bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of receiving a diagnosis of dementia on social engagement among older adults
Authors: Amano Takashi , Jia Yuane , Reynolds Addam , Scher Clara 
Published: February 2022

AbstractBackgroundEarly diagnosis has been recognized as a key strategy to improve outcomes of Alzheimer’s disease and related dementias. However, due to the stigmatization of these disorders, practitioners need to recognize and weigh the relative dis/advantages of receiving a diagnosis on patients’ well‐being. Despite this need, the social consequences of receiving a diagnosis have not been well investigated. This study closes this gap by examining if receiving a diagnosis of dementia influences social engagement among older adults.MethodData from Health and Retirement Study (HRS; 2012, 2014, and 2016), a nationally representative sample of people who are 51 years or older in the United States, were utilized. Social engagement is measured with a previously developed scale using five social activities (e.g. volunteering, attending educational courses, attending clubs). Dementia diagnosis was measured by asking respondents or their proxy whether they had ever been told by a doctor that they had dementia or Alzheimer’s disease. Propensity score weighted Poisson regression was utilized with adjustment (doubly robust) to control for potential systematic selection of receiving a diagnosis of dementia such as functional, sociodemographic, and health service‐related factors.ResultReceiving a new diagnosis of dementia statistically significantly reduced the score of social engagement two years later (b = ‐0.360, p = 0.031, 95% CI = [‐0.686, ‐0.033]). The effect of receiving a diagnosis was more salient in the model which adjusted for socioeconomic factors (b = ‐0.649, p < 0.001, 95% CI = [‐0.866, ‐0.432]). An interaction between receiving a diagnosis of dementia and household income was significant, indicating that the effect of receiving a diagnosis varies depending on the household income of the recipient.ConclusionResults suggest that receiving a new diagnosis of dementia may discourage people from maintaining their level of social engagement, regardless of their abilities to participate in activities. The significant interaction term implies that people with lower household income may be more susceptible to the negative impact of receiving a diagnosis of dementia on staying socially engaged. Practical attention should be paid especially to social disengagement among people with disadvantaged socioeconomic status after receiving a diagnosis of dementia.
Cross‐sectional association between the area deprivation index (ADI) and cognitive impairment in community‐dwelling older veterans
Authors: Ysea‐Hill Otoniel , Gomez Christian , Shah Aakashi , Ruiz Sergio J , Ruiz Jorge G 
Published: February 2022

AbstractBackgroundThe evidence that lower socio‐economic status (SES) may be associated with late‐onset dementia is inconsistent. Differences may be related to how SES was assessed. The area deprivation index (ADI) is a validated, composite measure of neighborhood socioeconomic status representing a geographic area level of social deprivation. Previous research has shown an association between ADI and cognitive impairment, but less is known about the Veteran population. The study aim was to determine the cross‐sectional association between the area deprivation index (ADI) and cognitive impairment in a Veteran population.MethodThis is a cross‐sectional study among community‐dwelling Veterans aged ≥50 years enrolled in a VA primary care clinic from July 2019‐May 2020. Patients were mailed questionnaires including sociodemographic, the Self‐Administered Gerocognitive Examination (SAGE). Clinical information was extracted from the electronic health records (EHR). To assess frailty, we used a 31‐item VA Frailty Index (VA‐FI) generated from EHR data matched to the study date. We calculated the ADI from 17 socioeconomic indicators available from the US Census. ADIs were generated for census tracts corresponding to veterans’ addresses. Higher ADI values are consistent with increased deprivation. After adjusting for age, gender, marital status, race, ethnicity, body mass index and frailty, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using binomial logistic regression models with cognitive impairment (MCI and dementia) as the outcome variable and ADI as the independent variable.ResultParticipants were 1060 Veterans (response rate of 20.14%), mean age of 68.39 (SD=8.52) years, 59.0% Caucasian, 69.2% non‐Hispanic, 95.8% male, 12.0% screened positive for MCI and 15.6% for dementia. Among ADI quintile groups, patients in the highest group had a significantly higher proportion of cognitive impairment (40.59%) when compared to lower groups, p<.001. No significant differences were found between quintiles 1 to 4. The highest ADI quintile was the only group associated with cognitive impairment, adjusted OR: 1.916 (95% CI:1.187‐3.093), p=.008.ConclusionThis study shows a cross‐sectional association between ADI and dementia in community‐dwelling older Veterans. Further investigation of the links between neighborhood social deprivation and cognitive impairment may assist in the development of strategies that lower the incidence of cognitive impairment in these communities.
Diagnosis of Alzheimer’s disease and frontotemporal dementia using FDG‐PET: Application of genetic algorithms
Authors: Pytel Vanesa , Díaz‐Álvarez Josefa , Cabrera‐Martín María Nieves , García‐Gutiérrez Fernando , Hernández‐Lorenzo Laura , Matias‐Guiu Jorge , Carreras José Luis , Ayala José Luis , Matias‐Guiu Jordi A 
Published: February 2022

AbstractBackgroundGenetic algorithms are methods used in machine learning, which have a proven capability to explore a large space of solutions, deal with very large numbers of input features, and avoid local minima. Diagnosis of Alzheimer’s Disease (AD) and Frontotemporal dementia (FTD) is often challenging, and thorough costly assessments are often needed. We hypothesised that the application of machine learning, and specifically genetic algorithms, to 18F‐Fluorodeoxyglucose Positron Emission Tomography (FDG‐PET) may help in diagnosis by selecting the most meaningful features and automating diagnosis. In this study, we aimed to develop algorithms for three common situations: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioural FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants.MethodEighty‐one patients with bvFTD, 88 patients with AD, 68 patients with PPA, and 39 HC were enrolled. Patients underwent a comprehensive clinical and neuropsychological protocol, and FDG‐PET imaging. Genetic algorithms, customised with K‐Nearest Neighbor and BayesNet Naives as the fitness function, were developed applied to the PET imaging and compared with Principal Component Analysis. The highest accuracy rate and most relevant features were identified. K‐fold cross validation within the same sample and external validation with ADNI samples were performed.ResultDiscrimination accuracy of FDG‐PET was 92‐95% for AD vs HCs, 95‐96% for bvFTD vs HCs, 89‐90% for AD vs bvFTD, and 90‐91% for classification of PPA subtypes. A reduced number of features was achieved, with cutting rates from 62% to 95.69% regarding the total number of variables, and several key brain regions were selected. External validation with ADNI obtained an accuracy of 82.93% for BayesNet Naives algorithm for the differentiation between AD and HC.ConclusionGenetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG‐PET images. Overall, our study contributes to the development of an automated, and optimised diagnosis of neurodegenerative disorders using brain metabolism.
Beyond WMH volume: Coalescence score as a new measure of cerebral small‐vessel disease pattern
Authors: McPhillie Ross , Barnes Jo , Tillin Therese , Chaturvedi Nishi , Hughes Alun D. , Jäger H. Rolf , Sudre Carole H. 
Published: February 2022

AbstractBackgroundTotal white matter hyperintensity (WMH) volume is a hallmark of cerebral small vessel disease that is associated with accelerated cognitive decline and dementia onset. The shape of lesions is a key component of existing visual rating scales. While automated methods have been developed to assess WMH volume, evaluation of the lesion shape is still limited. We describe an automatic method to derive an overall coalescence score and investigate its association with Framingham cardiovascular risk score (FRS) and blood pressure (BP) in elderly individuals.Method750 individuals from the Southall And Brent Revisited cohort (age 71.1(6.9) years; 54.7% male) underwent brain MRI including 3D T1‐weighted and FLAIR images. WMH volumes were automatically segmented. Each non‐periventricular lesion (<25% of the border touching the ventricles) was automatically classified into a coalescence category ranging from 0 to 4 describing its level of confluency (0 punctuate – 4 fully confluent) using derived shape features. An overall coalescence score (CS) was generated based on the proportion of lesions residing in each of the five categories with respect to the global WMH volume. Biological relevance of the coalescence score (log‐transformed due to its skewness) was evaluated by assessing its association with FRS and BP using linear regression adjusting for global WMH volume and proportion of excluded periventricular lesions as well as age and sex (BP).ResultFigure 1 presents the CS frequency distribution (a), its relationship with global WMH volume (b) and an example of how CS captures differences in WMH pattern independent of classical volumetric measurements (c) (same load but different CS). After correction for global and periventricular WMH volume and other confounders, there was a positive association between CS and FRS (mean[95% confidence interval] +6.9[3.7;10.2] %CS per 10% increase FRS; p<0.0001), and with diastolic BP (+0.5[0;0.90] % per mmHg; p=0.04). There was no evidence of an association with systolic BP [+0.05[‐0.19;0.30]% per mmHg; p=0.63).ConclusionThe coalescence score provides an automated tool to quantify varying degrees of WMH confluency, which may lead to better understanding of the pathophysiology of WMH in the context of ageing, risk factors and dementia.
A comparison of longitudinal [18F]GTP1 tau pet with other imaging and cognitive endpoints in the Tauriel Phase II Study
Authors: Manser Paul T. , Sanabria‐Bohorquez Sandra , Wildsmith Kristin R. , Toth Balazs , Blendstrup Mira , Pickthorn Karen , Teng Edmond 
Published: February 2022

AbstractBackgroundThe Tauriel Study (NCT03289143) was a Phase 2 randomized, double‐blind, placebo‐controlled, parallel‐group clinical trial that assessed the safety and efficacy of semorinemab in patients with prodromal‐to‐mild Alzheimer’s disease over 73 weeks. We sought to assess the longitudinal performance of tau PET imaging and to determine the relationships between the multiple imaging and cognitive endpoints that were collected.MethodCognitive testing, [18F]GTP1 PET, and volumetric MRI imaging were performed at baseline, Week 49 and Week 73. Relationships between imaging and cognitive endpoints were assessed with Spearman correlations. Standardized change from baseline among imaging and cognitive endpoints were compared using Cohen’s D.Result375 participants had evaluable [18F]GTP1 tau PET scans at baseline. On average, [18F]GTP1 SUVR in a temporal metaROI (mean = 1.56, SD = 0.28) was significantly higher than SUVR in whole cortical gray matter (mean = 1.33, SD = 0.25). In spite of the heterogenous localization of baseline [18F]GTP1 signal within and across subjects, longitudinal changes in tau PET were spatially more uniform: whole cortical gray SUVR and temporal meta ROI performed similarly at Week 73 in terms of standardized longitudinal change (Cohen’s D = 0.61 and 0.65, respectively). Furthermore, correlations between local baseline SUVR, as parcellated across cortical Hammers Atlas ROIs, and change from baseline to week 73 were low (Avg Spearman r = 0.14, range = [‐0.34, 0.37]).ConclusionThe Tauriel trial successfully enrolled a prodromal‐to‐mild AD patient population that progressed on both clinical and imaging endpoints that were statistically sensitive to detect potential treatment effects. However, correlations between changes in imaging and cognitive endpoints were low. Longer observation periods may further elucidate the associations between imaging and clinical endpoints.
Disintegration of anterior thalamic radiation fibers in cerebrovascular disease subjects with periventricular white matter hyperintensities leads to lower executive function performance
Authors: Haddad Seyyed Mohammad Hassan , Scott Christopher J.M. , Ozzoude Miracle , Holmes Melissa F. , Arnott Stephen R. , Nanayakkara Nuwan D. , Kwan Donna , Tan Brian , Casaubon Leanne , Mandzia Jennifer , Sahlas Demetrios J. , Saposnik Gustavo , Hassan Ayman , Black Sandra E. , Dowlatshahi Dar , Strother Stephen C. , Swartz Richard H. , Symons Sean , Montero‐Odasso Manuel , Bartha Robert 
Published: February 2022

AbstractBackgroundWhite matter hyperintensities (WMH) are more prevalent in elder adults and those typically located in periventricular areas are known as periventricular WMHs (pWMH). Recent studies link WMHs to hypoperfusion and ischemic injuries arising from diverse cerebrovascular diseases (CVDs). Previous studies have reported strong associations between WMH burden and disintegration of normal‐appearing white matter (WM) and WM pathways evaluated using diffusion tensor imaging (DTI). This WM deterioration may be the underlying cause of cognitive dysfunction in people with WMHs. The purpose of this study is to identify major WM tracts in which there is evidence of microstructure damage associated with pWMH severity to determine whether this damage is associated with cognitive decline.MethodAssociation between pWMH volume and fractional anisotropy (FA) in 18 major WM tracts in 100 CVD subjects (aged 55‐84 years, 29% female, evidenced by an ischemic stroke event documented by MRI or CT, with a modified Rankin score 0‐3) from the Ontario Neurodegenerative Disease Research Initiative was examined using linear regression. DTI (30 directions, b=1000 s/mm2) was acquired on eight 3T MRI scanners. Mean FA in 18 WM tracts was quantified using TRACULA in FreeSurfer in each subject. Volumes of pWMHs were obtained by semi‐automated segmentation of T1‐weighted images in each subject. Cognitive assessments were completed as part of ONDRI. Associations between cognitive scores and FA in tracts that significantly correlated with pWMH severity were also examined.ResultLinear regression (adjusted by age and sex) detected significant correlation between FA in right anterior thalamic radiation (ATR) (Fig. 1) and pWMH volume after Bonferroni correction (p‐value=0.002, r=‐0.356) (Fig. 2). No other significant associations were identified. Since right ATR is part of fronto‐striato‐thalamic circuit contributing to executive function, FA in ATR predicted association with Trail Making Test‐Part B (adjusted by age, sex, and education) and a signhificant correlation was identified (p‐value=0.006, r=‐0.256) (Fig. 3).ConclusionATR disintegration was associated with pWMH severity in participants with CVD and with executive function. This study suggests that tissue microstructure deterioration in ATR measured by DTI may contribute to decline in executive function in CVD patients with pWMHs.
Ethnic differences in the frequency of β‐amyloid deposition in cognitively normal individuals
Authors: Kim Jaeho , Jung Sang‐Hyuk , Choe Yeongsim , Kim Soyeon , Kim Beomsu , Kim Hang‐Rai , Son Sang Joon , Hong Chang Hyung , Na Duk L. , Kim Hee Jin , Cho Soo‐Jin , Won Hong‐Hee , Seo Sang Won 
Published: February 2022

AbstractBackgroundPrevalence estimates of β‐amyloid (Aβ) pathology and their related factors in cognitively normal (CN) individuals are required to better understand the development of Alzheimer’s disease (AD) and to facilitate the design of clinical studies.MethodWe recruited 448 South Korean CN participants from a Korean tertiary hospital (Samsung Medical Center, SMC) and 323 European CN participants from the US Alzheimer’s Disease Neuroimaging Initiative (ADNI) who underwent amyloid PET. We compared the frequencies of Aβ (+) in CN Korean and European participants and investigated which factors explain these differences after conducting genome‐wide association study (GWAS) analysis in each cohort.ResultThe frequency of Aβ (+) was higher in the ADNI dataset (32.8%) than in the SMC dataset (18.9%; P <0.001). In particular, the differences between the two groups were prominent in elderly participants (P = 0.001 for 70–79 years and P = 0.004 for ≥80 years) and homozygous APOE ε3 carriers (P <0.001). In the SMC GWAS, a locus near the brain‐derived neurotrophic factor gene showed a genome‐wide significant association with Aβ deposition (P = 3.59 × 10‐9), while this association was not observed in the ADNI GWAS. The association was also observed in elderly participants (P <0.001), APOE ε3 homozygote carriers (P = 0.0024), and another Korean replication set (P = 0.0056). Furthermore, the interaction between population groups (SMC or ADNI) and rs7481773 was significant (P = 0.0125).ConclusionThe different frequencies of Aβ (+) between CN Koreans and Europeans might be related to decreased cognitive reserve through their distinct cultural environments or genetic factors.
Improving the multi‐class classification of Alzheimer’s disease with machine learning‐based techniques: An EEG‐fNIRS hybridization study
Authors: Ho Thi Kieu Khanh , Kim Minhee , Jeon YoungHoon , Na Eunchan , Ullah Zahid , Kim Byeong C , Lee Kun Ho , Song Jong‐In , Kim Jae Gwan , Gwak Jeonghwan 
Published: February 2022

AbstractBackgroundAs an incurable neurodegenerative disorder, Alzheimer’s Disease (AD) is being projected to become one of the most expensive diseases for understanding the pathophysiological mechanism. Exploring alterations in the AD brain network is therefore of central importance for early and accurate diagnosis of cognitive deficits levels, yet diagnostic uncertainties exist merely confirmed by postmortem brain examination. The hybrid approach of two modalities, Electroencephalography (EEG) and functional Near‐Infrared Spectroscopy (fNIRS) reveals an ideal technique‐derived diagnosis due to the inexpensive and noninvasive experiments coupled with the reliability and versatility that make it highly desirable for AD multi‐class classification tasks.MethodsIn this study, the hybrid EEG‐fNIRS was employed in constructing the classification machine learning (ML)‐based models to categorize four subject groups, including healthy controls (HC) and three AD patient classes. First, a concurrent EEG‐fNIRS setup was utilized to record data from 41 subjects during the Oddball – a cognitive ability test and 1‐back – a memory ability test. Second, while fNIRS features were computed and extracted, the event‐related spectral perturbation (ERSP) features measuring the average dynamic changes in amplitude of three main EEG frequency bands were extracted. Third, a conventional neural network and a hybrid deep Convolutional Neural Network (CNN) ‐ Long Short‐Term Memory Network (LSTM) was built for the multi‐class classification using the fNIRS and EEG features, respectively. To boost the classification accuracy, the output of different models was combined by majority voting methods.ResultsThe hybrid EEG‐fNIRS feature set was able to achieve a higher accuracy (71.01% ± 0.004) by combing their complementary properties, compared to using EEG (66.35% ± 0.016) or fNIRS (67.15% ± 0.017) alone. In addition, fNIRS and ERSP feature patterns displayed a significant difference between subject groups during the Oddball and 1‐back experimental tasks. Thus, the feature‐level ensemble shows a great ability to understand neurophysiological AD status and its related symptoms and develop a syndrome‐specific diagnostic tool for our future works.ConclusionsThese findings demonstrate the potential of the hybrid EEG‐fNIRS systems to enhance the AD diagnosis and assessment process and the capability of ML techniques to facilitate further AD classification studies.
Assessment of hearing and vision impairment in cohort studies collecting cognitive data in older adults
Authors: Liu Chelsea , Nagarajan Niranjani , Assi Lama , Jiang Kening , Powell Danielle S. , Pedersen Emily , Rosman Lori , Villavisanis Dillan , Carlson Michelle C. , Swenor Bonnielin K. , Deal Jennifer A. 
Published: February 2022

AbstractIntroductionThere are no standard practices for considering sensory impairment in studies measuring cognitive function among older adults. Exclusion of participants with impairments may inaccurately estimate the prevalence of cognitive impairment and dementia.MethodsWe surveyed prospective cohort studies measuring cognitive function in older adults, determined the proportion that excluded participants based on sensory impairment and the proportion that assessed each type of sensory impairment, and described the methods of sensory assessment.ResultsInvestigators/staff from 85 (of 192 cohorts) responded; 6 (7%) excluded participants with severe impairment; 80 (94%) measured hearing and/or vision impairment, while 5 (6%) measured neither. Thirty‐two (38%) cohorts assessed hearing objectively and 45 (53%) assessed vision objectively.DiscussionFindings indicate variation in methods used to assess sensory impairment, with potential implications for resource allocation. To ensure equitable inclusion of study participants, consensus is needed on best practices standardized protocols for assessment and accommodations of sensory impairment.
Fabrication and characterization of transparent underwater superoleophobic coatings based chitin nanofibers and polyvinyl alcohol
Authors: Wang Qian , Wang Peizhuang , Kou Lvheng , Wei Hongyuan , Zhou Jiang 
Published: February 2022

AbstractTransparent underwater superoleophobic coatings were prepared by blending chitin nanofibers (ChNFs) and polyvinyl alcohol (PVA) and then cross‐linked with glutaraldehyde (GA). The interactions among ChNFs, PVA, and GA in the coatings were analyzed via FT‐IR and XPS. Results of SEM observation and AFM analysis showed that higher concentrations of ChNFs and GA gave rise to rougher surface and lower transmittance of the coatings. By adjusting ratios of ChNFs, PVA, and GA, the coating surface could possess underwater superoleophobicity with oil (1, 2‐dichloroethane) contact angle up to 159.2° and slide angle ~3°, meanwhile, transmittance of the coatings in the wavelength range of 400–800 nm could be maintained between 40% and 90%. This work provides a new route to prepare environmentally friendly transparent underwater superoleophobic coatings.