Innovative diagnostic tools for early detection of Alzheimer's disease

Illiterate Addenbrooke's Cognitive Examination-III in Three Indian Languages: An Adaptation and Validation Study

BACKGROUND

Literacy is an important factor that predicts cognitive performance. Existing cognitive screening tools are validated only in educated populations and are not appropriate for older adults with little or no education leading to poor performance on these tests and eventually leading to misdiagnosis. This challenge for clinicians necessitates a screening tool suitable for illiterate or low-literate older individuals.

OBJECTIVES

The objective was to adapt and validate Addenbrooke's Cognitive Examination-III (ACE-III) for screening general cognitive functions in illiterate and low-literate older populations in the Indian context in three languages.

METHOD

The Indian illiterate ACE-III was systematically adapted by modifying the original items of the Indian literate ACE-III to assess the cognitive functions of illiterates and low-literates with the consensus of an expert panel of professionals working in the area of dementia and related disorders. A total of 180 illiterate or low-literate participants (84 healthy-controls, 50 with dementia, and 46 with mild cognitive impairment [MCI]) were recruited from three different centers speaking Bengali, Hindi, and Kannada to validate the adapted version.

RESULTS

The optimal cut-off score for illiterate ACE-III to distinguish controls from dementia in all 3 languages was 75. The optimal cut-off scores in distinguishing between controls and MCI ranged from 79 to 82, with a sensitivity ranging from 93% to 99% and a specificity ranging from 72% to 99%.

CONCLUSION

The test is found to have good psychometric properties and is a reliable cognitive screening tool for identifying dementia and MCI in older adults with low educational backgrounds in the Indian context.

Enhanced associations between subjective cognitive concerns and blood-based AD biomarkers using a novel EMA approach

BACKGROUND

Subjective cognitive concerns (SCC) have emerged as important early indicators of Alzheimer's disease (AD) risk. Traditional measures of SCC rely on recall-based assessments, which may be limited in capturing real-time fluctuations in cognitive concerns. Ecological Momentary Assessment (EMA) offers a promising alternative by providing real-time data. This study aimed to link SCC assessed via EMA and traditional measures with blood-based AD biomarkers in a diverse, dementia-free, community-based sample based in the Bronx, NY.

METHODS

Einstein Aging Study (EAS) participants underwent in-person, recall-based assessments of SCC during an in-clinic visit. Additionally, EMA SCC assessments were collected once per day over two weeks. Linear regressions were conducted to examine the relationships between SCC variables and plasma biomarkers adjusted for demographics and mild cognitive impairment (MCI) status.

RESULTS

In N = 254 participants, EMA-reported SCCs demonstrated significant associations with AD biomarkers, particularly p-tau181 (β = 0.21, p = 0.001). Further, significant associations remain across both cognitive (cognitively unimpaired vs. MCI) and racial groups. In contrast, traditional SCC measures exhibited limited associations with these biomarkers. The findings highlight the added value of EMA in capturing SCCs that could indicate early ADRD risk.

CONCLUSIONS

EMA provides a more dynamic and potentially sensitive method for detecting early AD risk compared to traditional SCC assessments. These real-time measures could enhance early detection and clinical intervention, particularly in diverse and under-resourced populations. This study underscores the potential of EMA for broad applicability and inclusivity in monitoring AD progression and facilitating early therapeutic interventions.

Nanozymes: Innovative Therapeutics in the Battle Against Neurodegenerative Diseases

Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD), represent a significant challenge to global health due to their progressive nature and the absence of curative treatments. These disorders are characterized by oxidative stress, protein misfolding, and neuroinflammation, which collectively contribute to neuronal damage and death. Recent advancements in nanotechnology have introduced nanozymes-engineered nanomaterials that mimic enzyme-like activities-as promising therapeutic agents. This review explores the multifaceted roles of nanozymes in combating oxidative stress and inflammation in neurodegenerative conditions. By harnessing their potent antioxidant properties, nanozymes can effectively scavenge reactive oxygen species (ROS) and restore redox balance, thereby protecting neuronal function. Their ability to modify surface properties enhances targeted delivery and biocompatibility, making them suitable for various biomedical applications. In this review, we highlight recent findings on the design, functionality, and therapeutic potential of nanozymes, emphasizing their dual role in addressing oxidative stress and pathological features such as protein aggregation. This synthesis of current research underscores the innovative potential of nanozymes as a proactive therapeutic strategy to halt disease progression and improve patient outcomes in neurodegenerative disorders.

Linguistic cues for automatic assessment of Alzheimer's disease across languages

BackgroundMost common forms of dementia, including Alzheimer's disease, are associated with alterations in spoken language.ObjectiveThis study explores the potential of a speech-based machine learning (ML) approach in estimating cognitive impairment, using inputs of speech audio recordings.MethodsWe develop an automatic ML pipeline that ingests multimodal inputs of audio and transcribed text, mapping speech and language to domain-specific biomarkers optimized for high explainability and predictive ability. The resulting features are fed through a multi-stage pipeline to determine efficient classification configurations.ResultsWe evaluated the system on large real-world datasets, achieving above 90% and 70% weighted average F1 scores for two-class (AD versus normal controls) and three-class (AD versus mild cognitive impairment versus normal controls) classification tasks, respectively. Model performance remains stable across different population characteristics.ConclusionsThe study introduces a robust, non-invasive method for gauging the cognitive status of AD and MCI patients from speech samples, with the potential of generalizing effectively to multiple types of diseases/disorders which may burden language.

What is the best pulse shape for pacing purposes?

Introduction

Cell pacing is a fundamental procedure for generating action potentials (AP) in excitable tissues. Various pulse shapes have been proposed for this purpose, with the aim of either facilitating the achievement of the excitation threshold or minimizing energy delivery to the patient. This study seeks to identify the optimal pulse shape for each of these objectives.

Methods

To determine the most effective pulse forms, we employed a mathematical model simulating nonlinear tissue responses to a range of pulse shapes.

Results

Our results demonstrate that the rectangular pulse is optimal for reaching the excitation threshold, while the Gaussian pulse is superior in minimizing energy delivery. Other pulse shapes examined, including ramp-up, ramp-down, half-sine, and triangular (tent-like), fall between these two in terms of performance.

Discussion

From a clinical perspective, the appropriate pulse shape should be selected based on the specific goal. For minimizing the pulse amplitude required to cross the excitation threshold, the rectangular pulse is recommended. In contrast, if reducing energy delivery to the patient is paramount, the Gaussian pulse is the preferred choice. In other scenarios, a judicious selection can be made based on the outcomes of our model and the clinical requirements.

The Validity of a Smartphone-Based Application for Assessing Cognitive Function in the Elderly

Background/Objectives: The early detection of individuals at risk of cognitive impairment is a clinical imperative. With the recent advancement of digital devices, smartphone application-based cognitive assessment is considered a promising tool for cognitive screening and monitoring inside and outside the clinic. This study examined whether a smartphone-based cognitive assessment, Brain OK, was valid for evaluating cognitive performance and identifying people at risk of cognitive impairment. Methods: We recruited 88 study participants aged over 60. They completed two cognitive tests with the Montreal Cognitive Assessment (MoCA), a validated paper-and-pencil cognitive screening tool, and Brain OK, a smartphone-based cognitive testing application. To examine convergent validity, we conducted analyses of Spearman correlations between MoCA and BrainOK, a Bland-Atman plot with regression analysis, and the area under the curve (AUC). Results: There was a significant positive association between Brain OK and the MoCA total score, with a coefficient of 0.9044 (SE = 0.057, t = 15.750, p < 0.001). The Bland-Altman plot represented a reasonable level of agreement between the two tests. We conducted the AUC analysis of Brain OK to compare the cognitively normal and impaired groups. The AUC value for the Brain OK score of 13.5 was the highest at 0.941. The sensitivity and specificity were 0.958 and 0.925, respectively. Conclusions: The smartphone app-based Brain OK test was feasible for assessing cognitive function and acceptable for identifying subjects with cognitive impairment. The results suggest Brain OK complements traditional in-person cognitive assessments and may help enhance cognitive health dialogue between doctors and patients.

Articulatory precision from connected speech as a marker of cognitive decline in Alzheimer's disease risk-enriched cohorts

BACKGROUND

Mild cognitive impairment (MCI) has been recognized as a possible precursor to Alzheimer's disease (AD). Recent research focusing on connected speech has uncovered various features strongly correlated with MCI due to AD and related dementias. Despite these advancements, the impact of early cognitive decline on articulatory precision has not been thoroughly investigated.

OBJECTIVE

This study introduced the phoneme log-likelihood ratio (PLLR) to assess the articulatory precision of speakers across different cognitive status levels and compared its effectiveness to existing well-studied acoustic features.

METHODS

The study utilized speech recordings from a picture description task, which included data from 324 cognitively unimpaired participants with low amyloid-β burden (CU, Aβ(- )), 47 cognitively unimpaired participants with high amyloid-β burden (CU, Aβ(+ )), 69 participants with MCI, and 20 participants with dementia. Nine acoustic features were extracted from the speech recordings, covering three categories: speech fluency, speech pace, and articulatory precision. Welch's t -test and Hedge's g were adopted to assess their discriminative ability.

RESULTS

A reduction in speech fluency and pace was observed among participants in the MCI and dementia groups. The PLLR shows large effect sizes in distinguishing between cognitively unimpaired participants with low Aβ burden and those diagnosed with MCI or dementia. Additionally, the test-retest reliability experiment indicated moderate repeatability of the features under study.

CONCLUSIONS

The study reveals PLLR as a sensitive indicator capable of detecting subtle articulatory variations across groups, while also providing further support for the association between reduced articulatory precision and early cognitive decline.

Advances and future trends in the detection of beta-amyloid: A comprehensive review

The neurodegenerative condition known as Alzheimer's disease is typified by the build-up of beta-amyloid plaques within the brain. The timely and precise identification of beta-amyloid is essential for understanding disease progression and developing effective therapeutic interventions. This comprehensive review explores the diverse landscape of beta-amyloid detection methods, ranging from traditional immunoassays to cutting-edge technologies. The review critically examines the strengths and limitations of established techniques such as ELISA, PET, and MRI, providing insights into their roles in research and clinical settings. Emerging technologies, including electrochemical methods, nanotechnology, fluorescence techniques, point-of-care devices, and machine learning integration, are thoroughly discussed, emphasizing recent breakthroughs and their potential for revolutionizing beta-amyloid detection. Furthermore, the review delves into the challenges associated with current detection methods, such as sensitivity, specificity, and accessibility. By amalgamating knowledge from multidisciplinary approaches, this review aims to guide researchers, clinicians, and policymakers in navigating the complex landscape of beta-amyloid detection, ultimately contributing to advancements in Alzheimer's disease diagnostics and therapeutics.

Alzheimer's disease: a comprehensive review of epidemiology, risk factors, symptoms diagnosis, management, caregiving, advanced treatments and associated challenges

Background

Alzheimer's disease (AD) is a chronic, progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired reasoning. It is the leading cause of dementia in older adults, marked by the pathological accumulation of amyloid-beta plaques and neurofibrillary tangles. These pathological changes lead to widespread neuronal damage, significantly impacting daily functioning and quality of life.

Objective

This comprehensive review aims to explore various aspects of Alzheimer's disease, including its epidemiology, risk factors, clinical presentation, diagnostic advancements, management strategies, caregiving challenges, and emerging therapeutic interventions.

Methods

A systematic literature review was conducted across multiple electronic databases, including PubMed, MEDLINE, Cochrane Library, and Scopus, from their inception to May 2024. The search strategy incorporated a combination of keywords and Medical Subject Headings (MeSH) terms such as "Alzheimer's disease," "epidemiology," "risk factors," "symptoms," "diagnosis," "management," "caregiving," "treatment," and "novel therapies." Boolean operators (AND, OR) were used to refine the search, ensuring a comprehensive analysis of the existing literature on Alzheimer's disease.

Results

AD is significantly influenced by genetic predispositions, such as the apolipoprotein E (APOE) ε4 allele, along with modifiable environmental factors like diet, physical activity, and cognitive engagement. Diagnostic approaches have evolved with advances in neuroimaging techniques (MRI, PET), and biomarker analysis, allowing for earlier detection and intervention. The National Institute on Aging and the Alzheimer's Association have updated diagnostic criteria to include biomarker data, enhancing early diagnosis.

Conclusion

The management of AD includes pharmacological treatments, such as cholinesterase inhibitors and NMDA receptor antagonists, which provide symptomatic relief but do not slow disease progression. Emerging therapies, including amyloid-beta and tau-targeting treatments, gene therapy, and immunotherapy, offer potential for disease modification. The critical role of caregivers is underscored, as they face considerable emotional, physical, and financial burdens. Support programs, communication strategies, and educational interventions are essential for improving caregiving outcomes. While significant advancements have been made in understanding and managing AD, ongoing research is necessary to identify new therapeutic targets and enhance diagnostic and treatment strategies. A holistic approach, integrating clinical, genetic, and environmental factors, is essential for addressing the multifaceted challenges of Alzheimer's disease and improving outcomes for both patients and caregivers.

What traditional neuropsychological assessment got wrong about mild traumatic brain injury. II: limitations in test development, research design, statistical and psychometric issues

PRIMARY OBJECTIVE

This is Part II of a four-part opinion review on traditional neuropsychological assessment methods and findings associated with mild traumatic brain injury (mTBI). This Part II review focuses on historical, psychometric and statistical issues involving traditional neuropsychological methods that have been used in neuropsychological outcome studies of mTBI, but demonstrates the critical limitations of traditional methods.

RESEARCH DESIGN

This is an opinion review.

METHODS AND PROCEDURES

Traditional neuropsychological tests are dated and lack specificity in evaluating such a heterogenous and complex injury as occurs with mTBI.

MAIN OUTCOME AND RESULTS

In this review, we demonstrate traditional neuropsychological methods were never developed as standalone measures for detecting subtle changes in neurocognitive or neurobehavioral functioning and likewise, never designed to address the multifaceted issues related to underlying mTBI neuropathology symptom burden from having sustained a concussive brain injury.

CONCLUSIONS

For neuropsychological assessment to continue to contribute to clinical practice and outcome literature involving mTBI, major innovative changes are needed that will likely require technological advances of novel assessment techniques more specifically directed to evaluating the mTBI patient. These will be discussed in Part IV.

Toolkit to Examine Lifelike Language v.2.0: Optimizing Speech Biomarkers of Neurodegeneration

INTRODUCTION

The Toolkit to Examine Lifelike Language (TELL) is a web-based application providing speech biomarkers of neurodegeneration. After deployment of TELL v.1.0 in over 20 sites, we now introduce TELL v.2.0.

METHODS

First, we describe the app's usability features, including functions for collecting and processing data onsite, offline, and via videoconference. Second, we summarize its clinical survey, tapping on relevant habits (e.g., smoking, sleep) alongside linguistic predictors of performance (language history, use, proficiency, and difficulties). Third, we detail TELL's speech-based assessments, each combining strategic tasks and features capturing diagnostically relevant domains (motor function, semantic memory, episodic memory, and emotional processing). Fourth, we specify the app's new data analysis, visualization, and download options. Finally, we list core challenges and opportunities for development.

RESULTS

Overall, TELL v.2.0 offers scalable, objective, and multidimensional insights for the field.

CONCLUSION

Through its technical and scientific breakthroughs, this tool can enhance disease detection, phenotyping, and monitoring.

Current Advances in Computerized Cognitive Assessment for Mild Cognitive Impairment and Dementia in Older Adults: A Systematic Review

INTRODUCTION

Timely detection of cognitive impairment such as mild cognitive impairment (MCI) or dementia is pivotal in initiating early interventions to improve patients' quality of life. Conventional paper-pencil tests, though common, have limited sensitivity in detecting subtle cognitive changes. Computerized assessments offer promising alternatives, overcoming time and manual scoring constraints while potentially providing greater sensitivity.

METHODS

A literature search yielded 26 eligible articles (2020-2023). The articles were reviewed according to PRISMA guidelines, and the computerized tools were categorized by diagnostic outcome (MCI, dementia, combined).

RESULTS

The subjects included in the studies were aged 55-77 years. The overall gender distribution comprised 60% females and 40% males. The sample sizes varied considerably from 22 to 4,486. Convergent validity assessments in 20 studies demonstrated strong positive correlations with traditional tests. Overall classification accuracy in detecting MCI or dementia, distinguishing from normal cognition (NC), reached up to 91%. Impressively, 46% of the studies received high-quality ratings, underscoring the reliability and validity of the findings.

CONCLUSION

The review highlights the advancements in computerized cognitive assessments for assessing MCI and dementia. This shift toward technology-based assessments could enhance detection capabilities and facilitate timely interventions for better patient outcomes.

Oxygen-enriching triphase platform for reliable sensing of femtomolar Alzheimer's neurofilament lights

Accurate quantification of neurofilament lights (NfLs), a prognostic blood biomarker, is highly required to predict neurodegeneration in the presymptomatic stages of Alzheimer's disease. Here, we report self-oxygen-enriching coral structures with triphase interfaces for the label-free photocathodic detection of NfLs in blood plasma with femtomolar sensitivities and high reliability. In conventional photocathodic immunoassays, the poor solubility and sluggish diffusion rate of the dissolved oxygen serving as electron acceptors have necessitated the incorporation of additional electron acceptors or aeration procedures. To address the challenge, we designed the coral-like copper bismuth oxides (CBO) with robust solid-liquid-air contact boundaries that enrich the interfacial oxygen levels without an external aeration source. By optimally assembling the perfluorododecyltrichlorosilane (FTCS) and platinum (Pt) co-catalysts into the silver-doped CBO (Ag:CBO), the stable solid-liquid-air contact boundaries were formed within the sensor interfaces, which allowed for the abundant supply of air phase oxygen through an air pocket connected to the atmosphere. The Pt/FTCS-Ag:CBO exhibited the stable background signals independent of the dissolved oxygen fluctuations and amplified photocurrent signals by 1.76-fold, which were attributed to the elevated interfacial oxygen levels and 11.15 times-lowered mass transport resistance. Under the illumination of white light-emitting diode, the oxygen-enriching photocathodic sensor composed of Pt/FTCS-Ag:CBO conjugated with NfLs-specific antibodies precisely quantified the NfLs in plasma with a low coefficient of variation (≤2.97%), a high degree of recovery (>97.0%), and a limit of detection of 40.38 fg/mL, which was 140 times lower than the typical photocathodic sensor with diphase interfaces.

Detection of Mild Cognitive Impairment From Non-Semantic, Acoustic Voice Features: The Framingham Heart Study

BACKGROUND

With the aging global population and the rising burden of Alzheimer disease and related dementias (ADRDs), there is a growing focus on identifying mild cognitive impairment (MCI) to enable timely interventions that could potentially slow down the onset of clinical dementia. The production of speech by an individual is a cognitively complex task that engages various cognitive domains. The ease of audio data collection highlights the potential cost-effectiveness and noninvasive nature of using human speech as a tool for cognitive assessment.

OBJECTIVE

This study aimed to construct a machine learning pipeline that incorporates speaker diarization, feature extraction, feature selection, and classification to identify a set of acoustic features derived from voice recordings that exhibit strong MCI detection capability.

METHODS

The study included 100 MCI cases and 100 cognitively normal controls matched for age, sex, and education from the Framingham Heart Study. Participants' spoken responses on neuropsychological tests were recorded, and the recorded audio was processed to identify segments of each participant's voice from recordings that included voices of both testers and participants. A comprehensive set of 6385 acoustic features was then extracted from these voice segments using OpenSMILE and Praat software. Subsequently, a random forest model was constructed to classify cognitive status using the features that exhibited significant differences between the MCI and cognitively normal groups. The MCI detection performance of various audio lengths was further examined.

RESULTS

An optimal subset of 29 features was identified that resulted in an area under the receiver operating characteristic curve of 0.87, with a 95% CI of 0.81-0.94. The most important acoustic feature for MCI classification was the number of filled pauses (importance score=0.09, P=3.10E-08). There was no substantial difference in the performance of the model trained on the acoustic features derived from different lengths of voice recordings.

CONCLUSIONS

This study showcases the potential of monitoring changes to nonsemantic and acoustic features of speech as a way of early ADRD detection and motivates future opportunities for using human speech as a measure of brain health.

Automated Offline Smartphone-Assisted Microfluidic Paper-Based Analytical Device for Biomarker Detection of Alzheimer's Disease

This paper presents a smartphone-assisted microfluidic paper-based analytical device (μPAD), which was applied to detect Alzheimer's disease biomarkers, especially in resource-limited regions. This device implements deep learning (DL)-assisted offline smartphone detection, eliminating the requirement for large computing devices and cloud computing power. In addition, a smartphone-controlled rotary valve enables a fully automated colorimetric enzyme-linked immunosorbent assay (c-ELISA) on μPADs. It reduces detection errors caused by human operation and further increases the accuracy of μPAD c-ELISA. We realized a sandwich c-ELISA targeting β-amyloid peptide 1-42 (Aβ 1-42) in artificial plasma, and our device provided a detection limit of 15.07 pg/mL. We collected 750 images for the training of the DL YOLOv5 model. The training accuracy is 88.5%, which is 11.83% higher than the traditional curve-fitting result analysis method. Utilizing the YOLOv5 model with the NCNN framework facilitated offline detection directly on the smartphone. Furthermore, we developed a smartphone application to operate the experimental process, realizing user-friendly rapid sample detection.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection.

RESULTS

This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1-42 (Aβ 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach.

SIGNIFICANCE

This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1-42, particularly in areas with low resources and limited communication infrastructure.

Development and External Validation of a Gait Test Based Diagnostic Model for Detecting Mild Cognitive Impairment

OBJECTIVE

To address the lack of large-scale screening tools for mild cognitive impairment (MCI), this study aimed to assess the discriminatory ability of several gait tests for MCI and develop a screening tool based on gait test for MCI.

DESIGN

A diagnostic case-control test.

SETTING

The general community.

PARTICIPANTS

We recruited 134 older adults (≥65 years) for the derivation sample, comprising -69 individuals in the cognitively normal group and -65 in the MCI group (N=134). An additional 70 participants were enrolled for the validation sample.

INTERVENTIONS

All participants completed gait tests consisting of a single task (ST) and 3 dual tasks (DTs): counting backwards, serial subtractions 7, and naming animals.

MAIN OUTCOME MEASURES

Binary logistic regression analyses were used to develop models, and the efficacy of each model was assessed using receiver operating characteristic (ROC) curve and area under the curve (AUC). The best effective model was the final diagnostic model and validated using ROC curve and calibration curve.

RESULTS

The DT gait test incorporating serial subtractions 7 as the cognitive task demonstrated the highest efficacy with the AUC of 0.906 and the accuracy of 0.831 in detecting MCI with "years of education" being adjusted. Furthermore, the model exhibited consistent performance across different age and sex groups. In external validation, the model displayed robust discrimination (AUC=0.913) and calibration (calibrated intercept=-0.062, slope=1.039).

CONCLUSIONS

The DT gait test incorporating serial subtractions 7 as the cognitive task demonstrated robust discriminate ability for MCI. This test holds the potential to serve as a large-scale screening tool for MCI, aids in the early detection and intervention of cognitive impairment in older adults.

Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study

BACKGROUND

The rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults.

OBJECTIVE

This work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline.

METHODS

A sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability.

RESULTS

In the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80% and 86%, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets.

CONCLUSIONS

This work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature.

Application of fiber loop ringdown spectroscopy technique for a new approach to beta-amyloid monitoring for Alzheimer Disease's early detection

A novel fiber optic biosensor was purposed for a new approach to monitor amyloid beta protein fragment 1-42 (Aβ42) for Alzheimer's Disease (AD) early detection. The sensor was fabricated by etching a part of fiber from single mode fiber loop in pure hydrofluoric acid solution and utilized as a Local Optical Refractometer (LOR) to monitor the change Aβ42 concentration in Artificial Cerebrospinal Fluid (ACSF). The Fiber Loop Ringdown Spectroscopy (FLRDS) technique is an ultra-sensitive measurement technique with low-cost, high sensitivity, real-time measurement, continuous measurement and portability features that was utilized with a fiber optic sensor for the first time for the detection of a biological signature in an ACSF environment. Here, the measurement is based on the total optical loss detection when specially fabricated sensor heads were immersed into ACSF solutions with and without different concentrations of Aβ42 biomarkers since the bulk refractive index change was performed. Baseline stability and the reference ring down times of the sensor head were measured in the air as 0.87% and 441.6μs ± 3.9μs, respectively. Afterward, the total optical loss of the system was measured when the sensor head was immersed in deionized water, ACSF solution, and ACSF solutions with Aβ42 in different concentrations. The lowest Aβ42 concentration of 2 ppm was detected by LOR. Results showed that LOR fabricated by single-mode fibers for FLRDS system design are promising candidates to be utilized as fiber optic biosensors after sensor head modification and have a high potential for early detection applications of not only AD but possibly also several fatal diseases such as diabetes and cancer.

Toolkit to Examine Lifelike Language (TELL): An app to capture speech and language markers of neurodegeneration

Automated speech and language analysis (ASLA) is a promising approach for capturing early markers of neurodegenerative diseases. However, its potential remains underexploited in research and translational settings, partly due to the lack of a unified tool for data collection, encryption, processing, download, and visualization. Here we introduce the Toolkit to Examine Lifelike Language (TELL) v.1.0.0, a web-based app designed to bridge such a gap. First, we outline general aspects of its development. Second, we list the steps to access and use the app. Third, we specify its data collection protocol, including a linguistic profile survey and 11 audio recording tasks. Fourth, we describe the outputs the app generates for researchers (downloadable files) and for clinicians (real-time metrics). Fifth, we survey published findings obtained through its tasks and metrics. Sixth, we refer to TELL's current limitations and prospects for expansion. Overall, with its current and planned features, TELL aims to facilitate ASLA for research and clinical aims in the neurodegeneration arena. A demo version can be accessed here:  https://demo.sci.tellapp.org/ .

Sensory and motor deficits as contributors to early cognitive impairment

INTRODUCTION

Age-related sensory and motor impairment are associated with risk of dementia. No study has examined the joint associations of multiple sensory and motor measures on prevalence of early cognitive impairment (ECI).

METHODS

Six hundred fifty participants in the Baltimore Longitudinal Study of Aging completed sensory and motor function tests. The association between sensory and motor function and ECI was examined using structural equation modeling with three latent factors corresponding to multisensory, fine motor, and gross motor function.

RESULTS

The multisensory, fine, and gross motor factors were all correlated (r = 0.74 to 0.81). The odds of ECI were lower for each additional unit improvement in the multisensory (32%), fine motor (30%), and gross motor factors (12%).

DISCUSSION

The relationship between sensory and motor impairment and emerging cognitive impairment may guide future intervention studies aimed at preventing and/or treating ECI.

HIGHLIGHTS

Sensorimotor function and early cognitive impairment (ECI) prevalence were assessed via structural equation modeling. The degree of fine and gross motor function is associated with indicators of ECI. The degree of multisensory impairment is also associated with indicators of ECI.

A novel approach to recognition of Alzheimer's and Parkinson's diseases: random subspace ensemble classifier based on deep hybrid features with a super-resolution image

Background

Artificial intelligence technologies have great potential in classifying neurodegenerative diseases such as Alzheimer's and Parkinson's. These technologies can aid in early diagnosis, enhance classification accuracy, and improve patient access to appropriate treatments. For this purpose, we focused on AI-based auto-diagnosis of Alzheimer's disease, Parkinson's disease, and healthy MRI images.

Methods

In the current study, a deep hybrid network based on an ensemble classifier and convolutional neural network was designed. First, a very deep super-resolution neural network was adapted to improve the resolution of MRI images. Low and high-level features were extracted from the images processed with the hybrid deep convolutional neural network. Finally, these deep features are given as input to the k-nearest neighbor (KNN)-based random subspace ensemble classifier.

Results

A 3-class dataset containing publicly available MRI images was utilized to test the proposed architecture. In experimental works, the proposed model produced 99.11% accuracy, 98.75% sensitivity, 99.54% specificity, 98.65% precision, and 98.70% F1-score performance values. The results indicate that our AI system has the potential to provide valuable diagnostic assistance in clinical settings.

The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer's Disease Using GPT Embeddings

The development of noninvasive and cost-effective methods of detecting Alzheimer's disease (AD) is essential for its early prevention and mitigation. We optimize the detection of AD using natural language processing (NLP) of spontaneous speech through the use of audio enhancement techniques and novel transcription methodologies. Specifically, we utilized Boll Spectral Subtraction to improve audio fidelity and created transcriptions using state-of-the-art AI services-locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI-evaluating their performance against traditional manual transcription methods. Support Vector Machine (SVM) classifiers were then trained and tested using GPT-based embeddings of transcriptions. Our findings revealed that AI-based transcriptions largely outperformed traditional manual ones, with Wav2Vec (enhanced audio) achieving the best accuracy and F-1 score (0.99 for both metrics) for locally-based systems and Rev AI (standard audio) performing the best for cloud-based systems (0.96 for both metrics). Furthermore, this study revealed the detrimental effects of interviewer speech on model performance in addition to the minimal effect of audio enhancement. Based on our findings, current AI transcription and NLP technologies are highly effective at accurately detecting AD with available data but struggle to classify probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, due to a lack of training data, laying the groundwork for the future implementation of an automatic AD detection system.

Plasma Aβ42/Aβ40 and phospho-tau217 concentration ratios increase the accuracy of amyloid PET classification in preclinical Alzheimer's disease

INTRODUCTION

Incorporating blood-based Alzheimer's disease biomarkers such as tau and amyloid beta (Aβ) into screening algorithms may improve screening efficiency.

METHODS

Plasma Aβ, phosphorylated tau (p-tau)181, and p-tau217 concentration levels from AHEAD 3-45 study participants were measured using mass spectrometry. Tau concentration ratios for each proteoform were calculated to normalize for inter-individual differences. Receiver operating characteristic (ROC) curve analysis was performed for each biomarker against amyloid positivity, defined by > 20 Centiloids. Mixture of experts analysis assessed the value of including tau concentration ratios into the existing predictive algorithm for amyloid positron emission tomography status.

RESULTS

The area under the receiver operating curve (AUC) was 0.87 for Aβ42/Aβ40, 0.74 for phosphorylated variant p-tau181 ratio (p-tau181/np-tau181), and 0.92 for phosphorylated variant p-tau217 ratio (p-tau217/np-tau217). The Plasma Predicted Centiloid (PPC), a predictive model including p-tau217/np-tau217, Aβ42/Aβ40, age, and apolipoprotein E improved AUC to 0.95.

DISCUSSION

Including plasma p-tau217/np-tau217 along with Aβ42/Aβ40 in predictive algorithms may streamline screening preclinical individuals into anti-amyloid clinical trials.

CLINICALTRIALS

gov Identifier: NCT04468659 HIGHLIGHTS: The addition of plasma phosphorylated variant p-tau217 ratio (p-tau217/np-tau217) significantly improved plasma biomarker algorithms for identifying preclinical amyloid positron emission tomography positivity. Prediction performance at higher NAV Centiloid levels was improved with p-tau217/np-tau217. All models generated for this study are incorporated into the Plasma Predicted Centiloid (PPC) app for public use.

Comparing Cognitive Tests and Smartphone-Based Assessment in 2 US Community-Based Cohorts

BACKGROUND

Smartphone-based cognitive assessments have emerged as promising tools, bridging gaps in accessibility and reducing bias in Alzheimer disease and related dementia research. However, their congruence with traditional neuropsychological tests and usefulness in diverse cohorts remain underexplored.

METHODS AND RESULTS

A total of 406 FHS (Framingham Heart Study) and 59 BHS (Bogalusa Heart Study) participants with traditional neuropsychological tests and digital assessments using the Defense Automated Neurocognitive Assessment (DANA) smartphone protocol were included. Regression models investigated associations between DANA task digital measures and a neuropsychological global cognitive Z score (Global Cognitive Score [GCS]), and neuropsychological domain-specific Z scores. FHS participants' mean age was 57 (SD, 9.75) years, and 44% (179) were men. BHS participants' mean age was 49 (4.4) years, and 28% (16) were men. Participants in both cohorts with the lowest neuropsychological performance (lowest quartile, GCS1) demonstrated lower DANA digital scores. In the FHS, GCS1 participants had slower average response times and decreased cognitive efficiency scores in all DANA tasks (P<0.05). In BHS, participants in GCS1 had slower average response times and decreased cognitive efficiency scores for DANA Code Substitution and Go/No-Go tasks, although this was not statistically significant. In both cohorts, GCS was significantly associated with DANA tasks, such that higher GCS correlated with faster average response times (P<0.05) and increased cognitive efficiency (all P<0.05) in the DANA Code Substitution task.

CONCLUSIONS

Our findings demonstrate that smartphone-based cognitive assessments exhibit concurrent validity with a composite measure of traditional neuropsychological tests. This supports the potential of using smartphone-based assessments in cognitive screening across diverse populations and the scalability of digital assessments to community-dwelling individuals.

The Optimization of a Natural Language Processing Approach for the Automatic Detection of Alzheimer's Disease Using GPT Embeddings

As the impact of Alzheimer's disease (AD) is projected to grow in the coming decades as the world's population ages, the development of noninvasive and cost-effective methods of detecting AD is essential for the early prevention and mitigation of the progressive disease, alleviating its expected global impact. This study analyzes audio processing techniques and transcription methodologies to optimize the detection of AD through the natural language processing (NLP) of spontaneous speech. We enhanced audio fidelity using Boll Spectral Subtraction and evaluated the transcription accuracy of state-of-the-art AI services-locally-based Wav2Vec and Whisper, alongside cloud-based IBM Cloud and Rev AI-against traditional manual transcription methods. The choice between local and cloud-based solutions hinges on a trade-off between privacy, ongoing costs, and computational requirements. Leveraging OpenAI's GPT for word embeddings, we enhanced the training of Support Vector Machine (SVM) classifiers, which were crucial in analyzing transcripts and refining detection accuracy. Our findings reveal that AI-driven transcriptions significantly outperform manual counterparts when classifying AD and Control samples, with Wav2Vec using enhanced audio exhibiting the highest accuracy and F-1 scores (0.99 for both metrics) for locally based systems and Rev AI using unenhanced audio leading cloud-based methods with comparable precision (0.96 for both metrics). The study also uncovers the detrimental effect of including interviewer speech in recordings on model performance, advocating for the exclusion of such interactions to improve data quality for AD classification algorithms. Our comprehensive evaluation demonstrates that AI transcription (both Cloud and Local) and NLP technologies in their current forms can classify AD, as well as probable AD and mild cognitive impairment (MCI), a prodromal stage of AD, accurately but suffer from a lack of available training data. The insights garnered from this research lay the groundwork for future advancements in the noninvasive monitoring and early detection of cognitive impairments through linguistic analysis.

"Guttmann Cognitest®," a digital solution for assessing cognitive performance in adult population: A feasibility and usability pilot study

Background

As the world population continues to age, the prevalence of neurological diseases, such as dementia, poses a significant challenge to society. Detecting cognitive impairment at an early stage is vital in preserving and enhancing cognitive function. Digital tools, particularly mHealth, offer a practical solution for large-scale population screening and prompt follow-up assessments of cognitive function, thus overcoming economic and time limitations.

Objective

In this work, two versions of a digital solution called Guttmann Cognitest® were tested.

Methods

Two hundred and one middle-aged adults used the first version (Group A), while 132 used the second one, which included improved tutorials and practice screens (Group B). This second version was also validated in an older age group (Group C).

Results

This digital solution was found to be highly satisfactory in terms of usability and feasibility, with good acceptability among all three groups. Specifically for Group B, the system usability scale score obtained classifies the solution as the best imaginable in terms of usability.

Conclusions

Guttmann Cognitest® has been shown to be effective and well-perceived, with a high potential for sustained engagement in tracking changes in cognitive function.

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Background

The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too.

Background

Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.

Methods

Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.

Results

The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.

Conclusions

The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

Causal functional connectivity in Alzheimer's disease computed from time series fMRI data

Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.

Speech and language markers of neurodegeneration: a call for global equity

In the field of neurodegeneration, speech and language assessments are useful for diagnosing aphasic syndromes and for characterizing other disorders. As a complement to classic tests, scalable and low-cost digital tools can capture relevant anomalies automatically, potentially supporting the quest for globally equitable markers of brain health. However, this promise remains unfulfilled due to limited linguistic diversity in scientific works and clinical instruments. Here we argue for cross-linguistic research as a core strategy to counter this problem. First, we survey the contributions of linguistic assessments in the study of primary progressive aphasia and the three most prevalent neurodegenerative disorders worldwide-Alzheimer's disease, Parkinson's disease, and behavioural variant frontotemporal dementia. Second, we address two forms of linguistic unfairness in the literature: the neglect of most of the world's 7000 languages and the preponderance of English-speaking cohorts. Third, we review studies showing that linguistic dysfunctions in a given disorder may vary depending on the patient's language and that English speakers offer a suboptimal benchmark for other language groups. Finally, we highlight different approaches, tools and initiatives for cross-linguistic research, identifying core challenges for their deployment. Overall, we seek to inspire timely actions to counter a looming source of inequity in behavioural neurology.

Deep Multi-Branch CNN Architecture for Early Alzheimer's Detection from Brain MRIs

Alzheimer's disease (AD) is a neurodegenerative disease that can cause dementia and result in a severe reduction in brain function, inhibiting simple tasks, especially if no preventative care is taken. Over 1 in 9 Americans suffer from AD-induced dementia, and unpaid care for people with AD-related dementia is valued at USD 271.6 billion. Hence, various approaches have been developed for early AD diagnosis to prevent its further progression. In this paper, we first review other approaches that could be used for the early detection of AD. We then give an overview of our dataset and propose a deep convolutional neural network (CNN) architecture consisting of 7,866,819 parameters. This model comprises three different convolutional branches, each having a different length. Each branch is comprised of different kernel sizes. This model can predict whether a patient is non-demented, mild-demented, or moderately demented with a 99.05% three-class accuracy. In summary, the deep CNN model demonstrated exceptional accuracy in the early diagnosis of AD, offering a significant advancement in the field and the potential to improve patient care.

Functional Nanomaterials for the Diagnosis of Alzheimer's Disease: Recent Progress and Future Perspectives

Alzheimer's disease (AD) is one of the main causes of dementia worldwide, whereby neuronal death or malfunction leads to cognitive impairment in the elderly population. AD is highly prevalent, with increased projections over the next few decades. Yet current diagnostic methods for AD occur only after the presentation of clinical symptoms. Evidence in the literature points to potential mechanisms of AD induction beginning before clinical symptoms start to present, such as the formation of amyloid beta (Aβ) extracellular plaques and neurofibrillary tangles (NFTs). Biomarkers of AD, including Aβ 40, Aβ 42, and tau protein, amongst others, show promise for early AD diagnosis. Additional progress is made in the application of biosensing modalities to measure and detect significant changes in these AD biomarkers within patient samples, such as cerebral spinal fluid (CSF) and blood, serum, or plasma. Herein, a comprehensive review of the emerging nano-biomaterial approaches to develop biosensors for AD biomarkers' detection is provided. Advances, challenges, and potential of electrochemical, optical, and colorimetric biosensors, focusing on nanoparticle-based (metallic, magnetic, quantum dots) and nanostructure-based biomaterials are discussed. Finally, the criteria for incorporating these emerging nano-biomaterials in clinical settings are presented and assessed, as they hold great potential for enhancing early-onset AD diagnostics.

Prediction of conversion to dementia disorders based on timed up and go dual-task test verbal and motor outcomes: a five-year prospective memory-clinic-based study

BACKGROUND

While assessment tools can increase the detection of cognitive impairment, there is currently insufficient evidence regarding clinical outcomes based on screening for cognitive impairment in older adults.

METHODS

The study purpose was to investigate whether Timed Up and Go dual-task test (TUGdt) results, based on TUG combined with two different verbal tasks (name different animals, TUGdt-NA, and recite months in reverse order, TUGdt-MB), predicted dementia incidence over a period of five years among patients (N = 186, mean = 70.7 years; 45.7% female) diagnosed with Subjective Cognitive Impairment (SCI) and Mild Cognitive Impairment (MCI) following assessment at two memory clinics. Associations between TUG parameters and dementia incidence were examined in Cox regression models.

RESULTS

During follow-up time (median (range) 3.7 (0.1-6.1) years) 98 participants converted to dementia. Novel findings indicated that the TUGdt parameter words/time, after adjustment for age, gender, and education, can be used for the prediction of conversion to dementia in participants with SCI or MCI over a period of five years. Among the TUG-related parameters investigated, words/time showed the best predictive capacity, while time scores of TUG and TUGdt as well as TUGdt cost did not produce significant predictive results. Results further showed that the step parameter step length during TUGdt predicts conversion to dementia before adjustment for age, gender, and education. Optimal TUGdt cutoffs for predicting dementia at 2- and 4-year follow-up based on words/time were calculated. The sensitivity of the TUGdt cutoffs was high at 2-year follow-up: TUGdt-NA words/time, 0.79; TUGdt-MB words/time, 0.71; reducing respectively to 0.64 and 0.65 at 4-year follow-up.

CONCLUSIONS

TUGdt words/time parameters have potential as cost-efficient tools for conversion-to-dementia risk assessment, useful for research and clinical purposes. These parameters may be able to bridge the gap of insufficient evidence for such clinical outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05893524: https://www.

CLINICALTRIALS

gov/study/NCT05893524?id=NCT05893524&rank=1 .

Advanced Overview of Biomarkers and Techniques for Early Diagnosis of Alzheimer's Disease

The development of early non-invasive diagnosis methods and identification of novel biomarkers are necessary for managing Alzheimer's disease (AD) and facilitating effective prognosis and treatment. AD has multi-factorial nature and involves complex molecular mechanism, which causes neuronal degeneration. The primary challenges in early AD detection include patient heterogeneity and lack of precise diagnosis at the preclinical stage. Several cerebrospinal fluid (CSF) and blood biomarkers have been proposed to show excellent diagnosis ability by identifying tau pathology and cerebral amyloid beta (Aβ) for AD. Intense research endeavors are being made to develop ultrasensitive detection techniques and find potent biomarkers for early AD diagnosis. To mitigate AD worldwide, understanding various CSF biomarkers, blood biomarkers, and techniques that can be used for early diagnosis is imperative. This review attempts to provide information regarding AD pathophysiology, genetic and non-genetic factors associated with AD, several potential blood and CSF biomarkers, like neurofilament light, neurogranin, Aβ, and tau, along with biomarkers under development for AD detection. Besides, numerous techniques, such as neuroimaging, spectroscopic techniques, biosensors, and neuroproteomics, which are being explored to aid early AD detection, have been discussed. The insights thus gained would help in finding potential biomarkers and suitable techniques for the accurate diagnosis of early AD before cognitive dysfunction.

Age-related macular degeneration is associated with probable cerebral amyloid angiopathy: A case-control study

BACKGROUND

Age-related macular degeneration (AMD) is a common retinal degenerative disorder among older individuals. Amyloid deposits, a hallmark of cerebral amyloid angiopathy (CAA), may be involved in the pathogenesis of AMD. Since amyloid deposits may contribute to the development of both AMD and CAA, we hypothesized that patients with AMD have a higher prevalence of CAA.

OBJECTIVE

To compare the prevalence of CAA in patients with or without AMD matched for age.

METHODS

We conducted a cross-sectional, 1:1 age-matched, case-control study of patients ≥40 years of age at the Mayo Clinic who had undergone both retinal optical coherence tomography and brain MRI from 2011 to 2015. Primary dependent variables were probable CAA, superficial siderosis, and lobar and deep cerebral microbleeds (CMBs). The relationship between AMD and CAA was assessed using multivariable logistic regression and was compared across AMD severity (none vs early vs late AMD).

RESULTS

Our analysis included 256 age-matched pairs (AMD 126, no AMD 130). Of those with AMD, 79 (30.9%) had early AMD and 47 (19.4%) had late AMD. The mean age was 75±9 years, and there was no significant difference in vascular risk factors between groups. Patients with AMD had a higher prevalence of CAA (16.7% vs 10.0%, p=0.116) and superficial siderosis (15.1% vs 6.2%, p=0.020), but not deep CMB (5.2% vs 6.2%, p=0.426), compared to those without AMD. After adjusting for covariates, having late AMD was associated with increased odds of CAA (OR 2.83, 95% CI 1.10-7.27, p=0.031) and superficial siderosis (OR 3.40, 95%CI 1.20-9.65, p=0.022), but not deep CMB (OR 0.7, 95%CI 0.14-3.51, p=0.669).

CONCLUSIONS

AMD was associated with CAA and superficial siderosis but not deep CMB, consistent with the hypothesis that amyloid deposits play a role in the development of AMD. Prospective studies are needed to determine if features of AMD may serve as biomarkers for the early diagnosis of CAA.

A novel method for diagnosing Alzheimer's disease using deep pyramid CNN based on EEG signals

Background

The diagnosis of Alzheimer's disease (AD) using electroencephalography (EEG) has garnered more attention recently.

New methods

In this paper, we present a novel approach for the diagnosis of AD, in terms of classifying the resting-state EEG of AD, mild cognitive impairment (MCI), and healthy control (HC). To overcome the hurdles of limited data available and the over-fitting problem of the deep learning models, we studied overlapping sliding windows to augment the one-dimensional EEG data of 100 subjects (including 49 AD subjects, 37 MCI subjects and 14 HC subjects). After constructing the appropriate dataset, the modified DPCNN was used to classify the augmented EEG. Furthermore, the model performance was evaluated by 5 times of 5-fold cross-validation and the confusion matrix has been obtained.

Results

The average accuracy rate of the model for classifying AD, MCI, and HC is 97.10%, and the F1 score of the three-class classification model is 97.11%, which further proves the model's excellent performance.

Conclusions

Therefore, the DPCNN proposed in this paper can accurately classify the one-dimensional EEG of AD and is worthy of reference for the diagnosis of the disease.

Lipidomics and proteomics: An integrative approach for early diagnosis of dementia and Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder and considered to be responsible for majority of worldwide prevalent dementia cases. The number of patients suffering from dementia are estimated to increase up to 115.4 million cases worldwide in 2050. Hence, AD is contemplated to be one of the major healthcare challenge in current era. This disorder is characterized by impairment in various signaling molecules at cellular and nuclear level including aggregation of Aβ protein, tau hyper phosphorylation altered lipid metabolism, metabolites dysregulation, protein intensity alteration etc. Being heterogeneous and multifactorial in nature, the disease do not has any cure or any confirmed diagnosis before the onset of clinical manifestations. Hence, there is a requisite for early diagnosis of AD in order to downturn the progression/risk of the disorder and utilization of newer technologies developed in this field are aimed to provide an extraordinary assistance towards the same. The lipidomics and proteomics constitute large scale study of cellular lipids and proteomes in biological matrices at normal stage or any stage of a disease. The study involves high throughput quantification and detection techniques such as mass spectrometry, liquid chromatography, nuclear mass resonance spectroscopy, fluorescence spectroscopy etc. The early detection of altered levels of lipids and proteins in blood or any other biological matrices could aid in preventing the progression of AD and dementia. Therefore, the present review is designed to focus on the recent techniques and early diagnostic criteria for AD, revealing the role of lipids and proteins in this disease and their assessment through different techniques.

Functional changes in brain oscillations in dementia: a review

A growing body of evidence indicates that several characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) play a functional role in cognition and could be linked to the progression of cognitive decline in some neurological diseases such as dementia. The present paper reviews previous studies investigating changes in brain oscillations associated to the most common types of dementia, namely Alzheimer's disease (AD), frontotemporal degeneration (FTD), and vascular dementia (VaD), with the aim of identifying pathology-specific patterns of alterations and supporting differential diagnosis in clinical practice. The included studies analysed changes in frequency power, functional connectivity, and event-related potentials, as well as the relationship between electrophysiological changes and cognitive deficits. Current evidence suggests that an increase in slow wave activity (i.e., theta and delta) as well as a general reduction in the power of faster frequency bands (i.e., alpha and beta) characterizes AD, VaD, and FTD. Additionally, compared to healthy controls, AD exhibits alteration in latencies and amplitudes of the most common event related potentials. In the reviewed studies, these changes generally correlate with performances in many cognitive tests. In conclusion, particularly in AD, neurophysiological changes can be reliable early markers of dementia.

The Accuracy of Speech and Linguistic Analysis in Early Diagnostics of Neurocognitive Disorders in a Memory Clinic Setting

OBJECTIVE

To investigate whether automatic analysis of the Semantic Verbal Fluency test (SVF) is reliable and can extract additional information that is of value for identifying neurocognitive disorders. In addition, the associations between the automatically derived speech and linguistic features and other cognitive domains were explored.

METHOD

We included 135 participants from the memory clinic of the Maastricht University Medical Center+ (with Subjective Cognitive Decline [SCD; N = 69] and Mild Cognitive Impairment [MCI]/dementia [N = 66]). The SVF task (one minute, category animals) was recorded and processed via a mobile application, and speech and linguistic features were automatically extracted. The diagnostic performance of the automatically derived features was investigated by training machine learning classifiers to differentiate SCD and MCI/dementia participants.

RESULTS

The intraclass correlation for interrater reliability between the clinical total score (golden standard) and automatically derived total word count was 0.84. The full model including the total word count and the automatically derived speech and linguistic features had an Area Under the Curve (AUC) of 0.85 for differentiating between people with SCD and MCI/dementia. The model with total word count only and the model with total word count corrected for age showed an AUC of 0.75 and 0.81, respectively. Semantic switching correlated moderately with memory as well as executive functioning.

CONCLUSION

The one-minute SVF task with automatically derived speech and linguistic features was as reliable as the manual scoring and differentiated well between SCD and MCI/dementia. This can be considered as a valuable addition in the screening of neurocognitive disorders and in clinical practice.

Gait Indicators Contribute to Screening Cognitive Impairment: A Single- and Dual-Task Gait Study

Background: Screening cognitive impairment is complex and not an appliance for early screening. Gait performance is strongly associated with cognitive impairment. Objectives: We aimed to explore gait indicators that could potentially screen cognitive dysfunction. Methods: A total of 235 subjects were recruited from June 2021 to June 2022. Four gait tasks, including the walking test, the timed “Up & Go” test (TUG), foot pressure balance (FPB), and one-legged standing with eyes closed test (OLS-EC), were performed. Moreover, in the walking test, participants were instructed to walk at their usual pace for the single-gait test. For the dual-task tests, participants walked at their usual pace while counting backward from 100 by 1s. The data were analyzed by the independent sample t-test, univariate and multivariate logistic regression, a linear trend, stratified and interaction analysis, the receiver operating characteristic (ROC) curve, and Pearson’s correlations. Results: Among the 235 participants, 81 (34.5%) were men and 154 (65.5%) were women. The mean age of participants was 72 ± 7.836 years. The control, MCI, mild AD, and severe AD groups had means of 71, 63, 71, and 30, respectively. After adjusting for age, sex, education, and body mass index (BMI), the dual-task toe-off-ground angle (TOA) (odds ratio (OR) = 0.911, 95% confidence interval (CI): 0.847, 0.979), single-task TOA (OR = 0.904, 95% CI: 0.841−0.971), and the timed “Up & Go” time (TUGT) (OR = 1.515, 95% CI: 1.243−1.846) were significantly associated with an increased risk of cognitive impairment. In addition, the trend test and stratified analysis results had no significant differences (all p > 0.05). The area under the roc curve (AUC) values of TOA in the dual-task and TUGT were 0.812 and 0.847, respectively. Additionally, TOA < 36.75° in the dual-task, TOA < 38.90° in the single-task, and TUGT > 9.83 seconds (s) are likely to indicate cognitive impairment. The cognitive assessment scale scores were significantly correlated with TOA (all r > 0.3, p < 0.001) and TUGT (all r > 0.2), respectively. Conclusion: TOA and TUGT scores are, in some circumstances, associated with cognitive impairment; therefore, they can be used as simple initial screenings to identify patients at risk.

Evaluation of Blood-Based Plasma Biomarkers as Potential Markers of Amyloid Burden in Preclinical Alzheimer's Disease

BACKGROUND

Participant eligibility for the A4 Study was determined by amyloid PET imaging. Given the disadvantages of amyloid PET imaging in accessibility and cost, blood-based biomarkers may serve as a sufficient biomarker and more cost-effective screening tool for patient enrollment into preclinical AD trials.

OBJECTIVE

To determine if a blood-based screening test can adequately identify amyloid burden in participants screened into a preclinical AD trial.

METHODS

In this cross-sectional study, 224 participants from the A4 Study received an amyloid PET scan (18Florbetapir) within 90 days of blood sample collection. Blood samples from all study participants were processed within 2 h after phlebotomy. Plasma amyloid measures were quantified by Shimazdu and C2 N Diagnostics using mass spectrometry-based platforms. A corresponding subset of blood samples (n = 100) was processed within 24 h after phlebotomy and analyzed by C2 N.

RESULTS

Plasma Aβ42/Aβ40 demonstrated the highest association for Aβ accumulation in the brain with an AUC 0.76 (95%CI = 0.69, 0.82) at C2 N and 0.80 (95%CI = 0.75, 0.86) at Shimadzu. Blood samples processed to plasma within 2 h after phlebotomy provided a better prediction of amyloid PET status than blood samples processed within 24 h (AUC 0.80 versus 0.64; p < 0.001). Age, sex, and APOE ɛ4 carrier status did not the diagnostic performance of plasma Aβ42/Aβ40 to predict amyloid PET positivity in A4 Study participants.

CONCLUSION

Plasma Aβ42/Aβ40 may serve as a potential biomarker for predicting elevated amyloid in the brain. Utilizing blood testing over PET imaging may improve screening efficiency into clinical trials.

Association between walking energy utilisation and longitudinal cognitive performance in older adults

BACKGROUND

Human motor function is optimised for energetic efficiency, however, age-related neurodegenerative changes affects neuromotor control of walking. Energy utilisation has been associated with motor performance, but its association with cognitive performance is unknown.

METHODS

The study population included 979 Baltimore Longitudinal Study of Aging participants aged $\ge$50 years (52% female, mean age: 70$\pm$10.2 years) with a median follow-up time of 4.7 years. Energy utilisation for walking was operationalised as a ratio of the energy cost of slow walking to peak walking energy expenditure during standardised tasks ('cost-ratio'). Cognitive functioning was measured using the Trail Making Tests, California Verbal Learning Test, Wechsler Adult Intelligence Scale (WAIS), letter and category fluency and card rotation tests. Linear mixed models adjusted for demographics, education and co-morbidities assessed the association between baseline cost-ratio and cognitive functioning, cross-sectionally and longitudinally. To investigate the relationship among those with less efficient energy utilisation, subgroup analyses were performed.

RESULTS

In fully adjusted models, a higher cost-ratio was cross-sectionally associated with poorer performance on all cognitive tests except WAIS (P < 0.05 for all). Among those with compromised energy utilisation, the baseline cost-ratio was also associated with a faster decline in memory (long-delay free recall: β = -0.4, 95% confidence interval [CI] = [-0.8, -0.02]; immediate word recall: β = -1.3, 95% CI = [-2.7, 0.1]).

CONCLUSIONS

These findings suggest cross-sectional and longitudinal links between energy utilisation and cognitive performance, highlighting an intriguing link between brain function and the energy needed for ambulation. Future research should examine this association earlier in the life course to gauge the potential for interventive mechanisms.

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.

Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening

Background: Mild cognitive impairment (MCI) is a transitional stage between normal aging and probable Alzheimer's disease. It is of great value to screen for MCI in the community. A novel machine learning (ML) model is composed of electroencephalography (EEG), eye tracking (ET), and neuropsychological assessments. This study has been proposed to identify MCI subjects from normal controls (NC). Methods: Two cohorts were used in this study. Cohort 1 as the training and validation group, includes184 MCI patients and 152 NC subjects. Cohort 2 as an independent test group, includes 44 MCI and 48 NC individuals. EEG, ET, Neuropsychological Tests Battery (NTB), and clinical variables with age, gender, educational level, MoCA-B, and ACE-R were selected for all subjects. Receiver operating characteristic (ROC) curves were adopted to evaluate the capabilities of this tool to classify MCI from NC. The clinical model, the EEG and ET model, and the neuropsychological model were compared. Results: We found that the classification accuracy of the proposed model achieved 84.5 ± 4.43% and 88.8 ± 3.59% in Cohort 1 and Cohort 2, respectively. The area under curve (AUC) of the proposed tool achieved 0.941 (0.893-0.982) in Cohort 1 and 0.966 (0.921-0.988) in Cohort 2, respectively. Conclusions: The proposed model incorporation of EEG, ET, and neuropsychological assessments yielded excellent classification performances, suggesting its potential for future application in cognitive decline prediction.

Rational Design, Synthesis of Fluorescence Probes for Quantitative Detection of Amyloid-β in Alzheimer's Disease Based on Rhodamine-Metal Complex

The efficient detection and monitoring of amyloid-β plaques (Aβ42) can greatly promote the diagnosis and therapy of Alzheimer's disease (AD). Fluorescence imaging is a promising method for this, but the accurate determination of Aβ42 still remains a challenge. The development of a reliable fluorescent probe to detect Aβ42 is essential. Herein, we report a rational design strategy for Aβ42 fluorescence probes based on rhodamine-copper complexes, Rho1-Cu-Rho4-Cu, among them Rho4-Cu exhibits the best performance including high sensitivity (detection limit = 24 nM), high affinity (K_d = 23.4 nM), and high selectivity; hence, Rho4-Cu is selected for imaging Aβ42 in AD mice, and the results showed that this probe can differentiate normal mice and AD mice effectively.

An Attention-Based CoT-ResNet With Channel Shuffle Mechanism for Classification of Alzheimer’s Disease Levels

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 × 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.

Disrupted olfactory functional connectivity in patients with late-life depression

BACKGROUND

Odor identification (OI) impairment increases the risk of Alzheimer's disease and brain abnormalities in patients with late-life depression (LLD). However, it remains unclear whether abnormal functional connectivity (FC) of olfactory regions is involved in the relationship between OI impairment and dementia risk in LLD patients. The current study aims to explore the olfactory FC patterns of LLD patients and how olfactory FCs mediate the relationship between OI and cognition.

METHODS

A total of 150 participants underwent resting-state functional magnetic resonance imaging and psychometric and olfactory assessments. The primary and secondary olfactory regions were selected as regions of interest to investigate olfactory FC patterns and their association with OI and cognitive performance in LLD patients.

RESULTS

Compared with LLD patients without OI impairment and normal controls, LLD patients with OI impairment exhibited increased FC between the left orbital frontal cortex (OFC) and left calcarine gyrus, between the left OFC and right lingual gyrus, between the right OFC and right rectus gyrus, and decreased FC between the right piriform cortex and right superior parietal lobule. Additionally, these abnormal FCs were associated with scores of OI, global cognition and language function. Finally, the FC between the right piriform cortex and right superior parietal lobule exhibited a partially mediated effect on the relationship between OI and MMSE scores.

LIMITATIONS

The present study did not exclude the possible effect of drugs.

CONCLUSION

LLD patients with OI impairment exhibited more disrupted olfactory FC (a decrease in the primary olfactory cortex and an increase in the secondary olfactory cortex) than LLD patients with intact OI, and these abnormal FCs may serve as potential targets for neuromodulation in LLD patients to prevent them from developing dementia.

Verbal fluency response times predict incident cognitive impairment

Introduction

In recent decades, researchers have defined novel methods for scoring verbal fluency tasks. In this work, we evaluate novel scores based on speed of word responses.

Methods

We transcribed verbal fluency recordings from 641 cases of incident cognitive impairment (ICI) and matched controls, all participants in a large national epidemiological study. Timing measurements of utterances were used to calculate a speed score for each recording. Traditional raw and speed scores were entered into Cox proportional hazards (CPH) regression models predicting time to ICI.

Results

Concordance of the CPH model with speed scores was 0.599, an improvement of 3.4% over a model with only raw scores and demographics. Scores with significant effects included animals raw and speed scores, and letter F speed score.

Discussion

Novel verbal fluency scores based on response times could enable use of remotely administered fluency tasks for early detection of cognitive decline.

Highlights

 The current work evaluates prognostication with verbal fluency speed scores. These speed scores improve survival models predicting cognitive decline. Cases with progressive decline have some characteristics suggestive of Alzheimer's disease. The subset of acute decliners is probably pathologically heterogeneous.