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Objectives: To explore patient and care partner experiences of receiving an amyloid scan result, with a focus on how clinician disclosure practices influenced patient and care partner emotional responses to the scan result and/or diagnosis. Methods: Semi-structured interviews with 38 people with mild cognitive impairment or dementia and 62 care partners who experienced the disclosure of results from an amyloid PET scan as part of the CARE-IDEAS study. We used thematic analysis to analyze interview transcripts. Results: We identified four aspects of the disclosure process that could influence patient and care partner emotional experiences of the scan result/diagnosis: (1) mode of delivery, (2) presence of a care partner, (3) clarity of the scan result explanation, and (4) discussion of post-scan treatment and support options. Conclusions: Emotional experiences of an amyloid scan result can vary depending on how results are communicated. These findings support previous efforts to develop standard disclosure protocols. Scan results should be delivered in person with the care partner present. Clinicians should give a clear explanation of the result and its implications in an empathetic manner. Options for treatment and support should be discussed for all patients. [ABSTRACT FROM AUTHOR]

Introduction: Timely diagnosis is critical for persons with Alzheimer's disease and related dementias (ADRD) to ensure they receive adequate services; however, timely diagnosis may be prevented by a person's English language skills. The purpose of this integrative review was to understand how limited English proficiency (LEP) impacts older Latino's ability to access a timely ADRD diagnosis. Methods: Whittemore and Knafl's methodological approach guided the review. Searches in five databases yielded 12 articles for inclusion. Results: Lack of culturally congruent health care systems, health care providers, and knowledge of ADRD resulted in delays in obtaining a timely ADRD diagnosis among older Latinos with LEP. Discussion: Latinos with LEP and risk for ADRD benefit from language assistance and support in navigating the health care system. Nurses must be advocates, even when a language barrier is present, and recognize that interpreters are not a single source solution. [ABSTRACT FROM AUTHOR]

Background: Eye-movement can reflect cognition and provide information on the neurodegeneration, such as Alzheimer's disease (AD). The high cost and limited accessibility of eye-movement recordings have hindered their use in clinics. Aims: We aim to develop an AI-driven eye-tracking tool for assessing AD using mobile devices with embedded cameras. Methods: 166 AD patients and 107 normal controls (NC) were enrolled. The subjects completed eye-movement tasks on a pad. We compared the demographics and clinical features of two groups. The eye-movement features were selected using least absolute shrinkage and selection operator (LASSO). Logistic regression (LR) model was trained to classify AD and NC, and its performance was evaluated. A nomogram was established to predict AD. Results: In training set, the model showed a good area under curve (AUC) of 0.85 for identifying AD from NC, with a sensitivity of 71%, specificity of 84%, positive predictive value of 0.87, and negative predictive value of 0.65. The validation of the model also yielded a favorable discriminatory ability with the AUC of 0.91, sensitivity, specificity, positive predictive value, and negative predictive value of 82%, 91%, 0.93, and 0.77 to identify AD patients from NC. Discussion and Conclusions: This novel AI-driven eye-tracking technology has the potential to reliably identify differences in eye-movement abnormalities in AD. The model shows excellent diagnostic performance in identifying AD based on the current data collected. The use of mobile devices makes it accessible for AD patients to complete tasks in primary clinical settings or follow up at home. [ABSTRACT FROM AUTHOR]

Background: Evidence suggests that risk factors for Alzheimer's disease and related dementias (ADRD) are at least partially modifiable, and that lifestyle risk accumulates as we age. However, the prevalence and impact of lifestyle-related risk factors in young adulthood (i.e., 18–39 years) remain poorly understood, with some risk factors that are developed in early adulthood being difficult to remove and reverse at midlife. The Next Generation (NextGen) Brain Health Survey is the first of its kind to be designed specifically for young adults, with the aim of exploring attitudes, understanding and exposure to ADRD risk and protective factors in this life stage. Methods: The NextGen survey is an international, cross-sectional survey of young adults aged 18–39 years. The survey was developed in three phases with ongoing input from public advisors (i.e., young adults from Europe, North America, and Africa). First, we adapted items from existing literature for the target population. Second, we conducted focus groups with young adults to review the items and explore new themes. Third, we piloted the survey in an international network, including brain health researchers, clinicians, and advocacy groups. Feedback was integrated to create the finalized survey. Discussion: The NextGen survey is conducted online and made available to individuals aged 18–39 years internationally. Results will contribute new knowledge about young adults and ADRD risk exposure before mid-life, including much-needed evidence in populations that are traditionally under-represented in research. Findings will help identify mediators and modifiers of associations between knowledge, attitudes, and risk exposure, and provide the basis for comparison with middle-aged and older populations. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease and mild cognitive impairment are often difficult to differentiate due to their progressive nature and overlapping symptoms. The lack of reliable biomarkers further complicates early diagnosis. As the global population ages, the incidence of cognitive disorders increases, making the need for accurate diagnosis critical. Timely and precise diagnosis is essential for the effective treatment and intervention of these conditions. However, existing diagnostic methods frequently lead to a significant rate of misdiagnosis. This issue underscores the necessity for improved diagnostic techniques to better identify cognitive disorders in the aging population. Methods: We used Graph Neural Networks, Multi-Layer Perceptrons, and Graph Attention Networks. GNNs map patient data into a graph structure, with nodes representing patients and edges shared clinical features, capturing key relationships. MLPs and GATs are used to analyse discrete data points for tasks such as classification and regression. Each model was evaluated on accuracy, precision, and recall. Results: The AI models provide an objective basis for comparing patient data with reference populations. This approach enhances the ability to accurately distinguish between AD and MCI, offering more precise risk stratification and aiding in the development of personalized treatment strategies. Conclusion: The incorporation of AI methodologies such as GNNs and MLPs into clinical settings holds promise for enhancing the diagnosis and management of Alzheimer's disease and mild cognitive impairment. By deploying these advanced computational techniques, clinicians could see a reduction in diagnostic errors, facilitating earlier, more precise interventions, and likely to lead to significantly improved outcomes for patients. [ABSTRACT FROM AUTHOR]

Introduction: Subjective visual impairment (VI) is related to cognition in cognitively unimpaired (CU) older adults, mild cognitive impairment (MCI) patients, and Alzheimer's disease (AD) patients. The utility of subjective VI as an indicator for domain-specific cognitive impairment is unknown. Methods: We used the National Eye Institute Visual Function Questionnaire (NEI-VFQ-25 item) and a neuropsychological battery to assess the relationship between subjective VI and domain-specific cognitive performance in CU older adults (N = 58) and MCI patients (N = 16). Results: The CU group showed a positive relationship between subjective VI and visuospatial performance. CU older adults at high risk for AD demonstrated a unique relationship between subjective VI and attention, processing speed, and executive function. Peripheral vision was related to domain-specific performance in the patient group. Discussion: Subjective VI complaints may indicate potential for domain-specific cognitive decline in visuospatial performance, executive function, processing speed, and attention in older adults. [ABSTRACT FROM AUTHOR]

Background: Accumulating evidence suggested that Alzheimer's disease (AD) was associated with altered gut microbiota. However, the relationships between gut microbiota and specific cognitive domains of AD patients have yet been fully elucidated. The aim of this study was to explore microbial signatures associated with global cognition and specific cognitive domains in AD patients and to determine their predictive value as biomarkers. Methods: A total of 64 subjects (18 mild AD, 23 severe AD and 23 healthy control) were recruited in the study. 16 s rDNA sequencing was performed for the gut bacteria composition, followed by liquid chromatography electrospray ionization tandem mass spectrometry (LC/MS/MS) analysis of short-chain fatty acids (SCFAs). The global cognition, specific cognitive domains (abstraction, orientation, attention, language, etc.) and severity of cognitive impairment, were evaluated by Montreal Cognitive Assessment (MoCA) scores. We further identified characteristic bacteria and SCFAs, and receiver operating characteristic (ROC) curve was used to determine the predictive value. Results: Our results showed that the microbiota dysbiosis index was significantly higher in the severe and mild AD patients compared to the healthy control (HC). Linear discriminant analysis (LDA) showed that 12 families and 17 genera were identified as key microbiota among three groups. The abundance of Butyricicoccus was positively associated with abstraction, and the abundance of Lachnospiraceae_UCG-004 was positively associated with attention, language, orientation in AD patients. Moreover, the levels of isobutyric acid and isovaleric acid were both significantly negatively correlated with abstraction, and level of propanoic acid was significantly positively associated with the attention. In addition, ROC models based on the characteristic bacteria Lactobacillus , Butyricicoccus and Lachnospiraceae_UCG-004 could effectively distinguished between low and high orientation in AD patients (area under curve is 0.891), and Butyricicoccus and Agathobacter or the combination of SCFAs could distinguish abstraction in AD patients (area under curve is 0.797 and 0.839 respectively). Conclusion: These findings revealed the signatures gut bacteria and metabolite SCFAs of AD patients and demonstrated the correlations between theses characteristic bacteria and SCFAs and specific cognitive domains, highlighting their potential value in early detection, monitoring, and intervention strategies for AD patients. [ABSTRACT FROM AUTHOR]

Background: Abnormal eye movements occur at the early stages of Alzheimer's disease (AD). However, the characteristics of abnormal eye movements of patients with AD and their relationship with clinical symptoms remain inconsistent, and their predictive value for diagnosing and monitoring the progression of AD remains unclear. Methods: A total of 42 normal controls, 63 patients with mild cognitive impairment due to AD (AD-MCI), and 49 patients with dementia due to AD (AD-D) were recruited. Eye movements were assessed using the EyeKnow eye-tracking and analysis system. Cognitive function, neuropsychiatric symptoms, and activities of daily living were evaluated using various rating scales, and correlation analyses and receiver operating characteristic curves were performed. Results: Patients with AD exhibited increased number of offsets and offset degrees, prolonged offset duration, and decreased accuracy in lateral fixation; reduced accuracy, prolonged saccadic duration, and decreased velocity in prosaccade; decreased accuracy and corrected rate, prolonged corrected antisaccadic duration, and reduced velocity in antisaccade; and reduced accuracy and increased inhibition failures in memory saccade. Eye movement parameters were correlated with global cognition and the cognitive domains of memory, language, attention, visuospatial ability, execution function, and activities of daily living. Subgroup analysis indicated that the associations between eye movements and clinical symptoms in patients with AD were influenced by disease severity and history of diabetes. In the AD-D and AD with diabetes groups, these associations diminished. Nevertheless, the associations persisted in the AD-MCI and AD without diabetes groups. The areas under the curves for predicting AD, AD-MCI, and AD-D were 0.835, 0.737, and 0.899, respectively (all p < 0.05). Conclusion: Patients with AD exhibit distinct patterns of abnormal eye movements. Abnormal eye movements are significantly correlated with global cognition, multiple cognitive domains, and activities of daily living. Abnormal eye movements have a considerable predictive value for the diagnosis and progression of AD. [ABSTRACT FROM AUTHOR]

Background: The CAMDEX-DS is an instrument to diagnose Alzheimer's disease (AD) in Down syndrome consisting of an informant interview and a cognitive test battery (CAMCOG-DS). Measurement properties of the German CAMDEX-DS were investigated. Method: Fifty-five adults with Down syndrome (19–58 years) participated in this observational study. "Dementia" and "Alzheimer's dementia" (Alzheimer's disease) were diagnosed clinically and operationalised CAMDEX-ICD-10 criteria were applied to evaluate criterion validity. Validity and reliability of the CAMCOG-DS were analysed. Results: Specificity of the interview was 69–93%; sensitivity 0–80% for "dementia"; and 0-20% for Alzheimer's disease. A complete CAMCOG-DS score was obtained in 85% (item difficulty 0.11–0.96). Construct validity and retest-reliability were low to moderate (τ =.04–.79), inter-rater reliability excellent (τ =.70–.89), internal consistency and selectivity acceptable to excellent. Conclusions: Currently, the CAMDEX-DS including the CAMCOG-DS are the outcome assessments for assessing dementia in Down syndrome with the best psychometric properties; however, revision is recommended. [ABSTRACT FROM AUTHOR]

Objective: The diagnosis and treatment of biomarkers in Alzheimer's disease has emerged as a prominent topic within Alzheimer's disease research. In this paper, we conducted a bibliometric analysis of data from a wide range of literature in this field to enhance the in-depth understanding of this area. Method: The core collection of the Science Citation Index database (web of science) was used to search for relevant literature in the above fields from 1 January 2006 to 14 November 2022 and Citespace software was used to visualize and analyze the literature data. Results: A total of 1,138 papers were included, of which the United States ranked first with 607 papers and China ranked 6th in the world with 84 papers. The value of mediational centrality is 0.49 in the United States and 0.05 in China. In terms of the number of articles published by the research authors, the Swedish scholar Blennow Kaj ranks first with 82 articles published, and the scholars who rank second and third are Zetterberg Henrik (78 articles) and Morris John C (64 articles), respectively; in terms of the mediational centrality, the American scholar Trojanowski John Q ranked first in the world with 0.1, and the second and third ranked scholars were Blennow Kaj (0.09) and Zetterberg Henrik (0.06) respectively. Scholar JACK CR ranked first with 377 citation frequency. The journal NEUROLOGY is ranked first with 943 citations. Conclusion: In recent years, global research in the field of biomarkers related to Alzheimer's disease has shown signs of softening, and the momentum of research has slightly diminished. However, this trend does not imply a decline in the quality of research. It is essential to enhance collaboration among countries, major research institutions, and scholars, with a particular emphasis on fostering international partnerships in the future. [ABSTRACT FROM AUTHOR]

Introduction Approximately 2 million people in Italy are currently living with dementia or mild cognitive impairment (MCI), and 4 million are involved as family members or caregivers. Considering the significant impact of dementia, the Italian Ministry of Health entrusted the Italian National Institute of Health (Istituto Superiore di Sanità) with the development of a guideline within the Italian National Guideline System (Sistema Nazionale Linee Guida, SNLG) on the diagnosis and treatment of dementia and MCI. The main objective was to provide evidence-based recommendations aimed at reducing the variability and ensuring the appropriateness of clinical practices throughout the whole care process from identification and diagnosis to the end of life for people with dementia (PwD) or MCI and their families/caregivers. Methods The GRADE-ADOLOPMENT approach was used to adopt, adapt and update the guideline developed by the National Institute for Health and Care Excellence in 2018 (NG97). The methodology was based on the Methodological Handbook produced by the SNLG. A multidisciplinary panel of 29 experts and four representatives of family members/caregivers discussed and approved 47 review questions. Of these, 34 questions were adopted from the NG97, and 13 were new questions, including 10 questions referring to MCI. Systematic literature reviews were performed for each question, and a team of methodological and clinical experts qualitatively assessed and summarised results from included studies based on the GRADE approach. To facilitate the implementation and dissemination of the contents of this guideline, a care pathway and a leaflet dedicated to PwD or MCI and their families/caregivers were also developed. Results The literature review for this guideline included studies published up to November 2023. More than 1000 peer-reviewed publications were included, covering the following areas: (i) identification, diagnosis and post-diagnostic support; (ii) care models and care coordination; (iii) pharmacological interventions for cognitive symptoms; (iv) non-pharmacological interventions for cognitive symptoms; (v) non-cognitive symptoms, intercurrent illnesses and palliative care. The multidisciplinary panel discussed and approved 167 clinical practice recommendations and 39 research recommendations. Commentary Italy's first National Guideline on dementia and MCI addresses diagnosis, treatment and care within the National Healthcare System. It includes recommendations on pharmacological and non-pharmacological approaches, and emphasises tailored interventions, comprehensive cognitive assessment, staff training and palliative care. The guideline also underlines the need to involve PwD in decision-making and supporting caregivers throughout the entire course of the disease. Conclusions Structured strategies for the dissemination and implementation of the guideline will be defined within the Italian Fund for Alzheimer and other Dementias 2024–2026. An interactive care pathway and a leaflet dedicated to PwD and their carers are already available. The guideline will be updated starting January 2027, but early updates may be planned in case of breakthrough advancements. [ABSTRACT FROM AUTHOR]

The article focuses on the potential of antiamyloid monoclonal antibodies (MABs) as a promising treatment for Alzheimer's disease (AD). Topics include the role of amyloid plaques in the development of AD and the evolution of the amyloid hypothesis, advancements in diagnostic tools such as blood tests and retinal hyperspectral imaging, and recent treatment breakthroughs like Aducanumab and Lecanemab, which target amyloid-beta plaques in the brain.

Objective: We urgently need to understand Alzheimer's disease (AD) stigma among Black adults. Black communities bear a disproportionate burden of AD, and recent advances in early diagnosis using AD biomarkers may affect stigma associated with AD. The goal of our study is to characterize AD stigma within our cohort of self-identified Black participants and test how AD biomarker test results may affect this stigma. Design: We surveyed a sample of 1,150 self-identified Black adults who were randomized to read a vignette describing a fictional person, who was described as either having a positive or negative biomarker test result. After reading the vignette, participants completed the modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). We compared FS-ADS scores between groups defined by age, gender, and United States Census region. We examined interactions between these groupings and AD biomarker test result. Results: Participants over age 65 had lower scores (lower stigma) on all 7 FS-ADS domains compared to those under 65: structural discrimination, negative severity attributions, negative aesthetic attributions, antipathy, support, pity, and social distance. In the biomarker positive condition, worries about structural discrimination were greater than in the biomarker negative condition and statistically similar in the two age groups (DOR, 0.39 [95%CI, 0.22–0.69]). This pattern of results was similar for negative symptom attributions (DOR, 0.51 [95%CI, 0.28–0.90]). Conclusion: While older adults reported less AD stigma than younger adults, AD biomarker testing caused similarly high concerns about structural discrimination and negative severity attributions. Thus, use of AD biomarker diagnosis may increase AD stigma and exacerbate healthcare disparities known to effect AD diagnosis in some Black adults. Advances in AD diagnosis may interact with social and structural factors to differentially affect groups of Black adults. [ABSTRACT FROM AUTHOR]

Purpose: Due to the indistinguishable clinical features of corticobasal syndrome (CBS), the antemortem differentiation between corticobasal degeneration (CBD) and its mimics remains challenging. However, the utility of conventional magnetic resonance imaging (MRI) for the diagnosis of CBD has not been sufficiently evaluated. This study aimed to investigate the diagnostic performance of conventional MRI findings in differentiating pathologically confirmed CBD from its mimics. Methods: Semiquantitative visual rating scales were employed to assess the degree and distribution of atrophy and asymmetry on conventional T1-weighted and T2-weighted images. Additionally, subcortical white matter hyperintensity (SWMH) on fluid-attenuated inversion recovery images were visually evaluated. Results: In addition to 19 patients with CBD, 16 with CBD mimics (progressive supranuclear palsy (PSP): 9, Alzheimer's disease (AD): 4, dementia with Lewy bodies (DLB): 1, frontotemporal lobar degeneration with TAR DNA-binding protein of 43 kDa(FTLD-TDP): 1, and globular glial tauopathy (GGT): 1) were investigated. Compared with the CBD group, the PSP-CBS subgroup showed severe midbrain atrophy without SWMH. The non-PSP-CBS subgroup, comprising patients with AD, DLB, FTLD-TDP, and GGT, showed severe temporal atrophy with widespread asymmetry, especially in the temporal lobes. In addition to over half of the patients with CBD, two with FTLD-TDP and GGT showed SWMH, respectively. Conclusion: This study elucidates the distinct structural changes between the CBD and its mimics based on visual rating scales. The evaluation of atrophic distribution and SWMH may serve as imaging biomarkers of conventional MRI for detecting background pathologies. [ABSTRACT FROM AUTHOR]

The concept of subjective cognitive decline (SCD) is currently receiving much attention, as SCD has a high risk of transitioning to mild cognitive impairment (MCI) and dementia. The ATN biomarker diagnostic framework can accurately diagnose SCD as preclinical Alzheimer's disease (AD), which is an important window for precise prevention and treatment of AD. Based on the present diagnostic paradigms of clinical diagnosis and biomarker diagnosis for SCD, it is important to have fine management during the diagnostic process and precise communication and support after diagnosis for SCD patients, including diagnostic management specification, interpretation and recommendation diagnostic of biomarker disclosure, patients health management, and possible treatment for specific underlying causes. Previous studies have shown heterogeneity between clinical research and practice, and many doctors still feel unfamiliar with the concept of SCD and lack a systematic understanding. SCD diagnosis can provide patients with a certain degree of certainty, but it may also bring uncertainty about the expected risk of disease, and there is an urgent need to provide guidance to clinical doctors. So far, there is still a lack of Chinese expert consensus on diagnostic management specification, biomarker disclosure, and patient management of SCD. Based on the systematic summary of the current domestic and international research on the SCD, the consensus is written and aimed to improve the diagnosis and treatment level of SCD, guide high-quality preclinical AD research and lay the foundation for further clinical translation. [ABSTRACT FROM AUTHOR]

Accurately diagnosing Alzheimer’s disease is essential for improving elderly health. Meanwhile, accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer’s disease. However, most of the existing methods perform Alzheimer’s disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks. To address this challenging problem, we propose a novel multi-task learning method, which uses feature interaction to explore the relationship between Alzheimer’s disease diagnosis and mini-mental state examination score prediction. In our proposed method, features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction. Then, we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches, which can promote the learning of each task. We validate the effectiveness of our proposed method on multiple datasets. In Alzheimer’s disease neuroimaging initiative 1 dataset, the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86% and 2.5, respectively. Experimental results show that our proposed method outperforms most state-of-the-art methods. Our proposed method enables accurate Alzheimer’s disease diagnosis and mini-mental state examination score prediction. Therefore, it can be used as a reference for the clinical diagnosis of Alzheimer’s disease, and can also help doctors and patients track disease progression in a timely manner.

Objective: In this study, we examined the effectiveness of hyperbaric oxygen (HBO) therapy in ameliorating cognitive deficits in mice with Alzheimer’s disease (AD), while also assessing its impact on the autophagic pathway within the context of AD. Methods: 20 double-transgenic mice expressing the amyloid precursor protein and presenilin 1 (APP/PS1) were purposefully selected and randomly assigned to groups A and B. Concurrently, 20 C57BL/6 mice were chosen and randomly categorized into groups C and D, each consisting of 10 mice. Mice in groups B and D received HBO treatment. The Morris water maze assay was used to assess changes in mouse behavior. Immunohistochemistry techniques were used to quantify the expression levels of amyloid-beta 42 (Aβ42) and microtubuleassociated protein 1A/1B-light chain 3 (LC3) in hippocampal tissues, while western blot analysis was used to investigate the levels of LC3-II, p62, phosphoinositide 3-kinase (PI3K), and mammalian target of rapamycin (mTOR) proteins within hippocampal tissues. Results: Mice allocated to group B exhibited reduced escape latency and prolonged dwell time in the target quadrant compared to other groups. Histological examination revealed conspicuous plaque-like deposits of Aβ42 in the hippocampal tissues of mice in groups A and B. Group B displayed diminished Aβ42-positive reactants and augmented microtubule-associated protein 1A/1B-LC3-positive reactants compared to group A. LC3-positive reactants were also detected in the hippocampal tissues of mice in groups C and D, surpassing the levels observed in groups A and B. Furthermore, group B demonstrated significantly lower expression of mTOR protein and markedly higher expression of LC3-II protein in mouse hippocampal tissues when compared to group A (P < 0.05). Conversely, there were no significant disparities noted in PI3K and p62 protein expression between groups B and A. Notably, no discernible discrepancies were observed in the expression levels of mTOR, PI3K, LC3-II, and p62 proteins between groups C and D within mouse hippocampal tissues. Conclusion: HBO treatment demonstrates efficacy in enhancing cognitive function in mice with AD and holds promise as a potential therapeutic intervention for AD by facilitating the activation of the mTOR pathwaymediated autophagy. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases such as Alzheimer's disease and polyglutamine diseases are characterized by abnormal accumulation of misfolded proteins, leading to neuronal dysfunction and subsequent neuron death. However, there is a lack of studies that integratemolecular, morphological, and functional analyses in neurodegenerative models to fully characterize these time-dependent processes. In this study, we used C. elegans models expressing Ab1-42 and polyglutamine to investigate early neuronal pathogenic features in olfactory neurons. Both models demonstrated significant reductions in odor sensitivity in AWB and AWC chemosensory neurons as early as day 1 of adulthood, while AWA chemosensory neurons showed no such decline, suggesting cell-type-specific early neuronal dysfunction. At the molecular level, Ab1-42 or Q40 expression caused age-dependent protein aggregation and morphological changes in neurons. By day 6, both models displayed prominent protein aggregates in neuronal cell bodies and neurites. Notably, AWB neurons in both models showed significantly shortened cilia and increased instances of enlarged cilia as early as day 1 of adulthood. Furthermore, AWC neurons expressing Ab1-42 displayed calcium signaling defects, with significantly reduced responses to odor stimuli on day 1, further supporting early behavioral dysfunction. In contrast, AWA neuron did not exhibit reduced calciumresponses, consistent with the absence of detectable decreases in olfactory sensitivity in these neurons. These findings suggest that decreased calcium signaling and dysfunction in specific sensory neuron subtypes are early indicators of neurodegeneration in C. elegans, occurring prior to the formation of visible protein aggregates. We found that the ER unfolded protein response (UPR) is significantly activated in worms expressing Ab1-42. Activation of the AMPK pathway alleviates olfactory defects and reduces fibrillar Ab in these worms. This study underscores the use of C. elegans olfactory neurons as a model to elucidate mechanisms of proteostasis in neurodegenerative diseases and highlights the importance of integrated approaches. [ABSTRACT FROM AUTHOR]

Introduction: Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer's disease (AD), but the relation between linguistic features and cognitive tests or biomarkers remains unclear. This study aimed to investigate how linguistic features help identify cognitive impairments in patients in the early stages of AD. Method: This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid-ß positron emission tomography scans, brain magnetic resonance imaging, and comprehensive neuropsychological testing. Participants' speech data from a picture description task were examined. A total of 15 linguistic features were analyzed to classify groups and predict cognitive performance. Results: We found notable linguistic differences between the early-AD and NC groups in lexical diversity, syntactic complexity, and language disfluency. Using machine learning classifiers (SVM, KNN, and RF), we achieved up to 88% accuracy in distinguishing early-AD patients from normal controls, with mean length of utterance (MLU) and long pauses ratio (LPR) serving as core linguistic indicators. Moreover, the integration of linguistic indicators with biomarkers significantly improved predictive accuracy for AD. Regression analysis also highlighted crucial linguistic features, such as MLU, LPR, Type-to-Token ratio (TTR), and passive construction ratio (PCR), which were sensitive to changes in cognitive function. Conclusion: Findings support the efficacy of linguistic analysis as a screening tool for the early detection of AD and the assessment of subtle cognitive decline. Integrating linguistic features with biomarkers significantly improved diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop and validate machine learning models (MLMs) to diagnose Alzheimer's disease (AD) using cortical complexity indicated by fractal dimension (FD). Methods: A total of 296 participants with normal cognitive (NC) function and 182 with AD from the AD Neuroimaging Initiative database were randomly divided into training and internal validation cohorts. Then, FDs, demographic characteristics, baseline global cognitive function scales [Montreal Cognitive Assessment (MoCA), Functional Activities Questionnaire (FAQ), Global Deterioration Scale (GDS), Neuropsychiatric Inventory (NPI)], phospho-tau (p-tau 181), amyloidß-42/40, apolipoprotein E (APOE) and polygenic hazard score (PHS) were collected to establish multiple MLMs. Receiver operating characteristic curves were used to evaluate model performance. Participants from our institution (n = 66; 33 with NC and 33 with AD) served as external validation cohorts to validate the MLMs. Decision curve analysis was used to estimate the models' clinical values. Results: The FDs from 30 out of 69 regions showed significant alteration. All MLMs were conducted based on the 30 significantly different FDs. The FD model had good accuracy in predicting AD in three cohorts [area under the receiver operating characteristic (ROC) curve (AUC) = 0.842, 0.808, and 0.803]. There were no statistically significant differences in AUC values between the FD model and the other combined models in the training and internal validation cohorts except MoCA + FD and FAQ + FD models. Among MLMs, the MoCA + FD model showed the best predictive efficiency in three cohorts (AUC = 0.951, 0.931, and 0.955) and had the highest clinical net benefit. Conclusion: The FD model showed favorable diagnostic performance for AD. Among MLMs, the MoCA + FD model can predict AD with the highest efficiency and could be used as a non-invasive diagnostic method. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common neurodegenerative disease, with an increasing prevalence. Currently, there is no ideal diagnostic molecular imaging agent for diagnosing AD. Antibodies (Abs) have been proposed to close this gap as they can bind selectively and with high affinity to amyloid β (Aβ)--one of the molecular hallmarks of AD. Abs can even be designed to selectively bind Aβ oligomers or isoforms, which are difficult to target with small imaging agents. Conventionally, Abs must be labeled with long-lived radionuclides which typically results in in high radiation burden to healthy tissue. Pretargeted imaging could solve this challenge as it allows for the use of short-lived radionuclides. To develop pretargeted imaging tools that can enter the brain, AD mouse models are useful as they allow testing of the imaging approach in a relevant animal model that could predict its clinical applicability. Several mouse models for AD have been developed with different characteristics. Commonly used models are: 5xFAD, APP/PS1 and tg-ArcSwe transgenic mice. In this study, we aimed to identify which of these models were best suited to investigate pretargeted imaging approaches beyond the blood brain barrier. We evaluated this by pretargeted autoradiography using the Aβ-targeting antibody 3D6 and an 111In-labeled Tz. Evaluation criteria were target-to-background ratios and accessibility. APP/PS1 mice showed Aβ accumulation in high and low binding brain regions and is as such less suitable for pretargeted purposes. 5xFAD and tg-ArcSwe mice showed similar uptake in high binding regions whereas low uptake in low binding regions and are better suited to evaluate pretargeted imaging approaches. 5xFAD mice are advantaged over tg-ArcSwe mice as pathology can be traced early (6 months compared to 18 months of age) and as 5xFAD mice are commercially available. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection. [ABSTRACT FROM AUTHOR]

Objective: To investigate the association between suspected pseudobulbar affect (PBA), clinical diagnosis, cognitive testing, and self‐reported mood in older adults presenting for evaluation of dementia. Participants: Patients presenting to an outpatient memory disorders clinic (N = 311). Methods: We used traditional and novel network modeling approaches to examine associations between neuropsychological (NP) tests, patient and clinician rating scales, and the Center for Neurological Study‐Lability Scale (CNS‐LS) among patients with suspected AD (n = 133) and other neurocognitive diagnosis (n = 178). We then examined differences in test performance between patients with and without suspected PBA (CNS‐LS cut‐off of ≥ 13), while accounting for demographic and psychiatric covariates with propensity score matching. Group differences were assessed with Bayesian models. Results: Prevalence of suspected PBA in AD was slightly less than half (44.4%) and at a similar rate in other dementias (e.g., 46.9% in CVD and 45.5% in LBD). In network models, the CNS‐LS was associated with higher anxiety and better word list recall. After accounting for covariates, AD patients with suspected PBA performed better on word list recall βM = 0.40, 95% CI [0.15, 0.66], and committed fewer false positive errors on recognition βM = −1.51, 95% CI [−2.34, −0.59] than AD patients without suspected PBA. There were no differences in patients with any other diagnostic impression, nor group differences on other NP measures. Conclusions: Patients with suspected PBA and AD diagnosis had better memory recall and recognition than those without suspected PBA, suggesting that impaired emotional regulation may be an early sign of AD in patients with less prominent memory decline. Better understanding PBA in neurodegenerative diseases, including prevalence and comorbidity with psychiatric conditions, could help with early identification, education, and initiation of treatment. [ABSTRACT FROM AUTHOR]

Beta amyloid PET scans are a minimally invasive biomarker that may inform Alzheimer's disease (AD) diagnosis. The Caregiver's Reactions and Experience (CARE) study, an IDEAS supplement, aimed to understand experiences of PET scan recipients and their care partners regarding motivations for scans, reporting and interpreting results, and impact of results. Patients with mild cognitive impairment or dementia who agreed to join the CARE‐IDEAS study and their care partners participated in a baseline survey and follow‐up survey approximately 18 months later, supplemented by in‐depth qualitative interviews with subsets of participants. Patients who received scans and volunteered for follow‐up research were more likely to be male, better educated, and have higher income than the general population. Survey information was merged with Medicare data. This article integrates findings from several CARE‐IDEAS publications and provides implications for practice and research. Although most participants accurately reported scan results, they were often confused about their meaning for prognosis. Some participants reported distress with results, but there were no significant changes in measured depression, burden, or economic strain over time. Many respondents desired more information about prognosis and supportive resources. Scan results were not differentially associated with changes in service use over time. Findings suggest a need for carefully designed and tested tools for clinicians to discuss risks and benefits of scans and their results, and resources to support patients and care partners in subsequent planning. Learning of scan results provides a point‐of‐contact that should be leveraged to facilitate shared decision‐making and person‐centered longitudinal AD care. [ABSTRACT FROM AUTHOR]

The article discusses a study from Sweden where blood biomarkers, particularly phosphorylated tau 217 and amyloid-beta 42/40 ratios, improved diagnostic accuracy for Alzheimer's disease in both primary care and specialist settings. Topics discussed include the increasing prevalence of Alzheimer's, the role of blood biomarkers in diagnosis, and the comparison of clinical and biomarker-based diagnostic methods.

Objectives: To understand how psychosocial factors associated with physical activity differ based on disease severity in people with dementia, and how these factors are associated with physical activity participation. Methods: Eighty‐seven people with dementia, alongside their family carer were asked to complete a series of questions related to physical activity participation, including barriers, motivators, and facilitators. Regression models were developed to understand how psychosocial factors were associated with physical activity participation in the cohort. Results: In the final models, only the absence of intrapersonal barriers was associated with overall physical activity and regular moderate‐to‐vigorous physical activity. Feelings of relatedness were associated with regular moderate‐to‐vigorous physical activity only. Conclusion: Reducing intrapersonal barriers would appear to be a potentially useful strategy to promote physical activity in people with dementia. However, a tailored approach is needed depending on the desired physical activity outcome. [ABSTRACT FROM AUTHOR]

The newly proposed revised criteria for diagnosis and staging of Alzheimer's disease (AD) by the Alzheimer's Association (AA) Workgroup represent a significant milestone in the field. These criteria offer objective measures for diagnosing and staging biological AD, bridging the gap between research and clinical care. Although implementation feasibility may vary across regions and settings, improving the availability and accuracy of biomarkers, especially plasma biomarkers, is expected to enhance the applicability of these criteria in clinical practice. The Fall 2023 Alzheimer's Association Research Roundtable (AARR) meeting served as a forum for gathering industry perspectives and feedback on these revised criteria, ensuring that the new criteria inform research, clinical trial design, and clinical care. In this article, we outline a summary of the newly proposed "Revised Criteria for Diagnosis and Staging of AD: AA Workgroup" and provide highlights from the AARR meeting in fall 2023. Highlights: The Alzheimer's Association Research Roundtable (AARR) convened leaders from industry, academia, and government, to review the Revised Criteria for Diagnosis and Staging of AD: AA Workgroup, and gather industry perspectives and feedback on these revised criteria before its publication.The newly proposed revised criteria for diagnosis and staging of Alzheimer's disease (AD) by the AA's Workgroup represent a significant milestone, offering objective measures for the biological and staging of AD and bridging the gap between research and clinical care.Improving the availability and accuracy of biomarkers, especially blood‐based biomarkers (BBMs) is expected to improve clinical research and enhance the applicability of these criteria in clinical practice. [ABSTRACT FROM AUTHOR]

Introduction: The prediction of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is an important clinical challenge. This study aimed to identify the independent risk factors and develop a nomogram model that can predict progression from MCI to AD. Methods: Data of 141 patients with MCI were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We set a follow-up time of 72 months and defined patients as stable MCI (sMCI) or progressive MCI (pMCI) according to whether or not the progression of MCI to AD occurred. We identified and screened independent risk factors by utilizing weighted gene co-expression network analysis (WGCNA), where we obtained 14,893 genes after data preprocessing and selected the soft threshold b = 7 at an R² of 0.85 to achieve a scale-free network. A total of 14 modules were discovered, with the midnightblue module having a strong association with the prognosis of MCI. Using machine learning strategies, which included the least absolute selection and shrinkage operator and support vector machine-recursive feature elimination; and the Cox proportional-hazardsmodel, which included univariate and multivariable analyses, we identified and screened independent risk factors. Subsequently, we developed a nomogram model for predicting the progression from MCI to AD. The performance of our nomogram was evaluated by the C-index, calibration curve, and decision curve analysis (DCA). Bioinformatics analysis and immune infiltration analysis were conducted to clarify the function of early B cell factor 1 (EBF1). Results: First, the results showed that 40 differentially expressed genes (DEGs) related to the prognosis of MCI were generated by weighted gene co-expression network analysis. Second, five hub variables were obtained through the abovementioned machine learning strategies. Third, a low Montreal Cognitive Assessment (MoCA) score [hazard ratio (HR): 4.258, 95% confidence interval (CI): 1.994-9.091] and low EBF1 expression (hazard ratio: 3.454, 95% confidence interval: 1.813-6.579) were identified as the independent risk factors through the Cox proportional-hazards regression analysis. Finally, we developed a nomogrammodel including the MoCA score, EBF1, and potential confounders (age and gender). By evaluating our nomogram model and validating it in both internal and external validation sets, we demonstrated that our nomogrammodel exhibits excellent predictive performance. Through the Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes Genomes (KEGG) functional enrichment analysis, and immune infiltration analysis, we found that the role of EBF1 in MCI was closely related to B cells. Conclusion: EBF1, as a B cell-specific transcription factor, may be a key target for predicting progression from MCI to AD. Our nomogram model was able to provide personalized risk factors for the progression from MCI to AD after evaluation and validation. [ABSTRACT FROM AUTHOR]

Background: Dementia can be caused by numerous different diseases that present variable clinical courses and reveal multiple patterns of brain atrophy, making its accurate early diagnosis by conventional examinative means challenging. Although highly accurate and powerful, magnetic resonance imaging (MRI) currently plays only a supportive role in dementia diagnosis, largely due to the enormous volume and diversity of data it generates. AI-based software solutions/algorithms that can perform automated segmentation and volumetry analyses of MRI data are being increasingly used to address this issue. Numerous commercial and non-commercial software solutions for automated brain segmentation and volumetry exist, with FreeSurfer being the most frequently used. Objectives: This Review is an account of the current situation regarding the application of automated brain segmentation and volumetry to dementia diagnosis. Methods: We performed a PubMed search for "FreeSurfer AND Dementia" and obtained 493 results. Based on these search results, we conducted an in-depth source analysis to identify additional publications, software tools, and methods. Studies were analyzed for design, patient collective, and for statistical evaluation (mathematical methods, correlations). Results: In the studies identified, the main diseases and cohorts represented were Alzheimer's disease (n = 276), mild cognitive impairment (n = 157), frontotemporal dementia (n = 34), Parkinson's disease (n = 29), dementia with Lewy bodies (n = 20), and healthy controls (n = 356). The findings and methods of a selection of the studies identified were summarized and discussed. Conclusion: Our evaluation showed that, while a large number of studies and software solutions are available, many diseases are underrepresented in terms of their incidence. There is therefore plenty of scope for targeted research. [ABSTRACT FROM AUTHOR]

Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups. [ABSTRACT FROM AUTHOR]

Objective: Routine health care visits offer the opportunity to screen older adults for symptoms of Alzheimer's disease (AD). Many women see their gynecologist as their primary health care provider. Given this unique relationship, the Women's Preventive Services Initiative and the American College of Obstetrics and Gynecology advocate for integrated care of women at all ages. It is well-established that women are at increased risk for AD, and memory screening of older women should be paramount in this effort. Research is needed to determine the feasibility and value of memory screening among older women at the well-woman visit. Materials and Methods: Women aged 60 and above completed a 5-item subjective memory screener at their well-woman visit at the Columbia University Integrated Women's Health Program. Women who endorsed any item were considered to have a positive screen and were given the option to pursue clinical evaluation. Rates of positive screens, item endorsement, and referral preferences were examined. Results: Of the 530 women approached, 521 agreed to complete the screener. Of those, 17.5% (n = 91) were classified as positive. The most frequently endorsed item was difficulty with memory or thinking compared with others the same age. Among women with positive screens, 57.5% were interested in pursuing clinical referrals to a memory specialist. Conclusion: Results support the feasibility and potential value of including subjective memory screening as part of a comprehensive well-woman program. Early identification of memory loss will enable investigation into the cause of memory symptoms and longitudinal monitoring of cognitive change. [ABSTRACT FROM AUTHOR]

The Remote Assessment of Disease and Relapse – Alzheimer's Disease (RADAR-AD) consortium evaluated remote measurement technologies (RMTs) for assessing functional status in AD. The consortium engaged with the European Medicines Agency (EMA) to obtain feedback on identification of meaningful functional domains, selection of RMTs and clinical study design to assess the feasibility of using RMTs in AD clinical studies. We summarized the feedback and the lessons learned to guide future projects. [ABSTRACT FROM AUTHOR]

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Alzheimer's Disease (AD) is a significant challenge in neurodegenerative disorders, characterized by a gradual decline in cognitive functions. Diagnosis typically occurs at advanced stages when therapeutic options are less effective, underscoring the importance of early detection. Traditional diagnostic methods are often invasive and costly, spurring interest in more accessible and economical alternatives. The eye, as a direct link to the brain through the optic nerve, suggests that ocular changes could serve as early indicators of AD. This has led to the exploration of non-invasive ocular diagnostic tools. Technologies such as Optical Coherence Tomography (OCT), OCT Angiography (OCT-A), pupillometry, and eye-tracking, along with electrophysiological methods like Electroretinography (ERG) and Pattern Electroretinography (PEV), are being utilized to investigate potential ocular biomarkers. Further, tear fluid analysis has suggested that presence of amyloid-beta (Aβ) protein might reflect neurogenerative processes, providing a non-invasive window into disease progression. Exploring ocular changes as potential early indicators of Alzheimer's Disease (AD), we aimed to provide an overview of promising biomarkers for earlier diagnosis and intervention. Our review further investigates the connections between AD and other ocular degenerative diseases such as age-related macular degeneration (AMD) and glaucoma, uncovering shared pathogenic pathways that could offer new therapeutic targets. To establish the sensitivity and specificity of these ocular biomarkers, comprehensive studies are required. Moreover, larger, longitudinal studies are essential to confirm the effectiveness of ocular assessments in the preemptive diagnosis of Alzheimer's Disease. Clin Ter 2024; 175 (5):352-361 doi: 10.7417/CT.2024.5125 [ABSTRACT FROM AUTHOR]

Background: Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict ADspecific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Methods: Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Results: Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Conclusion: Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a chronic clinical condition that is predominantly seen in age groups above 60 years. The early detection of the disease through image classification aids in effective diagnosis and suitable treatment. The magnetic resonance imaging (MRI) data on Alzheimer's disease have been collected from Kaggle which is a freely available data source. These datasets are divided into training and validation sets. The present study focuses on training MRI datasets using TinyNet architecture that suits small‐scale image classification problems by overcoming the disadvantages of large convolutional neural networks. The architecture is designed such that convergence time is reduced and overall generalization is improved. Though the number of parameters used in this architecture is lesser than the existing networks, still this network can provide better results. Training MRI datasets achieved an accuracy of 98% with the method used with a 2% error rate and 80% for the validation MRI datasets with a 20% error rate. Furthermore, to validate the model‐supporting data collected from Kaggle and other open‐source platforms, a comparative analysis is performed to substantiate TinyNet's applicability and is projected in the discussion section. Transfer learning techniques are employed to infer the differences and to improve the model's efficiency. Furthermore, experiments are included for fine‐tuning attempts at the TinyNet architecture to assess how the nuances in convolutional neural networks have an impact on its performance. [ABSTRACT FROM AUTHOR]

Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer's Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA's potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments. [ABSTRACT FROM AUTHOR]

The most prevalent cause of dementia is Alzheimer's disease. Cognitive decline and accelerating memory loss characterize it. Alzheimer's disease advances sequentially, starting with preclinical stages, followed by mild cognitive and/or behavioral impairment, and ultimately leading to Alzheimer's disease dementia. In recent years, healthcare providers have been advised to make an earlier diagnosis of Alzheimer's, prior to individuals developing Alzheimer's disease dementia. Regrettably, the identification of early-stage Alzheimer's disease in clinical settings can be arduous due to the tendency of patients and healthcare providers to disregard symptoms as typical signs of aging. Therefore, accurate and prompt diagnosis of Alzheimer's disease is essential in order to facilitate the development of disease-modifying and secondary preventive therapies prior to the onset of symptoms. There has been a notable shift in the goal of the diagnosis process, transitioning from merely confirming the presence of symptomatic AD to recognizing the illness in its early, asymptomatic phases. Understanding the evolution of disease-modifying therapies and putting effective diagnostic and therapeutic management into practice requires an understanding of this concept. The outcomes of this study will enhance indepth knowledge of the current status of Alzheimer's disease's diagnosis and treatment, justifying the necessity for the quest for potential novel biomarkers that can contribute to determining the stage of the disease, particularly in its earliest stages. Interestingly, latest clinical trial status on pharmacological agents, the nonpharmacological treatments such as behavior modification, exercise, and cognitive training as well as alternative approach on phytochemicals as neuroprotective agents have been covered in detailed. [ABSTRACT FROM AUTHOR]

Introduction: Anticipating the diagnosis of Alzheimer's disease (AD) at an early asymptomatic at-risk stage, where therapeutics can more effectively delay conscious cognitive decline, is currently among the biggest challenges in the field. Herein, we aimed to compare the capacity of the Memory Binding Test (MBT) with the official diagnostic tool, the Free and Cued Selective Reminding Test (FCSRT), to anticipate AD diagnosis at an early preclinical stage based on the associative memory component of MBT (binding), suggested as more sensitive to the emergence of subtle episodic memory (EM) deficits (AD hallmark). Methods: We assessed the tests performance longitudinally (over 5 years) in 263 cognitively-normal elderly individuals at risk of AD (>6 months of subjective memory complaints) using linear mixed-effect models controlled for age, sex, and education. We stratified participants in 2 models: amyloid-β (Aβ)/neurodegeneration (N) model, assessing Aβ burden and neurodegeneration effect [3 groups: controls (Aβ-/N-); stable/N- (Aβ+); stable/N+ (Aβ+)]; and the stable/progressors model, assessing progression to prodromal-AD effect [2 groups: stable (Aβ+); progressors (Aβ+)], based on 15 subjects who progressed to AD during follow-up (excluded once diagnosed). Results: Aβ burden was associated with significantly less MBT-intrusions, while Aβ burden and neurodegeneration together, with the most. Progression status had a strong negative effect on both tests performance. When compared with the FCSRT, the MBT seems to anticipate diagnosis based on a worst performance in a higher number of scores (including binding) in at least a year. Discussion: Anticipation of diagnosis to an asymptomatic at-risk stage, while participants remain cognitively-normal according to FCSRT cut-offs and unaware of objective EM deficits, has the potential to delay the onset of AD-linked cognitive decline by applying promising therapeutics before decline becomes too advanced. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is the most prevalent form of dementia. Apart from its traditional clinical diagnostic methods, novel ocular imaging biomarkers have the potential to significantly enhance the diagnosis of Alzheimer's disease. Ophthalmologists might be able to play a crucial role in this multidisciplinary approach, aiding in the early detection and diagnosis of Alzheimer's disease through the use of advanced retinal imaging techniques. This systematic literature review the utilization of optical coherence tomography angiography biomarkers, specifically vessel density and the foveal avascular zone, for the diagnosis of Alzheimer's disease. A comprehensive search was performed across multiple academic journal databases, including 11 relevant studies. The selected studies underwent thorough analysis to assess the potential of these optical coherence tomography angiography biomarkers as diagnostic tools for Alzheimer's disease. The assessment of vessel density and the foveal avascular zone have emerged as a promising avenue for identifying and diagnosing Alzheimer's disease. However, it is imperative to acknowledge that further targeted investigations are warranted to address the inherent limitations of the existing body of literature. These limitations encompass various factors such as modest sample sizes, heterogeneity among study populations, disparities in optical coherence tomography angiography imaging protocols, and inconsistencies in the reported findings. In order to establish the clinical utility and robustness of these biomarkers in Alzheimer's disease diagnosis, future research endeavors should strive to overcome these limitations by implementing larger-scale studies characterized by standardized protocols and comprehensive assessments. [ABSTRACT FROM AUTHOR]

Background: Administrative healthcare databases, such as Medicare, are increasingly used to identify groups at risk of a crash. However, they only contain information on crash-related injuries, not all crashes. If the driver characteristics associated with crash and crash-related injury differ, conflating the two may result in ineffective or imprecise policy interventions. Methods: We linked 10 years (2008–2017) of Medicare claims to New Jersey police crash reports to compare the demographics, clinical diagnoses, and prescription drug dispensings for crash-involved drivers ≥ 68 years with a police-reported crash to those with a claim for a crash-related injury. We calculated standardized mean differences to compare characteristics between groups. Results: Crash-involved drivers with a Medicare claim for an injury were more likely than those with a police-reported crash to be female (62.4% vs. 51.8%, standardized mean difference [SMD] = 0.30), had more clinical diagnoses including Alzheimer's disease and related dementias (13.0% vs. 9.2%, SMD = 0.20) and rheumatoid arthritis/osteoarthritis (69.5% vs 61.4%, SMD = 0.20), and a higher rate of dispensing for opioids (33.8% vs 27.6%, SMD = 0.18) and antiepileptics (12.9% vs 9.6%, SMD = 0.14) prior to the crash. Despite documented inconsistencies in coding practices, findings were robust when restricted to claims indicating the injured party was the driver or was left unspecified. Conclusions: To identify effective mechanisms for reducing morbidity and mortality from crashes, researchers should consider augmenting administrative datasets with information from police crash reports, and vice versa. When those data are not available, we caution researchers and policymakers against the tendency to conflate crash and crash-related injury when interpreting their findings. [ABSTRACT FROM AUTHOR]

Background: Gold standard dementia assessments are rarely available in large real‐world datasets, leaving researchers to choose among methods with imperfect but acceptable accuracy to identify nursing home (NH) residents with dementia. In healthcare claims, options include claims‐based diagnosis algorithms, diagnosis indicators, and cognitive function measures in the Minimum Data Set (MDS), but few studies have compared these. We evaluated the proportion of NH residents identified with possible dementia and concordance of these three. Methods: Using a 20% random sample of 2018–2019 Medicare beneficiaries, we identified MDS admission assessments for non‐skilled NH stays among individuals with continuous enrollment in Medicare Parts A, B, and D. Dementia was identified using: (1) Chronic Conditions Warehouse (CCW) claims‐based algorithm for Alzheimer's disease and non‐Alzheimer's dementia; (2) MDS active diagnosis indicators for Alzheimer's disease and non‐Alzheimer's dementias; and (3) the MDS Cognitive Function Scale (CFS) (at least mild cognitive impairment). We compared the proportion of admissions with evidence of possible dementia using each criterion and calculated the sensitivity, specificity, and agreement of the CCW claims definition and MDS indicators for identifying any impairment on the CFS. Results: Among 346,013 non‐SNF NH admissions between 2018 and 2019, 57.2% met criteria for at least one definition (44.7% CFS, 40.7% CCW algorithm, 26.0% MDS indicators). The MDS CFS uniquely identified the greatest proportion with evidence of dementia. The CCW claims algorithm had 63.7% sensitivity and 78.1% specificity for identifying any cognitive impairment on the CFS. Active diagnosis indicators from the MDS had lower sensitivity (47.0%), but higher specificity (91.0%). Conclusions: Claims‐ and MDS‐based methods for identifying NH residents with possible dementia have only partial overlap in the cohorts they identify, and neither is an obvious gold standard. Future studies should seek to determine whether additional functional assessments from the MDS or prescriptions can improve identification of possible dementia in this population. [ABSTRACT FROM AUTHOR]

Objectives Early diagnosis of Alzheimer's disease (AD) using brain scans and other biomarker tests will be essential to increasing the benefits of emerging disease-modifying therapies, but AD biomarkers may have unintended negative consequences on stigma. We examined how a brain scan result affects AD diagnosis confidence and AD stigma. Methods The study used a vignette-based experiment with a 2 × 2 × 3 factorial design of main effects: a brain scan result as positive or negative, treatment availability and symptom stage. We sampled 1,283 adults ages 65 and older between June 11and July 3, 2019. Participants (1) rated their confidence in an AD diagnosis in each of four medical evaluations that varied in number and type of diagnostic tools and (2) read a vignette about a fictional patient with varied characteristics before completing the Modified Family Stigma in Alzheimer's Disease Scale (FS-ADS). We examined mean diagnosis confidence by medical evaluation type. We conducted between-group comparisons of diagnosis confidence and FS-ADS scores in the positive versus negative brain scan result conditions and, in the positive condition, by symptom stage and treatment availability. Results A positive versus negative test result corresponds with higher confidence in an AD diagnosis independent of medical evaluation type (all p  < .001). A positive result correlates with stronger reactions on 6 of 7 FS-ADS domains (all p  < .001). Discussion A positive biomarker result heightens AD diagnosis confidence but also correlates with more AD stigma. Our findings inform strategies to promote early diagnosis and clinical discussions with individuals undergoing AD biomarker testing. [ABSTRACT FROM AUTHOR]