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Smart mobile phone use is increasing worldwide, as is the ability of mobile devices to monitor daily routines, behaviors, and even cognitive changes. There is a growing opportunity for users to share the data collected with their medical providers which may serve as an accessible cognitive impairment screening tool. Data logged or tracked in an app and analyzed with machine learning (ML) could identify subtle cognitive changes and lead to more timely diagnoses on an individual and population level. This review comments on existing evidence of mobile device applications designed to passively and/or actively collect data on cognition relevant for early detection and diagnosis of Alzheimer’s disease (AD). The PubMed database was searched to identify existing literature on apps related to dementia and cognitive health data collection. The initial search deadline was December 1, 2022. Additional literature published in 2023 was accounted for with a follow-up search prior to publication. Criteria for inclusion was limited to articles in English which referenced data collection via mobile app from adults 50+ concerned, at risk of, or diagnosed with AD dementia. We identified relevant literature (n = 25) which fit our criteria. Many publications were excluded because they focused on apps which fail to collect data and simply provide users with cognitive health information. We found that although data collecting cognition- related apps have existed for years, the use of these apps as screening tools remains underdeveloped; however, itmay serve as proof of concept and feasibility as there is much supporting evidence on their predictive utility. Concerns about the validity of mobile apps for cognitive screening and privacy issues remain prevalent. Mobile applications and use of ML is widely considered a financially and socially viable method of compiling symptomatic data but currently this large potential dataset, screening tool, and research resource is still largely untapped. [ABSTRACT FROM AUTHOR]

The entorhinal cortex (EC) stands out as a critical brain region affected in the early phases of Alzheimer's disease (AD), with some of the disease's pathological processes originating from this area, making it one of the most crucial brain regions in AD. Recent research highlights disruptions in the brain's network activity, characterized by heightened excitability and irregular oscillations, may contribute to cognitive impairment. These disruptions are proposed not only as potential therapeutic targets but also as early biomarkers for AD. In this paper, we will begin with a review of the anatomy and function of EC, highlighting its selective vulnerability in AD. Subsequently, we will discuss the disruption of EC network activity, exploring changes in excitability and neuronal oscillations in this region during AD and hypothesize that, considering the advancements in neuromodulation techniques, addressing the disturbances in the network activity of the EC could offer fresh insights for both the diagnosis and treatment of AD. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors fromspeech data and transcripts are reviewed, whichmainly contains acoustic features fromspeech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges. [ABSTRACT FROM AUTHOR]

Motivation. At present, the research methods for image genetics of Alzheimer's disease based on machine learning are mainly divided into three steps: the first step is to preprocess the original image and gene information into digital signals that are easy to calculate; the second step is feature selection aiming at eliminating redundant signals and obtain representative features; and the third step is to build a learning model and predict the unknown data with regression or bivariate correlation analysis. This type of method requires manual extraction of feature single-nucleotide polymorphisms (SNPs), and the extraction process relies on empirical knowledge to a certain extent, such as linkage imbalance and gene function information in a group sparse model, which puts forward certain requirements for applicable scenarios and application personnel. To solve the problems of insufficient biological significance and large errors in the previous methods of association analysis and disease diagnosis, this paper presents a method of correlation analysis and disease diagnosis between SNP and region of interest (ROI) based on a deep learning model. It is a data-driven method, which has no obvious feature selection process. Results. The deep learning method adopted in this paper has no obvious feature extraction process relying on prior knowledge and model assumptions. From the results of correlation analysis between SNP and ROI, this method is complementary to other regression model methods in application scenarios. In order to improve the disease diagnosis performance of deep learning, we use the deep learning model to integrate SNP characteristics and ROI characteristics. The SNP feature, ROI feature, and SNP-ROI joint feature were input into the deep learning model and trained by cross-validation technique. The experimental results show that the SNP-ROI joint feature describes the information of the samples from different angles, which makes the diagnosis accuracy higher. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) is when brain function declines. MCI is the gray area transitioning from normal aging to the AD stage. Currently, the majority of early MCI diagnoses are processed through comprehensive neuropsychological tests. These tests may take the form of interviews, paper-and-pencil tests, or computer-based tests. There may be resistance from the subject if he/she has to undergo many screening tests simultaneously for multiple evaluation information, resulting in execution difficulty. The objectives of this study are to use 3D virtual reality to create an entertaining test scenario integrating the Mini-Cog, SPMSQ, MMSE, SLUMS, CDR, and CASI for middle-aged to older adults, furthermore, to employ fuzzy logic control (FLC) technology to develop a "MCI assessment system" for obtaining some pilot information for MCI assessment. There were 24 middle-aged to older adults aged from 50 to 65 years who participated in the evaluation experiment. The results showed that the MCI assessment system developed in this study is highly correlated with the traditional screening tests, including the Mini-Cog, SPMSQ, MMSE, SLUMS, and CASI. The assessment system can provide an integrated reference score for clinic workers in making judgments. In addition, the distribution of the System Usability Scale (SUS) evaluation scores for the MCI assessment system revealed that 87.5% were grade C (good to use) or above and 29.2% were grade B (extremely good to use) or above. The assessment system received positive feedback from the subjects. [ABSTRACT FROM AUTHOR]

Alzheimer's is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual's cognitive abilities to process and analyze information gradually deteriorate, resulting in mental decline. In recent years, there has been a notable increase in endeavors aimed at identifying Alzheimer's disease and addressing its progression. Research studies have demonstrated the significant involvement of genetic factors, stress, and nutrition in developing this condition. The utilization of computer-aided analysis models based on machine learning and artificial intelligence has the potential to significantly enhance the exploration of various neuroimaging methods and non-image biomarkers. This study conducts a comparative assessment of more than 80 publications that have been published since 2017. Alzheimer's disease detection is facilitated by utilizing fundamental machine learning architectures such as support vector machines, decision trees, and ensemble models. Furthermore, around 50 papers that utilized a specific architectural or design approach concerning Alzheimer's disease were examined. The body of literature under consideration has been categorized and elucidated through the utilization of data-related, methodology-related, and medical-fostering components to illustrate the underlying challenges. The conclusion section of our study encompasses a discussion of prospective avenues for further investigation and furnishes recommendations for future research activities on the diagnosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a complex neurodegenerative disease. Subjects with Mild Cognitive Impairment will progress to Alzheimer's disease, thus how to effectively diagnose Alzheimer's disease or Mild Cognitive Impairment using the clinical tabular data and Magnetic Resonance Images of the brain together has been a major concern of researches. Deep multi-modal learning-based methods can improve Alzheimer's disease diagnostic accuracy compared to the single modality-based methods. However, most existing multi-modal fusion methods only focus on learning global features fusion from image and clinical tabular data by concatenation, lacking the ability to jointly analyze and integrate global–local information of image with clinical tabular data. To address these limitations, this paper explored a novel Multi-Modal Global–Local Fusion method to perform multi-modal Alzheimer's disease classification through 3D Magnetic Resonance Images and clinical tabular data. Specifically, we adopt a global module that uses concatenation to fuse features to learn the global information. Moreover, we design an attention-based local module which encourages clinical tabular features to guide the learning of local 3D Magnetic Resonance Images information, thus, enhancing the power of features fusion from each modality. Our method considers both global and local information of the two modalities for multi-modal fusion. Experiment results show that our method in this paper is highly effective in combining 3D Magnetic Resonance Images and clinical tabular data for Alzheimer's disease classification with accuracy of 86.34% and 86.77% in ADNI and OASIS-1 datasets respectively, which outperforms the current state-of-the-art methods. Detailed ablation experiments are conducted to highlight the contribution of various components. code is available at: https://github.com/nananana0701/MMGLF. • We propose a multi-model fusion method to perform Alzheimer's disease classification using 3D MRI and clinical tabular data by learning the global and local fusion information. • We propose an attention-based local fusion strategy to realize the local fusion and then enforce the feature extractor concentrating on areas related to Alzheimer's disease. • The experimental results demonstrate that our method performs better compared with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Mild cognitive impairment (MCI) could be a transitory stage to Alzheimer's disease (AD) and underlines the importance of early detection of this stage. In MCI stage, though the older adults are not completely dependent on others for day-to-day tasks, mild impairments are seen in memory, attention, etc., subtly affecting their daily activities/routines. Smart sensing technologies, such as wearable and non-wearable sensors, coupled with advanced predictive modeling techniques enable daily activities/routines based early detection of MCI symptoms. Non-wearable sensors are less intrusive and can monitor activities at naturalistic environment with no interference to an individual's daily routines. This review seeks to answer the following questions: (1) What is the evidence for use of non-wearable sensor technologies in early detection of MCI/AD utilizing daily activity data in an unobtrusive manner? (2) How are the machine learning methods being employed in analyzing activity data in this early detection approach? A systematic search was conducted in databases such as IEEE Explorer, PubMed, Science Direct, and Google Scholar for the papers published from inception till March 2019. All studies that fulfilled the following criteria were examined: a research goal of detecting/predicting MCI/AD, daily activities data to detect MCI/AD, noninvasive/non-wearable sensors for monitoring activity patterns, and machine learning techniques to create the prediction models. Out of 2165 papers retrieved, 12 papers were eligible for inclusion in this review. This review found a diverse selection of aspects such as sensors, activity domains/features, activity recognition methods, and abnormality detection methods. There is no conclusive evidence on superiority of one or more of these aspects over the others, especially on the activity feature that would be the best indicator of cognitive decline. Though all these studies demonstrate technological developments in this field, they all suggest it is far in the future it becomes an effective diagnostic tool in real-life clinical practice. [ABSTRACT FROM AUTHOR]

As Alzheimer’s disease (AD) is featured with degeneration and irreversibility, the diagnosis of AD at early stage is important. In recent years, some researchers have tried to apply neural network (NN) to classify AD patients from healthy controls (HC) based on functional MRI (fMRI) data. But most study focus on a single NN and the classification accuracy was not high. Therefore, this paper used the random neural network cluster which was composed of multiple NNs to improve classification performance. Sixty one subjects (25 AD and 36 HC) were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This method not only could be used in the classification, but also could be used for feature selection. Firstly, we chose Elman NN from five types of NNs as the optimal base classifier of random neural network cluster based on the results of feature selection, and the accuracies of the random Elman neural network cluster could reach to 92.31% which was the highest and stable. Then we used the random Elman neural network cluster to select significant features and these features could be used to find out the abnormal regions. Finally, we found out 23 abnormal regions such as the precentral gyrus, the frontal gyrus and supplementary motor area. These results fully show that the random neural network cluster is worthwhile and meaningful for the diagnosis of AD. [ABSTRACT FROM AUTHOR]

"Alzheimer's disease" (AD) is a neurodegenerative disorder in which the memory shrinks and neurons die. "Dementia" is described as a gradual decline in mental, psychological, and interpersonal qualities that hinders a person's ability to function autonomously. AD is the most common degenerative brain disease. Among the first signs of AD are missing recent incidents or conversations. "Deep learning" (DL) is a type of "machine learning" (ML) that allows computers to learn by doing, much like people do. DL techniques can attain cutting-edge precision, beating individuals in certain cases. A large quantity of tagged information with multi-layered "neural network" architectures is used to perform analysis. Because significant advancements in computed tomography have resulted in sizable heterogeneous brain signals, the use of DL for the timely identification as well as automatic classification of AD has piqued attention lately. With these considerations in mind, this paper provides an in-depth examination of the various DL approaches and their implementations for the identification and diagnosis of AD. Diverse research challenges are also explored, as well as current methods in the field. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of AD automatically. In the proposed system, we use segmented MRI scans. Input data samples of three classes include 110 normal control (NC), 110 mild cognitive impairment (MCI) and 105 AD subjects are used in this paper. The data is acquired from the ADNI database and gray matter (GM) images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models. The proposed approaches segregate among NC, MCI, and AD. While testing both methods applied on the segmented data samples, the highest performance results of the classification in terms of accuracy on NC vs. AD are 95.33% and 89.87%, respectively. The proposed methods distinguish between NC vs. MCI and MCI vs. AD patients with a classification accuracy of 90.74% and 86.69%. The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing. [ABSTRACT FROM AUTHOR]

A letter to the editor about the redefined role of primary healthcare in the dementia research and policy agenda is presented.

Detection of early morphological changes in the brain and early diagnosis are important for Alzheimer's disease (AD), and high-resolution magnetic resonance imaging (MRI) can be used to help diagnose and predict the disease. In this paper, we proposed two improved ResNet algorithms that introduced the Contextual Transformer (CoT) module, group convolution, and Channel Shuffle mechanism into the traditional ResNet residual blocks. The CoT module is used to replace the 3 x 3 convolution in the residual block to enhance the feature extraction capability of the residual block, while the Channel Shuffle mechanism is used to reorganize the feature maps of different groups in the input layer to improve the communication between the feature maps from different groups. Images of 503 subjects, including 116 healthy controls (HC), 187 subjects with mild cognitive impairment (MCI), and 200 subjects with AD, were selected and collated from the ADNI database, and then, the data were pre-processed and sliced. After that, 10,060 slices were obtained and the three groups of AD, MCI and HC were classified using the improved algorithms. The experiments showed that the refined ResNet-18-based algorithm improved the top-1 accuracy by 2.06%, 0.33%, 1.82%, and 1.52% over the traditional ResNet-18 algorithm for four medical image classification tasks, namely AD: MCI, AD: HC, MCI: HC, and AD: MCI: HC, respectively. The enhanced ResNet-50-based algorithm improved the top-1 accuracy by 1.02%, 2.92%, 3.30%, and 1.31%, respectively, over the traditional ResNet-50 algorithm in four medical image classification tasks, demonstrating the effectiveness of the CoT module replacement and the inclusion of the channel shuffling mechanism, as well as the competitiveness of the improved algorithms. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes. It has become an effective aid in the early diagnosis of AD by exploiting abnormal brain activity. Because the original EEG has the characteristics of weak amplitude, strong background noise and randomness, the research on intelligent AD recognition based on machine learning is still in the exploratory stage. This paper proposes the discriminant subspace low-rank representation (DSLRR) algorithm for EEG-based AD and mild cognitive impairment (MCI) recognition. The subspace learning and low-rank representation are flexibly integrated into a feature representation model. On the one hand, based on the low-rank representation, the graph discriminant embedding is introduced to constrain the representation coefficients, so that the robust representation coefficients can preserve the local manifold structure of the EEG data. On the other hand, the least squares regression, principle component analysis, and global graph embedding are introduced into the subspace learning, to make the model more discriminative. The objective function of DSLRR is solved by the inexact augmented Lagrange multiplier method. The experimental results show that the DSLRR algorithm has good classification performance, which is helpful for in-depth research on AD and MCI recognition. [ABSTRACT FROM AUTHOR]

The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer's disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an age-related disease that affects a large proportion of the elderly. Currently, the neuroimaging techniques [e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)] are promising modalities for AD diagnosis. Since not all brain regions are affected by AD, a common technique is to study some region-of-interests (ROIs) that are believed to be closely related to AD. Conventional methods used ROIs, identified by the handcrafted features through Automated Anatomical Labeling (AAL) atlas rather than utilizing the original images which may induce missing informative features. In addition, they learned their framework based on the discriminative patches instead of full images for AD diagnosis in multistage learning scheme. In this paper, we integrate the original image features from MRI and PET with their ROIs features in one learning process. Furthermore, we use the ROIs features for forcing the network to focus on the regions that is highly related to AD and hence, the performance of the AD diagnosis can be improved. Specifically, we first obtain the ROIs features from the AAL, then we register every ROI with its corresponding region of the original image to get a synthetic image for each modality of every subject. Then, we employ the convolutional auto-encoder network for learning the synthetic image features and the convolutional neural network (CNN) for learning the original image features. Meanwhile, we concatenate the features from both networks after each convolution layer. Finally, the highly learned features from the MRI and PET are concatenated for brain disease classification. Experiments are carried out on the ADNI datasets including ADNI-1 and ADNI-2 to evaluate our method performance. Our method demonstrates a higher performance in brain disease classification than the recent studies. [ABSTRACT FROM AUTHOR]

Introduction: Regulatory bodies recommend that outcome measures used in Alzheimer's disease (AD) clinical trials capture clinically meaningful changes for the trial participant. However, commonly used outcome measures do not reflect the individual's views on what matters to them individually. The aim of the electronic Person-Specific Outcome Measure (ePSOM) programme is to better understand what outcomes matter to patients in early Alzheimer's disease. Methods: As part of the ePSOM programme, we designed and ran an online study to understand what matters to individuals when developing new treatments for AD. The ePSOM survey ran Aug 2019–Dec 2019 (UK) and collected primarily free text responses which were analysed using Natural Language Processing (NLP) techniques. In this paper, we focus our analyses on individuals who reported having a neurodegenerative disease diagnosis (primarily Mild Cognitive Impairment (MCI) or AD), reporting the most frequent and most important brain health priorities for this group. Due to a small sample size, the Diagnosis group was analysed as a whole. Finally, we compared the Diagnosis group to an age and gender matched control group using chi-squared tests to look for any differences between the Diagnosis and control groups' priorities. Results: The survey was completed by 5808 respondents, of whom 167 (2.9%) (women n = 91, men n = 69, other n = 7) had received one of our pre-defined neurodegenerative disease diagnosis: most commonly MCI n = 52, 1.1% (mean age 69.42, SD = 10.8); or Alzheimer's disease n = 48, 1.0% (mean age 71.24, SD = 9.79). Several thematic clusters were significantly more important for the target diagnostic group, e.g.: Expressing opinions; and less important, e.g., Cognitive Games. Conclusion: We conclude there are a range of outcomes which individuals consider important and what potential new treatments should help maintain or improve, suggesting that outcomes that matter shift along the preclinical, prodromal and overt dementia continuum. This has important implications for the development of outcome measures in long term prevention studies that last several years where participants may pass through different stages of disease. In the final stage of our project, we will design an electronic outcomes app which will employ the methodology tested in the large-scale survey to capture what matters to individuals about their brain health at an individual level. [ABSTRACT FROM AUTHOR]

Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and healthy controls (HC) based on neuroimaging data. But previous studies have only used a single SVM to classify AD and HC, and the accuracy is not very high and generally less than 90%. The method of random support vector machine cluster was proposed to classify AD and HC in this paper. From the Alzheimer's Disease Neuroimaging Initiative database, the subjects including 25 AD individuals and 35 HC individuals were obtained. The classification accuracy could reach to 94.44% in the results. Furthermore, the method could also be used for feature selection and the accuracy could be maintained at the level of 94.44%. In addition, we could also find out abnormal brain regions (inferior frontal gyrus, superior frontal gyrus, precentral gyrus and cingulate cortex). It is worth noting that the proposed random support vector machine cluster could be a new insight to help the diagnosis of AD. [ABSTRACT FROM AUTHOR]

Background and objectives: Alzheimer's disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool. Materials and methods: Thirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student's t-test and Mann-Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic. Results: Kinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters (p < 0.05, p < 0.01). The results found that patients with AD showed slower writing speed, tremendous writing pressure, and poorer writing stability. We built statistically significant features into a classification model, among which the model built by XGB was the most effective with a maximum accuracy of 96.55%. The handwriting characteristics also achieved good diagnostic value in the ROC analysis. Task 2 had a better classification effect than task 1. ROC curve analysis showed that the best threshold value was 0.084, accuracy = 96.30%, sensitivity = 100%, specificity = 93.41%, PPV = 92.21%, NPV = 100%, and AUC = 0.991. Task 4 had a better classification effect than task 3. ROC curve analysis showed that the best threshold value was 0.597, accuracy = 96.55%, sensitivity = 94.20%, specificity = 98.37%, PPV = 97.81%, NPV = 95.63%, and AUC = 0.994. Conclusion: This study's results prove that handwriting characteristic analysis is promising in auxiliary AD screening or AD diagnosis. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but we also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method. [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]

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]

Alzheimer's disease is the third most expensive disease, only after cancer and cardiopathy. It is also the fourth leading cause of death in the elderly after cardiopathy, cancer, and cerebral palsy. The disease lacks specific diagnostic criteria. At present, there is still no definitive and effective means for preclinical diagnosis and treatment. It is the only disease that cannot be prevented and cured among the world's top ten fatal diseases. It has now been proposed as a global issue. Computer-aided diagnosis of Alzheimer's disease (AD) is mostly based on images at this stage. This project uses multi-modality imaging MRI/PET combining with clinical scales and uses deep learning-based computer-aided diagnosis to treat AD, improves the comprehensiveness and accuracy of diagnosis. The project uses Bayesian model and convolutional neural network to train experimental data. The experiment uses the improved existing network model, LeNet-5, to design and build a 10-layer convolutional neural network. The network uses a back-propagation algorithm based on a gradient descent strategy to achieve good diagnostic results. Through the calculation of sensitivity, specificity and accuracy, the test results were evaluated, good test results were obtained. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) affects millions of people and is a major rising problem in health care worldwide. Recent research suggests that AD could have different subtypes, presenting differences in how the disease develops. Characterizing those subtypes could be key to deepen the understanding of this complex disease. In this paper, we used a multivariate, non-supervised clustering method over blood-based markers to find subgroups of patients defined by distinctive blood marker profiles. Our analysis on ADNI database identified 4 possible subgroups, each with a different blood profile. More importantly, we show that subgroups with different profiles have a different relationship between brain phenotypes detected in magnetic resonance imaging and disease condition. [ABSTRACT FROM AUTHOR]

Background: Early diagnosis of Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis (MVPA) for the diagnosis of brain disorders are explicitly attracting attention in the neuroimaging community. In this paper, we propose a voxel-wise discriminative framework applied to multi-measure resting-state fMRI (rs-fMRI) that integrates hybrid MVPA and extreme learning machine (ELM) for the automated discrimination of AD and MCI from the cognitive normal (CN) state. Materials and methods: We used two rs-fMRI cohorts: the public Alzheimer’s disease Neuroimaging Initiative database (ADNI2) and an in-house Alzheimer’s disease cohort from South Korea, both including individuals with AD, MCI, and normal controls. After extracting three-dimensional (3-D) patterns measuring regional coherence and functional connectivity during the resting state, we performed univariate statistical t-tests to generate a 3-D mask that retained only voxels showing significant changes. Given the initial univariate features, to enhance discriminative patterns, we implemented MVPA feature reduction using support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO), in combination with the univariate t-test. Classifications were performed by an ELM, and its efficiency was compared to linear and nonlinear (radial basis function) SVMs. Results: The maximal accuracies achieved by the method in the ADNI2 cohort were 98.86% (p<0.001) and 98.57% (p<0.001) for AD and MCI vs. CN, respectively. In the in-house cohort, the same accuracies were 98.70% (p<0.001) and 94.16% (p<0.001). Conclusion: From a clinical perspective, combining extreme learning machine and hybrid MVPA applied on concatenations of multiple rs-fMRI biomarkers can potentially assist the clinicians in AD and MCI diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we developed a new method to detect Alzheimer’s disease (AD)-related amyloid precursor protein (APP). A composite material containing horseradish peroxidase (HRP), APP antibody, and Cu3(PO4)2 was synthesized as the biosensor by co-precipitation method. In this competitive immunoassay, APP was first conjugated onto the microplate surface with the help of poly-L-lysine as the coating reagent; the composite materials were then attached onto the microplate through the interaction of APP and antibody; the HRP can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) and formed colored species. Therefore, the more APP in the detection solution (free form), the less composite material was combined with the immobilized APP on the microplate, resulting in the production of less colored TMB species. A series of detection parameters were studied, such as the composite material synthesis process, the concentration, and reaction time of different compounds. Our method has higher sensitivity compared with the similar immunoassay without using composite materials (the limits of detection are 0.3 and 3 ng/mL, respectively), and can be used for real samples (human serum) detection. The detection results using our method are consistent with the ELISA results, which is useful for the AD detection.ᅟ [ABSTRACT FROM AUTHOR]

Case-control Genome-Wide Association Studies (GWAS) provide a rich resource for studying the genetic architecture of complex diseases. A key is to elucidate how the genetic effects vary by the environment, what is traditionally defined by Gene-Environment interactions (GxE). The overlooked complication is that multiple, distinct pathophysiologic mechanisms may lead to the same clinical diagnosis and often these mechanisms have distinct genetic bases. In this paper, we first show that using the clinically diagnosed status can lead to severely biased estimates of GxE interactions in situations when the frequency of the pathologic diagnosis of interest, as compared to other diagnoses, depends on the environment. We then propose a pseudo-likelihood solution to correct the bias. Finally, we demonstrate our method in extensive simulations and in a GWAS of Alzheimer’s disease. [ABSTRACT FROM AUTHOR]

Machine learning approaches have been increasingly used in the neuroimaging field for the design of computer-aided diagnosis systems. In this paper, we focus on the ability of these methods to provide interpretable information about the brain regions that are the most informative about the disease or condition of interest. In particular, we investigate the benefit of group-based, instead of voxel-based, analyses in the context of Random Forests. Assuming a prior division of the voxels into non overlapping groups (defined by an atlas), we propose several procedures to derive group importances from individual voxel importances derived from Random Forests models. We then adapt several permutation schemes to turn group importance scores into more interpretable statistical scores that allow to determine the truly relevant groups in the importance rankings. The good behaviour of these methods is first assessed on artificial datasets. Then, they are applied on our own dataset of FDG-PET scans to identify the brain regions involved in the prognosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is an irreversible brain degenerative disorder affecting people aged older than 65 years. Currently, there is no effective cure for AD, but its progression can be delayed with some treatments. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Fluorodeoxyglucose positrons emission tomography (FDG-PET) is a functional molecular imaging modality, which proves to be powerful to help understand the anatomical and neural changes of brain related to AD. Most existing methods extract the handcrafted features from images, and then design a classifier to distinguish AD from other groups. These methods highly depends on the preprocessing of brain images, including image rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNNs), which learns the intra-slice and inter-slice features for classification after decomposition of the 3D PET image into a sequence of 2D slices. The 2D CNNs are built to capture the features of image slices while the gated recurrent unit (GRU) of RNN is cascaded to learn and integrate the inter-slice features for image classification. No rigid registration and segmentation are required for PET images. Our method is evaluated on the baseline FDG-PET images acquired from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an area under receiver operating characteristic curve (AUC) of 95.3% for AD vs. NC classification and 83.9% for MCI vs. NC classification, demonstrating the promising classification performance. [ABSTRACT FROM AUTHOR]

Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its early stage (mild cognitive impairment, MCI), has attracted more and more attention recently. Researchers have constructed threshold brain function networks and extracted various features for the classification of brain diseases. However, in the construction of the brain function network, the selection of threshold is very important, and the unreasonable setting will seriously affect the final classification results. To address this issue, in this paper, we propose a minimum spanning tree (MST) classification framework to identify Alzheimer's disease (AD), MCI, and normal controls (NCs). The proposed method mainly uses the MST method, graph-based Substructure Pattern mining (gSpan), and graph kernel Principal Component Analysis (graph kernel PCA). Specifically, MST is used to construct the brain functional connectivity network; gSpan, to extract features; and subnetwork selection and graph kernel PCA, to select features. Finally, the support vector machine is used to perform classification. We evaluate our method on MST brain functional networks of 21 AD, 25 MCI, and 22 NC subjects. The experimental results show that our proposed method achieves classification accuracy of 98.3, 91.3, and 77.3%, for MCI vs. NC, AD vs. NC, and AD vs. MCI, respectively. The results show our proposed method can achieve significantly improved classification performance compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Although the hippocampus is one of the most studied structures in the human brain, limited quantitative data exist on its 3D organization, anatomical variability, and effects of disease on its subregions. Histological studies provide restricted reference information due to their 2D nature. In this paper, high-resolution (~200 x 200 x 200 µm³) ex vivo MRI scans of 31 human hippocampal specimens are combined using a groupwise diffeomorphic registration approach into a 3D probabilistic atlas that captures average anatomy and anatomic variability of hippocampal subfields. Serial histological imaging in 9 of the 31 specimens was used to label hippocampal subfields in the atlas based on cytoarchitecture. Specimens were obtained from autopsies in patients with a clinical diagnosis of Alzheimer's disease (AD; 9 subjects, 13 hemispheres), of other dementia (nine subjects, nine hemispheres), and in subjects without dementia (seven subjects, nine hemispheres), and morphometric analysis was performed in atlas space to measure effects of age and AD on hippocampal subfields. Disproportional involvement of the cornu ammonis (CA) 1 subfield and stratum radiatum lacunosum moleculare was found in AD, with lesser involvement of the dentate gyrus and CA2/3 subfields. An association with age was found for the dentate gyrus and, to a lesser extent, for CA1. Three-dimensional patterns of variability and disease and aging effects discovered via the ex vivo hippocampus atlas provide information highly relevant to the active field of in vivo hippocampal subfield imaging. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disorder that is characterized by amyloid plaques in patients' brain tissue. The plaques are mainly made of b-amyloid peptides and trace elements including Zn2+, Cu2+, and Fe2+. Some studies have shown that AD can be considered a type of metal dyshomeostasis. Among metal ions involved in plaques, numerous studies have focused on copper ions, which seem to be one of the main cationic elements in plaque formation. The involvement of copper in AD is controversial, as some studies show a copper deficiency in AD, and consequently a need to enhance copper levels, while other data point to copper overload and therefore a need to reduce copper levels. In this paper, the role of copper ions in AD and some contradictory reports are reviewed and discussed. [ABSTRACT FROM AUTHOR]

Neuroscience is of emerging importance along with the contributions of Operational Research to the practices of diagnosing neurodegenerative diseases with computer-aided systems based on brain image analysis. Although multiple biomarkers derived from Magnetic Resonance Imaging (MRI) data have proven to be effective in diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI), no specific system has yet been a part of routine clinical practice. This paper aims to introduce a fully-automated voxel-based procedure, Voxel-MARS, for detection of AD and MCI in early stages of progression. Performance was evaluated on a dataset of 508 MRI volumes gathered from the Alzheimer's Disease Neuroimaging Initiative database. Data were transformed into a high-dimensional space through a feature extraction process. A novel 3-step feature selection procedure was applied. Multivariate Adaptive Regression Splines method was used as a classifier for the first time in the field of brain MRI analysis. The results were compared to those presented in a previous study on 28 voxel-based methods in terms of their ability to separate control normal (CN) subjects from the ones diagnosed with AD and MCI. It was observed that our method outperformed all of the others in sensitivity (83.58% in AD/CN and 78.38% in MCI/CN classification) with acceptable specificity values (over 85% in both cases). Furthermore, the method worked for discriminating MCI patients which converted to AD in 18 months (MCIc) from non-converters (MCInc) with a sensitivity outcome better than 27 of 28 methods. Overall, it was shown that the proposed method is promising in early detection of AD. [ABSTRACT FROM AUTHOR]

Background: Changes in everyday functioning constitute a clinically meaningful outcome, even in the early stages of Alzheimer’s disease. Performance-based assessments of everyday functioning might help uncover these early changes. We aimed to investigate how changes over time in everyday functioning relate to tau and amyloid in cognitively unimpaired older adults. Methods: Seventy-six cognitively unimpaired participants (72 ± 6 years old, 61% female) completed multiple Harvard Automated Phone Task (APT) assessments over 2.0 ± 0.9 years. The Harvard APT consists of three tasks, performed through an automated phone system, in which participants refill a prescription (APT-Script), select a new primary care physician (APT-PCP), and transfer money to pay a bill (APT-Bank). Participants underwent Pittsburgh compound-B and flortaucipir positron emission tomography scans at baseline. We computed distribution volume ratios for a cortical amyloid aggregate and standardized uptake volume ratios for medial temporal and neocortical tau regions. In separate linear mixed models, baseline amyloid by time and tau by time interactions were used to predict longitudinal changes in performance on the Harvard APT tasks. Three-way amyloid by tau by time interactions were also investigated. Lastly, we examined associations between tau and change in Harvard APT scores in exploratory voxel-wise whole-brain analyses. All models were adjusted for age, sex, and education. Results: Amyloid [unstandardized partial regression coefficient estimate (β) =-0.007, 95% confidence interval (95% CI) = (-0.013,-0.001)], and medial temporal tau [β =-0.013, 95% CI = (-0.022,-0.004)] were associated with change over time in years on APT-PCP only, i.e., higher baseline amyloid and higher baseline tau were associated with steeper rate of decline of APT-PCP. Voxel-wise analyses showed widespread associations between tau and change in APT-PCP scores over time. Conclusion: Even among cognitively unimpaired older adults, changes over time in the performance of cognitively complex everyday activities relate to cortical amyloid and widespread cerebral tau burden at baseline. These findings support the link between Alzheimer’s disease pathology and function and highlight the importance of measuring everyday functioning in preclinical disease stages. [ABSTRACT FROM AUTHOR]

Graph Convolutional Networks (GCNs) are widely applied in classification tasks by aggregating the neighborhood information of each sample to output robust node embedding. However, conventional GCN methods do not update the graph during the training process so that their effectiveness is always influenced by the quality of the input graph. Moreover, previous GCN methods lack the interpretability to limit their real applications. In this paper, a novel personalized diagnosis technique is proposed for early Alzheimer's Disease (AD) diagnosis via coupling interpretable feature learning with dynamic graph learning into the GCN architecture. Specifically, the module of interpretable feature learning selects informative features to provide interpretability for disease diagnosis and abandons redundant features to capture inherent correlation of data points. The module of dynamic graph learning adjusts the neighborhood relationship of every data point to output robust node embedding as well as the correlations of all data points to refine the classifier. The GCN module outputs diagnosis results based on the learned inherent graph structure. All three modules are jointly optimized to perform reliable disease diagnosis at an individual level. Experiments demonstrate that our method outputs competitive diagnosis performance as well as provide interpretability for personalized disease diagnosis. • Interpretable learning is used to output interpretable diagnosis results. • Dynamic graph learning coupled with GCN to optimize graph structure. • GCN coupled with above learning are used to output personalized diagnosis. [ABSTRACT FROM AUTHOR]

Background: Increasing age is the biggest risk factor for dementia, of which Alzheimer's disease is the commonest cause. The pathological changes underpinning Alzheimer's disease are thought to develop at least a decade prior to the onset of symptoms. Molecular positron emission tomography and multi-modal magnetic resonance imaging allow key pathological processes underpinning cognitive impairment - including β-amyloid depostion, vascular disease, network breakdown and atrophy - to be assessed repeatedly and non-invasively. This enables potential determinants of dementia to be delineated earlier, and therefore opens a pre-symptomatic window where intervention may prevent the onset of cognitive symptoms.Methods/design: This paper outlines the clinical, cognitive and imaging protocol of "Insight 46", a neuroscience sub-study of the MRC National Survey of Health and Development. This is one of the oldest British birth cohort studies and has followed 5362 individuals since their birth in England, Scotland and Wales during one week in March 1946. These individuals have been tracked in 24 waves of data collection incorporating a wide range of health and functional measures, including repeat measures of cognitive function. Now aged 71 years, a small fraction have overt dementia, but estimates suggest that ~1/3 of individuals in this age group may be in the preclinical stages of Alzheimer's disease. Insight 46 is recruiting 500 study members selected at random from those who attended a clinical visit at 60-64 years and on whom relevant lifecourse data are available. We describe the sub-study design and protocol which involves a prospective two time-point (0, 24 month) data collection covering clinical, neuropsychological, β-amyloid positron emission tomography and magnetic resonance imaging, biomarker and genetic information. Data collection started in 2015 (age 69) and aims to be completed in 2019 (age 73).Discussion: Through the integration of data on the socioeconomic environment and on physical, psychological and cognitive function from 0 to 69 years, coupled with genetics, structural and molecular imaging, and intensive cognitive and neurological phenotyping, Insight 46 aims to identify lifetime factors which influence brain health and cognitive ageing, with particular focus on Alzheimer's disease and cerebrovascular disease. This will provide an evidence base for the rational design of disease-modifying trials. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this systematic review was (1) to give an overview of the available short screening instruments for the early detection of Alzheimer's disease (AD) and (2) to review the psychometric properties of these instruments. Methods: First, a systematic search of titles and abstracts of PubMed and Web of Science was conducted between February and July 2015 and updated in April 2016 and May 2018. Only papers written in English or Dutch were considered. All full-text papers about cognitive screening instruments for the early detection of AD were included, resulting in the identification of 38 pencil and paper tests and 12 computer tests. In a second step, the psychometric quality of these instruments was evaluated. Therefore, the same databases were searched again to identify papers that described the psychometric properties of the instruments meanwhile applying diagnostic criteria for the diagnostic groups included. Results: Out of 1454 papers, 96 clearly discussed the psychometric properties of the instruments. Eighty-nine papers discussed pencil and paper tests of which 80 were validated in a memory clinic setting. Based on the number of studies (31 articles) and the sensitivity (84%) and specificity (74%) values, the Montreal Cognitive Assessment (MoCA) seems to be a promising (pencil and paper) screening test for memory clinic testing as well as for population screening. Regarding computer tests, validation studies were only available for 7 out of 12 tests. Conclusions: A large number of screening tests for AD are available. However, most tests are only validated in a memory clinic setting and description of the psychometric properties of the instruments is limited. Especially, computer tests require further research. The MoCA is a promising instrument, but the specificity to detect early AD is rather low. [ABSTRACT FROM AUTHOR]

Introduction: Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring. Methods: A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated. Results: The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups. Discussion: This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers. [ABSTRACT FROM AUTHOR]

The precise identification of Alzheimer's disease and other prevalent neurodegenerative diseases remains a difficult issue that requires the development of early detection of the disease and inexpensive biomarkers that can replace the present cerebrospinal fluid and imaging biomarkers. Blood biomarkers, such as amyloid and neurofilament light, have been emphasized as an important and practical tool in a testing or examination procedure thanks to advancements in ultra‐sensitive detection techniques. Although saliva is not currently being researched for neurodegenerative diseases, it is an important source of biomarkers that can be used for the identification of diseases and has some advantages over other biofluids. While this may be true for most people, getting saliva from elderly people presents some significant challenges. In this overview, we will first discuss how saliva is created and how aging‐related illnesses may affect the amount and kind of saliva produced. The findings support the use of salivary amyloid protein, tau species, and novel biomarkers in the diagnosis of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Introduction: Idiopathic normal pressure hydrocephalus (INPH) is a potentially reversible syndrome characterized by complex symptoms, difficulty in diagnosis and a lack of detailed clinical description, and it is difficult to distinguish from Alzheimer's disease (AD). The objective of this study was to design a method for measuring the actual amount of hydrocephalus in patients with INPH and to evaluate INPH. Methods: All subjects underwent a 3D T1-weighted MRI. Statistical parametric mapping 12 was used for preprocessing images, statistical analysis, and voxelbased morphometric gray matter (GM) volume, white matter (WM) volume, and cerebrospinal fluid (CSF) volume analysis. The demographic and clinical characteristics of the groups were compared using a t-test for continuous variables and a chi-square test for categorical variables. Pearson's correlation analysis and Bonferroni's statistic-corrected one-way ANOVA were used to determine the relationship among demographic variables. Receiver operating characteristic (ROC) curves were used to assess the accuracy of the callosal angle (CA), WM ratio, and CSF ratio in distinguishing probable INPH from AD. Results: The study included 42 patients with INPH, 32 patients with AD, and 24 healthy control subjects (HCs). There were no differences among the three groups in basic characteristics except for Mini-Mental State Examination (MMSE). There was a correlation between the intracranial CSF ratio and CA. The WM ratio and CSF ratio in patients with INPH and AD were statistically different. Furthermore, the combination of CA, WM ratio, and CSF ratio had a greater differential diagnostic value between INPH and AD patients than CA alone. Conclusion: INPH can be accurately assessed by measuring intracranial CSF ratio, and the addition of WM ratio and CSF ratio significantly improved the differential diagnostic value of probable INPH from AD compared to CA alone. [ABSTRACT FROM AUTHOR]

The global impact of Alzheimer's disease (AD) continues to increase, and focused efforts are needed to address this immense public health challenge. National leaders have set a goal to prevent or effectively treat AD by 2025. In this paper, we discuss the path to 2025, and what is feasible in this time frame given the realities and challenges of AD drug development, with a focus on disease-modifying therapies (DMTs). Under the current conditions, only drugs currently in late Phase 1 or later will have a chance of being approved by 2025. If pipeline attrition rates remain high, only a few compounds at best will meet this time frame. There is an opportunity to reduce the time and risk of AD drug development through an improvement in trial design; better trial infrastructure; disease registries of well-characterized participant cohorts to help with more rapid enrollment of appropriate study populations; validated biomarkers to better detect disease, determine risk and monitor disease progression as well as predict disease response; more sensitive clinical assessment tools; and faster regulatory review. To implement change requires efforts to build awareness, educate and foster engagement; increase funding for both basic and clinical research; reduce fragmented environments and systems; increase learning from successes and failures; promote data standardization and increase wider data sharing; understand AD at the basic biology level; and rapidly translate new knowledge into clinical development. Improved mechanistic understanding of disease onset and progression is central to more efficient AD drug development and will lead to improved therapeutic approaches and targets. The opportunity for more than a few new therapies by 2025 is small. Accelerating research and clinical development efforts and bringing DMTs to market sooner would have a significant impact on the future societal burden of AD. As these steps are put in place and plans come to fruition, e.g., approval of a DMT, it can be predicted that momentum will build, the process will be self-sustaining, and the path to 2025, and beyond, becomes clearer. [ABSTRACT FROM AUTHOR]

The quantification of neurofibrillary tangles (NFTs) using specific PET tracers can facilitate the diagnosis of Alzheimer's disease (AD) and allow monitoring of disease progression and treatment efficacy. [18F]-THK523 has shown high affinity and selectivity for tau pathology. However, its high retention in white matter, which makes simple visual inspection difficult, may limit its use in research or clinical settings. In this paper, we optimized the automated radiosynthesis of [11C]-TKF and evaluated its biodistribution and toxicity in C57 mice. [11C]-TKF can be made by reaction precursor with [11C]MeOTf or 11CH3I, but [11C]MeOTf will give us higher labeling yields and specific activity. [11C]-TKF presented better brain uptake in normal mouse than [18F]-THK523 (3.23% ± 1.25% ID.g-1 vs. 2.62% ± 0.39% ID.g-1 at 2 min post-injection). The acute toxicity studies of [11C]-TKF were unremarkable. [ABSTRACT FROM AUTHOR]

Multitemplate-based brain morphometric pattern analysis using magnetic resonance imaging has been recently proposed for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage (i.e., mild cognitive impairment or MCI). In such methods, multiview morphological patterns generated from multiple templates are used as feature representation for brain images. However, existing multitemplate-based methods often simply assume that each class is represented by a specific type of data distribution (i.e., a single cluster), while in reality, the underlying data distribution is actually not preknown. In this paper, we propose an inherent structure-based multiview leaning method using multiple templates for AD/MCI classification. Specifically, we first extract multiview feature representations for subjects using multiple selected templates and then cluster subjects within a specific class into several subclasses (i.e., clusters) in each view space. Then, we encode those subclasses with unique codes by considering both their original class information and their own distribution information, followed by a multitask feature selection model. Finally, we learn an ensemble of view-specific support vector machine classifiers based on their, respectively, selected features in each view and fuse their results to draw the final decision. Experimental results on the Alzheimer's Disease Neuroimaging Initiative database demonstrate that our method achieves promising results for AD/MCI classification, compared to the state-of-the-art multitemplate-based methods. [ABSTRACT FROM AUTHOR]

Background: Neuroimaging-biomarkers of Mild Cognitive Impairment (MCI) allow an early diagnosis in preclinical stages of Alzheimer's disease (AD). The goal in this paper was to review of biomarkers for Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD), with emphasis on neuroimaging biomarkers.Methods: A systematic review was conducted from existing literature that draws on markers and evidence for new measurement techniques of neuroimaging in AD, MCI and non-demented subjects. Selection criteria included: 1) age ≥ 60 years; 2) diagnosis of AD according to NIAAA criteria, 3) diagnosis of MCI according to NIAAA criteria with a confirmed progression to AD assessed by clinical follow-up, and 4) acceptable clinical measures of cognitive impairment, disability, quality of life, and global clinical assessments.Results: Seventy-two articles were included in the review. With the development of new radioligands of neuroimaging, today it is possible to measure different aspects of AD neuropathology, early diagnosis of MCI and AD become probable from preclinical stage of AD to AD dementia and non-AD dementia.Conclusions: The panel of noninvasive neuroimaging-biomarkers reviewed provides a set methods to measure brain structural and functional pathophysiological changes in vivo, which are closely associated with preclinical AD, MCI and non-AD dementia. The dynamic measures of these imaging biomarkers are used to predict the disease progression in the early stages and improve the assessment of therapeutic efficacy in these diseases in future clinical trials. [ABSTRACT FROM AUTHOR]

The high feature-dimension and low sample-size problem is one of the major challenges in the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this problem, feature selection and subspace learning have been playing core roles in the literature. Generally, feature selection methods are preferable in clinical applications due to their ease for interpretation, but subspace learning methods can usually achieve more promising results. In this paper, we combine two different methodological approaches to discriminative feature selection in a unified framework. Specifically, we utilize two subspace learning methods, namely, linear discriminant analysis and locality preserving projection, which have proven their effectiveness in a variety of fields, to select class-discriminative and noise-resistant features. Unlike previous methods in neuroimaging studies that mostly focused on a binary classification, the proposed feature selection method is further applicable for multiclass classification in AD diagnosis. Extensive experiments on the Alzheimer's disease neuroimaging initiative dataset showed the effectiveness of the proposed method over other state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

Curcumin derived from turmeric is well documented for its anti-carcinogenic, antioxidant and anti-inflammatory properties. Recent studies show that curcumin also possesses neuroprotective and cognitive-enhancing properties that may help delay or prevent neurodegenerative diseases, including Alzheimer’s disease (AD). Currently, clinical diagnosis of AD is onerous, and it is primarily based on the exclusion of other causes of dementia. In addition, phase III clinical trials of potential treatments have mostly failed, leaving disease-modifying interventions elusive. AD can be characterised neuropathologically by the deposition of extracellular β amyloid (Aβ) plaques and intracellular accumulation of tau-containing neurofibrillary tangles. Disruptions in Aβ metabolism/clearance contribute to AD pathogenesis. In vitro studies have shown that Aβ metabolism is altered by curcumin, and animal studies report that curcumin may influence brain function and the development of dementia, because of its antioxidant and anti-inflammatory properties, as well as its ability to influence Aβ metabolism. However, clinical studies of curcumin have revealed limited effects to date, most likely because of curcumin’s relatively low solubility and bioavailability, and because of selection of cohorts with diagnosed AD, in whom there is already major neuropathology. However, the fresh approach of targeting early AD pathology (by treating healthy, pre-clinical and mild cognitive impairment-stage cohorts) combined with new curcumin formulations that increase bioavailability is renewing optimism concerning curcumin-based therapy. The aim of this paper is to review the current evidence supporting an association between curcumin and modulation of AD pathology, including in vitro and in vivo studies. We also review the use of curcumin in emerging retinal imaging technology, as a fluorochrome for AD diagnostics. [ABSTRACT FROM PUBLISHER]

Complex biological systems are organized across various spatiotemporal scales with particular scientific disciplines dedicated to the study of each scale (e.g. genetics, molecular biology and cognitive neuroscience). When considering disease pathophysiology, one must contemplate the scale at which the disease process is being observed and how these processes impact other levels of organization. Historically Alzheimer's disease has been viewed as a disease of abnormally aggregated proteins by pathologists and molecular biologists and a disease of clinical symptoms by neurologists and psychologists. Bridging the divide between these scales has been elusive, but the study of brain networks appears to be a pivotal inroad to accomplish this task. In this study, we were guided by an emerging systems-based conceptualization of Alzheimer's disease and investigated changes in brain networks across the disease spectrum. The default mode network has distinct subsystems with unique functional-anatomic connectivity, cognitive associations, and responses to Alzheimer's pathophysiology. These distinctions provide a window into the systems-level pathophysiology of Alzheimer's disease. Using clinical phenotyping, metadata, and multimodal neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative, we characterized the pattern of default mode network subsystem connectivity changes across the entire disease spectrum (n = 128). The two main findings of this paper are (i) the posterior default mode network fails before measurable amyloid plaques and appears to initiate a connectivity cascade that continues throughout the disease spectrum; and (ii) high connectivity between the posterior default mode network and hubs of high connectivity (many located in the frontal lobe) is associated with amyloid accumulation. These findings support a system model best characterized by a cascading network failure--analogous to cascading failures seen in power grids triggered by local overloads proliferating to downstream nodes eventually leading to widespread power outages, or systems failures. The failure begins in the posterior default mode network, which then shifts processing burden to other systems containing prominent connectivity hubs. This model predicts a connectivity 'overload' that precedes structural and functional declines and recasts the interpretation of high connectivity from that of a positive compensatory phenomenon to that of a load-shifting process transiently serving a compensatory role. It is unknown whether this systems-level pathophysiology is the inciting event driving downstream molecular events related to synaptic activity embedded in these systems. Possible interpretations include that the molecular-level events drive the network failure, a pathological interaction between the network-level and the molecular-level, or other upstream factors are driving both. [ABSTRACT FROM AUTHOR]

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a progressive decline in cognitive abilities. Since AD starts several years before the onset of the symptoms, its early detection is challenging due to subtle changes in biomarkers mainly detectable in different neuroimaging modalities. Developing computer-aided diagnostic models based on deep learning can provide excellent opportunities for the analysis of different neuroimage modalities along with other non-image biomarkers. In this survey, we perform a comparative analysis of about 100 published papers since 2019 that employ basic deep architectures such as CNN, RNN, and generative models for AD diagnosis. Moreover, about 60 papers that have applied a trending topic or architecture for AD are investigated. Explainable models, normalizing flows, graph-based deep architectures, self-supervised learning, and attention mechanisms are considered. The main challenges in this body of literature have been categorized and explained from data-related, methodology-related, and clinical adoption aspects. We conclude our paper by addressing some future perspectives and providing recommendations to conduct further studies for AD diagnosis. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a common neurodegenerative disease in the elderly. Early diagnosis and prediction plays an important role in early intervention and delaying disease progression of AD. This paper focused on the principles and process of pattern classification method, and its application in the clinical study and auxiliary diagnosis of AD. The biomarkers, neuroimaging and cognitive ability scales are important features for pattern classification. Various classification algorithms including Bayesian networks, decision trees, support vector machines (SVM) and multilayer perception have been adopted to distinguish AD, mild cognitive impairment (MCI) and normal aging subjects. Besides, they can effectively trace and analyze MCI patients. [ABSTRACT FROM AUTHOR]