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Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This study investigated dynamic features of resting-state electroencephalography (EEG) functional connectivity to identify characteristic patterns of dementia subtypes, such as Alzheimer’s disease (AD) and frontotemporal dementia (FD), and to evaluate their potential as biomarkers. We extracted distinctive statistical features, including mean, variance, skewness, and Shannon entropy, from brain connectivity measures, revealing common alterations in dementia, specifically a generalized disruption of Alpha-band connectivity. Distinctive characteristics were found, including generalized Delta-band hyperconnectivity with increased complexity in AD and disrupted phase-based connectivity in Theta, Beta, and Gamma bands for FD. We also employed a convolutional neural network model, enhanced with these dynamic features, to differentiate between dementia subtypes. Our classification models achieved a multiclass classification accuracy of 93.6% across AD, FD, and healthy control groups. Furthermore, the model demonstrated 97.8% and 96.7% accuracy in differentiating AD and FD from healthy controls, respectively, and 97.4% accuracy in classifying AD and FD in pairwise classification. These establish a high-performance deep learning framework utilizing dynamic EEG connectivity patterns as potential biomarkers, offering a promising approach for early screening and diagnosis of dementia spectrum disorders using EEG. Our findings suggest that analyzing brain connectivity dynamics as a network and during cognitive tasks could offer more valuable information for diagnosis, assessing disease severity, and potentially identifying personalized neurological deficits.

Background Dementia is a neurological syndrome marked by cognitive decline. Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementia cases (AD and FTD) is crucial for effective medical care, as both conditions have similar early-symptoms. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI). Methods This study aims to develop a deep learning-based classification system for dementia by analyzing EEG derived scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. Scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique are utilized. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to validate the efficacy of the proposed method: the online BrainLat dataset (128 channels, comprising 16 AD, 13 FTD, and 19 healthy controls (HC)) and the in-house IITD-AIIA dataset (64 channels, including subjects with 10 AD, 9 MCI, and 8 HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes in both data sets. Results The best results were achieved using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yield accuracies of 94.17 % and 77.72 % on the BrainLat and IITD-AIIA datasets, respectively. Conclusions The results highlight that the image representation-based deep learning approach has the potential to differentiate various stages of dementia. It pave the way for more accurate and early diagnosis, which is crucial for the effective treatment and management of debilitating conditions.

In the complex realm of cognitive disorders, Alzheimer’s disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types, presenting entangled symptoms yet requiring distinct treatment approaches. The crux of effective treatment in slowing neurodegeneration lies in early, accurate diagnosis, as this significantly assists doctors in determining the appropriate course of action. However, current diagnostic practices often delay VaD diagnosis, impeding timely intervention and adversely affecting patient prognosis. This paper presents an innovative multi-omics approach to accurately differentiate AD from VaD, achieving a diagnostic accuracy of 89.25%. The proposed method segments the longitudinal MRI scans and extracts advanced radiomics features. Subsequently, it synergistically integrates the radiomics features with an ensemble of clinical, cognitive, and genetic data to provide state-of-the-art diagnostic accuracy, setting a new benchmark in classification accuracy on a large public dataset. The paper’s primary contribution is proposing a comprehensive methodology utilizing multi-omics data to provide a nuanced understanding of dementia subtypes. Additionally, the paper introduces an interpretable model to enhance clinical decision-making coupled with a novel model architecture for evaluating treatment efficacy. These advancements lay the groundwork for future work not only aimed at improving differential diagnosis but also mitigating and preventing the progression of dementia.

We tested the hypothesis that neuroticism moderates the association between APOE (apolipoprotein E) genotype and two major outcomes, cognitive function and Alzheimer disease. We also explored whether other personality dimensions (extraversion, openness to experience, agreeableness, and conscientiousness) moderate the associations of APOE with these outcomes. Primary analyses of existing randomized clinical trial data. Six-hundred two older adults (mean age of 78 years at baseline). APOE genotype, the NEO-Five Factor Inventory, the Alzheimer's Disease Assessment Scale-Cognitive: measured every 6 months for 6.5 years) and relevant covariates. Fully adjusted multivariate analyses showed that the association between the presence of APOE [Latin Small Letter Open E]-4 allele(s) and both outcomes was evident among individuals with high levels of neuroticism and extraversion but not among persons with low levels of these traits. Phenotypic personality dimensions, primarily neuroticism and extraversion, moderate the relationship between APOE [Latin Small Letter Open E]-4 genotype and cognitive outcomes among older adults. Future research is needed to elucidate the physiological processes involved in these particular phenotype–genotype interactions.

As our population ages, diseases affecting memory and daily functioning will affect an increasing number of individuals, their families and the healthcare system. The social, financial and economic impacts will be profound. This article provides an overview of current dementia syndromes to assist clinicians in evaluating, educating and treating these patients.

Background Index measures for health-related quality of life (HRQoL) quantify the desirability (utility) of a certain health state. The commonly used generic index measure, e.g. EuroQol: EQ-5D, may underestimate relevant areas of specific diseases, resulting in lower validity. Disease-specific index measures on the other hand combine disease-specificity and quantification of perceived quality on several health domains of a certain disease into one single figure. These instruments have been developed for several diseases, but a dementia-specific HRQoL index instrument was not yet available. Facing the increasing individual and societal burden of dementia, specific HRQoL values with metric characteristics are especially useful because they will provide vital information for health outcome research and economic evaluations. Aims of the study To develop and validate the prototype of a dementia-specific HRQoL index measure: Dementia Quality of life Instrument (DQI), as the first step towards valuation of the dementia health state. Methods For development of the DQI we created a conceptual framework based on a review of the literature, qualitative interviews with people with dementia and their carers, expert opinion and team discussion. To assess validity we undertook a survey under 241 dementia professionals. Measurements consisted of ranking (1–5) and rating (1–10) of 5 dementia-specific DQI domains (memory, orientation, independence, social activities and mood) and simultaneously rating of 9 DQI-derived health states on a visual analogue scale (VAS). We also performed a cross-sectional study in a large sample of people with very mild to moderate dementia and their caregivers (N = 145) to assess feasibility and concurrent validity. In addition, caregivers valued 10 DQI and 10 EQ-5D + C derived health states of the patient simultaneously on the same VAS. Setting: outpatient clinics, nursing homes and patient residences. Results All professionals judged the selected DQI domains to be relevant. Differences in ranking and rating behaviors were small. Mood was ranked (≥3.3) and rated (≥8.2) as most, orientation as least important (rank ≤2.6, value 7.5) health domain for dementia. For the validation part of this study the completion rates for all domains were above 98% for patients and 100% for caregivers on patients. A priori hypothesized DQI versus QOL-AD correlations that were significant in both patients and caregivers were: memory/memory, orientation/memory, independence/physical health, social activities/energy and mood/mood. Patient/caregiver inter-rater agreement was low (K < 0.2) for memory/independence, fair (K 0.2-0.4) for orientation/mood, and moderate (K 0.4-0.6) for social activities. Concurrent validity of the DQI with the EQ-5D + C was moderate. The fact that most of the correlations between the domains of these two instruments were low (≤0.40) showed that both instruments measure different elements of health status. As expected, modest correlations (≥0.40) were observed between corresponding domains of the two instruments. Conclusions Professionals judged all domains as relevant. The DQI prototype proved valid and feasible for patients and caregivers and is appropriate for very mild to moderate dementia. The differences in concurrent correlations with generic health status instruments imply that the dementia-specific DQI health domains indeed provide different information. The finding that patient HRQoL measured with the DQI was lower supports this notion. The new DQI shows comparable psychometric properties to the best available dementia-specific (QOL-AD) and generic (EQ-5D + C) measures. Further research is needed to generate values in the general population for each of the possible DQI states and to derive an algorithm that converts the 5 separate DQI domain scores into one single DQI Index score. Introducing the DQI Index will advance dementia-related HRQoL measurement by overcoming the shortcomings of generic and non-index instruments. This will allow more unequivocal interpretation of subjective dementia HRQoL states in dementia research.

Alzheimer's disease (AD) is classically defined as a dual clinicopathological entity. The recent advances in use of reliable biomarkers of AD that provide in-vivo evidence of the disease has stimulated the development of new research criteria that reconceptualise the diagnosis around both a specific pattern of cognitive changes and structural/biological evidence of Alzheimer's pathology. This new diagnostic framework has stimulated debate about the definition of AD and related conditions. The potential for drugs to intercede in the pathogenic cascade of the disease adds some urgency to this debate. This paper by the International Working Group for New Research Criteria for the Diagnosis of AD aims to advance the scientific discussion by providing broader diagnostic coverage of the AD clinical spectrum and by proposing a common lexicon as a point of reference for the clinical and research communities. The cornerstone of this lexicon is to consider AD solely as a clinical and symptomatic entity that encompasses both predementia and dementia phases.

Background: The clinical syndrome of dementia consists of several subtypes that are distinct in their etiology, clinical profile, management, and outcome. Limited specialized services are available for dementia patients in India. We report the profile of dementia subtypes from a clinic-based dementia registry in India. Methods: Consecutive dementia patients were investigated with clinical evaluation, neuropsychological tests modified for local use, and brain imaging. Results: In 347 consecutive dementia patients, Alzheimer’s disease was the most common subtype of dementia (38.3%), followed by a high proportion of vascular dementia (25.4%). Frontotemporal dementia syndromes were not uncommon (18.7%). Dementia with Lewy bodies was encountered in 8.9% of the patients, and mixed dementia was found in 8.6%. The mean age of the group at presentation was 66.3 years, nearly a decade younger than in developed countries. The proportion of patients with early-onset dementia was high (49.9%). Conclusions: Our results demonstrate that the clinical profiles of dementia subtypes in a clinic population are influenced by the population’s demographic profile, cardiovascular risk factor burden, sociocultural attitudes about cognitive impairment, and possibly genetic factors.

Key Points The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) provides a framework for the diagnosis of neurocognitive disorders based on three syndromes: delirium, mild neurocognitive disorder and major neurocognitive disorder Major neurocognitive disorder is mostly synonymous with dementia, although the criteria have been modified so that impairments in learning and memory are not necessary for diagnosis DSM-5 describes criteria to delineate specific aetiological subtypes of mild and major neurocognitive disorder The diagnostic certainty of an aetiological diagnosis is based on clinical features and biomarkers, and can be qualified as probable or possible The DSM-5 criteria are consistent with those developed by various expert groups for the different aetiological subtypes of neurocognitive disorders Further validation in clinical practice is necessary, but we expect these criteria will have high reliability and validity, and widespread adoption will bring consistency to the diagnosis of diverse neurocognitive disorders The fifth edition of the American Psyciatric Association's Diagnostic and Statistical Manual for Mental Disorders (DSM-5) was published in 2013, and with it came new diagnostic criteria for mild cognitive impairment and dementia. In this Review, members of the working group tasked with writing the DSM-5 criteria for neurocognitive disorders present the new approach to categorization and diagnosis. Three key syndromes are recognized—delirium, mild neurocognitive disorder and major neurocognitive disorder—and each can have distinct aetiological subtypes. Neurocognitive disorders—including delirium, mild cognitive impairment and dementia—are characterized by decline from a previously attained level of cognitive functioning. These disorders have diverse clinical characteristics and aetiologies, with Alzheimer disease, cerebrovascular disease, Lewy body disease, frontotemporal degeneration, traumatic brain injury, infections, and alcohol abuse representing common causes. This diversity is reflected by the variety of approaches to classifying these disorders, with separate groups determining criteria for each disorder on the basis of aetiology. As a result, there is now an array of terms to describe cognitive syndromes, various definitions for the same syndrome, and often multiple criteria to determine a specific aetiology. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) provides a common framework for the diagnosis of neurocognitive disorders, first by describing the main cognitive syndromes, and then defining criteria to delineate specific aetiological subtypes of mild and major neurocognitive disorders. The DSM-5 approach builds on the expectation that clinicians and research groups will welcome a common language to deal with the neurocognitive disorders. As the use of these criteria becomes more widespread, a common international classification for these disorders could emerge for the first time, thus promoting efficient communication among clinicians and researchers.

Background: Many people participating in dementia research may lack capacity to give informed consent and the relationship between cognitive function and capacity remains unclear. Recent changes in the law reinforce the need for robust and reproducible methods of assessing capacity when recruiting people for research. Aims: To identify numbers of capacitous participants in a pragmatic randomised trial of dementia treatment; to assess characteristics associated with capacity; to describe a legally acceptable consent process for research. Methods: As part of a pragmatic randomised controlled trial of Ginkgo biloba for mild-moderate dementia, we used a consenting algorithm that met the requirements of existing case law and the exigencies of the new Mental Capacity Act. We decided who had capacity to give informed consent for participation in the trial using this algorithm and sought predictors of capacity. Results: Most participants (76%) with mild-moderate dementia in this trial were unable to give informed consent according to the legal criteria. When adjusted for confounding, the Mini Mental State examination did not predict the presence of capacity. Conclusion: Cognitive testing alone is insufficient to assess the presence of capacity. Researchers and clinicians need to be aware of the challenging processes regarding capacity assessment. We outline a procedure which we believe meets the ethical and legal requirements.