Peripheral Biomarkers for Alzheimer's Disease: Update and Progress

Virtual Reality and Serious Videogame-Based Instruments for Assessing Spatial Navigation in Alzheimer's Disease: A Systematic Review of Psychometric Properties

Over the past decade, research using virtual reality and serious game-based instruments for assessing spatial navigation and spatial memory in at-risk and AD populations has risen. We systematically reviewed the literature since 2012 to identify and evaluate the methodological quality and risk of bias in the analyses of the psychometric properties of VRSG-based instruments. The search was conducted primarily in July-December 2022 and updated in November 2023 in eight major databases. The quality of instrument development and study design were analyzed in all studies. Measurement properties were defined and analyzed according to COSMIN guidelines. A total of 1078 unique records were screened, and following selection criteria, thirty-seven studies were analyzed. From these studies, 30 instruments were identified. Construct and criterion validity were the most reported measurement properties, while structural validity and internal consistency evidence were the least reported. Nineteen studies were deemed very good in construct validity, whereas 11 studies reporting diagnostic accuracy were deemed very good in quality. Limitations regarding theoretical framework and research design requirements were found in most of the studies. VRSG-based instruments are valuable additions to the current diagnostic toolkit for AD. Further research is required to establish the psychometric performance and clinical utility of VRSG-based instruments, particularly the instrument development, content validity, and diagnostic accuracy for preclinical AD screening scenarios. This review provides a straightforward synthesis of the state of the art of VRSG-based instruments and suggests future directions for research.

An Early Detection and Classification of Alzheimer's Disease Framework Based on ResNet-50

OBJECTIVE

The objective of this study is to develop a more effective early detection system for Alzheimer's disease (AD) using a Deep Residual Network (ResNet) model by addressing the issue of convolutional layers in conventional Convolutional Neural Networks (CNN) and applying image preprocessing techniques.

METHODS

The proposed method involves using Contrast Limited Adaptive Histogram Equalizer (CLAHE) and Boosted Anisotropic Diffusion Filters (BADF) for equalization and noise removal and K-means clustering for segmentation. A ResNet-50 model with shortcut links between three residual layers is proposed to extract features more efficiently. ResNet-50 is preferred over other ResNet types due to its intermediate depth, striking a balance between computational efficiency and improved performance, making it a widely adopted and effective architecture for various computer vision tasks. While other ResNet variations may offer higher depths, they are more prone to overfitting and computational complexity, which can hinder their practical application. The proposed method is evaluated on a dataset of MRI scans of AD patients.

RESULTS

The proposed method achieved high accuracy and minimum losses of 95% and 0.12, respectively. While some models showed better accuracy, they were prone to overfitting. In contrast, the suggested framework, based on the ResNet-50 model, demonstrated superior performance in terms of various performance metrics, providing a robust and reliable approach to Alzheimer's disease categorization.

CONCLUSION

The proposed ResNet-50 model with shortcut links between three residual layers, combined with image preprocessing techniques, provides an effective early detection system for AD. The study demonstrates the potential of deep learning and image processing techniques in developing accurate and efficient diagnostic tools for AD. The proposed method improves the existing approaches to AD classification and provides a promising framework for future research in this area.

Discovery of Novel Markers for Identifying Cognitive Decline Using Neuron-Derived Exosomes

Alzheimer's disease (AD), the predominant cause of late-life dementia, has a multifactorial etiology. Since there are few therapeutic options for symptomatic AD, research is increasingly focused on the identification of pre-symptomatic biomarkers. Recently, evaluation of neuron-derived exosomal markers has emerged as a promising novel approach for determining neuronal dysfunction. We aimed to identify novel neuron-derived exosomal markers that signify a transition from normal aging to Mild Cognitive Impairment (MCI) and then to clinically established AD, a sequence we refer to as AD progression. By using a Tandem Mass Tag-based quantitative proteomic approach, we identified a total of 360 neuron-derived exosomal proteins. Subsequent fuzzy c-means clustering revealed two clusters of proteins displaying trends of gradually increasing/decreasing expression over the period of AD progression (normal to MCI to AD), both of which were mainly involved in immune response-associated pathways, proteins within these clusters were defined as bridge proteins. Several differentially expressed proteins (DEPs) were identified in the progression of AD. The intersections of bridge proteins and DEPs were defined as key proteins, including C7 (Complement component 7), FERMT3 (Fermitin Family Member 3), CAP1 (Adenylyl cyclase-associated protein 1), ENO1 (Enolase 1), and ZYX (Zyxin), among which the expression patterns of C7 and ZYX were almost consistent with the proteomic results. Collectively, we propose that C7 and ZYX might be two novel neuron-derived exosomal protein markers, expression of which might be used to evaluate cognitive decline before a clinical diagnosis of AD is warranted.

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer's Disease

The early diagnosis of Alzheimer's disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer's patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer's disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

Predicting Thioflavin Fluorescence of Retinal Amyloid Deposits Associated With Alzheimer's Disease from Their Polarimetric Properties

Purpose

To use machine learning in those with brain amyloid to predict thioflavin fluorescence (indicative of amyloid) of retinal deposits from their interactions with polarized light.

Methods

We imaged 933 retinal deposits in 28 subjects with post mortem evidence of brain amyloid using thioflavin fluorescence and polarization sensitive microscopy. Means and standard deviations of 14 polarimetric properties were input to machine learning algorithms. Two oversampling strategies were applied to overcome data imbalance. Three machine learning algorithms: linear discriminant analysis, supporting vector machine, and random forest (RF) were trained to predict thioflavin positive deposits. For each method; accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were computed.

Results

For the polarimetric positive deposits, using 1 oversampling method, RF had the highest area under the receiver operating characteristic curve (0.986), which was not different from that with the second oversampling method. RF had 95% accuracy, 94% sensitivity, and 97% specificity. After including deposits with no polarimetric signals, polarimetry correctly predicted 93% of thioflavin positive deposits. Linear retardance and linear anisotropy were the dominant polarimetric properties in RF with 1 oversampling method, and no polarimetric properties were dominant in the second method.

Conclusions

Thioflavin positivity of retinal amyloid deposits can be predicted from their images in polarized light. Polarimetry is a promising dye-free method of detecting amyloid deposits in ex vivo retinal tissue. Further testing is required for translation to live eye imaging.

Translational Relevance

This dye-free method distinguishes retinal amyloid deposits, a promising biomarker of Alzheimer's disease, in human retinas imaged with polarimetry.