bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamic Light Scattering Spectroscopy as an Early Biomarker of Alzheimer’s Disease
Authors: Weiss Jeffrey Neill 
Published: January 2025

AbstractBackgroundThe early detection of neurologic damage at the microscopic level when the disease is subclinical would facilitate intervention preventing progression or potentially reversing the condition. The early determination of drug efficacy could shorten the length of drug studies, thereby reducing research costs. The eye is the only place in the body where an artery, vein, and nerve can be directly visualized The nerve fiber layer of the retina is an outgrowth of the brain.MethodDynamic Light Scattering (DLS) Spectroscopy measures the thermal random movement (Brownian Motion) of particles by analyzing the temporal fluctuations of scattered light. A proof‐of‐concept instrument for making retinal measurements provides optical power well below the maximal permissible exposure recommended by the ANSI Z136.1 standard. Scattered light is analyzed by a digital autocorrelator with an extended delay option for baseline determination. The testing duration is 5 seconds. DLS measurements were made from the macular area of 4 patients diagnosed with mild cognitive impairment and 4 age‐matched controls with no history of neurologic disease. Both groups of patients had normal retinal examinations, including normal Optical Coherence Tomography, (OCT).ResultFigure 1 Screenshot ‐ 62‐year‐old female recently with mild cognitive impairment, subsequently diagnosed with Alzheimer’s disease 1 year after DLS testing. Figure 2 Screenshot ‐ Age‐matched controlConclusionThe 4 patients with mild cognitive impairment were subsequently diagnosed with Alzheimer’s disease approximately 1 year after the abnormal DLS measurement. The differences between the patients subsequently diagnosed with Alzheimer’s disease, compared to the normal age‐matched controls, was consistent across the tested patients. Alzheimer’s measurements begin above 0.6 g 2(t)‐1 and exhibit flattening of the initial line as opposed to the curve seen in non‐Alzheimer’s patients. The development of an early, noninvasive, quantitative test to diagnose Alzheimer’s disease will lead to breakthroughs in drug development and hopefully, to the successful treatment of patients before dementia is irreversible.
Intelligent Residential Environments Catering to Dementia‐Friendly Needs
Authors: Khan Arshia A 
Published: January 2025

AbstractThis study was approved by the ethics review board at the University of Minnesota. In conclusion, the successful design and testing of an intelligent living space tailored for dementia care were conducted in a controlled lab environment with healthy participants. The primary aim was to assess the viability of integrating robots, wearable sensors, and spatial technology to support the well‐being of individuals affected by dementia. Utilizing pressure sensors in chairs alongside the robot’s emotion detection system effectively evaluated participants' moods, aligning with self‐reported moods in 95∖% of cases. Additionally, door sensors accurately detected exiting behavior 100∖% of the time, while wall‐mounted PIR sensors successfully identified wandering behavior. Wearable sensors, including EDA and blood pressure sensors, coupled with robot interactions, detected physiological changes during various activities, indicating the most significant impact during interactions with the robot. These findings suggest that an intelligent living space specifically designed for dementia care can effectively preserve the quality of life for affected individuals, particularly through robot interactions, showcasing promising effectiveness.
Fostering Equity in Dementia Care: The Vital Role of Culturally Sensitive Technology
Authors: Khan Arshia A , Imtiaz Rana Z 
Published: January 2025

AbstractThis perspective abstract highlights the critical demand for culturally sensitive technology in dementia caregiving due to the heightened risk and unique challenges faced by individuals from diverse backgrounds. The escalating prevalence of dementia necessitates a caregiving paradigm shift, especially in the context of diverse cultural backgrounds. Research consistently underscores the existence of cultural disparities in dementia risk, diagnosis, and care outcomes, emphasizing the urgent need for culturally sensitive technology in dementia care. Individuals from diverse cultural backgrounds often face unique challenges in accessing and receiving adequate dementia care. These challenges may stem from variations in communication styles, beliefs, and societal attitudes toward dementia. Culturally sensitive technology has the potential to bridge these gaps by tailoring interventions to align with specific cultural contexts. It can enhance communication, understanding, and overall caregiving effectiveness, providing personalized support that respects the cultural nuances of those affected by dementia. Moreover, incorporating cultural sensitivity into technology design not only addresses disparities but also contributes to more inclusive healthcare practices. Culturally tailored technologies can empower caregivers with tools that recognize and respect diverse perspectives, fostering a more collaborative and patient‐centered approach. In essence, the need for culturally sensitive technology in dementia care is paramount to ensuring equitable access, improving outcomes, and promoting a holistic understanding of the multifaceted challenges faced by individuals from diverse cultural backgrounds affected by dementia. This imperative aligns with the broader goal of enhancing the quality of life for all individuals affected by dementia while fostering a more inclusive and compassionate healthcare environment.
Parsing disease heterogeneity using normative modelling and Generative Adversarial Networks (GANs)
Authors: Chintapalli Sai Spandana , Govindarajan Sindhuja Tirumalai , Shou Haochang , Huang Hao , Davatzikos Christos 
Published: January 2025

AbstractBackgroundStructural and functional heterogeneity in the brains of patients with Alzheimer’s disease (AD) leads to diagnostic and prognostic uncertainty and confounds clinical treatment planning. Normative modelling, where individual‐level deviations in brain measures from a reference sample are computed to infer personalized effects of disease, allows parsing of disease heterogeneity. In this study, GAN based normative modelling technique quantifies individual level neuroanatomical abnormality thereby facilitating measurement of personalized disease related effects in AD patients.MethodWe adapt the pix2pix GAN to translate a subject with disease to a corresponding subject without disease. We train this model using a dataset comprising of healthy controls and synthetically simulated patients. Healthy controls (n = 6000) are selected from the ISTAGING consortium. Our neuroanatomical brain measures are the 10 region of interest (ROI) volumes (covering left and right frontal, parietal, occipital, temporal lobes as well as deep brain structures) computed using a multi‐atlas segmentation technique. To simulate patients, for each healthy control we introduce 10‐30% atrophy or expansion in a random combination of ROIs while preserving clinical covariate effects. The model learns to synthesize patient‐specific controls by removing disease‐related variations from patient’s brain measures (Fig.1). Deviation of the patient from the synthesized disease‐free control acts as an image‐based biomarker that is sensitive to disease effects and severity. For performance assessment, we select 200 controls (CN) and 200 AD participants (PT) from the OASIS dataset and compute their deviations across the 10 ROI volumes using the model pretrained on the ISTAGING dataset. Logistic regression is used to assess the overall discriminative power of the derived deviations in AD classification.ResultLarger deviations in PT compared to CN suggest disease related abnormality in brain measures (Fig.2.a). Additionally, GAN’s deviations provide a considerable gain over raw ROI volumes in AD classification as quantified using the 5‐fold AUC scores (Fig.2.b).ConclusionGAN‐based normative modelling technique introduced here is a useful tool to parse heterogeneity in brain measures at an individual level. We see that self‐supervised training of the model using pseudo‐synthetically simulated patient data that is agnostic to disease patterns can help detect real disease related effects.
Acceptance of wearable technology for the early detection of dementia‐causing diseases: perspectives from the CODEC II cohort
Authors: Wilson Sarah , Beswick Emily , Morrell Rachel , Bhogal Sharandeep , Tolley Clare , Whitfield Tim , Wing Kieran , Ardle Ríona Mc , Walker Zuzana , Slight Sarah 
Published: January 2025

AbstractBackgroundWith global dementia prevalence estimated to reach 139 million by 2050,(1) early detection of dementia‐causing diseases is crucial for promoting preventative interventions. Wearable technologies have the potential to detect early signs; however, they need to be acceptable amongst users. We explored user’s perspectives on the acceptability of wearable devices.MethodA sub‐group of participants from a research cohort, Predictors of COgnitive DECline using digital devices (CODEC‐II), were recruited. Participants used four wearables (smartwatch, EEG headband, active and passive smartphone app) for two weeks every three‐months over a year. Semi‐structured interviews were conducted with participants after the first two weeks. A framework thematic analysis approach was used, assisted by N‐Vivo (QSR, version 14.23.2). The Intelligent Systems Technology Acceptance Model(2) aided in refining key themes.ResultTwenty‐one participants were interviewed, including those with subjective cognitive decline (n = 10), mild cognitive impairment (MCI) (n = 7), dementia with Lewy bodies (n = 1), and caregivers (n = 3). Key themes included ease of use, data security, usefulness, intention of use. Many participants expressed low digital self‐efficacy, relying on the research team for support with technology setup and troubleshooting. Those with MCI needed more assistance with synching data, and experience difficulty with some cognitive testing games in the active smartphone app. Some participants expressed concerns surrounding the potential for someone to hack data generated from their device. Others questioned the accuracy of the study smartwatch, as they compared its outputs to their personal ones. Participants with professional or caregiving responsibilities described how their busy lifestyle hindered their ability to use the active smartphone app every day; this was not expressed by retired individuals.ConclusionAcceptance of using different wearable technologies for the early detection of dementia‐causing diseases varied amongst participants. Researchers should consider the difficulty level of cognitive testing games for those with cognitive impairment. Future work must explore ways to support individuals with low digital self‐efficacy, ensuring digital health equity is supported. References: • Prince et al., 2015, The global impact of dementia, World Alzheimer report • Vorm and Combs, 2022, Integrating transparency, trust, and acceptance: The intelligent systems technology acceptance model (ISTAM). International Journal of Human–Computer Interaction.
Early identification of participants at risk of cognitive decline: An online tool for patient prediction of cognitive decline
Authors: Cespedes Marcela Ines , Canovas Rodrigo , Gillis Cai , Maserejian Nancy N , Maruff Paul , Fowler Christopher J , Rainey‐Smith Stephanie R , Martins Ralph N , Masters Colin L , Doecke James D. 
Published: January 2025

AbstractBackgroundThe success of therapeutic options for treatment of Alzheimer’s disease (AD) and the growing emphasis for such treatment to commence in the pre‐clinical phase makes it necessary to have robust empirical models of clinical disease progression to understand findings from clinical trials, allow clinicians to evaluate effects of new drugs, and to select individuals for future trials. Such models have been developed from relatively small samples, with incomplete data/substantial loss to follow‐up. The ADOPIC consortium provides the largest complete AD natural history sample to date. We applied our sigmoid models of disease progression to cognitive data from ADOPIC, and developed an algorithm to predict cognitive change.MethodWe developed an online application (Prediction of Alzheimer’s Disease Progression Tool, [PADPT]) to estimate decline in cognition over the course of AD within 2,861 ADOPIC participants (1,434 Cognitively unimpaired [CU], 342 MCI [Mild Cognitive Impairment], 211 AD, and 847 progressed from CU→MCI or MCI→AD), with ≥1 PET Amyloid (Aβ) scan and ≥36 months of cognitive assessment with the pre‐clinical Alzheimer’s cognitive composite (PACC). Individual participant slopes were defined according to age and gender using linear mixed effects models. Aβ+ (≥50CL) participant rates of decline vs their mean PACC score were investigated using quantile polynomial regression. Within PADPT, single visit information on age, clinical classification and APOE ε4 allele status is entered to compute the estimated annual rate of decline. Candidate results are mapped to the sigmoid curve to determine their predicted rate of cognitive decline over the chosen time period.ResultQuantile regression bands (QRB’s) aligned with participant cognitive decline. Individuals with stable cognition clustered to the top right of the plot (Figure 1). Mean slopes for APOE ε4 allele status separated QRB’s for CU and AD, but not for MCI groups. All participants allocated to cognitive decline groups (past the first sigmoid inflection point) demonstrated decline in PACC score that remained in the QRB’s, regardless of initial clinical classification.ConclusionUsed in conjunction with baseline profile information, the PADPT application can assist with clinical decision making both pre and post‐treatment and also identify suitable clinical trial candidates who, untreated, will show subtle decline in cognition.
RoboZen: Humanoid Robots Administer Mindfullness Based Stress Releif Therapy to people with dementia and help them relax and improve mood and cognition
Authors: Khan Arshia A 
Published: January 2025

AbstractBackgroundIn this study humanoid robots were programmed to deliver mindfulness‐based stress relief therapy to individuals with dementia. Precisely programmed, these robots aim to enhance well‐being, providing tailored interventions for relaxation, mood enhancement, and heightened cognition in dementia care, showcasing technology’s promise in improving overall quality of life [1, 2, 3, 4].MethodsIn this study, the humanoid robot was programmed with a carefully AI based design protocol incorporating calming activities, guided breathing exercises, and interactive engagement, tailored to the cognitive abilities and preferences of individuals with dementia, to effectively deliver mindfulness‐based stress relief therapy. This mixed‐methods study assessed the effectiveness of humanoid robots in delivering mindfulness‐based stress relief therapy to individuals with dementia. The sample included 9 participants diagnosed with dementia residing in nursing homes. Ethical approval was obtained from the Institutional Review Board. The intervention involved a humanoid robot delivering mindfulness‐based stress relief sessions. Quantitative data, obtained through pre‐ and post‐intervention assessments, included standardized scales measuring stress levels, mood, and cognitive function. Physiological indicators such as heart rate variability and electrodermal activity were recorded using wearable sensors during sessions. Qualitative data were gathered through participant observations and post‐session interviews to capture subjective experiences and perceptions of humanoid robot‐administered therapy. Thematic analysis identified recurring patterns and insights. The study aimed to offer insights into the feasibility, acceptability, and potential benefits of integrating humanoid robots into dementia care for stress relief, contributing to innovative approaches in dementia interventions.ResultsAnalysis of data from participants undergoing humanoid robot‐administered mindfulness‐based stress relief therapy showed significant improvements. The Brief Introspection Mood Scale (BMIS) indicated a highly significant enhancement in mood scores post‐intervention. Participants reported consistently positive shifts in emotional states, supported by electrodermal data showing reduced stress levels during and after sessions. Cognitive assessments, using the Montreal Cognitive Assessment (MoCA), consistently demonstrated improved cognitive function following the intervention.ConclusionIn conclusion, this study demonstrates the promising efficacy of humanoid robot‐administered mindfulness‐based stress relief therapy, revealing significant improvements in mood, physiological stress indicators, and cognitive function among individuals with dementia. This offers a valuable avenue for holistic care interventions.
Machine‐learning analysis of real‐world multimodal data collected autonomously at home detects dementia more precisely than a traditional composite scale
Authors: Rueda‐Delgado Laura M , Buick Alison R , Nolan Hugh , Rowe James B , Murphy Brian 
Published: January 2025

AbstractBackgroundCurrent tools for Alzheimer’s disease screening and staging used in clinical research (e.g. ACE‐3, ADAS‐Cog) require substantial face‐to‐face time with trained professionals, and may be affected by subjectivity, “white coat syndrome” and other biases. Alzheimer’s symptomology is multi‐factorial and varies day‐to‐day. To enable stratification for precision treatments, more effective composite endpoints are required, with accurate quantification of functional impairment in individual domains. Ideally these would be measured objectively and frequently in real‐world contexts to improve validity, and to reduce clinical burden in trial and care settings.MethodWe present a machine‐learning stratification analysis using data from the Cumulus real‐world multi‐domain neuroassessment platform (Figure 1), in CNS101, a year‐long study designed with a consortium of 10 pharma companies. Participants completed a range of tablet‐based functional tasks with wake EEG, and separately recorded EEG during sleep. This cross‐sectional analysis uses 1111 sessions from repeated sampling with 101 participants (47 patients with mild Alzheimer’s type dementia, and 54 corresponding controls, recruited across 7 sites in the UK), during an early two‐week burst period of autonomous at‐home use. Bagging of decision trees was used to compare the power of different data sources in discriminating dementia from neurotypical states. This algorithm is widely used for its performance across learning tasks (linear and non‐linear), and heterogeneous datasets of differing size, levels of noise, and collinearity. For each set of input data, 100 classifiers were trained and evaluated (10‐fold cross‐validation in each of 10 random partitions), and the mean Area Under the Curve (AUC) of sensitivity/specificity trade‐off was calculated.ResultA full set of 691 multimodal features yielded a classifier with AUC performance of 0.953 and error rate 4.7% (Figures 2, 3) – compared to AUC of 0.921 and error rate 7.9% for the set of 13 ADAS‐Cog subscores. While individual modalities/domains all performed above chance, the 50 features from Cumulus cognitive tasks alone had an AUC performance of 0.955.ConclusionPatients using digital technology autonomously in the home can yield data that matches or exceeds the discriminative power of a traditional composite scale. This can enable objective precision measurement of disease severity at scale.
Feasibility and Acceptability of Digital Cognitive Screening Approaches for Older Adults in Primary Care
Authors: Thompson Louisa I. , Lawrence Molly , Rosenbaum Jennifer A , Czech Stephanie , Anthony David C , Chandran Rabin F , Goldberg Arnold R , Vyshedskiy Andrey , Elwy Anashua R , Eaton Charles B 
Published: January 2025

AbstractBackgroundNew immunotherapies for early‐stage Alzheimer’s disease (AD) have ushered in fresh hope for AD research and clinical care, but also highlight barriers to AD screening and timely diagnosis in the US. Digital cognitive assessments could potentially streamline screening and referrals for AD treatment and/or clinical trials. We report preliminary data on the feasibility and acceptability of three digital cognitive approaches for older adults completing Annual Wellness or routine follow‐up visits with a primary care provider (PCP).MethodsData were collected for an ongoing primary care‐based study in Rhode Island. Cognitive screening approaches included: 1) remote online screening with the Boston Online Cognitive Assessment (BOCA) prior to the PCP appointment, 2) self‐administered screening with the BOCA in the waiting room immediately before or after the appointment, and 3) provider‐administered screening during the appointment using the Digital Clock and Recall (Linus Health DCRTM). Participants also completed a 30‐minute, in‐clinic cognitive health consultation including the Montreal Cognitive Assessment (as a reference standard) to receive feedback and referral options. Five PCPs aided in protocol development through focus groups and participated in data collection. Potential participants were identified via EMR. Recruitment included mailings, MyChart messages and phone calls, and direct referrals from PCPs. Exclusion criteria: dementia or other neurological disease, score of <13 on the telephone version of the MoCA.Results34 older adults were screened, 23 enrolled, and 2 withdrew. The sample is currently 52% female, 81% White, and mean age is 66. Most participants completed remote BOCA screening on a smartphone. Most reported that they preferred screening at home compared to in the clinic. 11 out of 11 participants completed at least one instance of the BOCA screening online prior to their PCP appointment. DCR tablet screening was successful in 9 out of 11 PCP appointments. PCP time constraints and technological issues were reasons for incomplete administration. In‐clinic self‐administered BOCA screening was discontinued due to space and time constraints, and the lack of a designated coordinator onsite.ConclusionsBoth remote online screening and tablet‐based, provider‐administered screening in‐clinic may be feasible and acceptable approaches to cognitive screening for older adults in primary care.