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.

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

Purpose: Social isolation and loneliness are increasingly recognized as critical psychosocial determinants of cognitive aging and dementia risk. By adopting a multidimensional approach, this study examines how both objective and subjective features of social connectedness relate to cognitive functioning across healthy and older cognitively normal and impaired Italian adults. Methods: In Experiment 1, 662 healthy participants ( M _age= 51.7 years) completed a brief assessment, including measures of general cognition, associative memory and social cognition, subjective cognitive decline and questionnaires on social connectedness. In Experiment 2, 141 older adults ( M _age= 64.29 years) were categorized as cognitively healthy (CH; n=68) and cognitively impaired (CI; n=73) after a comprehensive neuropsychological evaluation. Results: Experiment 1 regression analyses revealed that larger social networks and cohabitation were associated with better associative memory and social cognition, while higher loneliness predicted greater subjective cognitive decline. Notably, the results observed in the larger sample were partially confirmed in experiment 2, with CI group having worse scores in delayed free recall of the Italian Face-Name Association Test. Furthermore, depression, rather than loneliness, was a significant predictor of subjective cognitive complaints, independent of cognitive status. Conclusion: These findings underscore the distinct and interactive roles of objective and subjective social isolation in shaping cognitive trajectories and highlight the importance of integrating psychosocial variables into dementia prevention strategies.

Growing interest in noninvasive neuromodulation posits 40 Hz sensory stimulation as a potential intervention for Alzheimer's disease. Early studies suggested that 40 Hz visual flicker entrains gamma activity and reduces amyloid pathology; however, later work found either absent entrainment or even increased amyloid burden, revealing strong dependence on disease stage, network integrity, and stimulation parameters. Initial clinical investigations suggest that 40 Hz stimulation is safe, well-tolerated, and capable of modulating cortical network dynamics and improving cognition, despite limited effects on amyloid burden. These findings suggest a shift from molecular pathology to network plasticity as the primary target of gamma stimulation.

The concept of brain reserve offers a framework for understanding the mechanisms of cognitive resilience in later life. This study investigated whether a static measure of brain structure, a proxy for accumulated reserve, or a dynamic measure of atrophy is a more powerful predictor of future cognitive changes. Using Alzheimer's disease (AD) as a model of accelerated cognitive aging, we investigated this question across the neurocognitive continuum. We analyzed longitudinal structural MRI and neuropsychological data from 75 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (Healthy, MCI, Dementia) using LASSO regularization and logistic regression to identify associations with cognitive change and model MCI conversion. Baseline brain volumes were robust predictors of cognitive decline, significantly outperforming short-term volumetric changes (atrophy rate). Specifically, larger lateral ventricles and smaller hippocampal and thalamic volumes, particularly in the anterior and medial thalamic nuclei, were associated with greater impairment in memory and executive function. Importantly, while APOE ϵ4 carrier status was high across the MCI group (∼56%), it did not differentiate converters from non-converters, indicating that genetic risk alone does not account for divergent clinical trajectories. Our model, based on these baseline volumes, predicted MCI conversion with good accuracy (AUC = .82). These findings highlight the importance of structural brain reserve, as a proxy for the resilience of integrated neural networks against neurodegeneration. Our results underscore the clinical utility of a single baseline MRI scan for identifying individuals at risk and advancing a network-based understanding of neurodegeneration.