bluuebunny/crossref_metadata_2025_split huggingface dataset:
Validity of online versus in‐clinic self‐reported Everyday Cognition Scale (ECog): Online measures performed as well as repeating the in‐clinic assessment
Authors: Howell Taylor , Neuhaus John , Glymour M Maria , Nosheny Rachel L , Weiner Mike W. 
Published: December 2021

AbstractBackgroundClinical assessment of cognition and functional abilities plays an important role in identifying individuals at risk for cognitive decline, mild cognitive impairment (MCI), and dementia due to Alzheimer’s (AD). In‐person clinical assessments can be logistically challenging and deter broad participation in clinical studies, reducing diversity in AD research. Online assessments are an appealing alternative, but the validity of online compared to clinical in‐person assessments has not been established. Our goal was to determine whether the Everyday Cognition (ECog) Scale, which assesses decline in instrumental activities of daily living, collected online corresponds well with ECog collected in‐clinic. The correspondence of in‐clinic and online measures could not logically exceed the test‐retest reliability of the in‐clinic measure, so we used this as a benchmark for validity of the online measure.MethodSelf‐reported ECog was collected both in‐clinic and online from 94 older adults (characteristics described in Table 1) enrolled in both the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Brain Health Registry (BHR). Associations between in‐clinic and online ECog scores were estimated using linear regression. Prediction error was corrected for optimism with 10‐fold cross‐validation. We estimated test‐retest reliability of in‐clinic ECog in 259 cognitively normal ADNI2 participants (characteristics described in Table 2) with two in‐clinic ECog measures, completed six months apart. Reliability was estimated using the intraclass correlation coefficient (ICC) and linear regression. Age, gender, education, and time between assessments were included as covariates in all regression models.Result
(1) Validity of online ECog. Mean online ECog scores were significantly associated with mean in‐clinic ECog scores (β=0.81, 95% CI 0.64‐0.98, p=0.00). The optimism‐corrected R2 was 0.61 (95% CI 0.40‐0.82). (2) Reliability of In‐clinic ECog: The ICC was estimated to be 0.67 (95% CI 0.54‐0.79). Mean in‐clinic ECog scores collected at 6 months were significantly associated with in‐clinic ECog score collected at baseline (β=0.71, 95% CI 0.63‐0.78, p=0.00). The optimism‐corrected R2 was 0.61 (95% CI 0.51‐0.72).ConclusionThe online ECog corresponded well with in‐clinic ECog and provided as much information as repeating the in‐clinic ECog. This supports the validity of online ECog, a measure of functional and cognitive decline.
The development and validation of a new cross‐cultural naming test
Authors: Franzen Sanne , Satoer Djaina , van den Berg Esther , Papma Janne M. 
Published: December 2021

AbstractBackgroundTraditional naming tests such as the Boston Naming Test are unsuitable to assess naming impairment in diverse populations, given the large influence of culture, language, and education on performance. A more fair naming test is urgently needed. The goal of this study was therefore to develop and validate a new cross‐cultural naming test to diagnose naming impairment in Alzheimer’s disease in diverse populations.MethodWe carried out a multistage pilot study. First, we generated a list of 149 potentially cross‐cultural items, and selected the items with a homogeneous age of acquisition and word frequency across multiple languages. For the 74 items that remained, we selected several colored photographs (examples provided in Figure 1), out of which 194 controls selected the best matching photograph. The 74‐item test was piloted in 15 diverse healthy controls, after which 14 items were removed. The final 60‐item test was validated in 39 controls and 61 culturally, educationally, and linguistically diverse patients – mainly with dementia (30%), psychiatric illness (20%), MCI (16%), or subjective memory impairment (15%). Participants were mostly from North Africa, Suriname, Eastern Europe, and the Middle East, and the majority of participants had an education level of primary school or lower (51%).ResultThe new cross‐cultural naming test was well able to discriminate between patients with Alzheimer’s/mixed dementia (n=11, median score: 48/60) and the rest of the sample (median: 58/60, AUC=0.87). Preliminary item analyses indicated that colors, as well as animals (ant, worm, snake) and occupations with a later age of acquisition/lower word frequency were the most sensitive. There were robust correlations with other measures of language (Recall of Pictures Test naming ρ=0.26, p=.04, animal fluency ρ=0.66, p<.001, foods fluency ρ=0.57, p<.001), as well as a culture‐sensitive cognitive screener (ρ=0.69, p<.001). The scores were not correlated with education level, age, or number of years of residence in the Netherlands (analysis in controls).ConclusionThis new naming test is an important step towards a more fair neuropsychological assessment and more accurate dementia diagnosis in culturally, linguistically, and educationally diverse individuals.
LANGaware: Leveraging machine learning on natural language for the early detection of neurodegenerative and psychiatric diseases
Authors: Rentoumi Vassiliki , Vassiliou Evangelos , Demiraj Admir , Pittaras Nikiforos , Mandalis Petros , Alexandridou Martha , Kemp Hollie , Eleftheriou Ioanna , Danezi Maria , Hatzopoulou Maria , Kamtsadeli Vasiliki , Paliouras George , Papatriantafyllou John D 
Published: December 2021

AbstractBackgroundVarious neurodegenerative and psychiatric diseases are related with changes in spoken language [1], although they have seldom been investigated. We evaluate the effectiveness of our language‐agnostic Machine Learning (ML) system to detect subtle changes in spoken language that manifest early signs of cognitive decline, thus assisting with its diagnosis. We evaluate our methodology using recordings of speech samples from multiple languages obtained from patient cohorts in early stages of cognitive decline and matched healthy controls.MethodOur methodology involves capturing patient audio recordings while they are performing one or more predefined cognitive assessment tasks, involving, e.g., the description of a picture or recounting of an everyday activity. Afterwards, multi‐language audio recordings and generated transcripts are analyzed with audio and NLP feature extraction methods [3], ranging from semantic, morpho‐syntactic, phonological representations of the input, as well as, more sophisticated linguistic measures. The feature pool is filtered by a Pearson's rho threshold of 0.85. We build a Random Forest classifier out of 100 Decision trees, using the Gini impurity criterion, 5‐fold cross‐validation for training, elimination and composition‐based feature selection, as well as post‐selection retraining / fine‐tuning. The model's diagnostic performance is evaluated on a test set unseen during training.ResultOur results are validated against the diagnosis that is provided by medical experts. Our performance in terms of accuracy (∼82%), f1 (84%) and ROC‐AUC score (∼82%) are clear indicators of the effectiveness of speech analysis towards detecting cognitive decline. Moreover, our tree‐based classifier produces probability scores that closely follow the proportion of pathological cases in the input data, with a correlation of 94%.ConclusionIn the current evaluation we verified our conjectures regarding the strong capacity of speech to predict cognitive decline. Audio analysis and machine learning are proven to be invaluable tools in the prediction of early signs of cognitive decline, which are coupled with a wide spectrum of neurodegenerative and psychiatric diseases.[1] Boschi, Veronica, et al., Frontiers in psychology 8 (2017): 269.[2] Vassiliki Rentoumi et al., Alzheimer's & Dementia, Wiley, volume 16, 2020.[3] Alberdi, Ane et al., Artificial intelligence in medicine 71 (2016): 1‐29.
Current management strategies: The DIAMOND‐Lewy toolkit
Authors: Taylor John‐Paul , Pillai Manjunadh 
Published: December 2021

AbstractBackgroundDementia with Lewy bodies (DLB) is a challenging condition to treat. People with DLB present with a range of cognitive, neuropsychiatric, sleep, motor, and autonomic symptoms. These symptoms can vary in severity between patients and fluctuate over time within an individual. Treating one symptom can led to the worsening of another and often symptoms are treated in isolation by different specialists leading to sub‐optimal care. The evidence base, particularly for pharmacological interventions is building and this is bolstered by an increasing evidence base in related conditions such as Parkinson's disease which have relevance for DLB. A way forward would be to provide a holistic symptom‐based management toolkit, inclusive of the existing evidence base and drawing upon consensus expert opinion.MethodTaking a tri‐partite approach we 1) reviewed the existing evidence base in DLB 2) ran public‐patient workshops to identify best practice in DLB management based on lived experience 3) conducted a Delphi consensus process with a panel of experts in the field of Lewy body disease to determine best management practise. These activities led to the production of a holistic management toolkit which consisted of clinical summary sheets and detailed management guidelines. This toolkit was then trialled in a cluster randomized control study across 23 clinical services (e.g. memory clinics, movement disorders services etc.) in two UK regions, assessing the outcomes for patients and carers as secondary measures.ResultThe toolkit produced included 161 management statements covering a range of symptoms (cognitive, motor, neuropsychiatric, sleep and autonomic). In the trial 131 participants were recruited with 6 month data in 108 and provided preliminary evidence to suggest less marked patient deterioration on the global outcome measure (p<0.05) as well as improvements in care‐giver burden (p<0.01) and depression (p<0.05) in services using the toolkit compared to treatment as usual.ConclusionDeveloping a holistic management toolkit based on evidence base and expert opinion may have benefits for people living with DLB and their care‐givers.
Considerations for designing DLB trials
Authors: Armstrong Melissa J 
Published: December 2021

AbstractBackgroundDementia with Lewy bodies (DLB) is a common neurodegenerative dementia, but symptomatic and disease‐modifying treatments are lacking. Researchers designing clinical trials for DLB are faced with disease‐related challenges including choosing priority targets, populations, biomarkers, and outcomes.MethodThis presentation will review recent research relating to clinical trial design in DLB. Specific topics include research priorities of individuals living with DLB and caregivers, selecting the appropriate population for DLB trials (e.g. whether to combine with Parkinson disease dementia [PDD], subtyping based on presence of Alzheimer disease biomarkers), limitations of current outcome measures for use in DLB, and other challenges (e.g. fluctuations) inherent to DLB research.ResultIndividuals with DLB and caregivers identified research needs relating to determining the cause of DLB, improving diagnosis, investigating what to expect/disease stages, treating symptoms, identifying therapies to affect progression, and identifying approaches to improve daily function, quality of life, caregiving, and education. Determining whether to combine populations with DLB and PDD is challenging due to overlapping clinical and pathological features but also distinct experiences/trajectories and recruitment considerations. Recent research identified that disease‐modifying trials would use different endpoints for DLB versus PDD if prioritizing sample size. Research also demonstrated different trajectories in the face of Alzheimer disease co‐pathology, which is common in individuals with DLB. A systematic review of clinical outcome measures used in prior DLB trials identified the most common measures used to assess core DLB features in clinical trials. However, most of these outcome measures have limitations for use in DLB and no measure is specifically validated in DLB. Biomarker outcomes face challenges relating to lack of validation and DLB heterogeneity. The presence of fluctuations, the co‐existence of cognitive‐behavioral and motor symptoms, and limited specialty centers also challenge DLB trialists.ConclusionIndividuals living with DLB identify a large number of research needs, but numerous challenges face individuals designing DLB trials. Researchers need to thoughtfully weigh DLB‐related decisions in trial design based on individual study goals. Further research is needed to optimize DLB trial design, including development/validation of outcome measures and novel trial approaches to account for challenges such as fluctuations and travel limitations.
Feasibility and acceptance of robotic care of Alzheimer’s disease and related dementia
Authors: Yuan Fengpei , Lopez Ruth Palan , Crane Monica , Montgomery Austin , Zhao Xiaopeng 
Published: December 2021

AbstractBackgroundAn estimated 44 million people worldwide live with Alzheimer’s disease and related dementias (ADRD). There is increasing interest in using emerging technologies such as robotics and artificial intelligence to provide assistance to people living with ADRD and their caregivers.MethodWe conducted an anonymous, online survey to study the public's attitude and acceptance of using a human‐like robot to assist people with ADRD. The survey was distributed via social media (e.g., Facebook, Twitter, LinkedIn), emails, and Listservs. Participants above age 18 were recruited to complete the survey. After informed consent, participants watched a brief (3 minute) video, which provided examples of how humanoid robots could be used to help people with ADRD. This was followed by questions, which took approximately 10 minutes to complete and assessed perceptions of the robot’s appearance and functions (e.g., companion, communication, providing entertainment, medication, emergence call). The participant could skip any questions that they do not wish to answer.ResultInputs to the survey were collected between November 13, 2020 and January 15, 2021. During that time, 1,004 people viewed the survey. The survey was started by 249 people from 16 different countries and districts (7 in Asia, 5 in Europe, 2 in North America, 1 in Oceania, and 1 in Africa). Ninety‐seven people dropped out before finishing all the questions and 152 people completed the survey. Among the complete responses, 11 persons reported to have dementia and 79 people had experience in providing care to ADRD patients. Among a list of robot’s functions, reminding to take medicine, making emergency call, and helping to contact medical services were rated as the most important by participants with dementia. Monitoring medication and making emergency communication/alert were rated as the most important by participants with experience in dementia caregiving.ConclusionThe study presents statistical results from an anonymous online survey that collect international inputs on the use of social robots in Alzheimer’s care. Results of the survey show an overall positive attitude towards using humanoid robots for people with ADRD. Results may shed insight for design and development of assistive technology in robotics and AI for ADRD.
Disturbed sleep and reduced hippocampal integrity at midlife: Implications for AD risk in women with early surgical menopause
Authors: Gervais Nicole J , Brown Alana , Gravelsins Laura , Nicoll Gina , Sun Dorothy Leqi , Ge Jennifer Xiangning , Laird Kaz , Almey Anne , Reuben Rebekah , Karkaby Laurice , Duchesne Annie , Perovic Mateja , Gosselin Nadia , Grady Cheryl , Olsen Rosanna K , Einstein Gillian 
Published: December 2021

AbstractBackgroundWomen bear the greatest burden of Alzheimer’s disease (AD), and early (<45y) ovarian hormone deprivation via surgical menopause further increases risk (Rocca et al., 2007). Given that sleep disturbance is implicated in AD progression (Lim et al., 2013), an important area of investigation is determining whether younger women with early surgical menopause demonstrate disturbed sleep at midlife, and whether this may confer additional risks by contributing to changes in hippocampal integrity. Thus, we investigated whether middle‐aged women with bilateral salpingo‐oophorectomy not taking hormone therapy demonstrate sleep disturbance, and reduced hippocampal volume and memory. Additionally, we assessed whether sleep and hippocampal volume predict memory performance.MethodWomen with an early BSO that were either taking estradiol‐based hormone therapy (BSO+ET: age=43y; n=22), or not taking ET (BSO: age=46y; n=18) were recruited to the study, and were compared to age‐matched premenopausal (AMC: n=24) and spontaneously postmenopausal women who were ∼10y older (SM: 55y; n=20). Sleep was assessed via portable polysomnography (Vitaport‐5/REMbo‐234, Temec Technologies) for 1‐3 nights, and sleep staging was acquired using automated scoring (Z3score, Neurobit technologies). High‐resolution T2‐weighted scans were acquired using a Siemens 3T Prisma scanner, and hippocampal subfields (CA1, Subiculum, combined region: DGCA23), were manually segmented (Olsen et al., 2017) in FSLView. The interference match‐to‐sample (IMTS) task was used to asses and recognition memory for scenes (Watson et al., 2013).ResultBoth hormone‐deprived groups demonstrated increased sleep latency (BSOvsAMC: p<.05, d= 0.74; SMvsAMC: d= 0.69) and decreased sleep efficiency (BSOvsAMC: p<.05, d= 0.79; SMvsAMC: p<.05, d= 0.67). Only the BSO group demonstrated reduced volume in the anterior CA1 and DGCA23 relative to AMC (p<.05, d= 0.96‐1.06) and BSO+ET (p<.05, d= 0.67‐0.84). Additionally, the BSO demonstrated reduced efficiency (accuracy/RTcorrect) on scene recognition memory (p<.05, d=0.70‐0.86). Multiple regression analyses show that DGCA23 volume and group membership predict scene recognition memory performance.ConclusionWhile both hormone‐deprived groups demonstrated disturbed sleep, women with early BSO also showed reduced hippocampal integrity, despite being ∼10 years younger than SM women. These findings are consistent with the elevated AD risk among women with BSO. It remains to be seen whether sleep disturbance at a younger age exacerbates risk.
Increased cost of cognitive effort in preclinical Alzheimer’s disease
Authors: Aschenbrenner Andrew J. , Hassenstab Jason , Fagan Anne M. , Benzinger Tammie L.S. , Morris John C. 
Published: December 2021

AbstractBackgroundScores on neuropsychological tests depend on willingness to expend the necessary cognitive effort to complete each task. Group differences on cognitive test scores may partially reflect differences in perceived cognitive cost, or the willingness to exert cognitive effort, which will obscure estimates of “true” cognitive ability. It is critical, therefore, to obtain estimates of cognitive effort during neuropsychological testing. Subjective, questionnaire‐based measures of effort are prone to self‐report bias, so we administered an objective measure of cognitive effort (Cognitive Effort Discounting: COGED) to participants at the Knight ADRC. In COGED, participants complete increasingly difficult levels of a working memory task (the N‐Back) and then are asked to rate which “level” they would complete again for a given amount of monetary reward. We hypothesized that objective (COGED) but not subjective measures of effort would decrease in preclinical AD and this would be related to cognitive performance.MethodSixty‐five healthy older adults (Clinical Dementia Rating = 0) completed both the COGED task and a subjective effort rating scale (the NASA task load index). Participants were classified as amyloid‐positive (N=24) or negative (N=41) based either on amyloid PET scan or CSF Ab42/Ab40 ratio. Group differences on the effort task were analyzed using linear regression.ResultEffort ratings decreased across levels of N‐back (p<0.001), indicating the task accurately tracks perceived effort. As expected, amyloid‐positive participants scored lower on the N‐back task (p=.03) but more importantly, they also scored lower on COGED than the amyloid‐negative group (Figure 1, p=0.05), after controlling for age, education and working memory. There were no group differences on the NASA (Figure 2). Surprisingly, neither COGED scores nor the NASA were related to performance on the N‐back, suggesting that effort ratings are independent of task performance.ConclusionWe successfully adapted the COGED paradigm for remote assessment and administered the task to a group of healthy older adults from the Knight ADRC. This task accurately reflects effort / reward processing. Objective (but not subjective) measures of the ability to make these computations decline in preclinical AD but does not appear to affect concurrent cognitive assessments.
Loneliness experience through COVID‐19 pandemic and lockdown is related with resting‐state brain networks functional connectivity
Authors: Cabello‐Toscano María R. , Macià Dídac , Cattaneo Gabriele , Solana Javier , Abellaneda‐Pérez Kilian , Vaqué‐Alcázar Lídia , Tormos‐Muñoz Josep M. , Pascual‐Leone Alvaro , Bartres‐Faz David 
Published: December 2021

AbstractBackgroundThe reduction in social interactions often associated with old age frequently initiates or aggravates loneliness, increasing the risk of a number of diseases including depression and dementia (Mushtaq et al., JCDR. 2014;8(9):E01–WE4.). Loneliness has been related to particular patterns of brain resting‐state networks (RSNs; Spreng et al., Nat Commun. 2020;11(1):6393). However, the role of RSNs in a resilient response to reductions of social interactions remains unknown and the COVID‐19 pandemic provides a unprecedent opportunity to investigate it.MethodRepeated loneliness assessments were collected by the Barcelona Brain Health Initiative (BBHI; www.bbhi.cat/en/) using the short UCLA Loneliness Scale (Russell et al., J Pers Assess. 1978; 42(3):290–294). Two timepoints were measured over two years prior to COVID‐19 outbreak and four thereafter (March, April, June and October 2020). We used baseline resting‐state functional magnetic resonance imaging (rs‐fMRI), from 281 participants (mean age 53.91±7.35; 137 women), acquired within 1‐2 years prior to the pandemic to compute resting‐state functional connectivity (rs‐FC) for each pair of nodes defined by the Schaefer‐Yeo atlas. Then, we utilized partial least squares (PLS) analysis for the decomposition of two multivariate sets (i.e., rs‐FC and loneliness scores) into latent variables of maximal covariation.ResultWe identified two latent trajectories significantly correlated with two latent connectivity patterns, each of them describing approximately opposite baseline rs‐FC and loneliness levels during the months with the most severe restrictions (i.e., March and April). The first latent trajectory showed greater baseline loneliness improving during the pandemic, associated with higher within‐network connectivity in the limbic and salience networks. The second trajectory captured individuals with baseline loneliness levels similar to the average population, improving in March, at the beginning of outbreak, and worsening in April, which was related to lower baseline within‐network connectivity in the DMN and higher in the dorsal attention and visual circuits.ConclusionPresent findings reveal specific associations between RSNs’ functional connectivity and opposite changes in feelings of loneliness during an unanticipated period of severe social restrictions. Specifically, lower rs‐FC of limbic networks combined with high connectivity of salience system at baseline suggested greater resilience to the negative impact during the pandemic on loneliness.