bluuebunny/crossref_metadata_2025_split huggingface dataset:
Further Validation of a Self‐administered Smartphone‐based Verbal Learning and Memory Assessment
Authors: Edgar Chris J. , Cummins Hannah , Bransby Lisa , Bird Laura J. , Fredrickson Amy , Maruff Paul , Lim Yen Ying 
Published: December 2023

AbstractBackgroundThe International Shopping List test (ISLT) is a verbal word list learning test set within a realistic shopping list context, supporting ease of cultural adaptation, and addressing content validity and patient relevance. Improvements to and the wide adoption of natural language processing (NLP) software has enabled the possibility for self‐administered tests of verbal learning. LilaTM a self‐administered, smartphone‐application (app) was developed, using the virtual assistant and NLP. We have previously reported acceptability and usability data from qualitative analysis of concept elicitation interviews. We now seek to provide preliminary validation data around its use.MethodA convenience sample of healthy young adults (n = 40), M(SD)age = 35 (16), were invited to install and complete one assessment of the LilaTM app. A usability survey was also administered to explore participants’ experiences. The sample was predominantly female (70%) and of Asian ethnicity (62.5%). Data were collected in Australia, using an English word pool developed for the USA.ResultAcceptability was generally high with N = 38 completing immediate recall rounds and N = 32 completing the delayed recall round. Eight‐two percent found it extremely or somewhat easy to hear and understand what items were on the shopping list and 95% indicated that it was extremely or somewhat easy to understand what they had to do to complete the assessment. Scoring accuracy was 88% at the word item level, with reprocessing to address accent, context setting, and other NLP approaches to improve accuracy underway e.g., homophones biased to the grocery item.ConclusionWhilst acceptability was high, the importance of localization of test stimuli was confirmed as well as the need for additional data processing options to account for accent. Continued optimization of data processing to score participant responses and word item pools to reduce ambiguity is expected to further improve accuracy e.g., accounting for regional accents and consideration of homophones and compound words. However, specific characteristics of speech including in those for whom English may be a second/foreign language will be an area of focus for future development.
Humanoid Robots administering Cognitive Stimulation Therapy to help improve cognition and reduce neuropsychiatric disorders
Authors: Khan Arshia A 
Published: December 2023

AbstractBackgroundDementia is a progressive disease that causes a continuous decline in cognition. People affected with dementia are also at high risk for neuropsychiatric disorders requiring frequent therapy sessions. The current healthcare worker shortages have made finding a therapist challenging, resulting in increased hospital admissions due to an increase in neuropsychiatric disorders in people affected with dementia. To address this problem humanoid robots and wearable sensors were programmed to administer Cognitive Stimulation Therapy(CST), a therapy that doesn’t require a clinician to be administered. CST is an evidence based therapy that is administered twice a week for seven weeks. The wearable sensors track and monitor the physiological data and are able to predict neuropsychiatric symptoms. The robots interact, communicate and administer the therapy while the participants are wearing the sensors along with artificial intelligence algorithms that track the physiological data to help predict any neuropsychiatric symptoms.MethodA humanoid robot, Pepper was programmed to administer Cognitive Stimulation Therapy to be deployed in nursing homes to help improve cognition and also help alleviate neuropsychiatric symptoms. The protocol involves conducting a cognitive assessment using the MMSE prior to initiation of the Cognitive Stimulation Therapy. The Cognitive Stimulation Therapy is administered twice a week for seven weeks. At the end of the fourteen sessions the MMSE is administered again to measure the cognition levels. Additionally pre and post surveys for depression, quality of life, Pittsburgh sleep index/ sleep quality and activities of daily living are conducted to monitor the neuropsychiatric symptoms. During the entire seven weeks the participants physiological data is tracked and analyzed using artificial intelligence algorithms to predict neuropsychiatric symptoms. The combination of the humanoid robots and the wearable sensors work in sync to improve cognition and reduce behavioral and psychological symptoms of dementia..ResultThe robot is successfully programmed and tested in the lab setting. The study is successfully designed.ConclusionRobots can be employed to administer cognitive stimulation therapy to help improve cognition and reduce neuropsychiatric symptoms in people affected with dementia. Robots have the potential to address healthcare worker shortages.
Machine Learning Approach to Identify Dysregulation in the Plasma Proteome of CADASIL
Authors: Keller Jonah , Fitzsimons Stephen , Park Ryan , Moury Luana , Oh Bradley C , Treiman Scott , Radabaugh Hannah , Ferguson Adam , Elahi Fanny M 
Published: December 2023

AbstractBackgroundThe plasma proteome provides information to identify dysregulated molecular pathways underlying disease. Cerebrovascular diseases often manifest a significantly different plasma proteomic signature. Cerebral‐Autosomal‐Dominant‐Arteriopathy‐Subcortical‐Infarcts‐and‐Leukoencephalopathy (CADASIL) is one such vascular brain disease. The CADASIL plasma proteome has yet to be investigated.MethodTo investigate the plasma proteome in the context of CADASIL, we used large‐scale proteomics, measuring over 7,000 proteins via aptamer‐based technology (SomaLogic) from 53 study participants (nCADASIL = 25; nControl = 28). We employed machine learning (ML) methods as an unbiased approach to uncover disease‐associated change in proteomic networks. This approach is based on the premise that protein sets that best classify disease state could be important biological drivers of disease. We developed a novel ML method: coupling recursive feature extraction with logistic regression (LR) and XGBoost evaluators, as well maximum‐relevance‐minimum‐redundancy with Random‐Forest and F‐Statistic evaluators. The results of all four models were selectively aggregated in order to delineate the CADASIL plasma proteome signature while minimizing overfitting to the low sample size data. We developed a 45 protein model of the CADASIL proteomic signature from these results. To evaluate the classification ability, we tested the Repeated‐Stratified‐10‐fold‐Cross‐Validation accuracy of a LR classifier. Next, we investigated whether the CADASIL proteomic signature was relevant for other neurodegenerative diseases with vascular components; the classifier was applied to an Alzheimer’s Disease (AD) dataset. Finally, the functional signature of the 45 proteins was investigated through gene ontology (GO).ResultThe LR classifier in CADASIL is 99.8% accurate. In AD, the accuracy is 71.2%. This implies the CADASIL signature shares commonalities as well as pertinent differences with other neurodegenerative diseases. The GO of the 45 proteins resulted in GO terms corresponding to extracellular matrix and collagen, cellular membrane lipoproteins and glycolipids, and proteasome biological pathways.ConclusionThe proposed method allows for an initial unbiased discovery of important proteomic changes associated with disease state. The identified proteins would provide starting points for mechanistic studies. In addition, a panel of proteins could be used for screening at population level for risk stratification and more in‐depth phenotyping. Next steps include investigating the biological relevance of findings in mechanistic models for therapeutic developments.
Monitoring Daytime Napping in Community Dwelling Older Adults Using Contactless Sleep Technologies
Authors: Ravindran Kiran K.G. , Monica Ciro Della , Atzori Giuseppe , Lambert Damion , Revell Victoria Louise , Dijk Derk‐Jan 
Published: December 2023

AbstractBackgroundIncreased daytime napping and excessive sleepiness are associated with cognitive decline in older adults, especially in people living with dementia (PLWD) [1]. Subjective assessments of naps are burdensome and maybe unreliable in PLWD and hence there is a need for technologies that provide objective longitudinal assessment of the incidence and duration of naps. Here we compare two contactless sleep technologies (CSTs) against sleep diary and actigraphy for monitoring daytime napping in community dwelling non‐demented older adults.MethodTwo under‐mattress CSTs (Withings Sleep Analyser [WSA] and Emfit QS [Emfit]) along with actigraphy (Actiwatch Spectrum [AWS]) were deployed in the home of 17 older adults for a period of 14 days ( = 65 years; Mean Age ± SD = 72 ± 4.49; 6 Women). The ground truth nap information was collected using an extended consensus sleep diary that included additional information about naps (timing, duration, and location). We analyzed the agreement of the napping events and duration estimated by WSA, Emfit and AWS against the sleep diary reported events.ResultThe CSTs only detected in‐bed naps whilst the AWS detected both in‐bed and not‐in‐bed naps. Although all the compared devices detected spurious naps unreported in the sleep diary, it was highest in AWS (81% of total naps detected) followed by Emfit (63%) and WSA (16%) as shown in Figure 1. Among the CSTs, WSA accurately detected more in bed naps while registering less spurious naps compared to Emfit but had lower duration agreement to sleep diary (Figure 2). Further, when the contribution of daytime naps to 24‐h total sleep time was computed, the WSA estimate (12.8±6.1%) was closest to the sleep diary estimate (12.9±9.1%) followed by AWS (15.6±9.9%) and Emfit (17.2±11.1%).ConclusionCSTs, with their ability to provide both contextual location information and objective measures of napping, such as timing and duration, offer a reliable and unobtrusive alternative to traditional methods such as sleep diary and actigraphy for long‐term round‐the‐clock monitoring of sleep in older adults. References: [1] Li, P, Gao, L, Yu, L, et al. Daytime napping, and Alzheimer’s dementia: A potential bidirectional relationship. Alzheimer’s Dement. 2023; 19: 158– 168. https://doi.org/10.1002/alz.12636
Rapid, scalable assay of amylin‐β amyloid co‐aggregation in brain tissue and blood
Authors: Kotiya Deepak , Leibold Noah S , Verma Nirmal , Jicha Gregory , Goldstein Larry B , Despa Florin 
Published: December 2023

AbstractBackgroundIslet amyloid polypeptide (amylin) secreted from the pancreas crosses the blood brain barrier into the brain parenchyma and interacts with Alzheimer’s disease (AD) associated cerebral ß‐amyloid (Aß) plaques, which are found in both sporadic and early‐onset familial AD. Immunoprecipitated amylin from human AD brain tissue analyzed by Western blot using an anti‐Aß antibody detects Aß immunoreactivity, which indicates that the amylin and Aß peptides aggregate in the brains of humans with AD, forming amylin‐Aß hetero‐oligomers that are sodium dodecyl sulfate (SDS)‐soluble. We report the development of an ELISA to detect amylin‐Aß hetero‐oligomers in brain tissue and blood.MethodThe amylin‐Aß ELISA relies on a monoclonal anti‐Aß middomain antibody (detection) and a polyclonal anti‐amylin antibody (capture) designed to recognize an epitope that is distinct from the high affinity amylin‐Aß binding sites. We screened temporal cortex homogenates obtained from 13 cognitively unaffected (CU) humans and 47 persons with sporadic AD (sAD) for amylin‐Aß hetero‐oligomers. Individuals were of similar age (sAD, 85.65 ± 7.04 years vs. CU, 87.22 ± 7.30 years; p = 0.482). Brain tissue amylin and Aß concentrations were measured using conventional ELISAs. Amylin‐Aß oligomer levels were also measured in whole blood lysates from APP/PS1 rats and APP/PS1 rats expressing amyloid forming amylin in the pancreas (APP/PS1/HIP rats; all rats age‐matched, 16‐months).ResultThe brains of most AD patients had detectable amylin‐Aß hetero‐oligomer concentrations, whereas about half of those CU had brain tissue amylin‐Aß hetero‐oligomers below the detection limit (0.02 ng/mg total protein; one‐way ANOVA, P < 0.0001). Amylin‐Aß hetero‐oligomers were more common in brains with frequent neuritic plaques (CERAD C). Higher brain tissue amylin‐Aß hetero‐oligomer levels were associated with greater brain tissue amylin concentrations (r = 0.38; P < 0.05). There was no correlation between cerebral amylin‐Aß hetero‐oligomer accumulation and age in the analysis of samples from sAD and CU groups (r = ‐0.22; P > 0.05). Levels of amylin‐Aß hetero‐oligomers in whole blood lysates obtained from APP/PS1/HIP rats were higher than those from APP/PS1 littermates (P < 0.05).ConclusionStudies are needed to determine the extent to which amylin‐Aß hetero‐oligomerization can be detected in human blood.
Tele‐Dementia in Africa: Setting roadmap for implementation
Authors: Farombi Temitope Hannah , Akinyemi Rufus O. , Ogunniyi Adesola 
Published: December 2023

AbstractBackgroundAlzheimer’s disease and dementia related disorder incidence and prevalence continue to increase in Africa due to changing ageing demographics. Despite this rise, overall quality and quantity of available evidence to inform best practices in recruitment, retention and inclusion of underrepresented population in ADRD research is very low. Africa needs creative innovations to bridge the dementia care gap and telemedicine offers a unique opportunity for prompt diagnosis, follow up and care supportMethodThis was an observational study of remote video assessment of persons living with Dementia and their caregivers. Mobile phone and web‐based applications for virtual consultation and health information management system were built to engage patients with various clinical conditions across multiple specialties. The assessment and follow up were done between September 2020 and December 2022.Cognitive assessment tools were administered via telemedicine platform for screening and post diagnostic management due to the COVID‐19 lock downResultA total of 50 patients with Dementia and their caregivers attending Neurology clinic at the Geriatric centre UCH were engaged on the platform for clinical follow up. Mean(+SD) age of was 74.0(+7.2) year and 56% were females, 82.8% had some form of formal education, 67.2% married and 97.9 lived with other persons. Over 31.9% participants had at least one comorbidity. 93% of the participants expressed satisfaction with video conferencing platform. 87% retention rate on follow up on the platform,13% of drop out rate due to technical fault and poor internet connectionConclusionwe believe implementation of telemedicine will improve recruitment and retention of persons living with dementia and it offers a promising dementia management strategy for Africa
Combining working memory and speech data in a digital app for early detection of cognitive impairment in primary care
Authors: Hilsabeck Robin C. , Keller Jeffrey N. , Henry Maya L. , Li J. Jessy , Pugalenthi Lokesh , Rathouz Paul , Toprac Paul , Chang Patrick , Chang Joshua , Schmitz Suzanne , Largent Avery , Foil Heather , Brouillette Robert , Lester‐Smith Rosemary A 
Published: December 2023

AbstractBackgroundCognitive screening to detect mild cognitive impairment (MCI) and dementia in primary care settings has proven to be a challenging task. The ideal solution would be a brief, low‐burden assessment tool appropriate for use with individuals from diverse educational and cultural backgrounds that requires limited time and expertise from primary care providers. The purpose of this project was to develop a digital cognitive screening app derived from widely used cognitive and speech tasks using machine learning techniques.MethodParticipants were 104 old adults, 53 who were cognitively normal (CN) and 51 who were cognitively impaired (CI). All participants completed a working memory task (WM), followed by four speech‐language tasks, followed by a second administration of WM (WM2) to investigate the added utility of practice effects. Participants also completed the Quick Mild Cognitive Impairment (Qmci) screen. Bayesian adaptive regression trees were used to test 11 models. All models contained sex, age, education, and marital status. One model contained WM and the WM2‐WM difference. Four models contained 26 acoustic and 34 linguistic variables derived from each of the four speech tasks, and four more models were identical, except with the two WM variables added. For comparison, two models with Qmci were fitted: one with and one without the two WM variables.ResultThree models achieved cross‐validated classification accuracy of c = 0.90: WM by itself (estimated c = 0.91; Figure 1), WM and a personal narrative task (c = 0.94; Figure 2), and WM and a counting backwards task (c = 0.90). Models with picture description and procedural discourse tasks performed the worst. The QMCI‐only model yielded c = 0.91.ConclusionCombining WM, acoustic and linguistic variables involved in recounting personal memories, and the ability to benefit from prior exposure to a task discriminated CN from CI groups with a high degree of accuracy. This unique combination assesses key cognitive abilities known to be affected early in neurodegenerative diseases, executive functioning and memory, and includes both verbal and nonverbal tasks. Importantly, individuals with varying levels of cognitive functioning, including mild dementia, were able to use the app successfully, and the automated nature of the tool required minimal staff time or expertise.
Digital Elder Abuse Intervention for Older People Living with Dementia
Authors: Abujarad Fuad , Edwards Chelsea , Neugroschl Judith A. , Hwang Ula , Marottoli Richard 
Published: December 2023

AbstractBackgroundA growing number of older adults experience elder abuse (EA) each year, and people living with dementia (PLWD) are at higher risk. Emergency departments (EDs) are opportune settings to identify EA and can provide interventions to those at high risk and direct them to appropriate health and social services. There is evidence that PLWD can report abuse despite cognitive challenges, but research is limited regarding self‐administered interventions to detect EA.MethodWe developed and tested our tablet‐based intervention to increase self‐reporting of EA among PLWD. The VOICES EA intervention combines educational resources, screening, multimedia elements, and brief psychoeducational interviewing to motivate PLWD to self‐identify and self‐report abuse. The goal of our study is to evaluate using VOICES among PLWD (ages 60+ years) at a geriatric memory clinic (N = 30) and the ED setting (N = 101). We previously demonstrated VOICES’ feasibility and efficacy with 1,000 cognitively intact participants (ages 60+) in the ED and wanted to compare patterns of use between users (with or without dementia). Participants were recruited, consented, and enrolled at Yale New Haven Health System in New Haven, Connecticut, USA. We used the Montreal Cognitive Assessment (MoCA) to assess cognition of participants, excluding participants with severe dementia.ResultOlder adults with MoCA scores between 14 and 25 were able to use VOICES similarly to those without cognitive impairments (i.e., MoCA >25) and were able to complete using the VOICES independently. From the memory clinic, 83% agreed that audio and visual tool aids were helpful, and 87% agreed that the tool’s instructions were easy to follow. In the ED setting, of the 101 participants who started VOICES, 100 completed VOICES independently (99% completion rate). Of participants who completed the post‐use questions (N = 99), 84% were satisfied with their ability to complete VOICES on their own without assistance, and 91% agreed VOICES was appropriate for learning about EA.ConclusionOur results suggest that the VOICES EA intervention is feasible (i.e., acceptable, practical, and satisfactory) for detecting EA in PLWD, specifically those with MoCA scores ranging from 14‐25. This may allow for enhanced early EA detection and intervention in this highly vulnerable population.
Are Digital Markers of Everyday Driving Comparable to Fluid Biomarkers in Identifying Preclinical Alzheimer Disease?
Authors: Bayat Sayeh , Babulal Ganesh M. 
Published: December 2023

AbstractBackgroundPreclinical Alzheimer’s disease (AD) may have a subtle functional signature reflected in changes in everyday driving behaviour. Driving has the potential to serve as a digital marker for AD when captured continuously and characterized accurately. This study directly compares the diagnostic performance of digital markers of everyday driving, cerebrospinal fluid (CSF), and blood biomarkers in detecting amyloid deposition in AD, using amyloid positron emission tomography (PET) as the “gold standard”.MethodParticipants were enrolled in a longitudinal study on driving and preclinical AD biomarkers at Washington University School of Medicine. The Driving Real‐World In‐Vehicle Evaluation System (DRIVES) Project uses in‐vehicle GPS dataloggers to collect daily driving among cognitive normal drivers. This study included 121 drivers (aged 65+) with complete blood, CSF, and PET biomarker data. We employed three artificial neural network (ANN) to examine the relationship between everyday driving and PET‐amyloid status. The first model solely included driving features. The second ANN model (i.e., “non‐intrusive” model) added age and education level as additional features. The third model included all of the previous features plus APOE e4 status. Plasma Aß42/Aß40<0.1013 and CSF Aß42/Aß40<0.0673 were used to detect PET‐amyloid positivity. To compare the performance of the ANN models and the CSF and plasma biomarkers, area under the receiver operating curve (AUC) from 5‐fold cross‐validation were calculated.ResultIndividuals who were PET‐amyloid positive (n = 46) were more likely to be older (p<.05) and carry an APOE e4 allele (p<.0001). The two groups did not differ in sex or education. In predicting PET‐amyloid positivity, the five most important driving features were the number of left turns per mile, 80th percentile of jerk, minimum vehicle speed, speed variability, and the number of speeding incidents. The CSF biomarker achieved the highest performance (AUC = 0.96±0.04), followed by the model with driving, age, education and APOE e4 status (AUC = 0.88±0.05). The model with only driving features achieved lower performance (AUC = 0.74±0.07), while the non‐intrusive ANN model achieved comparable performance to the plasma biomarker (AUC: 0.82±0.11vs.0.83±0.12).ConclusionDigital markers of everyday driving along with age and education level offer a non‐intrusive and scalable diagnostic tool for preclinical AD that provides ongoing assessments.