bluuebunny/crossref_metadata_2025_split huggingface dataset:
A multiple cohort study exploring the influence of partial volume correction on age and sex effects on tau PET imaging
Authors: Hobbs Diana A , Flores Shaney , Keefe Sarah J. , Morris John C. , Benzinger Tammie L.S. , Gordon Brian A. 
Published: January 2025

AbstractBackgroundTau PET imaging has become pivotal in understanding the pathophysiological processes underlying Alzheimer disease (AD). In individuals without amyloid pathology, there is evidence tau levels are elevated with increase age and that females show greater levels of binding. An unknown question is how consistent these effects are, and whether they are susceptible to methodological choices impacting PET quantification. We explored the impact of partial volume correction (PVC) on tau regional values in data from the Charles F. and Joanne Knight Alzheimer Disease Research Center (ADRC) and the Alzheimer Disease Neuroimaging Initiative (ADNI).MethodData came from 323 participants (age: 67.89±8.57) at the Knight ADRC and 274 individuals (age: 70.14±6.61) from ADNI. Forty‐two regional values of tau PET binding were measured using flortaucipir with and without partial‐volume correction as implemented using study specific geometric transfer matrix approaches. Participants were selected to be amyloid‐negative. Similarity in regional estimates was determined using spatial permutation testing.ResultWithin the ADRC cohort the value of the regression coefficients changed with PVC by the spatial pattern of the effects remained highly similar for both age (Figure 1A: r=0.66, p<0.001) and sex (Figure 1C: r=0.85, p<0.001). In ADNI there were moderate association between the maps of age‐related effects (Figure 1B: r=0.36, p<0.05) and sex (Figure 1D: r=0.31, p<0.05). When comparing between the ADRC and ADNI cohorts using non‐PVC data there were highly significant associations for both age (Figure 2A: r=0.83, p<0.001) and sex (Figure 2C: r=0.95, p<0.001). When using PVC, data no significant associations were found for age (Figure 2B: r=0.32, p=0.06) or sex (Figure 2D: r=0.02, p=0.92).ConclusionThe implementation of, and choice of PVC approach can drastically impact the detection of PET signals. This is a particular concern in the context of cohorts where there may be low to moderate levels of underlying pathology and where non‐specific binding may dominate the signal. These results emphasize the importance of considering PVC in tau PET analyses and highlight the reliability of observed age‐ and sex‐related effects across different cohorts.
The potential of plasma TDP‐43 as a biomarker for limbic predominant age related TDP‐43 encephalopathy
Authors: Sordo Lorena , Leiby Anne‐Marie C , Malhas Rond , Barlow James , Rissman Robert A. , Kawas Claudia H. , Corrada María M. M. , Head Elizabeth , Sajjadi S. Ahmad 
Published: January 2025

AbstractBackgroundDespite recent advances in biomarkers discovery for neurodegenerative diseases, there are currently no in vivo biomarkers for limbic‐predominant age‐related TDP‐43 encephalopathy (LATE‐NC). A few studies have suggested that plasma TDP‐43 can be an in vivo marker of frontotemporal dementia due to TDP‐43 pathology and amyotrophic lateral sclerosis. In this pilot study, we aimed to assess plasma TDP‐43 concentrations as a biomarker in individuals with autopsy confirmed LATE‐NC.MethodsWe measured plasma TDP‐43 levels in a convenience sample of 48 participants of The 90+ study (n=40) and UCI Alzheimer’s Disease Research Center (n=8) with available brain autopsy results. Of these, 26 (54.2%) were TDP‐43(+) [LATE stages 2 (n=21) and 3 (n=5)], and 22 (45.8%) were TDP‐43(‐) cases. Cases with LATE Stage 1 were not included in this study. TDP‐43 levels were measured using the Simoa TDP‐43 Advantage kit on the HD‐X platform following manufacturer’s protocol.ResultsWe were able to successfully measure TDP‐43 concentrations in all plasma samples. Samples with coefficient of variation (%CV) >20% were excluded from analysis (n=5). Participants ages ranged from 72 to 104 years old (mean=95.2, SD=7.3). Of these, 29 (67.4%) were female. Demographics and neuropathological characteristics stratified by LATE‐NC presence and stage are depicted in table 1. Although non‐parametric tests showed no statistically significant differences between the groups, we found that TDP‐43 levels were higher in LATE‐NC(+), relative to LATE‐NC(‐) cases [mean=626.7 (SD=557.4) vs. 449.6 (395.1) pg/mL]. Moreover, the mean concentration of TDP‐43 tended to increase with increased staging of LATE‐NC (Table 1 and Figure 1).ConclusionOur results provide preliminary evidence that plasma TDP‐43 levels have the potential to be an in vivo biomarker for LATE‐NC either in isolation or combined with other potential measures. To address this, our future work will include assessing plasma TDP‐43 levels in combination with measures of inflammation and/or neurodegeneration.
Precision Medicine for Treating Alzheimer’s Disease in Down Syndrome
Authors: Sukreet Sonal , Kim Elise K , Petersen Melissa , Zhang Fan , Sano Mary , Aisen Paul S. S. , Andrews Howard F , O'Bryant Sid E. , Rafii Michael , Rissman Robert A. 
Published: January 2025

AbstractBackgroundIndividuals with Down Syndrome (DS) exhibit a genetic form of Alzheimer's disease (AD). In this study, we leveraged biobanked plasma samples from a completed clinical trial focused on anti‐inflammatory treatment, Vitamin E, for DS. The trial's primary endpoint was on cognition, and Vitamin E treatment was not found to be significantly beneficial. We examined whether applying precision medicine using an established proinflammatory endophenotype algorithm for AD therapy would reveal responders to anti‐inflammatory therapy as we have with other AD within the DS trial sample.MethodWe utilized biobanked plasma samples from the Phase 3 clinical trial titled "Vitamin E in Aged Persons with Down Syndrome (NCT00056329)." A pro‐inflammatory panel of proteins, including pre‐defined predictive protein and neuropathology markers, were assayed in baseline (n=138) and 36‐month (n=138) plasma samples. Response status was determined by evaluating the annual rate of change as measured by a combination of primary and secondary endpoints (BPT, VT, and BFDS). The predictive biomarker profile was established through SVM analyses to predict treatment response (yes/no) as the outcome variable.ResultPro‐inflammatory biomarkers IFN, IL10, IL12p70, 1L13, IL1B, IL2, IL4, IL6, IL8, and TNF𝛼 (along with APOE4 and demographic factors) were highly accurate in predicting the treatment responders with an AUC of 99.41%. Neuropathology markers, including Aβ40, Aβ42, Aβ42/Aβ40 ratio, NfL, GFAP, total tau, pTau181, and pTau231 with added demographic and APOE factors, predicted the responder status with an AUC of 73.98%. When the inflammatory and neuropathological data were combined into one algorithm, it produced an AUC of 97.35% (sensitivity of 81.69%, specificity of 100%) with an optimized cut‐off of 0.442 for distinguishing responder status.ConclusionOur findings confirmed the utility of our established AD pro‐inflammatory endophenotype for use in the DS population. We demonstrate that a protein algorithm encompassing markers related to inflammation and neuropathology can be used to generate a predictive biomarker endophenotype that can identify DS participants who are more likely to respond and benefit from anti‐inflammatory treatment. Our results demonstrate the feasibility of using precision medicine approaches for treating AD in DS. Our ongoing studies focus on understanding how these predictive algorithms relate to neuropathological progression in DS.
Cognitive Assessment through Writing Tasks
Authors: Chen Casey , Thebaud Thomas , Butala Ankur , Dehak Najim , Moro‐Velazquez Laureano , Oh Esther S 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disease which affects motor‐cognitive abilities including handwriting.Past studies have used simple writing tasks to analyze handwriting fluidity, while more complex tasks can detect visual‐spatial deficits through spatial organization analysis. In this study, we extracted explainable features from the handwriting of AD patients performing various writing tasks using signal processing techniques and analyzed their importance in the cognitive assessment of AD.MethodWe collected a dataset from 60 participants, 23 with AD or MCI due to AD and 37 controls (HC), using a digital tablet. The writing tasks used involved various degrees of freedom: In Copy Text and Copy Read Text participants copied a paragraph, with and without reading it out loud, adding a multi‐tasking complexity layer, and in Freewrite participants wrote a paragraph spontaneously. In‐air and on‐tablet movements and writing pressure were collected. Multiple explainable features were extracted, such as pressure standard deviation, writing time/completion time ratio, displacements between writing, and completion time. Features from AD and HC participants were compared using Welch’s t‐test and adjusted using the false discovery rate (FDR) correction.ResultThe results show that the on‐tablet time/completion time ratio was significantly different between AD and HC (p‐value < 0.05) across all three tasks, and pressure variation was significantly different between groups for the Freewrite and Copy Read Text tasks. For Copy Read Text, there was a significant difference between groups in completion time. AD patients spent more time in‐air than on‐tablet with greater writing pressure variation.For Freewrite, in‐air displacements between writing segments were significantly different (p‐value < 0.01) between groups. AD patients had longer average in‐air time and smaller horizontal displacement.The results suggest a possible hesitation effect and a visual‐spatial deficit in AD patients.ConclusionAnalysis of writing tasks with varying complexity proved useful in assessing the motor cognitive abilities of AD patients. Our results show statistically significant differences between AD and HC participants for the on‐tablet time/total task time ratio and in‐air compared to on‐tablet times across all three writing tasks suggesting greater hesitance in writing among AD patients.
Using Machine Learning to Explore Multimodal Digital Markers for Early Detection of Cognitive Impairment in Alzheimer’s Disease
Authors: Geraci Joseph , Searls Edward , Qorri Bessi , Low Spencer , Li Zexu , Joung Philip , Gifford Katherine A. , Pratap Abhishek , Tsay Mike , Cumbaa Christian , Pani Luca , Alphs Larry , Au Rhoda 
Published: January 2025

AbstractBackgroundRecent technological advancements have revolutionized our approach to healthcare, enabling us to harness the potential of smartphones and wearables to collect data that can be used to characterize Alzheimer’s disease (AD) heterogeneity and to develop digital biomarkers. Our focus is to create comprehensive cross‐domain digital datasets and establish an infrastructure that allows for seamless data sharing. Central to accelerating the potential of digital biomarkers for more accurate and early detection is privacy‐protecting data access, which when combined with deep molecular phenotyping, will enhance our understanding of the biological mechanisms underlying clinical expression.MethodsIn the preliminary phase of this project, we analyzed data from 64 participants from the Boston University Alzheimer’s Disease Research Center and encompassing approximately 1480 variables. Our analysis approach leverages a novel machine learning (ML) technology, Attractor AI, that is capable of differentiating causal and non‐causal subpopulations within small patient or study populations and large volumes of measures, enhancing the efficacy of predictive models.ResultsWe were able to subcategorize 50% of the 27 cognitively impaired (CI) subjects. A notable discovery was a distinct subpopulation of 8 individuals, 7 of whom were CI, characterized significantly by higher sleep‐derived variables such as various desaturation thresholds and periodicity measures (p=0.008‐0.00007). Additionally, incorporating maximum heart rate, revealed another group of 8 subjects, 6 identified as CI, distinguished by elevated heart rate during one or more of their measuring instances (p=10‐10).ConclusionsWhile these results are preliminary, they signal a promising direction to cluster subgroups of people along similar dimensions, laying the groundwork for a precision medicine solution. Our future endeavors include expanding the scope of multimodal digital data to encompass aspects like vocalization and speech patterns derived from cognitive assessments, gait analysis, physical activity, and other cognitive tests. The integration of these diverse data streams, coupled with our preliminary sleep analysis findings, has the potential to result in a robust and accurate subgrouping system for CI to help identification of AD risk pathways that might be amenable to early intervention and either delay or prevent potential transition to AD.
Cerebrospinal Fluid Neurogranin Levels and Cognitive Function in Alzheimer’s Disease and Frontotemporal Lobar Degeneration – Data from CBAS
Authors: Jurasova Vanesa , Andel Ross , Katonova Alzbeta , Zuntychová Terezie , Horakova Hana , Vyhnalek Martin , Matoska Vaclav , Veverova Katerina , Hort Jakub 
Published: January 2025

AbstractBackgroundSynaptic dysfunction occurs in the early stages of neurodegenerative diseases and leads to the breakdown of connections within neuronal networks. Therefore, biomarkers reflecting this process, such as neurogranin (Ng), might be useful to study disease pathophysiology and aid in diagnostics. Ng is crucial for long‐term potentiation and memory consolidation, and plays a central role in synaptic plasticity. It has been reported that Ng levels in cerebrospinal fluid (CSF) are elevated in Alzheimer’s disease (AD), but also in frontotemporal lobar degeneration (FTLD). The aim of this study was to investigate levels of Ng in AD and FTLD, and to evaluate Ng levels and cognitive function in AD.MethodParticipants with aMCI due to AD (n = 109), AD dementia (n = 67), MCI due to FTLD (n = 25), and FTLD dementia (n = 29) were recruited from the Czech Brain Aging Study. All individuals underwent clinical evaluation, MRI and analysis of biomarkers in CSF (Aß42, pTau181, tTau). Levels of Ng in CSF were measured using the LUMIPULSE® G600II instrument (Fujirebio, Ghent, Belgium). A one‐way analysis of covariance (ANCOVA) with post hoc Tukey’s HSD and multivariate linear regression with age, sex, and years of education as covariates were performed using the R statistical software.ResultPatients with aMCI due to AD and AD dementia had significantly higher Ng levels compared to patients with MCI due to FTLD and FTLD dementia. The association between Ng levels and assessments in memory, attention, executive, language, and visuospatial domains did not statistically differ between patients with AD dementia and those with aMCI due to AD.ConclusionNg levels are higher in AD compared to FTLD both in aMCI and dementia stage, but are not related to cognition in AD individuals. Our results indicate that higher levels of Ng are specifically associated with AD pathology and these changes are more prominent in aMCI due to AD suggesting Ng as an early biomarker of AD.
Greater locus coeruleus integrity predicts less accumulation of plasma pTau231 and GFAP over time
Authors: Williams McKenna E. , Fennema‐Notestine Christine , Bell Tyler R. , Franz Carol E. , Glatt Stephen J. , Panizzon Matthew S. , Rissman Robert A. , Kremen William S. , Elman Jeremy A. 
Published: January 2025

AbstractBackgroundThe locus coeruleus (LC) is one of the earliest sites of tau accumulation and integrity of the rostral‐middle LC may be a particularly early marker of AD‐related changes. LC dysfunction may also promote further pathological accumulation. We examined relationships between LC integrity and plasma phospho‐tau (pTau) and the astrocytic marker glial fibrillary acidic protein (GFAP), and whether baseline LC integrity moderates subsequent change in biomarker levels.MethodParticipants were 669 men from waves 3 and 4 of the Vietnam Era Twin Study of Aging (VETSA) (wave 3 age=67.423 [SD=2.59], follow‐up interval=5.52 years [SD=0.39]). All were cognitively unimpaired at wave 3. Linear mixed effects models tested concurrent and longitudinal relationships between baseline MRI‐assessed LC rostral‐middle integrity and pTau231 and GFAP across level of genetic risk for AD (APOE‐ε4+ versus APOE‐ε4‐). Covariates included storage time, testing site, and age.ResultAge was positively associated with pTau231 (β=0.11, p=0.024) and GFAP levels (β=0.26, p<0.001). APOE‐ε4+ carriers showed higher levels of GFAP compared non‐carriers (β=0.23, p=0.011); APOE‐ε4 groups did not differ in levels of pTau231 or LC integrity. Baseline LC integrity was not associated with concurrent biomarker levels in either APOE‐ε4 group. In longitudinal analyses, pTau231 and GFAP increased from waves 3 to 4 (β=0.94, p=0.006, β=1.23, p<0.001, respectively). However, baseline LC integrity moderated the rate of increases, such that higher LC integrity at wave 3 was associated with less pTau231 accumulation (interaction β=‐0.19, p=0.027) for both APOE‐ε4 groups. Additionally, baseline LC integrity moderated change in GFAP from waves 3‐4, and this effect significantly differed by APOE‐ε4 status (3‐way interaction β=‐0.38, p=0.004). Effects of baseline LC integrity on biomarker change were specific to rostral‐middle but not caudal regions. Wave 3 biomarker levels did not moderate longitudinal change in LC integrity.ConclusionResults support the idea that higher baseline rostral‐middle LC integrity is protective against factors that cause accumulation of pTau231 and GFAP. However, the nature of the moderating effect of LC integrity on change in GFAP may be dependent on APOE‐ε4 status. Baseline biomarker levels did not moderate change in LC integrity over time, suggesting changes in LC may be an upstream event.
Brain metabolic subtypes in Long Covid individuals from a Brazilian cohort
Authors: Jotz Arthur Viana , Bastiani Marco De , Machado Luiza Santos , Schlickmann Thomas Hugentobler , Silveira Maiele Dornelles , Senger Joana Emilia , Borelli Wyllians Vendramini , Ferrari‐Souza João Pedro , Povala Guilherme , da Silva Ana Paula Bornes , da Ros João Pedro Uglione , de Mello Guilherme Bastos , Kadan Matheus Fakhri , Kadan Matheus Fakhri , Radaelli Graciane , de Paula Faria Daniele de Paula , Coutinho Artur Martins , Lourenco Mychael V , Hernández Micaela A , Calandri Ismael Luis , Pascoal Tharick A. , Matushita Cristina Sebastião , Soder Ricardo Benardi , Schumacher‐Schuh Artur Francisco , Souza Diogo O. , da Costa Jaderson Costa , de Souza Débora Guerini , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundThe Coronavirus disease 2019 (COVID‐19), caused by SARS‐CoV‐2, is one of the biggest health concerns of the century. Long COVID is one of the major sequelae from the infection and include persistent neurological manifestations. Brain images study suggest that Long COVID patients present distinct brain metabolic alterations. However, whether brain subtypes of Long COVID exist remains unclear. Here, we aimed to evaluate metabolic patterns in FDG‐PET imaging in a Brazilian cohort of individuals presenting with Long COVID.MethodA total of 37 adult Brazilian individuals presenting with Long COVID symptoms were scanned with FDG positron emission tomography (FDG‐PET) and its standardized uptake value ratio (SUVr normalized by the whole brain) was obtained. Then, 89 volumes of interest (VOIs) were extracted and used as input features for a principal component analysis (PCA). Average silhouette method was used to determine the optimal number of clusters. K‐means clustering was used to split the dataset into a set of K groups. We employed ANCOVA and Tukey tests to compare the groups. Statistical analysis was made in the R environment, significance set at p <0.05.ResultPCA analysis identified three different FDG‐PET subtypes: C1 (n=4), C2 (n=6), and C3 (n=27). In the ANCOVA comparison, the regions with the highest increase in the mean regional SUVr were left lingual gyrus for C1 compared to C2 and C3, and corpus callosum for C2 against C3. Conversely, the greatest regional decrease observed were: left caudate nucleus for C1 compared to C2 and right globus pallidus for C1 and C2 compared to C3.ConclusionThese preliminary findings suggest that Long COVID individuals present a predominant metabolic signature (C3) but at least two variants. Our results corroborate that Long COVID is a heterogeneous condition that affects individuals in a different manner. More studies are needed to understand the regional brain vulnerability to Long COVID.
Association of CSF cortisol with cortical thickness in Alzheimer's disease continuum
Authors: Souza Laura Willers , Ros Lucas Uglione Da , Bieger Andrei , Ferrari‐Souza João Pedro , Borelli Wyllians Vendramini , de Bastiani Marco Antônio , Bauer‐Negrini Guilherme , DuBois Jonathan M. , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundMultiple studies have linked high cortisol levels, a frequently used biomarker of stress, with the Alzheimer's disease (AD) pathophysiology. However, the relationship between cerebrospinal fluid (CSF) cortisol levels and AD‐related brain atrophy is not fully understood. This study sought to investigate the cross‐sectional and longitudinal association between CSF cortisol levels and brain cortical thickness in patients across the biological and clinical continuum of AD.MethodsWe evaluated 310 individuals from the ADNI cohort with available baseline CSF cortisol concentrations, structural MRI and CSF Elecsys biomarkers (Aβ1‐42 and p‐tau181). Cross‐sectional analysis was conducted in Freesurfer (v7.1.1) through vertex‐wise analysis using general linear models (GLMs) corrected for multiple comparisons through cluster formation (p<0.01) and permutation (Monte Carlo simulation of 10,000 iterations). Longitudinal measures of an AD signature meta‐ROI were estimated from the surface area‐weighted average of the mean cortical thickness of the following ROIs: entorhinal, inferior temporal, middle temporal, and fusiform. Linear mixed‐effects (LME) models corrected for confounders were performed to evaluate cortical thickness longitudinal trajectories. The following models were used to assess the association between cortical thickness and cortisol levels: (1) cortisol as an independent variable; (2) cortisol, clinical diagnoses, and interaction; (3) cortisol, AD biomarkers positivity, and interaction.ResultsDemographics are depicted in Table 1. Cross‐sectional analysis revealed that higher cortisol levels were associated with higher brain atrophy in several brain regions, as depicted by cortical brain thickness measures (Figure 1). These analyses also showed that cortisol does not interact with clinical diagnoses or AD biomarkers to decrease cortical thickness. In the longitudinal analyses, the association between cortisol and cortical thickness and the interaction with clinical diagnoses were not significant. However, we found that cortisol significantly interacts with AD biomarkers positivity to reduce cortical thickness values over time (β=0.002, p=0.03; Figure 2).ConclusionsAltogether, our results support that cortisol levels potentiate AD pathophysiology effects on AD‐related brain atrophy. These findings suggest that CSF cortisol levels may affect brain vulnerability to AD pathophysiology in the long term through neurodegeneration. Thus, our results have potential implications for understating AD‐related brain atrophy and developing innovative therapeutic strategies.