictory. Here, we conducted a meta-analysis using random-effects models to investigate the linear association between WMC and creativity by pooling the individual effect size from the previous literature. Further, a subgroup analysis was performed to examine whether such association is specific for different WMC categories (i.e., verbal WMC, visual–spatial WMC and dual-task WMC). The main meta-analytic results showed a significantly positive association between WMC and creativity (r = .083, 95% CI: .050–.115, p < .001, n = 3,104, k = 28). The subgroup analysis demonstrated consistent results by showing a significantly positive association between them, irrespective of WMC category. We also found that cultural environments could moderate this association, and we identified a strong correlation in participants from an Asian cultural context. In conclusion, this study provides the evidence to clarify the positive association between WMC and creativity, and implies that the Asian cultural context may boost such an association.