bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing Dynamic White Lighting for practical application
Authors: Ansari MD. Azaharuddin , White Jonathon David 
Published: January 2025

AbstractBackgroundEffect of dynamic lighting on sleep were studied since 1980’s. Traditional light sources were used due to lack of advancement in technology and also researchers assumed illuminance as cause of melatonin suppression. This led researchers to use high illuminance to suppress melatonin at day time. Later, it was discovered that intrinsic photoreceptor retinal ganglion cell (ipRGC), sensitive to specific wavelength, has direct impact on the level of melatonin, that shifted the attention to the actual wavelength of the stimulating light. With the invention of LEDs, having various emission profiles has allowed researcher to propose numerous LED using 5 to 8 types of LED for appropriate delivery of dynamic light to control melatonin secretion whole day. Better lighting has been shown to improve sleep, reduce anxiety/depression, and improve mood in dementia patients.MethodA C‐program was developed to calculate various lighting parameters of combined spectra of LED strips. Brute force optimization was used to calculate the power to be supplied to each of the input LED strips to obtain a combined white light for dawn, day and dusk based on the required visual (e.g. CRI>90, Duv < 0.001) and non‐visual parameters for minimum power consumption and lowest material cost.ResultWe investigated 3 red, green, blue, ice blue, and warm white (R‐G‐B‐IB‐WW) systems from different manufactures. Only in one system, did the blue LED allow increased efficiency (<1%), demonstrating that a 4 LED system (R‐G‐IB‐WW) is sufficient i.e. blue LED is unnecessary. We added a monochromatic Cyan LED strip, its spectra overlap the action spectra of ipRGC, hoping it could help to achieve better stimulation of ipRGC cells with lower power consumption. While m‐EDI increased, but m‐EDI/W input power did not. Cyan is thus unnecessary.ConclusionWe have shown that an LED system with only 4 types of LEDS (2 monochrome and 2 broadband) can generate dawn to dusk dynamic white light suitable for both visual and non‐visual needs of dementia patients (as widely accepted by researchers over the world) with high electrical to optical efficiency. This approach, using fewer LED strips and lower energy consumption, is more financially appealing.
Precision Phenotyping of Mild Behavioral Impairment and the Early Detection of Dementia
Authors: Vahia Ipsit V 
Published: January 2025

AbstractThere is growing recognition of the importance of changes in behavior as an early clinical marker of the onset of Alzheimer’s disease. Behavior symptoms may precede the onset of cognitive symptoms by as many as three years. However, these symptoms are often confused for primary psychiatric pathology and there is an urgent need for markers that can help quantify behaviors with the eventual goal of helping distinguish behavior changes related to psychiatric pathology from behavior changes related to the onset of dementia. This talk will summarize the state of knowledge on mild behavioral impairment, and present data from three studies demonstrating how a range of sensor‐derived biomarkers can quantify subtle changes in behavior with precision. The talk will include case presentations around detection of apathy, agitation, pacing. It will also demonstrate how these markers can identify response to medications, which may be one way of distinguishing the presence of psychiatric versus neurodegenerative pathology. Finally, the talk will present data from ongoing clinical trials around how these biomarkers may translate into research and eventually practice.
Testing Measurement Invariance Across Gender in a Low Resource Setting Using the Computerized Battery for Neuropsychological Evaluation of Children (BENCI)
Authors: Maina Rachel W , Wicherts Jelte 
Published: January 2025

AbstractBackgroundDeveloping culture‐fair tests that measure constructs equivalently across different ethno‐lingual groups is challenging, given the diverse cultural variations that impact neurocognitive measurement. Multi‐level measurement invariance must be established before interpreting scores similarly across groups, both within and between cultures for meaningful comparisons.MethodWe set out to test whether a neurocognitive tool (BENCI) behaves the same way across the males (n = 311) and the females group (n = 291) using measurement invariance testing with multigroup confirmatory factor analysis. We used a four factor model that had an excellent fit in a Kenyan pooled sample when modified to have four correlated first‐order factors i.e., Fluency, Reasoning, Memory, and Flexibility. We evaluated configural, metric, scalar and lastly strict invariance model and then tested whether the different restraint in the models resulted in a significant chi‐square difference.ResultThe four‐factor model showed good model fit (RMSEA = .034, CFI = .959, TLI = .930, χ2 (92, n = 604) = 154.309, p < .001), with metric measurement invariance (RMSEA = .033, CFI = .957, TLI = .933, χ2 (100, n = 604) = 164.368, p < .001; Δχ2 = 10.059, DF = 8, p = 261, ΔCFI = 0.002), scalar measurement invariance (RMSEA = .032, CFI = .957, TLI = .938, χ2 (108, n = 604) = 172.831, p < .001; Δχ2 = 8.463, p = .390, ΔCFI = 0.000), and the strict measurement invariance (RMSEA = .031, CFI = .955, TLI = .940, χ2 (118, n = 604) = 186.563, p < .001; Δchi‐square = 22.195, df = 18, p = .223, ΔCFI 0.002) between the females and males.ConclusionMetric, scalar and strict invariance is achieved indicating that indicator items, indicator intercepts (same starting point on the BENCI) and residuals (except Visual Memory Delayed CA residuals) of the BENCI are comparable across the male and female samples, and we can compare the association of BENCI with other invariant constructs across the two groups, including the latent mean comparisons. These results are promising and suggest that cross‐gender comparisons may also be tenable for other clinical group comparisons in Kenya and cross‐cultural data harmonization in multi‐national research programs.
People with dementia as exercise video game testers: Gathering end‐user perspectives
Authors: Dove Erica , Wang Rosalie H. , Patterson Kara K. , Astell Arlene J. 
Published: January 2025

AbstractBackgroundCommercially available exercise video games (‘exergames') can be used by people with dementia with the right (human) prompting and support. However, more information is needed about what makes these systems and games technologically accessible for this population, considering their cognitive difficulties. This study explores what works and doesn’t work for people with dementia when introducing new exergame systems and games to broaden opportunities for physical activity.MethodsThirty‐two people living with dementia (mean Montreal Cognitive Assessment score: 12.75/30) were recruited from four community‐based adult day programs in Canada. Participants were recruited as ‘game testers' once weekly for six weeks at each day program. Participants with dementia tried different exergame systems (e.g., Xbox Kinect, Nintendo Switch) and games (e.g., darts, boxing, dancing, etc.). Concurrently, gameplay video recordings and feedback via the talk‐aloud protocol and audio‐recorded post‐game group debriefs were collected. Data were analyzed descriptively and are currently undergoing analysis using behavioural coding software to examine in‐game prompts and player movements.ResultsIn total, 292 gameplay turns occurred across the sessions. Some participants declined to play certain games after watching their peers experience accessibility challenges. Common reasons for ending a turn (beyond completing the game objective) included boredom, frustration, confusion, fatigue, and soreness. Systems with handheld controllers (e.g., Nintendo Wii) were less accessible for participants than gesture‐based controls due to the need to push buttons and attend to the controller while simultaneously performing physical motions. Additionally, games requiring multiple coordinated movements and those not accommodating additional age‐related impairments (e.g., range of motion impairments, mobility devices) were not widely accessible to this diverse group of participants.ConclusionsThis study highlights the importance of involving people living with dementia in game development. The findings reveal which elements of current exergames make them accessible or inaccessible for people living with dementia. These findings can help design new exergames to increase access to physical activity for people with dementia. The findings will also interest dementia service providers who want to use exergames to increase physical activity for people with dementia.
Multimodal Analysis of Behavior During Stroop Test for Characterization of Alzheimer’s Disease Signs
Authors: Meyer Trevor , Favaro Anna , Oh Esther S , Irazoqui Pedro , Dehak Najim , Moro‐Velazquez Laureano 
Published: January 2025

AbstractBackgroundClinical rating scales and neuropsychological tests are commonly used for assessing sign and disease severity, yet lack detail in the early stages Alzheimer’s Disease (AD). Existing evaluation methods can be subjective, nonlinear, expensive, or reliant on anecdotal evidence making objective and consistent characterization and phenotyping of AD difficult. Multimodal analysis of patient behavior, rather than scoring of patient‐generated output which can be skewed by compensation strategies, presents a unique opportunity to objectively quantify AD related changes. Using the Stroop Test (ST), we present a multimodal analysis of eye movement and spoken responses during completion of ST tasks, with the goal to characterize patient behavior.Method17 individual AD subjects and 24 age‐matched cognitively normal controls (CTL) were recruited from the Johns Hopkins Memory and Alzheimer’s Treatment Center and the Movement Disorders Clinic at the Johns Hopkins University School of Medicine. Eight subjects returned for two or three follow up sessions at least 20 weeks apart, totaling 29 AD recording sessions. An Eyelink Portable Duo in head‐free‐to‐move mode was used to record eye movements and spoken responses. We analyzed subjects' interaction with ST using several metrics relating to blinking, saccade, fixation, gaze, prosodic, accuracy, and timing characteristics. We summarize patient behavior abnormality by scaling behavioral metrics into positive z‐scores using CTL performance and summarize the overall abnormality as the average z‐score across 45 metrics.ResultThe average deviation across metrics correlates with MoCA scores (p<0.001). These metrics display significantly different distributions between the AD from CTL subjects (p<0.001). A visualization we've coined as a “sign profile” summarizes patient behavior abnormality during ST. In all subjects our abnormality measurement closely matches MoCA scores and clinician observations.ConclusionAD subjects execute the ST with eye movement and speech behaviors which significantly differ from CTL subjects, and subjects with longitudinal recordings show encouraging results for the use of this analysis in monitoring. By utilizing measures from more than one feature, we detect early clinical changes a single‐score test might miss. This analysis offers comprehensive patient profiling, providing an intuitive and quantitative understanding of patient signs enabling more accurate detection and management of disease.
A machine learning approach to predict amyloid and tau positivity using clinical features
Authors: Arnold Daniel , Borelli Wyllians Vendramini , Machado Luiza Santos , Rahmouni Nesrine , Therriault Joseph , Servaes Stijn , Stevenson Jenna , Macedo Arthur C. , Schumacher‐Schuh Artur Francisco , Mattjie Christian , Barros Rodrigo C. , de Bastiani Marco Antônio , Lussier Firoza Z , Chamoun Mira , Bezgin Gleb , Benedet Andrea L. , Pascoal Tharick , Rosa‐Neto Pedro , Zimmer Eduardo R. ,   Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: January 2025

AbstractBackgroundScreen failure due to amyloid negativity is yet a problem in clinical trials for anti‐amyloid drugs. In this context, clinical characteristics of patients presenting with cognitive decline may decrease the screen failure ratio by increasing the odds of selecting individuals with brain amyloid pathology. Herein, we aimed at estimating amyloid and tau positivity in individuals using clinical variables in a machine learning model of prediction.MethodWe selected 1694 participants with amyloid and tau status from ADNI, TRIAD, PPMI databases. Commonly shared clinical features selected between datasets were age, total MOCA scores, clinical diagnosis, sex, education, BMI, heart disease, stroke, hyperlipidemia and diabetes. Amyloid positivity was defined by amyloid‐PET (PIB‐PET, FBB‐PET or AZD4694‐PET) or CSF AB42 and Tau positivity defined by Tau‐PET (MK6240‐PET or AV1451‐PET) or CSF p‐tau181. The dataset was split into training (49%), validation (21%), and testing datasets (30%). A XGBoost model was tuned, and then used to predict the tau status outcome in the testing dataset.ResultA total of 927 men and 767 women were included with mean MOCA 25.7 ± 3.8 scores (Table). The population consisted of 1276 CN, 290 MCI and 128 dementia individuals (1357 A‐T‐ and 337 A+T+). The receiver‐operator characteristic (ROC) analysis showed that the area under the curve (AUC) was 0.86 for discriminating A‐T‐ vs. A⁺T⁺, with 0.82 as sensitivity, specificity as 0.90 and accuracy as 0.88. The top 3 most impactful features were found to be age, diagnosis and MOCA score through a SHAP value analysis (Figure).ConclusionPredicting amyloid and tau positivity with clinically collectible variables may improve selection of individuals for anti‐amyloid trials. A two‐step workflow using this approach may significantly reduce the costs of AD trials. The following studies may incorporate an individualized calculator using clinical variables to estimate amyloid and tau positivity.
Virtual support for family caregivers to reduce hospitalization and fall risk in people with Alzheimer’s disease
Authors: Laird Rosemary D. , Abrevaya Jason 
Published: January 2025

AbstractBackgroundThe Florida Department of Elder Affairs and Navigating Aging Needs, LLC (NAN) recently formed a public‐private partnership providing virtual support to family caregivers of people with Alzheimer’s disease (AD) living at home. The program targets a diverse population with high‐level daily care needs and at increased risk for continuing decline and costly Medicaid‐supported care. Objectives To reduce the risk of hospitalizations and falls for people living with Alzheimer’s disease.MethodsFamily caregivers were offered a virtual support program that included regular meetings (Zoom or phone) with a personal licensed social worker. Of the 50 family caregiver participants, 45% were Black and 12% were Hispanic. The initial assessment included 80 questions on the AD patient’s medical, emotional, social, and legal/financial well‐being and the validated battery of 12 questions comprising the Zarit Burden Scale. Following the assessment, the social worker provided the family caregiver with a personal plan that identified areas of risk that “need attention,” along with resources to address identified needs. The social worker and family caregiver then met monthly to resolve areas identified as high risk and discuss other relevant issues as they arose. Every 3 months the social worker re‐assessed patient status with the family caregiver.ResultsAfter 9 months of virtual support, family caregivers reported 75% fewer falls and 68% fewer hospitalizations. Average number of falls fell from 2.2 at baseline to 0.54 (p = 0.004), and average number of hospitalizations fell from 0.90 at baseline to 0.28 (p = 0.006). Caregiver assessments also demonstrated a positive impact on AD patients' depression, anxiety, advanced directives, and financial planning over the course of the program. Zarit Burden Scale scores for family caregivers remained stable despite AD progression.ConclusionProviding virtual support to family caregivers of people with AD was correlated with reduced hospitalizations and falls for people living at home with AD in this pilot program. The next step for the Florida Department of Elder Affairs and NAN is to assess the cost implications of expanding this approach on a larger scale.
Diagnostic performance of online cognitive assessment and plasma biomarkers in Alzheimer’s Disease and Subjective Cognitive Impairment using the Oxford Cognitive Testing Portal
Authors: Toniolo Sofia , Scholcz Anna , Amein Benazir , Ganse‐Dumrath Akke , Thompson Sian , Manohar Sanjay , Zetterberg Henrik , Husain Masud , Zhao Sijia 
Published: January 2025

AbstractBackgroundPlasma biomarkers have emerged as a promising tool to detect the presence of Alzheimer’s disease (AD) when cognitive symptoms have not yet emerged. However, there is also a pressing need to detect and track subtle cognitive change at the preclinical stage of AD for population screening purposes and to monitor disease progression at scale. A potential solution is remote cognitive assessment, yet it is still not extensively employed.Method114 participants (37 AD, 22 subjective cognitive impairment (SCI) patients and 55 age‐matched controls) were recruited from the Oxford Centre for Cognitive Disorders. They were tested on a newly developed, fully remote online cognitive assessment tool, Oxford Cognitive Testing Portal (OCTAL), which hosts a wide range of validated cognitive tasks, such as the Rey‐Osterrieth Complex Figure (ROCF), Trail Making Task (TMT), Digital Symbol Substitution task (DSST)), Corsi Block Task (CORSI), as well as novel visual short‐term memory (Oxford Memory task) and visual long‐term memory (Object‐in‐Scene task) tasks (Figure 1). All participants underwent standard in‐person cognitive testing, i.e. Addenbrooke’s Cognitive Examination‐III (ACE). Plasma p‐tau181, GFAP, NFL, Aß42/40 ratio were also measured. Logistic regression was used for group classification.ResultPerformance on OCTAL was able to discriminate between SCI from healthy controls with an AUC of 0.78 (Figure 2a), statistically outperforming standard neuropsychological testing (ACE), which had an AUC of 0.65. Combining plasma biomarkers to OCTAL further increased diagnostic accuracy, reaching an AUC of 0.82 in group classification. OCTAL also outperformed ACE in distinguishing individuals with AD from SCI (Figure 2b), where adding plasma biomarkers to performance at OCTAL achieved a perfect separation (AUC of 1) between the groups.ConclusionThese findings demonstrate the potential of OCTAL for widespread, cost‐effective cognitive testing. They also emphasise the utility of combining plasma biomarkers and digital cognitive tests to improve diagnostic accuracy across different stages of the disease. These accessible tools could pave the way to more scalable protocols for screening, stratification and monitoring of patients with preclinical AD.
Computerized testing to detect cognitive decline in biomarker‐confirmed Alzheimer’s disease: Longitudinal findings from the ARMADA study
Authors: Jutten Roos J , Ho Emily , Karpouzian‐Rogers Tatiana , Van Hulle Carol A. , Dodge Hiroko H , Nowinski Cindy J. , Gershon Richard C. , Weintraub Sandra , Rentz Dorene M 
Published: January 2025

AbstractBackgroundThe National Institutes of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) was developed to address the need for a brief yet comprehensive instrument to facilitate more uniform assessment in large‐scale research studies. Here, we investigated whether the cognitive measures of the NIHTB detect cognitive decline in biomarker‐confirmed Alzheimer’s disease (AD).MethodWe used data from N = 178 participants (age 76.5±8; 53% female) from the Assessing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging (ARMADA) study, who were recruited across nine research centers in the United States and had amyloid PET or amyloid‐beta42/tau CSF data available within two years of their baseline NIHTB assessment. Participants' cognitive status (normal (CN) or impaired (CI), based on standardized protocols) and amyloid status (Aß‐/Aß+, based on site‐specific cut‐offs) were combined to create three groups: healthy controls (CN Aß‐, n = 113), asymptomatic AD (CN Aß+, n = 24), and prodromal‐to‐mild AD (CI Aß+, n = 34) (Table 1). The NIHTB was assessed at baseline, 12 and 24 months and includes five tasks measuring fluid cognition (attention, executive functions, processing speed, working memory, and episodic memory) and two tasks reflecting crystallized cognition (oral word reading and auditory word comprehension). Linear mixed‐effect models correcting for age, sex, and education were used to examine cross‐sectional and longitudinal NIHTB scores in the three study groups.ResultCompared to healthy controls, the prodromal‐to‐mild AD group had lower baseline scores on most NIHTB fluid tasks (Flanker Inhibitory Control and Attention, Dimensional Change Card Sort, Picture Sequence Memory, List Sort Working Memory) and showed accelerated decline over 12‐24 months on Pattern Comparison Processing Speed, List Sorting Working Memory and Picture Vocabulary (Figure 1). None of the NIHTB tasks detected cross‐sectional or longitudinal differences in the asymptomatic Aß+ group compared to healthy controls over the course of this study.ConclusionThe NIHTB detects cross‐sectional impairment in episodic memory, attention, executive functions and working memory and longitudinal change in processing speed, working memory and auditory word comprehension in prodromal‐to‐mild AD. These findings further support the use of the NIHTB in AD research, but also imply that the optimal NIHTB composite to detect cognitive decline may differ across AD clinical stages.