bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of cerebrospinal fluid and plasma biomarkers with longitudinal tau accumulation
Authors: Moscoso Alexis , Karikari Thomas K. , Grothe Michel J. , Ashton Nicholas J. , Zetterberg Henrik , Blennow Kaj , Schöll Michael 
Published: December 2021

AbstractBackgroundDue to its tight relation with neurodegeneration and cognitive decline, tau pathology is increasingly recognized as an attractive therapeutic target in Alzheimer’s disease. To be more efficient, clinical trials testing anti‐tau drugs need to target subjects at risk for longitudinal tau accumulation. So far, only elevated signal in an amyloid‐β (Aβ) PET scan has been consistently identified as predictor of future tau burden. Here, we studied whether more accessible cerebrospinal fluid (CSF) and plasma biomarkers were associated with PET‐measured longitudinal tau accumulation.MethodWe included two partially overlapping cohorts of participants from the Alzheimer’s Disease Neuroimaging Initiative that underwent baseline and follow‐up [18F]flortaucipir (FTP) scans, as well as baseline Aβ PET scans. In cohort 1 (93 cognitively unimpaired (CU) and 70 impaired (CI)), all participants had available CSF biomarkers (Aβ1‐42, p‐tau181, and p‐tau181/Aβ1‐42 ratio) at baseline; in cohort 2 (37 CU and 37 CI), participants had a plasma p‐tau181 measurement within 2 years prior to the baseline FTP scan. Annual standardized uptake value ratio (SUVR) change on FTP scans was estimated at the voxel level and in a previously defined meta‐region of interest (ROI) using linear mixed effects models. In cohort 1, correlations of CSF markers with FTP SUVR annual change were determined using Pearson correlation coefficients, separately for CU and CI. In cohort 2, we instead used clinical diagnosis‐adjusted partial correlations in the combined cohort (CU+CI) due to lower sample size.ResultAmong CU individuals in cohort 1, all CSF biomarkers showed moderately strong correlations with FTP SUVR change; CSF p‐tau181/Aβ1‐42 ratio showed the strongest association with FTP SUVR change, slightly outperforming Aβ PET (Fig. 1). Similarly, CSF p‐tau181/Aβ1‐42 ratio was the strongest correlate of FTP SUVR change in CI subjects (Fig. 2). In cohort 2, plasma p‐tau181 was moderately correlated with FTP SUVR change, though not stronger than Aβ PET (Fig. 3).ConclusionCSF biomarkers might be as effective as Aβ PET for the prediction of longitudinal tau accumulation. Plasma p‐tau181, though not as strongly correlated as Aβ PET, might represent a cost‐effective solution for the prediction of tau accumulation. These findings may have implications for clinical trials with anti‐tau drugs.
Large‐scale cross‐sectional and longitudinal validation of a digital speech‐based measure of cognition
Authors: Stegmann Gabriela M , Hahn Shira , Liss Julie , Berisha Visar , Mueller Kimberly D 
Published: December 2021

AbstractBackgroundCognitive decline is associated with deficits in attention to tasks and attention to relevant details. We developed a metric, semantic relevance (SemR), which is algorithmically extracted from speech and measures overlap between a picture’s content and the words used to describe the picture. In this study, we validate it in a sample that was not used when developing it. We automatically extract SemR from transcripts of Cookie Theft (BDAE) and evaluate its cross‐sectional and longitudinal clinical validity using four groups: Normal Cognition (NC), Early MCI (EMCI), MCI, and Dementia (D).MethodDementia Bank and Wisconsin Registry for Alzheimer’s Prevention (WRAP) were combined, and participants (average age 63.7) included: NC (N = 918; 647 F), EMCI (n = 180; 110 F), MCI (n = 26, 9 F), and D (n = 195, 126 F). Participants provided Cookie Theft descriptions and Mini‐Mental State Exam (MMSE) assessments on an average of 2.1 occasions/participant, assessed on average 2.4 years apart (total n=2,717). SemR was algorithmically computed from each picture description transcript. Transcripts were also hand‐coded for “content information units” as a ground‐truth comparison with automated SemR. Cross‐sectionally, we used a mixed‐effects model to calculate relationships between SemR and ground truth, and between SemR and MMSE. We estimated within‐speaker SemR longitudinal trajectories using growth curve models (GCM) for each group.Result(Figure 1) Automatic and hand‐coded SemR were strongly correlated (r = .85, p<.05). (Figure 2) SemR was significantly related to MMSE (b = .002, p<.05), such that decrease in MMSE resulted in decrease in SemR. (Figure 3) Longitudinal GCMs showed that SemR declined with age for all groups. The decline was slowest for NCs, steepened for the EMCI and MCI groups, and then slowed again for D, who had the lowest scores. Figure 3 displays SemR trajectories and confidence bands for age ranges with the most data for each group. SemR has a standard error of measurement (SEM) of .05.ConclusionSemR is reliable, shows convergent validity with MMSE, and correlates strongly with manual hand‐counts. SemR declines with age and severity of cognitive impairment, with the speed of decline differing by level of impairment.
Associations between remote cognitive testing on an individual’s own digital device and amyloid burden on neuroimaging in clinically normal older adults: Results from Boston Remote Assessment for Neurocognitive Health (BRANCH)
Authors: Papp Kate V. , Rentz Dorene M. , Buckley Rachel F. , Schneider Olivia R , Hsieh Stephanie , Soberanes Daniel , Properzi Michael J , Schultz Aaron P. , Johnson Keith A. , Sperling Reisa A. , Amariglio Rebecca E. 
Published: December 2021

AbstractBackgroundDigital cognitive testing completed on an individual’s own device, independently, and remotely is a highly appealing solution to sensitively capture early memory changes in preclinical AD. However, few digital assessments have been designed specifically for a preclinical AD population and validated against paper‐and‐pencil measures and relevant AD biomarkers. Here, we describe initial validation steps for the Boston Remote Assessment of Neurocognitive Health (BRANCH), web‐based cognitive testing targeting cognitive domains with AD susceptibility (e.g., cross‐modal associative memory, semantically facilitated learning and recall, and pattern separation) and using task stimuli relevant to everyday life. To determine the validity of BRANCH, we explored correlations between remote BRANCH to in‐clinic paper and pencil measures and PET amyloid burden.MethodA link to BRANCH was either texted or emailed to 128 clinically normal (CN) older adults participating in the Harvard Aging Brain Study. Participants completed the 4 BRANCH measures (modified Face Name Test, groceries test, categories test, signs test) over a mean 19 minutes. Participants had previously completed in‐clinic paper and pencil tests to compute a Preclinical Alzheimer’s Cognitive Composite (PACC‐5) as well as PET imaging with 11CPittsburg Compound‐B to estimate global amyloid burden. A composite of accuracy across BRANCH tasks was computed.ResultParticipants had a mean age of 74.38(Range 51‐89), were 82% Caucasian, and 60.7% female. They completed BRANCH on smartphones (26%), tablets (18%), laptops (26%), and desktops(29%). Only 2.59% of participants reported difficulty completing BRANCH on a post‐test survey. Lower BRANCH composite performance was associated with worse performance on in‐person paper and pencil measures (Figure 1; PACC; r=0.637, p<0.001). Lower BRANCH performance was associated with greater amyloid burden (r=‐0.275, p=0.003).ConclusionA digital memory assessment with ecologically‐valid tasks and stimuli is feasible for CN older adults to complete independently on their own devices. The relatively strong correlation observed between BRANCH and PACC suggests that BRANCH captures valid information about cognitive performance despite being collected remotely on an individual’s own device. The significant association between BRANCH and amyloid burden suggests that these remotely captured tasks may be promising tools to detect and track AD‐specific cognitive decrements on a larger scale.
CS6253 ABCA1 agonist in cynomolgus monkeys increases APOA‐I subspecies PREβ‐HDL and lowers Aβ42 and Aβ40 in cerebrospinal fluid
Authors: Johansson Jan O 
Published: December 2021

AbstractBackgroundThen main genetic late‐onset Alzheimer’s disease (AD) risk factor, apolipoprotein E (APOE) ε4 genotype is characterized by the apoE4 protein having impaired interaction with astrocyte’s ATP‐binding cassette transporter A1 (ABCA1) resulting in; (1) poor cholesterol efflux (Bielicki 2016) to apoE and reduced lipid transport to cells in brain, (2) build‐up of residual cholesterol in lipid rafts, impeding astrocyte function (Rawat 2019), and (3) increased neuron cell death (Voskuhl 2018). ApoE as opposed to most other apolipoproteins does not cross the blood brain barrier (BBB). It has been suggested that the apoA‐I subspecies preβ‐HDL effectively crosses the BBB where it can compensate for ApoE4’s impaired interaction with ABCA1. We hypothesized that the ABCA1 agonist CS6253 would generate preβ‐HDL in plasma and decrease AD risk factors in brain including Aβ42.Method
In vitro: CS6253 was incubated with human plasma (Figure, A‐B) and generation of preβ‐HDL assessed by 2D electrophoreses using a.b. to apoA‐I and CS6253 generating EC50 values. In vivo: Cynomolgus monkeys (cynos) have 93% homology with human apoE, lipid and amyloid metabolism similar to humans making them representative models for humans. In cynos; (1) CS6253 single dose 25 mg/kg iv was administered (n=2) and plasma collected for preβ‐HDL analysis by ELISA (Daichii a.b.) (Figure, C); (2) CS6253 multiple dose 0 (control), 75, 150 and 225 mg/kg iv (n=2/sex/group) 5 times was administered over 10 days and plasma collected serially for preβ‐HDL analysis (Figure, D). CSF and plasma were collected before treatment and 6 hours after the 5th/last dosing and Aβ42 and 40 analyzed by SIMOA.ResultCS6253 increased preβ‐HDL in vitro in human plasma and in vivo in cynos, see Figure. In cynos CS6253 decreased Aβ42 and Aβ40 concentration in CSF following dose‐response pattern.ConclusionThe CS6253 ABCA1 agonist transiently increases plasma preβ‐HDL and reduces CSF concentrations of Aβ42 and Aβ40. The Aβ reduction is consistent with apoE targeted replacement mice models studies where CS6253 reduced Aβ42, prevented AD brain pathology and improved cognition. Studies in human APOE ε4 carriers with and without AD are needed to corroborate these findings and to investigate the therapeutic potential of CS6253 or similar compounds.
Effects of COVID‐19 on cognition of older African Americans
Authors: Kavcic Voyko , Podlesek Anja , DiCerbo Loraine M , Hanna Sophie , Shair Sarah , Giordani Bruno 
Published: December 2021

AbstractBackgroundThe Covid‐19 pandemic, with its diverse effects (e.g., on quality of life, health and illness, physical and psychological well‐being, social and economic life), has also been suggested to effect cognitive functioning. To better understand the possible effect of the COVID‐19 pandemic, we examined cognitive functioning of older African Americans.MethodStudy included 164 community‐dwelling older African Americans (mean age= 75.6, range=64‐94) recruited from the Wayne State Institute of Gerontology Healthier Black Elders Center and general Detroit area. Responses were gathered over the phone between 7/28/2020 to 1/14/2021. Change in cognitive functioning was evaluated with the newly developed Cognitive Change Questionnaire (CCQ) in which participants self‐evaluated their cognition related to daily activities (e.g., memorizing, paying attention, task switching) on a 5‐point scale (1–Much easier, 3–No difference, to 5–Much harder), comparing it to the pre‐pandemic time. In addition, all participants received the TICSm phone‐based cognitive screen. Responses to COVID‐19 were measured on these scales: positive emotions (e.g., gratitude, happiness), negative emotion (e.g., anger, fear), unmet needs (e.g., food, medical care), and personal issues (e.g., economic problems, reduced privacy).ResultFindings showed that 106 (65%) participants did not report any COVID‐19 related cognitive changes, 7 (4%) indicated improved cognitive functioning, and 50 (31%) indicated that their cognitive functioning was worse as compared to pre‐COVID‐19 times. Average CCQ scale score (range 8‐40) was 26.86. CCQ scale showed good psychometric properties (Cronbach alpha=.84). Significant correlations were obtained between CCQ and TICSm subscales: r=–.16 (p=.05) with total score, r=–.19 (p=.01) with immediate recall, and r=–.24 (p=.002) with delayed recall. Stepwise regression analysis showed that COVID‐19 related responses explained 24% of variability on CCQ: participants reported greater cognitive decline if they expressed greater level of negative emotions (21% explained variance) and higher anxiety (3% explained variance).ConclusionThese results demonstrate that a sizeable percentage of older community‐dwelling African Americans do self‐report cognitive decline following COVID‐19, associated with increased presence of negative emotions and anxiety. The possibility of altered cognitive function should be considered not only for quality‐of‐care issues, but also when recruiting for research trials during and after the pandemic.
Assessment of AD‐related plasma biomarkers in diverse ancestral populations
Authors: Griswold Anthony J. , Rajabli Farid , Garcia‐Serje Catherine , Hamilton‐Nelson Kara L. , Adams Larry D. , Tejada Sergio , Mena Pedro Ramon , Starks Takiyah D. , Whitehead Patrice L. , Silva‐Vergara Concepcion , Cuccaro Michael L. , Martinez Izri , Illanes‐Manrique Maryenela , Cornejo‐Olivas Mario R. , Laux Renee A. , Caywood Laura J. , Reitz Christiane , Beecham Gary W. , Byrd Goldie S. , Feliciano‐Astacio Briseida E. , Scott William K. , Haines Jonathan L. , Vance Jeffery M. , Pericak‐Vance Margaret A. 
Published: December 2021

AbstractBackgroundPlasma proteins as biomarkers for the differential diagnosis of AD from other similar neurodegenerative disorders, as well as the identification of preclinical AD, has recently been well supported across several large AD cohorts. However, these are composed primarily of individuals of non‐Hispanic European ancestry. Few studies have been performed in African‐American or Hispanic/Latinx AD populations to determine if plasma biomarkers are also useful in these populations. Given the differences in AD risk loci found across ancestries, the application of these biomarkers in diverse populations is not assured. Therefore, the aim of this study is to explore the utility of plasma biomarkers in AD, MCI and at‐risk family members from diverse ancestral backgrounds.MethodAs part of ongoing initiatives to understand AD in individuals of diverse ancestry, we are measuring the plasma level of biomarkers in a cohort of more than 3,000 individuals. This includes: 999 African Americans (248 AD cases, 591 controls, 160 MCI), 581 Puerto Ricans (223 AD cases, 208 controls, 150 MCI), 1052 Puerto Ricans in families (411 AD cases, 413 controls, 228 MCI), 98 Cubans (23 AD cases, 39 controls, 36 MCI), and 117 Peruvians (33 AD cases, 75 controls, 9 MCI). We will also have data on autopsy confirmed European AD cases (37) and a cohort of Amish individuals (∼400 AD cases, ∼200 MCI, and ∼500 controls). Plasma proteins tested are Aβ42, Aβ40, total Tau, and p‐Tau181 using Simoa chemistry assays (Quanterix HD‐X). All measurements are performed in duplicate and data analysis performed using HD‐X Analyzer Software v1.6.ResultMeasurement and analysis of biomarkers in this diverse dataset is currently underway and will be completed in a few months. These results will allow a direct comparison of biomarker analysis related to AD diagnosis between European, African, and Amerindian ancestries. Moreover, a family‐based design for over 1000 Puerto Rican individuals will be the first to identify potential heritable trends in biomarker levels.ConclusionThis study is critical to being to understand how plasma biomarkers for AD may vary across diverse ancestries and whether previous findings will be generalizable and useful for all individuals, regardless of ancestry.
Acoustic features of voice as a measure of cognitive load during performance of serial subtraction in a remote data collection context
Authors: Cormack Francesca K , Barnett Jennifer H , Taptiklis Nick 
Published: December 2021

AbstractBackgroundCognitive load is the mental demand a task imposes for a specific person. Performance declines when demand exceed capacity; therefore, increase of mental effort may precede measurable cognitive decline. Physiological indices of load (e.g. heart rate, skin conductance etc.) are sensitive to task demand (e.g. subtracting three vs seven), show increased cognitive load with ageing, and in MCI compared to healthy ageing. Voice features have promise as non‐invasive and scalable indicators of mental effort. Here, we aim to classify serial subtraction at high and low cognitive load using voice recordings captured using an automated remote data collection system.MethodParticipants (aged 17‐86) completed serial subtraction via the Neurovocalix web‐app on their own devices. From a pool of 5,742 participants, 100 were randomly selected for manual review. Seven participants were excluded for audio or performance issues. Responses were transcribed and the start and end of each subtraction attempt marked, producing 3,254 attempts for analysis. Low‐level acoustic features were extracted and aggregated over each attempt, then normalized within participant. Random Forest classifiers were trained and evaluated using Leave‐One‐Subject‐Out‐Cross‐Validation (LOSOCV) to predict high vs low load. LOSOCV repeatedly splits the dataset by subject, with one participant at a time used for testing, and the remainder used for training the model. This produces model predictions for each participant and attempt.ResultAverage cross‐validation accuracy was 0.81 (95% CI 0.78 to 0.84), with an average area under the curve (AUC) of 0.87 (95% CI 0.85 to 0.89). We tested predictions for specific numbers which appeared in both subtraction by seven and by three. Accuracy was 0.78, suggesting that predictions were not driven by specific numeric responses. We observed a significant negative correlation between behavioural performance on the task (response rate), and utterance load probability metric for utterances (ρ=‐0.32, p<0.001), suggesting that participants who were more fluent in serial subtraction exhibited lower cognitive load.ConclusionAcoustic features of voice can distinguish between utterances generated under conditions of high and low cognitive load during serial subtraction, adding a novel, independent and sensitive outcome measure to a cognitive task with established utility in the context of neurodegeneration.
Modeling brain‐predicted age from functional connectivity in early‐stage and preclinical Alzheimer disease
Authors: Millar Peter R , Luckett Patrick H , Benzinger Tammie L.S. , Fagan Anne M. , Morris John C. , Ances Beau M. 
Published: December 2021

AbstractBackgroundThe “brain‐predicted age” approach summarizes complex age relationships with neuroimaging features by quantifying apparent brain age compared to normative trajectories. Advanced brain‐predicted age has been observed in many studies of symptomatic Alzheimer disease (AD), but the effects of preclinical AD have not been investigated. Prior studies have typically modeled brain aging with structural MRI, but other modalities, including resting‐state functional connectivity (FC), are underexplored. Here we modeled FC‐predicted brain age and tested its relationships with preclinical and symptomatic AD.MethodWe calculated FC correlation matrices between 300 regions of interest in 493 cognitively normal (clinical dementia rating [CDR]=0), amyloid‐negative (CSF pTau/Aβ42 ratio and/or amyloid PET Centiloid) control participants (18‐91 years old). We trained a Gaussian process regression model to predict age from these FC features using 10‐fold cross‐validation. We applied the trained model to a separate set of 153 amyloid‐negative, 143 preclinical AD (CDR=0, amyloid‐positive), and 183 symptomatic AD participants (CDR>0). Finally, we tested group differences in FC‐predicted brain age between the AD samples and continuous relationships with biomarkers of amyloid (CSF Aβ42, amyloid PET Centiloid), tau (CSF pTau‐181, tau PET [AV‐1451]), and neurodegeneration (CSF NfL, hippocampal volume), controlling for true age as a covariate.ResultThe FC‐based model accurately predicted age in the training set (R2
=.72, mean absolute error=8.14, Figure 1). FC‐predicted brain age was 2.43 years older in symptomatic AD compared to cognitively normal controls (p<.001, Cohen’s d=0.38, Figure 2A), but was 1.90 years younger in preclinical AD compared to amyloid‐negative controls (p=.011, Cohen’s d=‐0.29, Figure 2B). Greater FC‐predicted brain age was associated with smaller hippocampal volume (p=.009, Figure 3F), but also with lower CSF pTau‐181 (p=.012, Figure 3C). Relationships between FC‐predicted age and other AD biomarkers were not significant (p’s>.20, Figure 3).ConclusionElevated FC‐predicted brain age may reflect advanced brain aging in FC networks during symptomatic AD. This finding is consistent with previous models based on structural MRI. Reduced FC‐predicted brain age in amyloid‐positive participants may reflect compensation during early preclinical stages, but this interpretation requires further investigation. FC‐predicted brain age may be a sensitive biomarker in future models of healthy aging and symptomatic AD.