Population-based neuropathological studies of dementia: design, methods and areas of investigation – a systematic review

Background Prospective population-based neuropathological studies have a special place in dementia research which is under emphasised. Methods A systematic review of the methods of population-based neuropathological studies of dementia was carried out. These studies were assessed in relation to their representativeness of underlying populations and the clinical, neuropsychological and neuropathological approaches adopted. Results Six studies were found to be true population-based neuropathological studies of dementia in the older people: the Hisayama study (Japan); Vantaa 85+ study (Finland); CC75C study (Cambridge, UK); CFAS (multicentre, UK); Cache County study (Utah, USA); HAAS (Hawaï, USA). These differ in the core characteristics of their populations. The studies used standardised neuropathological methods which facilitate analyses on: clinicopathological associations and confirmation of diagnosis, assessing the validity of hierarchical models of neuropathological lesion burden; investigating the associations between neuropathological burden and risk factors including genetic factors. Examples of findings are given although there is too little overlap in the areas investigated amongst these studies to form the basis of a systematic review of the results. Conclusion Clinicopathological studies based on true population samples can provide unique insights in dementia. Individually they are limited in power and scope; together they represent a powerful source to translate findings from laboratory to populations

Bilingualism Is Associated with a Delayed Onset of Dementia but Not with a Lower Risk of Developing it: a Systematic Review with Meta-Analyses.

Some studies have linked bilingualism with a later onset of dementia, Alzheimer's disease (AD), and mild cognitive impairment (MCI). Not all studies have observed such relationships, however. Differences in study outcomes may be due to methodological limitations and the presence of confounding factors within studies such as immigration status and level of education. We conducted the first systematic review with meta-analysis combining cross-sectional studies to explore if bilingualism might delay symptom onset and diagnosis of dementia, AD, and MCI. Primary outcomes included the age of symptom onset, the age at diagnosis of MCI or dementia, and the risk of developing MCI or dementia. A secondary outcome included the degree of disease severity at dementia diagnosis. There was no difference in the age of MCI diagnosis between monolinguals and bilinguals [mean difference: 3.2; 95% confidence intervals (CI): -3.4, 9.7]. Bilinguals vs. monolinguals reported experiencing AD symptoms 4.7 years (95% CI: 3.3, 6.1) later. Bilinguals vs. monolinguals were diagnosed with dementia 3.3 years (95% CI: 1.7, 4.9) later. Here, 95% prediction intervals showed a large dispersion of effect sizes (-1.9 to 8.5). We investigated this dispersion with a subgroup meta-analysis comparing studies that had recruited participants with dementia to studies that had recruited participants with AD on the age of dementia and AD diagnosis between mono- and bilinguals. Results showed that bilinguals vs. monolinguals were 1.9 years (95% CI: -0.9, 4.7) and 4.2 (95% CI: 2.0, 6.4) older than monolinguals at the time of dementia and AD diagnosis, respectively. The mean difference between the two subgroups was not significant. There was no significant risk reduction (odds ratio: 0.89; 95% CI: 0.68-1.16) in developing dementia among bilinguals vs. monolinguals. Also, there was no significant difference (Hedges' g = 0.05; 95% CI: -0.13, 0.24) in disease severity at dementia diagnosis between bilinguals and monolinguals, despite bilinguals being significantly older. The majority of studies had adjusted for level of education suggesting that education might not have played a role in the observed delay in dementia among bilinguals vs. monolinguals. Although findings indicated that bilingualism was on average related to a delayed onset of dementia, the magnitude of this relationship varied across different settings. This variation may be due to unexplained heterogeneity and different sources of bias in the included studies. Registration: PROSPERO CRD42015019100

Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.

OBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD

Survey on Early Detection of Alzhiemer’s Disease Using Capsule Neural Network

Alzheimer's disease (AD) is an disorder which is irreversible of the brain related to memory loss, mostly found in the old and aged population. Alzheimer's dementia results from the degeneration or loss of brain cells. The brain-imaging technologies most often used to diagnose AD is Magnetic resonance imaging (MRI). MRI or structural magnetic resonance is a very popular and actual technique used to diagnose AD.&nbsp;An&nbsp;MRI&nbsp;uses magnets and powerful radio waves to create a complete view of your brain. To actually detect the presence of Alzheimer’s, the MRI should me studied carefullyImplementation of CBIR Content Based Image Retrival which is a revolutionary computer aided diagnosis technique will create new abilities in MRI Magnetic resonance imaging in related image retrieval and training for recognition of development of AD in early stage

Enhancing Alzheimer's Detection Using a Multi-Modal Approach Hybrid Features Extraction Technique from MRI Images

The neurodegenerative illness Alzheimer's, which affects millions of people worldwide, poses significant obstacles to early detection and efficient treatment. The non-invasive technique of magnetic resonance imaging (MRI) has shown promise in identifying structural abnormalities in the brain linked to Alzheimer's disease. To address the complexity of Alzheimer's detection and enhance accuracy, this study proposes a novel hybrid feature extraction method that combines Convolutional Neural Networks (CNN), Local Binary Patterns (LBP), and Scale-Invariant Feature Transform (SIFT). After the feature extraction, PSO (Particle Swarm Optimization) and ABC (Ant Bee Colony) were applied for optimization. In this research, a dataset comprising MRI brain images from healthy individuals and Alzheimer's patients was curated. Preprocessing techniques were applied to enhance image quality and remove noise. The hybrid feature extraction method was then employed to extract distinctive and complementary features from the MRI images

Alzheimers Disease Diagnosis using Machine Learning: A Review

Alzheimers Disease AD is an acute neuro disease that degenerates the brain cells and thus leads to memory loss progressively. It is a fatal brain disease that mostly affects the elderly. It steers the decline of cognitive and biological functions of the brain and shrinks the brain successively, which in turn is known as Atrophy. For an accurate diagnosis of Alzheimers disease, cutting edge methods like machine learning are essential. Recently, machine learning has gained a lot of attention and popularity in the medical industry. As the illness progresses, those with Alzheimers have a far more difficult time doing even the most basic tasks, and in the worst case, their brain completely stops functioning. A persons likelihood of having early-stage Alzheimers disease may be determined using the ML method. In this analysis, papers on Alzheimers disease diagnosis based on deep learning techniques and reinforcement learning between 2008 and 2023 found in google scholar were studied. Sixty relevant papers obtained after the search was considered for this study. These papers were analysed based on the biomarkers of AD and the machine-learning techniques used. The analysis shows that deep learning methods have an immense ability to extract features and classify AD with good accuracy. The DRL methods have not been used much in the field of image processing. The comparison results of deep learning and reinforcement learning illustrate that the scope of Deep Reinforcement Learning DRL in dementia detection needs to be explored.Comment: 10 pages and 3 figure

DETERMINING EFFECTIVE LEVEL OF DEMENTIA DISEASE USING MRI IMAGES

Abstract The prevalence of dementia is growing as the world's population ages, making it a major public health issue. The key to successful management and treatment of dementia is an early and precise diagnosis. In this work, we will investigate the Dementia detection model DenseNet-169 in depth. The DenseNet-169 model has been used to classify almost 7,000 magnetic resonance imaging (MRI) scans of the brain. Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia are all categorized using this Convolution Neural Network (CNN) model. The use of deep learning and image processing presents intriguing new directions for the diagnosis and treatment of dementia, with the ultimate goal of enhancing the quality of life for those with the disease