bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early tau phosphorylation as a potential retinal biomarker for AD and other tauopathies
Authors: de Ruyter Frederique Jasmine Hart 
Published: December 2020

AbstractBackgroundThere is increased interest in in vivo labeling of Alzheimer’s disease (AD) pathology in the retina as a non‐invasive diagnostic approach (Jiang, Wang, Li, Cao, & Li, 2016; Koronyo et al., 2017). While the majority of studies focus on the detection of amyloid beta (Aβ) in the retina, the characterization of tau related pathology is scarce. In this study we assessed the presence of different tau isoforms in the retina in AD as well as other neurodegenerative diseases associated with and without tau pathology.MethodPost mortem eyes were collected through the Netherlands Brain Bank from donors with AD (n=6), progressive supranuclear palsy (PSP, n=2), frontotemporal lobar degeneration (FTLD; FUS and TDP43) (n=4), Lewy body disease (LBD, n=2), multiple system atrophy (MSA, n=2), Parkinson’s disease (PD, n=6), and non‐neurodegenerative controls (n=10). Superior‐temporal quadrants were cut in 10 µm sections. Sections were immunostained using primary antibodies directed against total tau (HT7), early tau phosphorylation (AT8), 3R and 4R tau isoforms (RD3/RD4) and late tau phosphorylation (pS422). The frontal cortex was used as a positive control.ResultTotal tau (HT7) was observed in non‐demented control and neurodegenerative cases (Figure 1). The presence of 3‐ and 4‐repeat isoforms of tau varied within the inner plexiform layer (IPL) and outer plexiform layer (OPL) with more 3‐repeat tau in the IPL and more 4‐repeat tau in the OPL. Only AD and PSP cases showed positive immunoreactivity for AT8, which was found differently distributed in the IPL and OPL (Figure 2). Tau phosphorylated at Ser422 was negative in all groups.ConclusionIn control and neurodegenerative cases, high levels of tau are present in the retina, mainly in the plexiform layers. Interestingly, we found different intensities for 3‐ and 4‐repeat tau in the IPL and OPL, implicating differences in tau expression by amacrine and horizontal cells (Chidlow, Wood, Manavis, Finnie, & Casson, 2017). Tau phosphorylated at Ser202/Thr205 clearly differentiates tauopathies from other neurodegenerative disease and non‐demented controls. Depending on the epitope, phosphorylated tau is a potential retinal biomarker for AD and other tauopathies.
Cognitive profiles in amyloid‐PET‐eligible patients: A retrospective study
Authors: Loreto Flavia , Gunning Stephen , Golemme Mara , Watt Hilary , Patel Neva , Win Zarni , Carswell Christopher J. , Perry Richard J. , Malhotra Paresh A. 
Published: December 2020

AbstractBackgroundThe early cognitive profile of Alzheimer’s Disease (AD) is typically characterised by predominant impairment in episodic memory, reflecting medial temporal lobe dysfunction. However, little is known about the cognitive profile associated with AD pathology in those patients who meet Appropriate Use Criteria (AUC, Johnson et al., 2013) for Amyloid PET Imaging (API). In this group, AD pathology is frequently associated with an atypical clinical course or presentation. Here, we systematically evaluate the cognitive profiles of AUC‐meeting patients in a real‐world clinical setting, combining the information provided by the neuropsychological assessment with the results of Amyloid PET Imaging.MethodFrom a larger sample of 396 patients who underwent Amyloid PET at the Imperial Memory Centre between December 2013 and June 2019, we included those individuals who also had at least one formal neuropsychological assessment (minimum of 4 tests) within 18 months of API (n=124). Both these were solely conducted for clinical purposes, and API was requested after multidisciplinary team discussion. Cognitive measures in amyloid positive (Aβ+) and negative (Aβ‐) patients were compared using non‐parametric tests.ResultsNeuropsychological assessment preceded Amyloid PET Imaging in most cases, for both amyloid‐positive (n=51, mean age 67.24±9.32 years, 58.8% female) and amyloid‐negative (n=73, mean age 67.9±9.64 years, 41.1% female) groups. Cognitive performance in the amyloid‐positive group was significantly worse in 6 of the 14 cognitive measures taken into account: visuo‐spatial functioning, executive functioning, working memory, verbal learning, verbal delayed recall, and confrontation naming (Figure 1). Verbal learning, verbal delayed recall and confrontation naming were the most frequently impaired measures in the amyloid‐positive group, with around 50% of patients performing below 2 standard deviations. Notably, self‐reported symptoms of depression were significantly higher in the amyloid‐negative (7.3±4.38) than in the amyloid‐positive group (4.68±3.7) (p=.002) (Figure 2).ConclusionsIn a sample of patients attending a tertiary Behavioural Neurology clinic, neuropsychological evaluation revealed worse performance in positive API across a range of domains, particularly verbal memory and confrontation naming, with higher rates of symptoms of depression being reported more frequently in the amyloid negative group.
Association of ideal cardiovascular health metrics and cognitive function in older adults: A population‐based study
Authors: Han Xiaolei , Wang Yongxiang , Du Yifeng , Qiu Chengxuan 
Published: December 2020

AbstractBackgroundThe American Heart Association (AHA) proposed the Life Simple’s 7 to promote cardiovascular health (CVH): four behaviors (e.g., smoking, body mass index (BMI), physical activity, and diet) and three biological factors (e.g., blood pressure, total cholesterol, and fasting blood glucose). We aimed to examine the association of ideal CVH metrics with cognitive performance and dementia among older adults living in the rural communities in China.MethodThis community‐based study included 4868 participants from the Multidomain Interventions to Delay Dementia and Disability in Rural China (MIND‐CHINA), who were aged ≥65 years (mean 71.5 years; 57.2% women). Data on demographic features and CVH metrics were collected through face‐to‐face interviews, clinical examinations, and laboratory tests. The six CVH metrics (except diet) were defined according to the modified AHA recommendations, and participants were divided into concurrently having ≤2 (reference), 3, 4, and ≥5 ideal CVH metrics. Global cognitive function was assessed with the Mini‐Mental State Examination (MMSE). Diagnoses of dementia were reached according to the DSM‐IV criteria. Data were analyzed using multiple binary logistic and general linear regression models.ResultOf all participants, 31.8% had ≤2, 34.6% had 3, 24.8% had 4, and 8.8% had ≥5 ideal CVH metrics. Multiple logistic regression analysis showed that ideal physical activity (OR = 0.70; 95% CI, 0.52–0.94) and ideal fasting blood glucose (FBG) (OR = 0.37; 95% CI, 0.25–0.54) were associated with a reduced likelihood of dementia. Multiple general linear regression analysis suggested that ideal individual CVH metrics (vs. poor levels) that were significantly associated with MMSE score were physical activity (β = 0.414; 95% CI, 0.119‐0.709), BMI (β = ‐0.751; 95% CI, ‐1.127‐ ‐0.375), and FBG (β = 0.729; 95% CI, 0.243‐1.214). The composite measurement of CVH metrics was not significantly associated with either dementia and MMSE score.ConclusionIn this cross‐sectional study, the composite measure of CVH metrics is not associated with global cognitive function among rural‐dwelling Chinese older adults, although optimal physical activity and ideal FBG are associated with good global cognitive performance and a low likelihood of dementia.
Amyloid‐β deposition in cognitively normal oldest‐old is associated with cortical thinning and faster memory decline
Authors: Pelkmans Wiesje , Legdeur Nienke , Kate Mara ten , Yaqub Maqsood , Barkhof Frederik , Van Berckel Bart N.M. , van Der Flier Wiesje , Visser Pieter Jelle , Tijms Betty M. 
Published: December 2020

AbstractBackgroundThe Alzheimer’s disease (AD) pathological cascade is hypothesized to start with the accumulation of amyloid‐β (Aβ). However, the precise effect of Aβ deposition on cognitive decline in the oldest‐old remains controversial, since very old individuals can have intact cognition despite abnormal AD biomarkers. Here we studied, in cognitively normal individuals of 90 years and older, the impact of Aβ on structural brain integrity and the association of Aβ and structural brain integrity on decline over time in memory functioning.MethodFifty‐seven cognitively normal individuals (age mean±sd = 92.7±2.9) were selected from the EMIF‐AD 90+ study. Aβ status was determined by visual reads on [18F]‐flutemetamol PET (normal=Aβ‐, abnormal=Aβ+); cortical thickness and hippocampal volume were quantified on structural MRI with FreeSurfer and FSL, respectively. Cortical thickness was computed according to the Desikan‐Killany atlas and averaged across hemispheres. Hippocampal volume was normalized for intracranial volume. Memory performance was evaluated using a composite score of the CERAD (delayed recall), Logical Memory (delayed recall), Rey Complex Figure (delayed copy), and the Visual Association Test, that were repeated after 20 months (n=43). Effects of Aβ status, regional cortical thickness, hippocampal volume, time, and their interactions on memory function were assessed with linear mixed models, adjusted for age and sex.ResultRoughly one out of three (n=19; 33%) individuals had an abnormal amyloid PET (Table1). Compared to Aβ‐, those with Aβ+ showed at baseline a thinner parahippocampal and medial orbitofrontal cortex (p<.05; Fig.1), while hippocampal volume did not differ. Over time, Aβ+ individuals showed a steeper decline (β±SE=‐.27±.09; p<.01;) on memory performance than Aβ‐ (Fig.1). Faster memory decline was also predicted by cortical thinning in predominantly temporal regions in the whole group (Fig.1).ConclusionIn the oldest‐old with initially intact cognition, Aβ pathology was associated with a thinner cortex and faster decline in memory performance. These results suggest that Aβ abnormality, even in the oldest old with apparent high reserve and maintenance mechanisms, is indicative of active neurodegeneration. Other pathologies may also contribute to decline in the oldest old as a thinner temporal lobe predicted decline in memory irrespective of Aβ pathology.
Dementias Platform UK (DPUK): Facilitating cross‐cohort analysis in a digital age
Authors: Bauermeister Sarah D 
Published: December 2020

AbstractBackgroundDementias Platform UK (DPUK) is a £53M public‐private partnership established by the MRC to provide access to large‐scale cohort data and accelerate the research and discovery of new treatments for dementia. The DPUK Data Portal facilitates multi‐modal remote data access to 3.4M individuals from 42 cohorts using a secure, robust, persistent, and fully auditable data repository. To facilitate the analysis of multiple independent datasets, DPUK curates cohort data to the C‐Surv data model. This model standardises data structure, variable naming and value labelling conventions to reduce data pre‐processing times and reduce unwanted variation. C‐Surv also enables the harmonisation of datasets for analysis feasibility testing. We present the C‐Surv ontology and demonstrate its impact on data discovery and knowledge management for multi‐cohort, multi‐modal analysis in dementia research.Method
Ontology: The C‐Surv ontology has 22 level‐1 and 132 level‐2 categories, written to be machine‐readable for mapping to other ontologies and is optimised for cohort (survey) data. C‐Surv accommodates heterogeneous data‐types across cohorts. Higher order data (imaging, genetics, devices) are pre‐processed prior to curation. Researchers may request access to native or curated data.Results
Standardisation: C‐Surv standardisation process: standardised naming structure of variables – maximum coding structure of 12 uppercase alphanumeric characters selected, with 5 additional characters for the imaging, linkage, genomic categories and accelerometer data. An intuitive coding structure is adopted whereby syllable‐based acronyms, word fragments, abbreviations and minimal numeric characters are utilised. Harmonisation: C‐Surv provides a harmonised dataset of 30 common data elements (variables) which are widely used in neurodegenerative and bio‐epidemiological research, and can be visualised in the DPUK Cohort Explorer. The C‐Surv standard data structure allows efficient data discovery and data selection. Building on the DPUK Cohort Matrix and Cohort Directory, the C‐Surv standard data structure provides a framework for the development of ergonomic data discovery and data selection tools.ConclusionThe C‐Surv ontology is a data model optimised for analysis of epidemiologic survey data. It enables analysts to conduct multi‐cohort multi‐modal analyses rapidly; offering reduced administrative load to researchers and data managers. It also reduces artifactual variation in data due to non‐standard data pre‐processing.
Pattern recognition to objectively differentiate the etiology of cognitive decay in longitudinal cognitive data: Analysis of stroke, Alzheimer’s disease, and normal aging
Authors: Clouston Sean , Richmond Lauren , Scott Stacey , Luhmann Christian , Natale Ginny , Hanes Douglas , Zhang Yun , Smith Dylan 
Published: December 2020

AbstractBackgroundEstimates suggest that Alzheimer’s disease and stroke account for a majority of age‐related cognitive decay. This study utilized novel pattern‐recognition protocols to estimate the extent to which cognitive decay might be attributable to Alzheimer’s disease and stroke.MethodSecondary analyses of data collected for the Health & Retirement Study (N=17,579) were used to objectively type Alzheimer’s disease and stroke‐related cognitive decay. Patterns of decay in episodic memory were the measure, while rate of linear decay in episodic memory and, separately, in orientation were the outcome.ResultAfter adjusting for demographics, Alzheimer’s disease and stroke accounted for approximately half of age‐associated decay in cognition (51.07‐55.6% for orientation and episodic memory respectively) and explained variance attributed to random slopes in longitudinal multilevel models.ConclusionResults support prior histopathological and neuroimaging efforts suggesting that approximately half of cognitive decay, commonly attributed directly to aging, may be due to stroke and other cerebrovascular disease, and Alzheimer’s disease.
Differences on autobiographical memory in healthy aging and Alzheimer’s disease
Authors: Escudero Joaquin V. , Melendez Juan C. , Satorres Encarnacion 
Published: December 2020

AbstractBackgroundAutobiographical memory (AM) refers to memories from one’s past that are characterized by a sense of subjective time and autonoetic consciousness which involves a feeling of re‐experiencing or reliving the past. Cognitive aging research indicates age‐related deficits in episodic AM, whereas semantic AM is unimpaired in healthy older adults. Regarding research involving subjects with dementia had showed that episodic memories deteriorate rapidly in Alzheimer's disease (AD), whereas semantic memories are preserved until moderate stages. This study aims to compare healthy older adults and AD patients, on the type of AM in order to identify differences between the groups.MethodThirty patients with initial AD, and 46 healthy older participants matched on age and education took part in the present study. Participants underwent a neurological and neuropsychological assessment. To evaluated AM, Autobiographical Memory Interview (AMI) was applied. AMI is a semi‐structured interview, applicable to subjects from 18 years old, that evaluates the two components of AM: semantic and episodic. For the study, MANOVA was performed to determine whether there were differences between groups on AM components.ResultThe multivariate contrasts revealed a main effect of group (Λ = .701; F(2, 73) = 85.57; p < .001). Therefore, post hoc Tukey tests were performed for each dependent variable, and significant differences were obtained on all the measures studied. Healthy older people obtained higher score than AD on the episodic AM (F(1, 75) = 8.14; p < .001) and on semantic AM (F(1, 75) = 164.5; p < .001).ConclusionAutobiographical recall in AD is mainly characterized by a substantial loss of episodic and semantic information, leading to a decontextualization of autobiographical memories and a shift from mentally reliving past events to a general sense of familiarity. This decline is exacerbated by anterograde and retrograde amnesia, leaving AD patients with limited access to memories that shape self‐consciousness, self‐knowledge, and self‐images.
Prediction of longitudinal clinical outcomes of MCI population using Alzheimer’s AT[N] biomarker profiling: Can machine learning methods improve our predictions?
Authors: Ezzati Ali , Harvey Danielle J. , Davatzikos Christos , Truelove‐Hill Monica , Sreepada Lasya P , Pomponio Raymond , Zammit Andrea R , Jack Clifford R , Aisen Paul S , Lipton Richard B 
Published: December 2020

AbstractBackgroundThe NIA‐AA Research Framework on Alzheimer’s disease (AD) represents an important advance in the biological characterization of the AD spectrum. While this framework predicts disease progression, definitions of amyloid, tau, and neurodegeneration positivity based on cut‐scores may limit the performance of predictive models.Method252 cognitively normal (CN), 424 amnestic mild cognitive impairment (aMCI) and 128 AD participants were included in this study. aMCI participants at baseline were classified using 2 different methods: 1) Using conventional AT[N] criteria based on proposed cut‐offs in CSF or brain imaging measures: participants were classified on the AD continuum (A+T‐N‐, A+T+N‐, A+T‐N+, A+T+N+) and non‐AD continuum (all other subgroups); and 2) using a machine learning (ML) based method (ensemble linear discriminant model built using CN and AD participants) to classify MCI participants as CN‐like or AD‐like based on continuous CSF biomarkers, regional amyloid PET, FDG‐PET, and volumetric MRI (variables contributing to AT[N] classification; Table 1). Predictive performance of AT[N]‐based classification and ML‐based classification were assessed using clinical outcomes at 0.5, 1, 2, 3, 4, and 5 years of follow‐up.ResultA total of 414 aMCI participants had at least one wave of follow‐up (N of 397, 400, 346, 297, 238, and 93 at 0.5, 1, 2, 3, 4, and 5 years, respectively). The progression rate from aMCI to AD was 5.8% at 6m, 9.5% at 1y, 17.9% at 2y, 19.9% at 3y, 20.2% at 4y, and 15% at 5y of follow‐up. Presence of AD pathology at baseline (AT[N]‐based AD continuum) predicted progression to AD with high sensitivity but modest specificity during follow up (Table 2). ML‐based classification was less sensitive but substantially more specific. ML‐based classification models were significantly more accurate than AT[N]‐based classification at all follow‐up periods (McNemar test, p‐value<0.001 for all).ConclusionA single cut‐point approach used in AT[N] classification lacks accuracy when research questions require high diagnostic or prognostic certainty. A multivariate approach using ML methods can improve accuracy of prediction of disease progression. PPVs were higher at all follow‐up time points for ML‐based classification, indicating that progression to AD occurs at a higher rate in persons identified by ML.
Gray and white matter damage are associated with motor symptoms in Parkinson’s disease
Authors: Dadar Mahsa , Gee Myrlene , Duchesne Simon , Camicioli Richard 
Published: December 2020

AbstractBackgroundPrevious studies have found associations between atrophy in Substantia Nigra (SN) and White Matter Hyperintensities (WMH) of vascular origin and motor deficits in Parkinson’s disease (PD) patients (Zeighami et al. 2015, Pozorski et al. 2019). Here we assess the relationship between WMHs and SN atrophy and motor symptoms in PD.MethodData included 50 PD patients and 45 age‐matched controls with T1‐weighted and FLAIR scans at baseline, month18, and month36. WMHs were segmented using T1‐weighted and FLAIR images and a random forests classifier (Dadar et al. 2017, Figure 1.a). Deformation‐based morphometry was used to measure atrophy in Substantia Nigra (SN) (Figure 1.b, Ashburner et al 1998). The relationship between MRI features and clinical scores was assessed using mixed‐effects models: where Cohort denotes a categorical variable contrasting PD versus controls, and ID denotes the categorical random effects. The variables of interest were MRI‐Feature (implying an overall association between the MRI feature and clinical score of interest) and the interaction term Cohort:MRI‐Score (implying an additional PD‐specific impact of the MRI‐Feature on the Clinical‐Score). Log‐transformed WMH volumes and mean DBM score in left and right SN were used as MRI features. The motor subscore of Unified Parkinson's Disease Rating Scale (UPDRSIII) was used as the dependent variable of interest, reflecting motor deficits.ResultWMH load significantly increased with age in both groups (t=9.95, p<0.0001). We did not find a significant group difference in WMH volumes. However, WMH load was significantly associated with UPDRSIII in PDs (t=2.63, p=0.008), but not in controls (t=0.02, p=0.86), with a marginal interaction (t=1.67, p=0.05, Figure 2). DBM in both left and right SN significantly decreased with age (tLeftSN=‐5.79, tRightSN=‐6.33, p<0.0001), and were significantly different between the cohorts (Figure 3, tLeftSN=‐4.19, tRightSN=‐3.95, p<0.00001). UPDRSIII significantly increased with age (t=4.75, p<0.00001) and decrease in SN DBM (tLeftSN=‐2.02, tRightSN=‐1.88, p<0.05), with a significantly greater impact in PDs (Figure 4, tLeftSN=‐2.10, tRightSN=‐1.73, p<0.04).ConclusionIn our sample, both SN atrophy and WMH burden were significantly associated with additional motor deficits in PD, implying an additive contribution of both gray and white matter damage to the motor deficits in PD.