A Survey of Alzheimer's Disease Early Diagnosis Methods for Cognitive Assessment

Novel techniques for early diagnosis and monitoring of Alzheimer's disease

INTRODUCTION

Alzheimer's disease (AD) is the most common neurodegenerative disorder, which is characterized by a progressive loss of cognitive functions. The high prevalence, chronicity, and multimorbidity are very common in AD, which significantly impair the quality of life and functioning of patients. Early detection and accurate diagnosis of Alzheimer's disease (AD) can stop the illness from progressing thereby postponing its symptoms. Therefore, for the early diagnosis and monitoring of AD, more sensitive, noninvasive, straightforward, and affordable screening tools are needed.

AREAS COVERED

This review summarizes the importance of early detection methods and novel techniques for Alzheimer's disease diagnosis that can be used by healthcare professionals.

EXPERT OPINION

Early diagnosis assists the patient and caregivers to understand the problem establishing reasonable goals and making future plans together. Early diagnosis techniques not only help in monitoring disease progression but also provide crucial information for the development of novel therapeutic targets. Researchers can plan to potentially alleviate symptoms or slow down the progression of Alzheimer's disease by identifying early molecular changes and targeting altered pathways.

Exercise Barriers in Older Individuals with Alzheimer's Disease: A Cross-Sectional Study

Background and Objectives: Defining the exercise habits of individuals with Alzheimer's Disease (AD) may help to determine optimal rehabilitation programs. This study aimed to investigate the physical and psychological parameters associated with exercise barriers in older individuals with AD, with the goal of informing more effective rehabilitation programs. Materials and Methods: A cross-sectional prospective study was conducted with 50 individuals with AD. The individuals were evaluated with the Exercise Benefit/Barriers Scale (EBBS), the Mini-Mental State Examination (MMSE), the Five Times Sit to Stand Test (FTSTS), the Barthel Index (BI), the Tampa Scale for Kinesiophobia (TSK), and the Hospital Anxiety and Depression Scale (HADS). Results: There was a significant positive correlation between age with EBBS-Exercise Barriers (r = 0.308; p = 0.029) and EBBS-Total Score (r = 0.295; p = 0.038). There were significant negative correlations between the time of diagnosis with EBBS-Exercise Benefits (r = -0.569; p = 0.000), EBBS-Exercise Barriers (r = -0.324; p = 0.022), and EBBS-Total Score (r = -0.508; p = 0.000). There was a positive correlation between MMSE and EBBS-Exercise Benefits (r = 0.465; p = 0.001), EBBS-Exercise Barriers (r = 0.471; p = 0.001) and EBBS-Total Score (r = 0.519; p = 0.000). There were significant positive correlations between FTSTS and EBBS-Exercise Barriers (r = 0.340; p = 0.016), and EBBS-Total Score (r = 0.280; p = 0.049). There were positive correlations between BI and EBBS-Exercise Benefits (r = 0.362; p = 0.010), EBBS-Exercise Barriers (r = 0.377; p = 0.007), and EBBS-Total Score (r = 0.405; p = 0.004). Conclusions: Exercise barriers/benefits were associated with cognition and post-diagnosis duration in individuals with AD. Individuals with lower physical function had lower exercise perception. In addition, living with relatives or caregivers led to better exercise benefit scores.

A Framework for Interpretability in Machine Learning for Medical Imaging

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research.

Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.

Biosensors toward behavior detection in diagnosis of alzheimer’s disease

In recent years, a huge number of individuals all over the world, elderly people, in particular, have been suffering from Alzheimer’s disease (AD), which has had a significant negative impact on their quality of life. To intervene early in the progression of the disease, accurate, convenient, and low-cost detection technologies are gaining increased attention. As a result of their multiple merits in the detection and assessment of AD, biosensors are being frequently utilized in this field. Behavioral detection is a prospective way to diagnose AD at an early stage, which is a more objective and quantitative approach than conventional neuropsychological scales. Furthermore, it provides a safer and more comfortable environment than those invasive methods (such as blood and cerebrospinal fluid tests) and is more economical than neuroimaging tests. Behavior detection is gaining increasing attention in AD diagnosis. In this review, cutting-edge biosensor-based devices for AD diagnosis together with their measurement parameters and diagnostic effectiveness have been discussed in four application subtopics: body movement behavior detection, eye movement behavior detection, speech behavior detection, and multi-behavior detection. Finally, the characteristics of behavior detection sensors in various application scenarios are summarized and the prospects of their application in AD diagnostics are presented as well.

Does Dementia Have a Microbial Cause?

The potential contribution of pathogenic microbes to dementia-inducing disease is a subject of considerable importance. Alzheimer’s disease (AD) is a neurocognitive disease that slowly destroys brain function, leading to cognitive decline and behavioral and psychiatric disorders. The histopathology of AD is associated with neuronal loss and progressive synaptic dysfunction, accompanied by the deposition of amyloid-β (Aβ) peptide in the form of parenchymal plaques and abnormal aggregated tau protein in the form of neurofibrillary tangles. Observational, epidemiological, experimental, and pathological studies have generated evidence for the complexity and possible polymicrobial causality in dementia-inducing diseases. The AD pathogen hypothesis states that pathogens and microbes act as triggers, interacting with genetic factors to initiate the accumulation of Aβ, hyperphosphorylated tau protein (p-tau), and inflammation in the brain. Evidence indicates that Borrelia sp., HSV-1, VZV (HHV-2), HHV-6/7, oral pathogens, Chlamydophila pneumoniae, and Candida albicans can infect the central nervous system (CNS), evade the immune system, and consequently prevail in the AD brain. Researchers have made significant progress in understanding the multifactorial and overlapping factors that are thought to take part in the etiopathogenesis of dementia; however, the cause of AD remains unclear.

A Revisit to Etiopathogenesis and Therapeutic Strategies in Alzheimer's Disease

Dementia is a cluster of brain abnormalities that trigger progressive memory deficits and other cognitive abilities such as skills, language, or executive function. Alzheimer's disease (AD) is the foremost type of age-associated dementia that involves progressive neurodegeneration accompanied by profound cognitive deficits in advanced stages that severely hamper social or occupational abilities with or without the involvement of any other psychiatric condition. The last two decades witnessed a sharp increase (~123%) in mortality due to AD type dementia, typically owing to a very low disclosure rate (~45%) and hence, the prophylactic, as well as the therapeutic cure of AD, has been a huge challenge. Although understanding of AD pathogenesis has witnessed a remarkable growth (e.g., tauopathy, oxidative stress, lipid transport, glucose uptake, apoptosis, synaptic dysfunction, inflammation, and immune system), still a dearth of an effective therapeutic agent in the management of AD prompts the quest for newer pharmacological targets in the purview of its growing epidemiological status. Most of the current therapeutic strategies focus on modulation of a single target, e.g., inhibition of acetylcholinesterase, glutamate excitotoxicity (memantine), or nootropics (piracetam), even though AD is a multifaceted neurological disorder. There is an impedance urgency to find not only symptomatic but effective disease-modifying therapies. The present review focuses on the risk / protective factors and pathogenic mechanisms involved in AD. In addition to the existing symptomatic therapeutic approach, a diverse array of possible targets linked to pathogenic cascades have been re-investigated to envisage the pharmacotherapeutic strategies in AD.

Development and Validation of a Deep Learning Model for Earlier Detection of Cognitive Decline From Clinical Notes in Electronic Health Records

IMPORTANCE

Detecting cognitive decline earlier among older adults can facilitate enrollment in clinical trials and early interventions. Clinical notes in longitudinal electronic health records (EHRs) provide opportunities to detect cognitive decline earlier than it is noted in structured EHR fields as formal diagnoses.

OBJECTIVE

To develop and validate a deep learning model to detect evidence of cognitive decline from clinical notes in the EHR.

DESIGN, SETTING, AND PARTICIPANTS

Notes documented 4 years preceding the initial mild cognitive impairment (MCI) diagnosis were extracted from Mass General Brigham's Enterprise Data Warehouse for patients aged 50 years or older and with initial MCI diagnosis during 2019. The study was conducted from March 1, 2020, to June 30, 2021. Sections of notes for cognitive decline were labeled manually and 2 reference data sets were created. Data set I contained a random sample of 4950 note sections filtered by a list of keywords related to cognitive functions and was used for model training and testing. Data set II contained 2000 randomly selected sections without keyword filtering for assessing whether the model performance was dependent on specific keywords.

MAIN OUTCOMES AND MEASURES

A deep learning model and 4 baseline models were developed and their performance was compared using the area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC).

RESULTS

Data set I represented 1969 patients (1046 [53.1%] women; mean [SD] age, 76.0 [13.3] years). Data set II comprised 1161 patients (619 [53.3%] women; mean [SD] age, 76.5 [10.2] years). With some overlap of patients deleted, the unique population was 2166. Cognitive decline was noted in 1453 sections (29.4%) in data set I and 69 sections (3.45%) in data set II. Compared with the 4 baseline models, the deep learning model achieved the best performance in both data sets, with AUROC of 0.971 (95% CI, 0.967-0.976) and AUPRC of 0.933 (95% CI, 0.921-0.944) for data set I and AUROC of 0.997 (95% CI, 0.994-0.999) and AUPRC of 0.929 (95% CI, 0.870-0.969) for data set II.

CONCLUSIONS AND RELEVANCE

In this diagnostic study, a deep learning model accurately detected cognitive decline from clinical notes preceding MCI diagnosis and had better performance than keyword-based search and other machine learning models. These results suggest that a deep learning model could be used for earlier detection of cognitive decline in the EHRs.

Intelligent Sensing Technologies for the Diagnosis, Monitoring and Therapy of Alzheimer's Disease: A Systematic Review

Alzheimer's disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer's patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer's disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer's. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer's disease.

Inflammasome NLRP3 Potentially Links Obesity-Associated Low-Grade Systemic Inflammation and Insulin Resistance with Alzheimer's Disease

Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia. Metabolic disorders including obesity and type 2 diabetes mellitus (T2DM) may stimulate amyloid β (Aβ) aggregate formation. AD, obesity, and T2DM share similar features such as chronic inflammation, increased oxidative stress, insulin resistance, and impaired energy metabolism. Adiposity is associated with the pro-inflammatory phenotype. Adiposity-related inflammatory factors lead to the formation of inflammasome complexes, which are responsible for the activation, maturation, and release of the pro-inflammatory cytokines including interleukin-1β (IL-1β) and interleukin-18 (IL-18). Activation of the inflammasome complex, particularly NLRP3, has a crucial role in obesity-induced inflammation, insulin resistance, and T2DM. The abnormal activation of the NLRP3 signaling pathway influences neuroinflammatory processes. NLRP3/IL-1β signaling could underlie the association between adiposity and cognitive impairment in humans. The review includes a broadened approach to the role of obesity-related diseases (obesity, low-grade chronic inflammation, type 2 diabetes, insulin resistance, and enhanced NLRP3 activity) in AD. Moreover, we also discuss the mechanisms by which the NLRP3 activation potentially links inflammation, peripheral and central insulin resistance, and metabolic changes with AD.

DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson's Disease

Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder affecting patient functioning and quality of life. Aside from the motor symptoms of PD, cognitive impairment may occur at early stages of PD and has a substantial impact on patient emotional and physical health. Detecting these early signs through actual daily functioning while the patient is still functionally independent is challenging. We developed DailyCog-a smartphone application for the detection of mild cognitive impairment. DailyCog includes an environment that simulates daily tasks, such as making a drink and shopping, as well as a self-report questionnaire related to daily events performed at home requiring executive functions and visual-spatial abilities, and psychomotor speed. We present the detailed design of DailyCog and discuss various considerations that influenced the design. We tested DailyCog on patients with mild cognitive impairment in PD. Our case study demonstrates how the markers we used coincide with the cognitive levels of the users. We present the outcome of our usability study that found that most users were able to use our app with ease, and provide details on how various features were used, along with some of the difficulties that were identified.

Dementia and Major Neurocognitive Disorders: Some Lessons Learned One Century after the first Alois Alzheimer's Clinical Notes

Over 100 years ago, Alois Alzheimer presented the clinical signs and symptoms of what has been later called "Alzheimer Dementia" in a young woman whose name was Augustine Deter [...].