Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring最新文献

Introduction: The clock drawing test (CDT) screens for dementia but requires trained scorers and lacks standardized criteria. Thus, we developed an automated vision transformer (ViT)-based diagnostic system with convolutional neural network preprocessing for analyzing hand-drawn CDT images.

Methods: The architecture implements fine-tuned ViT feature extraction with linear classification for dementia prediction. Training used the National Health and Aging Trends Study (NHATS) dataset (n = 54,027), with testing on an independent clinical cohort from the Toronto Dementia Research Alliance (TDRA; n = 862; 522 dementia, 340 normal cognition).

Results: The ViT approach predicted dementia with 76.5% balanced accuracy, outperforming human-scored features (74.3%) and existing deep learning models (MiniVGG = 73.3%, MobileNetV2 = 72.3%, relevance factor variational autoencoder = 69.1%) on the TDRA dataset.

Discussion: This pen-and-paper compatible diagnostic system enables scalable remote cognitive screening through automated CDT image analysis that is competitive with human-scored features, potentially increasing diagnostic accessibility for diverse populations across varied socioeconomic contexts.

Highlights: The vision transformer model achieves 76.5% accuracy in dementia detection from clock drawing tests, outperforming human scoring and existing deep learning methods.Novel convolutional neural network-based preprocessing automatically handles challenging image quality issues like shadows, irrelevant markings, and improper cropping.The system requires only a photo of a hand-drawn clock test, enabling scalable remote screening accessible across socioeconomic contexts.A feature-extraction model trained on 54,027 samples demonstrates robust generalization to an independent clinical dataset of 862 patients.This fully automated approach eliminates the need for trained scorers while maintaining diagnostic accuracy above manual methods.

Background: Optical coherence tomography (OCT) and OCT angiography (OCT-A) have been studied as biomarkers for Alzheimer's disease (AD), with promising results. Nevertheless, their potential in the logopenic variant of primary progressive aphasia (lvPPA), which shares the same amyloid pathology, has not yet been explored. This work aimed to characterize retinal changes in lvPPA compared to healthy controls.

Methods: Ten participants with lvPPA and eleven controls underwent OCT and OCT-A imaging. Amyloid pathology in lvPPA was confirmed by lumbar puncture. Retinal parameters included retinal nerve fiber layer (RNFL) thickness and foveal avascular zone (FAZ).

Results: Compared to controls, lvPPA participants exhibited reduced RNFL thickness in the temporal sector (p = 0.013) and significantly decreased FAZ circularity (p = 0.002).

Discussion: RNFL thinning may reflect trans-synaptic degeneration from cortical atrophy, while reduced FAZ circularity suggests early microvascular changes related to amyloid burden. Our findings support OCT and OCT-A as potential biomarkers for lvPPA.

Highlights: For the first time, OCT and OCT-A are studied as potential biomarkers for lvPPA.Compared to healthy controls, retinal nerve thickness is decreased in lvPPA patients.Retinal vasculature exhibits structural alterations in lvPPA patients.

[This corrects the article DOI: 10.1002/dad2.70174.].

Introduction: This study aimed to determine whether simulated driving performance can reliably predict cognitive impairment in stroke survivors.

Methods: Cognitively impaired (n = 35) and normal (n = 54) stroke survivors completed a simulated driving course with reactive, distracted, and route-planning sections. Performance was assessed using lane departures, average speed, brake reaction time, task completion time, and route accuracy.

Results: Logistic regression models correctly distinguished cognitive status in 77.5% of cases for reactive and distracted driving, and 80.9% for route planning. Notably, the route planning task also achieved the highest classification rate of cognitively impaired participants (∼70%). Receiver operating characteristic (ROC) analyses on the strongest predictors from each driving section revealed significant areas under the curve (AUCs), with optimal cutoffs identifying cognitively impaired participants at 70%-80% accuracy.

Discussion: These findings provide a critical foundation for developing simulator-based assessments as practical, functionally relevant screening tools for identifying cognitive impairment and determining driving readiness post-stroke.

Highlights: Stroke survivors were tested on simulated driving tasks.Driving metrics were lane departures, speed, reaction time, and route accuracy.Cognitive status was predicted with greater than 75% accuracy.Simulators may be a clinical tool for assessing post-stroke driving readiness.

Introduction: It is unclear how dementia affects loss in life expectancy (LE). In this registry-based study, we aimed to study sex differences in LE and loss in LE in dementia, mild cognitive impairment (MCI), and subjective cognitive decline (SCD).

Methods: A total of 16,358 patients diagnosed with dementia, MCI, or SCD from the Norwegian Registry of Persons Assessed for Cognitive Symptoms (NorCog) during 2009-2022 were included and followed up for mortality. Sex differences in LE and loss in LE were predicted using flexible parametric survival models and sex-specific mortality in the general population as reference.

Results: Among dementia patients, women with dementia had the largest loss in LE: 17 years loss at 60 years; correspondingly, men lost 13.5 years. Similar patterns were observed for MCI and dementia subtypes.

Discussion: Women with dementia or MCI had a larger loss in LE compared to men with these diagnoses.

Highlights: Women with dementia had the largest loss in life expectancy compared to the general population.The excess female loss in life expectancy was also evident for all the dementia subtypes and for mild cognitive impairment.The loss in life expectancy was more pronounced in younger patients with dementia, with a loss of 17 years in women at 60 years of age. Men, in comparison, lost 13.5 years at the same age.Subjective cognitive decline was associated with a minor loss in life expectancy in both sexes.

Introduction: The discrepancy between biological and modeled brain ages-the brain-age gap (BAG)-could indicate potential neuropsychological changes. This study verified if and how longitudinal BAG changes were associated with neuropsychological functions and Alzheimer's disease-related biomarkers in individuals with mild cognitive impairment (MCI).

Methods: One hundred thirty-eight individuals with MCI and 103 healthy controls (HCs) with three rounds of magnetic resonance imaging scanning were selected from the Alzheimer's Disease Neuroimaging Initiative. We applied support vector regression on functional connectivity for modeling the brain age and further calculated the BAG.

Results: Longitudinal BAG changes were higher in participants with MCI compared to HCs. Larger BAG fluctuations were correlated with poorer cognitive performance and more severe depressive symptoms in patients with MCI. Neurofilament light chain and phosphorylated tau levels were associated with the longitudinal BAG changes.

Discussion: Present findings demonstrated the necessity of incorporating longitudinal BAG in monitoring the neuropsychological status among cognitively vulnerable populations.

Highlights: Brain-age gap (BAG) changes are sensitive indicators of cognitive vulnerability in aging.BAG changes were larger in patients with mild cognitive impairment than in the controls.Longitudinal BAG changes were associated with worse cognitive-affective states.The plasma neurofilament light chain and cerebrospinal fluid phosphorylated tau levels were associated with the BAG changes.

Introduction: Machine learning applied to neuroimaging can help with medical diagnosis and early detection by identifying biomarkers of subtle changes in brain structure and function. The effectiveness of advanced diffusion MRI (dMRI) methods for pre-dementia classification remains largely unexplored, particularly when combined with CSF biomarkers.

Methods: We implemented XGBoost machine learning models to evaluate the classification potential of dMRI parameters (derived using NODDI, C-NODDI, MAP, or SMI), CSF biomarkers of Alzheimer's pathology (Tau, pTau, Aβ42, Aβ40), and pairwise dMRI + CSF combinations in distinguishing cognitive normality from mild cognitive impairment.

Results: MAP-RTAP (AUC = 0.78) and pTau/Aβ42 (AUC = 0.76) were the best performing individual biomarkers. Combining C-NDI derived using C-NODDI and Aβ42/Aβ40 achieved the highest performance (AUC = 0.84) and accuracy (0.84), while other combinations optimized either sensitivity (0.93) or specificity (0.88).

Discussion: dMRI biomarkers demonstrate comparable performance to CSF biomarkers, with notable improvements achieved when combined. This study highlights dMRI's effectiveness for enhancing early AD detection.

Highlights: Advanced multishell diffusion MRI provides equivalent performance as CSF biomarkers in classifying MCICombining diffusion MRI and CSF biomarkers improves classification performanceStatistical diffusion MRI models perform best when used individually to classify MCIThe pTau/Aβ42 ratio outperforms other individual CSF biomarkers in MCI diagnosisBiophysical diffusion MRI models achieve the best performance when combined with CSF data.

Introduction: Blood-based biomarkers for Alzheimer's disease (AD) have the potential to improve diagnostic accessibility, but their clinical interpretation requires understanding of variability and biological influences.

Methods: We repeatedly sampled blood from 57 adults referred for lumbar puncture as part of a cognitive evaluation at a memory clinic. We measured serum phosphorylated- tau-181 (s-p-tau181) and plasma amyloid beta (Aβ)42/40 ratio (p-Aβ42/Aβ40) and evaluated the impact of renal and blood-brain barrier (BBB) function.

Results: Test-retest analysis revealed large variability of s-p-tau181 and small for p-Aβ42/Aβ40. Markers of renal function and BBB integrity significantly influenced s-p-tau181 levels, whereas p-Aβ42/Aβ40 was not affected.

Discussion: This study emphasizes the need for caution when interpreting longitudinal changes in s-p-tau181. Inter-individual variability is to a large degree due to susceptibility to biological influences where a novel association with integrity of BBB function were identified. These results have implications for the clinical application of blood-based biomarkers in AD diagnostics and monitoring.

Highlights: Blood phosphorylated- tau-181 (p-tau181) shows high test-retest variability in memory clinic patients.Blood amyloid beta (Aβ)42/Aβ40 ratio is stable but has poor diagnostic accuracy.Renal function and blood-brain barrier (BBB) integrity affect blood p-tau181 levels.Caution is needed when interpreting longitudinal changes in blood p-tau181.Renal and BBB disorders should be considered when assessing blood p-tau181.

Introduction: Simple screening tools are critical for assessing Alzheimer's disease (AD)-related pre-dementia changes. This study investigated longitudinal scores from the Quick Dementia Rating System (QDRS), a brief study partner-reported measure, in relation to baseline levels of the AD biomarker plasma pTau217 in individuals unimpaired at baseline.

Methods: Data from the Wisconsin Registry for Alzheimer's Prevention (N = 639) were used to examine whether baseline plasma pTau217 (ALZpath assay on Quanterix platform) modified QDRS or Preclinical Alzheimer's Cognitive Composite (PACC3) trajectories (mixed-effects models; time = age). pTau217*age interaction effects (e.g., high vs low pTau217 simple age slopes) were compared across outcomes.

Results: Higher baseline pTau217 levels were associated with faster functional (QDRS) and cognitive (PACC3) decline. Effect sizes were similar between PACC3 and QDRS. Exploratory analyses showed increased risk of transitioning to impaired QDRS classifications in those with high-baseline pTau217.

Discussion: This study demonstrates the utility of QDRS for tracking pre-dementia AD-related decline.

There are a relatively small number of investigations into brain aging in those with intellectual and developmental disability (I/DD). This project seeks to (1) characterize the internationally available multi-omics Alzheimer's disease (AD) biomarker studies including those with I/DD, and (2) discuss future research directions. PubMed, Web of Science, and Scopus were searched under the following criteria: cross-sectional or longitudinal AD-omics studies on adults (18 +) with I/DD. 532 studies were identified, 186 studies were evaluated for full-text, 79 studies were excluded, and 117 studies were extracted. Most biological specimens were analyzed in blood, plasma, or serum. Metabolomics, hormonomics, and transcriptomics were most understudied. Sex differences were investigated in nine studies. Two studies included participants with non-Down syndrome neurodevelopmental disorders. European-based city populations were primarily represented across studies. Future studies including a broader range of I/DD presentations, and considering sex differences, comorbidities, and novel biomarkers beta synuclein are interesting future directions.

Highlights: Small sample sizes, cross-sectional designs, and few prospective and retrospective studies highlight the need for more rigorous research design.A focus on European-based city populations and Down syndrome (DS) clinical groups prompts the need for inclusive, community-based recruitment methods across broader clinical and ethnic groups.The vesicle-associated membrane protein 2 (VAMP2) shows promise for early detection of synaptic degeneration, potentially across I/DD groups, showing correlations with CSF biomarkers of Alzheimer's disease, axonal injury, and cognitive performance in DS.