Identifying cognitive impairment in routine care is essential, yet many brief screening instruments exhibit ceiling or floor effects. To our knowledge, no widely used brief cognitive screening instrument has incorporated the qualitative Clock Drawing Test (CDT) command–copy discrepancy, in which the copied clock is better organized than the clock drawn to command, into a composite score.

In this cross-sectional study, patients with amnestic multidomain mild cognitive impairment (aMD-MCI) or early Alzheimer’s disease (AD) were consecutively recruited from a tertiary memory clinic at Salamanca University Hospital, Spain, and compared with cognitively unimpaired community-dwelling controls. All participants underwent the Mini-Mental State Examination and the CDT. CogniScan was derived post hoc from nine elements retained through binary partial least squares regression: five temporal-orientation items, three delayed-recall words and one CDT command–copy discrepancy indicator. Factor maps from the fitted model were used to visualize group separation, and receiver operating characteristic analyses were used to estimate apparent discriminative performance and candidate cutoffs.

The final sample included 183 older adults (median age, 75 years): 35 with aMD-MCI, 75 with early AD and 73 cognitively unimpaired controls. All nine CogniScan elements were strongly associated with cognitive status, with large effect sizes. In the primary binary analysis, discrimination was excellent (area under the curve [AUC] 0.99, 95% confidence interval 0.98–1.00). The Youden-optimized cutoff was ≥2 errors (sensitivity 96%, specificity 99%). In secondary exploratory analyses restricted to cognitively impaired participants, discrimination between aMD-MCI and early AD was lower (AUC 0.89, 95% confidence interval 0.82–0.97), consistent with partial overlap on the latent maps. In exploratory models, categorical educational level was not significantly associated with CogniScan score among controls and was not an independent predictor of cognitive status in the adjusted model.

CogniScan showed excellent apparent discriminative performance, but external validation across broader cognitive phenotypes and wider age and educational ranges is needed. It appears best suited as a binary detector of impairment rather than as a definitive stage classifier.

Subjective cognitive decline (SCD) may represent the earliest observable stage of Alzheimer’s disease (AD), yet identifying individuals at risk of progressing remains challenging. Cognitive dispersion, or intra-individual variability (IIV-D), may serve as a sensitive early marker. This study examined IIV-D across diagnostic groups, focusing on SCD and amnestic mild cognitive impairment (aMCI) progressors (SCD-p, aMCI-p; progressing to a more advanced disease stage) versus non-progressors (SCD-np, aMCI-np; not progressing to a more advanced stage). We expected greater IIV-D across groups (AD > aMCI > SCD > controls) and in progressors.

A total of 308 participants aged 65–94 (67 healthy controls [HC], 126 SCD, 79 aMCI, 36 AD) from the Consortium for the Early Identification of Alzheimer’s Disease – Quebec (CIMA-Q) were included. SCD and aMCI participants were followed for up to eight years (34 SCD-p, 92 SCD-np; 29 aMCI-p, 50 aMCI-np). Analyses of covariance assessed baseline across- and verbal memory within-domain IIV-D, maximum discrepancy (MD), and domain-specific deviation.

IIV-D increased with disease severity (HC = SCD < aMCI < AD). Among SCD participants, progressors showed greater episodic memory deviation than non-progressors, primarily driven by poorer Logical Memory delayed recall. In aMCI, progressors showed higher IIV-D across all indices (across- and within-domain, IIV-D and MD), with domain-specific differences limited to episodic memory.

These findings indicate that IIV-D measures distinguish aMCI progressors from non-progressors, although they do not appear to enhance predictive accuracy for progression to AD and may not yet be a reliable marker at the SCD stage.

Few studies have examined how differing diagnostic criteria for mild cognitive impairment (MCI) relate to neuroimaging markers of cerebrovascular disease and neurodegeneration in Veterans. We compared three MCI diagnostic schemes on their associations with white matter hyperintensity (WMH) burden, hippocampal volume, and cortical thickness in nondemented Vietnam-era Veterans.

228 Veterans (mean age = 69.65) were classified using: (1) Alzheimer’s Disease Neuroimaging Initiative (ADNI) criteria (subjective memory concerns, impaired Logical Memory, global Clinical Dementia Rating = 0.5); (2) neuropsychological criteria (>1 standard deviation [SD] below norms on two tests within a domain or one test across three domains); and (3) typical criteria (subjective memory concerns and >1.5 SD below norms on one test). Regression models predicting WMH burden adjusted for age and intracranial volume and included MCI status and hippocampal volume; ADNI-based models also included posttraumatic stress disorder symptom severity.

Neuropsychological criteria were associated with greater WMH burden (β = 0.45, p = .024), whereas typical and ADNI criteria were not. Sensitivity analyses found that meeting neuropsychological criteria for amnestic MCI interacted with lower hippocampal volume to predict greater WMH burden (β = −0.001, p = .028). No criteria were associated with cortical thickness.

Neuropsychological criteria more sensitively identified Veterans with greater WMH burden and demonstrated a small, hippocampal volume-dependent effect in amnestic MCI. These findings support the clinical utility of multi-test neuropsychological approaches for detecting complex brain changes in high-comorbidity populations, with implications for risk stratification and targeted intervention in aging Veterans.

To characterize qualitative Clock Drawing Test (CDT) error profiles across dementia etiologies and mild cognitive impairment (MCI), and to propose a clinically interpretable six-class framework.

In a hospital-based study in Kolkata, India, consecutive adults with cognitive impairment completed a free-drawn “ten past ten” CDT. Errors were coded using classical qualitative categories and clock components (face, numbers, hands), then collapsed into six classes: conceptual, stimulus-bound/perseveration, spatial, planning, number-related, and graphic-conceptual. For nonexclusive domains, omnibus Pearson χ² tests summarized error distributions across diagnoses; Cramér’s V quantified effect size.

Participants included Alzheimer’s disease (AD; n = 36), vascular dementia (VaD; n = 16), behavioral variant frontotemporal dementia (bvFTD; n = 9), MCI (n = 19), and other conditions (n = 22). Although 50.0% drew a normal clock face, only 11.8% achieved perfect numbering and 12.7% set the hands correctly. Conceptual errors were most frequent (70.6%), followed by spatial errors (47.1%); neglect and counterclockwise numbering were rare (3.9% each). Error distributions differed by diagnosis for face, numbers, and hands (all p < 0.001; V = 0.4–0.5). The six-class scheme retained a significant distributional association with diagnosis (χ² = 43.365, p = 0.002; V = 0.3): bvFTD showed prominent conceptual and graphic-conceptual failures, AD combined conceptual and spatial errors, MCI emphasized spatial and number-related errors, and VaD was heterogeneous.

Qualitative CDT profiles vary meaningfully across cognitive disorders. This concise six-class framework captures clinically salient patterns, especially in severely degraded drawings, and may complement brief memory screening and digital CDT metrics.

Dementia is a group of symptoms, characterised by a loss of cognition that interferes with everyday tasks, difficulty focusing, planning, problem solving, and behavioural changes, such as apathy, anxiety, or depression. The leading cause of dementia is Alzheimer’s disease, but vascular dementia or mild cognitive impairment are also frequently occurring. There are six drugs legislated in Europe for use in the treatment of dementia. There are unmet clinical needs to find more effective, better tolerated or complementary therapeutic options. The aim of this study is to comprehensively analyse the results of clinical trials and other human studies regarding the efficacy and safety of herbal interventions used in patients with dementia.

We enrolled a total of 48 studies for this systematic review, of which 27 were included into the statistical analysis of effect size (Cohen’s d).

We found significant improvements mainly after administration of Ginkgo biloba, Crocus sativus, Salvia officinalis, and Melissa officinalis. It should be emphasised that some herbs and herbal formulations demonstrated efficacy comparable to that of donepezil, a widely used and approved medication, suggesting potential for phytopharmaceutical therapies as complementary approaches. In some studies, the observed effects were similar to those reported for conventional treatments, indicating promising directions for further research in Alzheimer’s disease and dementia.

In light of the evidence, phytopharmaceuticals have a promising role as a co-therapeutic option or alternative for patients with dementia who do not tolerate or have contraindications to standard medications. However, further research is necessary to translate these initial promising results into clinical practice.

This study aimed to make a head-to-head comparison of the diagnostic accuracy and cross-cultural applicability of abbreviated 20-item versions of the Copenhagen Cross-Linguistic Naming Test (C-CLNT20) and Naming Assessment in Multicultural Europe (NAME20).

The present study was conducted in a multicultural and multilingual patient sample from memory clinics across five European countries. Receiver operating characteristic curve analysis was used to assess the diagnostic accuracy of C-CLNT20 and NAME20 in classifying dementia and mild cognitive impairment (MCI). Binary logistic regression analysis was performed to evaluate the influence of demographic and cultural factors on diagnostic accuracy.

C-CLNT20 and NAME20 showed acceptable diagnostic accuracy for dementia with areas under the curve (AUC) of .75 and .82, respectively, but had low accuracy for MCI (AUC of .64 and .62, respectively). Compared to C-CLNT20, NAME20 had slightly higher, but statistically non-significant, AUCs for dementia in both in the full sample and in participants with immigrant background. The diagnostic accuracy of the C-CLNT20 and NAME20 was not significantly influenced by education and immigrant status in the full sample, or by acculturation and use of an interpreter in participants with immigrant background.

Both C-CLNT20 and NAME20 are promising brief alternatives to the full versions of the naming tests when time is limited. They also present a promising alternative to other established naming tests by maintaining diagnostic accuracy while showing minimal cross-cultural and cross-linguistic bias.

Subjective cognitive complaints are poor predictors of neurodegenerative disease and future dementia. Errors in metacognition, positive or negative differences between actual and perceived performance, may partially explain this. We aimed to assess whether hypothesized indicators of underlying neurodegenerative factors (e.g. hippocampal atrophy) in mild cognitive impairment (MCI) were associated with overestimation of actual cognitive performance, and hypothesized non-degenerative factors (e.g. depression) were associated with underestimation of performance.

Metacognitive error was estimated from paired subjective and objective cognitive assessments using the Multifactorial Memory Questionnaire and Addenbrooke’s Cognitive Examination – Revised, respectively. A normative model was developed with cognitively healthy older adults (n = 36), and applied to individuals with suspected MCI due to Alzheimer’s disease (AD) or MCI with Lewy bodies (total n = 88). Theorized predictors of subjective overestimation or underestimation of performance (metacognitive error) were assessed, including demographics, AD biomarkers, and mental and physical ill health. Metacognitive error was also assessed as a predictor of conversion to dementia.

Underestimation of cognitive function was associated with depressive symptoms, anxiety, and self-reported autonomic symptoms. Overestimation of cognitive function was associated with age, hippocampal atrophy, plasma glial fibrillary acidic protein, and subsequent dementia conversion.

Underestimation of cognitive function may reflect functional cognitive changes linked to mental and physical ill health, while overestimation of function may be a marker of neurodegenerative changes. Quantifying metacognitive error may provide a noninvasive screening tool for progressive MCI, requiring investigation in an independent sample.

Mild cognitive impairment (MCI) involves measurable cognitive decline that does not yet significantly disrupt daily functioning but may signal increased risk of dementia. Reliable prediction of dementia conversion in MCI is essential for early intervention and optimized clinical trial design. This study aimed to evaluate the predictive performance of various machine learning (ML) classification algorithms using clinical and neuropsychological data.

Data were drawn from the Gothenburg MCI Study and included 347 patients from a memory clinic, of whom 84 (24%) converted to dementia within two to six years. We applied 11 ML classification algorithms (logistic regression, linear discriminant analysis, naïve Bayes, k-nearest neighbors, LASSO, ridge regression, elastic net, decision tree, random forest, gradient boosting, and support vector machine (SVM)) to predict dementia conversion based on 54 clinical predictors (e.g., cerebrospinal fluid biomarkers, neuropsychological test scores, comorbidities, and demographics). In a second step, we included delta scores reflecting change in neuropsychological test performance from baseline to follow-up.

Without delta scores, LASSO, ridge, elastic net, random forest, and SVM performed best, achieving accuracy ≥0.87, kappa = 0.64, and AUC-ROC ≥0.90. These models demonstrated high specificity (0.94) but moderate sensitivity (0.68). Including delta scores improved performance, with ridge and elastic net achieving accuracy = 0.90, kappa = 0.73 and 0.72, AUC-ROC = 0.94, specificity = 0.96, and sensitivity = 0.73. The elastic net model yielded a positive predictive value of 0.85 and a negative predictive value of 0.92.

ML models incorporating clinical and cognitive change data can accurately predict dementia conversion in MCI, supporting their utility in clinical decision-making.

Individuals with mild cognitive impairment (MCI) and dementia often engage in self-regulatory driving behaviours, but less is known about individuals with subjective cognitive impairment (SCI), who perceive cognitive decline without objective evidence of it.

This study describes the driving status and habits of older Canadians in the COMPASS-ND data set (n = 955) across cognitive groups: cognitively unimpaired (CU), SCI, MCI, and dementia.

Most participants reported having a driver’s license without restrictions, including over half of the Dementia group, who differed from the other groups by driving less often, shorter distances, and with greater restrictions. Interestingly, on driving frequency and restriction measures, the SCI group showed greater similarity to the MCI than the CU group. Females reported driving less frequently, shorter distances, and with more restrictions than males across cognitive groups.

Results suggest that cognitive status and sex influence the driving habits of older adults, with potential implications for autonomy and independent mobility.

This study aimed to update normative data and establish cut-off scores for a fruit-based semantic verbal fluency (SVF) task among older Taiwanese adults as a method for detecting mild cognitive impairment (MCI). The task was chosen due to its familiarity and cultural neutrality for Mandarin-speaking populations.

SVF performance was evaluated in 245 healthy control participants and 360 individuals diagnosed with MCI. The influence of demographic variables was examined, and regression-based correction formulas were developed. Receiver operating characteristic (ROC) analyses determined optimal cut-off values according to established clinical classifications of MCI.

Age, education, and sex significantly influenced SVF performance. A demographically corrected 15th percentile threshold of 10 words was proposed for community screening. An optimal ROC-derived cut-off of 11.5 words yielded an AUC of .716 (95% CI: .68–.76), with sensitivity of 57.8% and specificity of 73.9%. SVF scores were significantly correlated with global cognition, memory, and processing speed.

The fruit-based SVF task is a quick, culturally relevant tool for detecting early cognitive impairment. Revised norms and cut-off scores can improve MCI identification in Mandarin-speaking seniors.

Engagement in social, physical, and cognitive activities is beneficial for maintaining cognitive health in later life by providing cognitive reserves against cognitive and neurodegenerative decline.

Insight is needed to understand how different activities combine to provide cognitive protection before and after the beginning of decline.

The current work used a cross-sectional data set of older adults who were cognitively unimpaired (CU), live with subjective cognitive impairment (SCI), live with mild cognitive impairment (MCI), or live with Alzheimer’s disease. Beneficial behaviors included easily modifiable risk factors for dementia in late life: engagement in social, creative, and physical activities. The study explored individual and combined effects on the relationships between hippocampal volume and memory.

Greater engagement in beneficial behaviors minimized the neural–cognitive relationship in the SCI group. Once disease progression continued to MCI, risk factors no longer modified the brain-cognition relationship.

Understanding how individual behaviors combine provides guidance when developing intervention trials or public policy procedures.

The trajectory of Mild Cognitive Impairment (MCI) to dementia within primary care is not well understood.

We investigated the 5-year trajectory of patients initially diagnosed with MCI, evaluated their risk of developing dementia considering age, sex, and Montreal Cognitive Assessment (MoCA) test scores and determined the annual conversion rate from MCI to dementia for patients assessed in a MINT (Multispecialty Interprofessional Team) memory clinic.

We conducted a longitudinal cohort study using a retrospective chart review of 751 patients assessed within a MINT memory clinic in Ontario, Canada. The conversion rate from MCI to dementia was estimated with the Kaplan-Meier method. Cox regression examined time to dementia diagnosis and the association between baseline MoCA scores and dementia risk.

The observed 5-year conversion rate from MCI to dementia was 28.0%, though with limited follow-up data. Accounting for missing data, the estimated 5-year conversion rate was 48.8% (39.5%, 59.2%) with an average annual rate of 9.8%. Each one-point increase in MoCA score at initial visit was associated with a 10% lower rate of conversion to dementia (aHR: 0.90, 95%CI: 0.85-0.96).

Findings highlight the profile of patients assessed in MINT clinics, cognitive trajectory of those diagnosed with MCI, and the importance of primary care-based memory clinics in early detection and intervention.