Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. FDRs-BD had significantly larger ICV (d = +0.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct.

As longevity increases, cognitive decline in older adults has become a growing concern. Consequently, an increasing interest in the potential of digital tools (e.g., serious games (SG) and virtual reality (VR)) for early screening of Mild Cognitive Impairment (MCI) is emerging. Traditional cognitive assessments like the Mini‐Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are widely used but have limitations related to cultural bias and manual scoring, while their digital adaptations, such as MOCA‐CC, maintain diagnostic accuracy while offering remote administration and automated scoring. Innovative tools, such as the Virtual Super Market (VSM) test and Panoramix Suite, instead, assess cognitive domains like memory, attention, and executive function while promoting engagement and preserving ecological validity, making assessments more reflective of real‐world tasks. Several studies show that these tools exhibit strong diagnostic performance, with sensitivity and specificity often exceeding 80%. However, although digital tools offer advantages in accessibility and user engagement, challenges remain concerning technological literacy, data privacy, and long‐term validation. Future research should focus on validating these tools across diverse populations and exploring hybrid models that combine traditional and digital assessments, as digital tools show promise in transforming cognitive screening and enabling earlier interventions for cognitive decline.This article is categorized under: Psychology > Development and Aging Neuroscience > Cognition

Mild cognitive impairment (MCI) represents an intermediate stage between typical aging and early cognitive decline. As such, an early and accurate diagnosis is essential in making timely interventions. Digital tools, including mobile applications, web platforms, wearable devices, and artificial intelligence-driven systems, have been developed and validated to capture multidimensional data, offering innovative screening solutions. This meta-analysis aims to evaluate the diagnostic accuracy of digital tools for MCI detection in different populations and settings, with a particular focus on three key issues: (i) the overall diagnostic performance of digital tools, (ii) the influence of methodological quality of studies, and (iii) the impact of demographic factors and familiarity with technologies on diagnostic accuracy. This meta-analysis assessed diagnostic accuracy across 32 studies, reporting pooled sensitivity (0.808, 95% CI: 0.775–0.838) and specificity (0.795, 95% CI: 0.757–0.828), but with considerable heterogeneity ( I ² = 71.5% sensitivity; 84.0% specificity). The HSROC analysis revealed significant intrinsic variability ( τ ₐ = 0.807) and minimal threshold variability ( τθ = 0.291). Meta-regression indicated that applicability concerns significantly reduced specificity ( p = 0.037), with older age also predicting lower specificity ( p = 0.029). Thus, implementing standardized protocols, rigorous validation processes, and targeted adaptations are crucial steps for enhancing the effectiveness of digital tools in detecting MCI.

Background: Previous models suggest biological and behavioral continua among healthy individuals (HC), at-risk condition, and full-blown schizophrenia (SCZ). Part of these continua may be captured by schizotypy, which shares subclinical traits and biological phenotypes with SCZ, including thalamic structural abnormalities. In this regard, previous findings have suggested that multivariate volumetric patterns of individual thalamic nuclei discriminate HC from SCZ. These results were obtained using machine learning, which allows case–control classification at the single-subject level. However, machine learning accuracy is usually unsatisfactory possibly due to phenotype heterogeneity. Indeed, a source of misclassification may be related to thalamic structural characteristics of those HC with high schizotypy, which may resemble structural abnormalities of SCZ. We hypothesized that thalamic structural heterogeneity is related to schizotypy, such that high schizotypal burden would implicate misclassification of those HC whose thalamic patterns resemble SCZ abnormalities.Methods: Following a previous report, we used Random Forests to predict diagnosis in a case–control sample (SCZ = 131, HC = 255) based on thalamic nuclei gray matter volumes estimates. Then, we investigated whether the likelihood to be classified as SCZ (π-SCZ) was associated with schizotypy in 174 HC, evaluated with the Schizotypal Personality Questionnaire.Results: Prediction accuracy was 72.5%. Misclassified HC had higher positive schizotypy scores, which were correlated with π-SCZ. Results were specific to thalamic rather than whole-brain structural features.Conclusions: These findings strengthen the relevance of thalamic structural abnormalities to SCZ and suggest that multivariate thalamic patterns are correlates of the continuum between schizotypy in HC and the full-blown disease.

Several studies underlined the negative effects of forced social isolation on emotional processes in younger population. The current study aimed to review existing evidence of the pandemic’s impact on the emotional regulation of Italian children aged 0–12 years in order to identify personal and contextual factors that may adversely impact their developmental process. Different electronic databases (Web of Science, APA PsycInfo, APA PsycArticles, MEDLINE, Psychology and Behavioral Sciences Collection, and Scopus) were used to identify peer-reviewed studies published in English and Italian. Thirteen studies were included in the review, covering a total of 18.843 children. All studies reported negative effects of the lockdown on a child’s emotional processes. The most affected were children aged 3–5 years, those living in Northern Italy, and those with low socioeconomic status (SES) families. Alterations in emotional processes were associated with sleep disturbances, quality of family relationships, personality structures, the coping strategies used, and time spent with technological devices. Finally, two- (time × parenting) and three-way (time × parenting × environmental sensitivity) interactions resulted significantly in predicting a child’s emotional regulation, respectively, in terms of externalizing and internalizing behaviors. This review remarks that children’s emotional processes were negatively impacted during social lockdown, especially where acute social isolation interacted with a set of dispositional and situational risk factors.

Recent investigations have suggested that both metacognition and emotions play important roles in second language (L2) learning. However, the majority of these studies focused on negative emotions, such as anxiety, or considered high school or university students, neglecting primary school age students, which is frequently the starting period in which a L2 is learned. Moreover, none of these studies investigated both metacognition and emotions in relation to L2 learning. Thus, the present study aimed to explore within a short-term longitudinal design the role of both metacognition and emotions in learning English as a L2 starting from an early stage, namely, in primary school. We included 305 Italian 2nd and 3rd graders ( M _age = 7.44 years; SD = 0.59; 48.5% females). They were evaluated twice, at the beginning (T0) and the end of the school year (T1), on an English vocabulary test, metacognitive monitoring and control of performance on the English vocabulary test, and achievement emotions (i.e., enjoyment, anxiety, and boredom) related to learning English as a L2. Basic cognitive abilities were also assessed at T0. The results showed that both metacognitive monitoring and control abilities significantly predicted children’s English vocabulary improvements from the first to the second test administration points. Among the achievement emotions, only boredom was a strong predictor of English vocabulary learning. These outcomes showed that metacognitive and emotional variables are relevant beginning at the early stage of L2 learning.