bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cerebrospinal fluid biomarkers at presentation associate with etiologic diagnosis and treatment responsiveness in patients with rapidly progressive dementia
Authors: Kuchenbecker Lindsey A , Tipton Philip W , Martens Yuka A , Brier Matthew R , Satyadev Nihal , Dunham S Richard , Lazar Evelyn , Dacquel Maxwell V , Bu Guojun , Geschwind Michael D , Morris John C , Schindler Suzanne E. , Fagan Anne M. , Graff‐Radford Neill R , Day Gregory S. 
Published: December 2023

AbstractBackgroundThe causes of rapidly progressive dementia are heterogeneous, including neurodegenerative, vascular, and autoimmune/inflammatory diseases. Overlap in presenting symptoms and signs, results of cognitive testing, and findings on brain imaging and other common diagnostic tests confounds the etiologic diagnosis, contributing to diagnostic delays, missed opportunities for treatment, and poorer outcomes in patients with RPD. New tools and approaches are needed to improve diagnostic accuracy, with particular emphasis on improving early recognition of patients with potentially treatable causes of RPD.MethodsEstablished and emerging biomarkers of Alzheimer disease neuropathology (Aβ42/40, p‐tau181, p‐tau231), neuroaxonal and neuronal injury (neurofilament light [NfL], VILIP‐1, total tau), neuroinflammation (YKL‐40, sTREM2, GFAP, MCP‐1), and synaptic dysfunction (SNAP‐25, neurogranin) were measured in cerebrospinal fluid obtained at presentation from 78 prospectively accrued patients with RPD due to neurodegenerative (n = 35), vascular (n = 9), and autoimmune/inflammatory (n = 34) diseases, and 72 age‐ and sex‐similar cognitively normal individuals (controls). Causes of RPD were further classified as potentially treatment‐responsive or nonresponsive, referencing the extant literature. Biomarker levels were compared across etiologic diagnoses and by treatment responsiveness, referencing control values. Analyses were adjusted for the effects of age (ANCOVA).ResultsAlzheimer disease biomarkers (low Aβ42/40 and elevated p‐tau181 and p‐tau231) were associated with neurodegenerative causes of RPD (ANCOVA, p = 0.02 p = 0.30, p = 0.15), while high NfL (p = 0.61), low VILIP‐1 (p = 0.009), and elevated markers of inflammation (sTREM2, p = 0.009; YKL‐40, p<0.001) identified patients with autoimmune/inflammatory diseases (Figure). MCP‐1 levels were highest in patients with vascular causes of RPD (p = 0.08). 44 patients (56.4%) had potentially treatment‐responsive causes of RPD. Treatment responsiveness was associated with higher levels of CSF Aβ42/40 (p = 0.003), NfL (p<0.001), sTREM2 (p<0.001) and YKL‐40 (p<0.001); and lower levels of p‐tau‐181 and p‐tau231 (p<0.001, p<0.001) and VILIP‐1 (p<0.001).ConclusionSelected biomarkers measured in CSF at presentation associate with etiologic diagnosis and treatment responsiveness in patients with neurodegenerative, vascular, and autoimmune/inflammatory causes of RPD. Biomarker panels may be adapted to improve recognition of patients with treatment‐responsive causes of RPD early in the symptomatic course when treatments may be most effective.
Neurofibrillary tangle distribution in posterior cortical atrophy and early‐onset Alzheimer disease
Authors: Dickinson Sophie M. , Smirnov Denis , Estrella Melanie , Headley Alison , Goodwill Vanessa , Pizzo Donald , Galasko Douglas R. , Salmon David P. , Hiniker Annie 
Published: December 2023

AbstractBackgroundEarly onset sporadic Alzheimer’s disease (AD) sometimes presents with non‐memory symptoms attributed to relatively greater neocortical than hippocampal tau neurofibrillary tangle (NFT) density. Posterior cortical atrophy (PCA) is a variant of AD presenting with visuospatial deficits and atrophy of the occipital cortex, yet the pathologic underpinning is poorly understood.MethodWe examined brains of 50 patients with early onset (age<65 y.), autopsy‐confirmed AD from the UCSD Alzheimer’s Disease Research Center, 12 of whom were clinically diagnosed with PCA. Immunohistochemistry for tau was performed in the hippocampus (HP), midfrontal cortex (MF), and occipital cortex (OC), and the density of NFTs was measured.ResultPCA and non‐PCA patients did not significantly differ in age (63.35±4.59 y. at death) or sex (40% female). The APOE ε4 allele was much less frequent in PCA (p = 0.002). While the PCA group was less impaired on the MMSE (p = 0.01), they performed comparably on the CDR sum‐of‐boxes. The OC NFT density did not differ between the groups; however, the OC/HP (p = 0.04) and OC/MF (p = 0.008) ratios demonstrate that PCA cases had relatively greater OC pathology than non‐PCA patients. Across the groups, age at symptom onset was inversely associated with OC NFT density (p = 0.01) and the OC/HP NFT ratio (p = 0.008), such that earlier onset was associated with greater OC NFT burden. Visuospatial performance, measured by the Block Design test, was associated with the OC NFT density (p = 0.006), as well as the OC/HP (p = 0.007) and OC/MF NFT ratios (p = 0.03), with the effects remaining significant when restricting to non‐PCA participants. In contrast, memory performance measured by the Logical Memory Immediate Recall measure was only associated with HP NFT density (p = 0.02).ConclusionDespite differences in the predominance of early visuospatial symptoms, PCA and non‐PCA patients did not differ in OC tangle density, with some of the highest densities in non‐PCA patients. However, the degree of visuospatial impairment was associated with the OC/HP NFT ratio across both groups. Results suggest that greater memory impairment in the non‐PCA patients associated with a higher HP tangle density may moderate the predominance of visuospatial impairment so that they do not receive a formal PCA diagnosis.
Generative deep learning model‐based manifold construction for ATN biomarkers
Authors: Song Yeong‐Hun , Seong Joon‐Kyung 
Published: December 2023

AbstractBackgroundGenerative models have the potential to be beneficial in neuroimaging, as they can produce new data samples that closely resemble the ones they were trained on. This can assist in addressing the scarcity of brain scan data, which is a significant issue in neuroscience.MethodIn this study, we developed a one‐ring convolution‐based generative adversarial network (GAN) to generate ATN biomarkers, which operate on an icosahedron cortical mesh structure on a cortical surface. Data from the Alzheimer’s Disease Neuroimaging Initiative cohort was used in the study, including AV45, AV1451, and T1 magnetic resonance imaging (MRI) scans from 2283 subjects. The cortical thickness of all T1 MRI images was calculated using a Freesurfer pipeline and resampled into icosahedron subdivision level 3. AV45 and AV1451 scans were then converted into standard uptake value ratio (SUVR) and mapped to the cortical surface before being resampled into icosahedron subdivision level 3. For each modality, the proposed GAN was trained and evaluated using density and coverage metrics, which measure the fidelity and diversity of generated samples. Also, we validated the efficacy of the constructed ATN manifolds using the data augmentation scenario. Specifically, we evaluated the performance of tau stage classifiers using cortical thickness data with and without augmenting tau PET data from our manifolds.ResultOur proposed GAN achieved high performance in density and coverage. Specifically, the density and coverage for the amyloid SUVR were 1.0 and 0.97, for the tau SUVR were 1.34 and 1.0, and for the cortical thickness were 1.66 and 0.98, respectively, showing high performance in generating realistic and diverse samples for all modalities. The effectiveness of our proposed GAN was further demonstrated to show improved performance in the Tau Braak stage classification experiment when using the generated data. When augmented with generated data, the classification accuracy was improved from 0.77 to 0.84 in terms of AUC scores.ConclusionOur proposed GAN achieved high performance in density and coverage, generating realistic and diverse samples for each ATN biomarker, and demonstrated improved performance in a Tau Braak stage classification experiment when using the generated data.
Proteomics analyses of CSF and Plasma for discovery of early vascular pathologies in neurodegenerative disease
Authors: Elahi Fanny M 
Published: December 2023

AbstractBackgroundCerebral small vessel disease (cSVD) is a major cause of brain degeneration and dementia for which we have no therapies. My lab applies new molecular omics technology to human biospecimen for an initial unbiased approach to discovery of clinically‐relevant dysregulated molecular pathways in humans living with disease. To this end, we have focused our efforts on an autosomal dominant form of disease–CADASIL (Cerebral Autosomal Dominant Arteriopathy Subcortical Infarcts and Leukoencephalopathy) caused by mutations in NOTCH3 . Given its autosomal dominant nature, CADASIL provides a unique opportunity to build an all‐in‐human modeling pipeline for therapeutic discovery.MethodWe have performed proteomics analyses in plasma samples (CADASIL n = 58, controls n = 60) as an unbiased starting point for discovery of dysregulated molecular pathways and are building patient iPSC derived multi‐cellular in vitro models (iCADASIL) to identify molecular drivers of pathological cell‐cell communications within the neuro‐glio‐vascular unit. Both bulk proteomics as well as spatial transcriptomics (GeoMx, Nanostring) from donated brain samples are used to extract disease‐associated molecular signatures from human tissue for (1) testing the validity of iCADASIL models, and for (2) comparison with blood proteomics and identification of promising plasma biomarkers for non‐invasive staging of neurovascular pathology in living humans for the purposes of future clinical trial design.ResultOur initial results indicate that dysregulations in angiogenesis, changes to extracellular matrix and immune dysfunction, including neutrophilic activation, may be features of early disease stages with relevance to mild brain phenotypes such as slowed processing speed and executive dysfunction. In vitro angiogenesis assays and immune cell extravasation assays are underway in our iCADASIL models.ConclusionThe unbiased proteomics performed on CADASIL ‐ control plasma confirmed known molecular dysregulations as well as identified new avenues of investigation that we are pursuing via iCADASIL models. The combination of omics methods applied to multiple biospecimen, including in vivo plasma and ex vivo brain tissue provides a powerful approach for discovery of therapeutically targetable pathways with relevance to disease‐associated phenotypes.
Using single cell transcriptomic data to elucidate cell‐type protein signatures of disease
Authors: Miedema Suzanne S.M. 
Published: December 2023

AbstractBackgroundKey to developing treatment strategies for neurodegenerative disease is identifying cell type‐specific pathways that drive disease initiation and progression. Advancements in single cell/nuclei transcriptomics (sc/snRNAseq) have elucidated how RNA expression differs between cell types in disease. In addition, these efforts have yielded detailed transcriptomic profiles belonging to specific cell subtypes. For the proteomic field, deep single cell measurements have been practically unreachable. What can we learn from sc/snRNAseq resources about cell type‐specific proteomic profiles? Can we use them as reference for elucidating involvement cell types in disease at the protein level?MethodWe discuss two studies performed in our lab. First, we use sc/snRNAseq resources [References 1,2,4] to infer which cell types are involved in the proteomic changes in FTD [5]. Secondly, we compare the proteomic profiles of granulovacuolar degeneration‐positive and tangle‐positive neurons from AD with transcriptomic resources [3].ResultWe demonstrate that applying sc/snRNAseq resources can elucidate involvement of major brain cell types in neurodegenerative disease processes at the protein level. Doing this for highly specified cell types is not advisable, due to the correlation gap between RNA and protein expression levels.ConclusionWe aim to reveal cell type‐specific disease mechanisms in neurodegenerative disease to pave the way for cell‐targeted treatments. Future endeavours will enlarge our insights in the single cell proteomics field.
References:[1] Darmanis S, Sloan SA, Zhang Y, et al (2015) A survey of human brain transcriptome diversity at the single cell level. PNAS 112:7285‐7290.[2] Hodge RD, Bakken TE, Miller JA, et al (2019) Conserved cell types with divergent features in human versus mouse cortex. Nature 573:61‐68.[3] Hondius, D. C., Koopmans, F., Leistner, C., et al. (2021). The proteome of granulovacuolar degeneration and neurofibrillary tangles in Alzheimer’s disease. Acta Neuropathologica, 141(3), 341‐358.[4] Lake BB, Chen S, Sos BC, et al (2018) Integrative single‐cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol 36:70‐80.[5] Miedema, S.S.M., Mol, M.O., Koopmans, F.T.W. et al. Distinct cell type‐specific protein signatures in GRN and MAPT genetic subtypes of frontotemporal dementia. Acta Neuropathol Comm 10, 100 (2022).
Predicting clinical decline over 36 months with a memory‐based digital biomarker
Authors: Bruno Davide , Zinkunegi Ainara Jauregi , Bock Jason R 
Published: December 2023

AbstractBackgroundWord‐list recall tests are routinely employed in clinical practice for dementia screening. Process scoring and latent modeling of these tests, where underlying neurocognitive mechanisms are exploited, have shown potential to enhance test accuracy without requiring test redesign. We examined Embic’s digital biomarker M, a measure of recall derived from multinomial processing trees and hierarchical Bayesian computational methods, as a predictor of conversion to mild cognitive impairment (MCI) and Alzheimer’s disease (AD), and of increase in Clinical Dementia Rating (CDR) score.MethodsSecondary data analyses were carried out from ADNI data. M was computed from Rey’s AVLT data, and compared to standard AVLT clinical metrics (total and delayed recall). We conducted logistic regression analyses with diagnosis at 36 months (normal vs. MCI + AD) as outcome; all 169 participants were classified as normal at baseline, and 13 of these progressed to a clinical diagnosis (see Table 1). We controlled for baseline age, gender and years of education; and ran separate analyses with other metrics to avoid multicollinearity. We then repeated the same analytical framework with CDR scores (stable at 0 vs. increased to 0.5 or 1) at 36 months as outcome: stable participants were 141, and 24 increased (see Table 2). Covariates and predictors were the same as in the previous analysis.Results
M predicted conversion to MCI or AD after three years (FDR‐corrected p value = 0.048), while neither AVLT total nor delayed recall did. Performance metrics showed that M generated 92% accuracy, or probability of yielding a correct classification, and 100% specificity, or no false positives (Figure 1). Analogously, M, but not standard AVLT metrics, also predicted a CDR increase after three years (FDR‐corrected p value = 0.003), with high accuracy (84%), specificity (96%), and precision (80%), or positive predictive value (Figure 2). Sensitivity analyses with ordinal regression broadly confirmed these findings.ConclusionsThese results indicate Embic’s digital biomarker M outperforms some of AVLT’s traditional metrics in identifying individuals who will progress to MCI and AD after three years, from a healthy baseline.
Rare copy number variation in the GR@ACE/DEGESCO dementia dataset of spanish population
Authors: de Rojas Itziar , García‐Ribas Guillermo , Sanchez‐Juan Pascual , Perez‐Tur Jordi , Pastor Pau , Bullido María J. , Álvarez‐Martínez Victoria , Real Luis Miguel , Mir Pablo , Piñol‐Ripoll Gerard , García‐Alberca Jose María , Medina Miguel , Sáez María Eugenia , Carracedo Angel , Tárraga Lluís , Boada Mercè , Van Der Lee Sven J , Ruiz Agustin ,   GR@ACE/DEGESCO
Published: December 2023

AbstractBackgroundRecent studies have found that duplications or deletions of DNA fragments, known as copy number variants (CNVs), may play a role in missing heritability for complex human diseases.MethodIn that sense, we conducted a scan for CNVs in the GR@ACE/DEGESCO dementia dataset of Spanish population1 (n = 20,080 individuals using Axiom 815K Spanish biobank array (Thermo Fisher)). We ran CNV calling algorithms from PennCNV software to obtain high‐confidence calls for CNVs. After extensive quality control (QC) for individuals (sex discrepancies, excess of heterozygosity, high missingness, familiar relations and population outliers were excluded), CNVs (>50kb and nSNPs>10 were included), removing spurious CNVs in telomeric/centromeric regions, gene QC (ANOVA comparisons between DNA origin source evaluated in controls), 8,275 controls and 7,818 dementia cases were selected for following analysis.ResultGlobal burden analyses revealed highly significant differences between dementia cases and controls (dem/ctrl) in CNV rate of deletions (p = 2.69E‐04, meanctrl‐dem = 1.53‐1.38) but not in duplications (p = 0.535, meanctrl‐dem = 1.63‐1.61). However, only the number of genes affected by CNVs was significantly different in deletions (p = 0.042, meanctrl‐dem = 3.62‐3.10; duplications p = 0.448, meanctrl‐dem = 5.93‐5.68). We also observed a nominal deletion‐gene‐affected association by genes related to nervous system development pathway or intellectual disability such as VPS13B2 (nine cases affected), PKP3 (Freqctrl‐dem 0.17‐0.25%) and FBRSL13 (Freqctrl‐dem 0.15‐0.26%). The current study did not detect significant differences in CNVs that affect known Alzheimer’s disease (AD) loci identified by recent genome‐wide association studies4 (deletions Fisher test p = 0.0875, duplications Fisher Test p = 0.5853). Nevertheless, we found three important AD genes with CNVs potentially associated to dementia. Specifically, we detected MAPT protective deletions (Freqctrl‐dem 0.16‐0.01%), ABCA7 risk deletions (Freqctrl‐dem 0.05‐0.14%) and APP CNVs detected in five dementia cases (0.064%). Because the technology used in our study has limitations in detecting small CNVs, future studies must carefully assess the presence of smaller CNVs and their relationship with dementia.ConclusionWe have detected potential CNVs in the Spanish population for Alzheimer’s disease, however, because the technology used in our study has limitations in detecting small CNVs, future studies must carefully assess the presence of smaller CNVs and their relationship with dementia.
APOEε4 facilitates the accumulation and connectivity‐mediated spreading of tau pathology at lower amyloid levels
Authors: Steward Anna , Biel Davina , Dewenter Anna , Roemer Sebastian Niclas , Wanger Fabian , Dehsarvi Amir , Svaldi Diana , Higgins Ixavier A. , Brendel Matthias , Shcherbinin Sergey , Ewers Michael , Franzmeier Nicolai 
Published: December 2023

AbstractBackgroundTargeting amyloid (Aß) may show highest clinical efficacy when preventing downstream tau spreading and subsequent neurodegeneration. Thus, it’s critical to establish Aß‐PET thresholds at which tau spreading is triggered, which may depend on ApoE4, i.e. a key genetic risk factor for Aß, tau and cognitive decline. Yet, ApoE4’s impact on Aß‐related tau spreading is unclear, which may bias intervention studies. Thus, we assessed whether ApoE4‐carriage facilitates Aß‐associated tau spreading and determined thresholds at which Aß‐related tau trajectories diverge between ApoE4‐carriers vs. non‐carriers. Lastly, we ran simulated trials using tau‐PET endpoints to test whether including ApoE4‐carriers increases the sensitivity to detect treatment effects at lower Aß‐levels.MethodWe included non‐demented ADNI subjects (n = 237), and a replication sample (18F‐AV‐1451‐A05 trial, n = 130). We combined ApoE4‐status, amyloid‐PET and longitudinal flortaucipir tau‐PET alongside resting‐state fMRI‐connectivity templates from n = 42 Aß‐ controls to assess connectivity‐based tau spreading (subject‐specific tau epicentre connectivity ranked regions i.e. Q1‐Q4) together with tau accumulation in Braak‐stage specific regions. We used i) mediation models to test whether ApoE4 effects on Braak‐stage specific tau accumulation were mediated by centiloid and ii) linear models to determine the centiloid by ApoE4 interaction on tau accumulation in Q1‐Q4 and Braak‐stage regions and iii) determined confidence interval‐based cut‐points of ApoE4‐related divergence in tau trajectories. Lastly, we estimated ApoE4‐stratified sample sizes to detect simulated treatments effects on tau accumulation across increasing centiloid windows.ResultUsing bootstrapped mediation we found that centiloid mediated faster ApoE4‐related tau‐PET increase in early Braak‐stage regions (ADNI/A05, BraakIII: B = 0.19/0.33, p<0.001/<0.001), and that ApoE4 facilitates Aß‐related tau spreading (ApoE4*centiloid interaction on tau‐PET increase) in early Braak‐stage and connectivity‐based ROIs. We identified centiloid cut‐points (∼10‐15) where Aß‐related tau accumulation rates diverged between ApoE4 carriers vs. non‐carriers through Braak and connectivity stages (Fig.1), which replicated across samples. Lastly, focusing on ApoE4‐carriers exclusively in simulated clinical trials increased the sensitivity for detecting treatment effects on tau accumulation at lower centiloid levels (Fig.2).ConclusionApoE4 is related to earlier Aß‐related tau accumulation, which may drive faster ApoE4‐related disease progression. Our results are relevant for clinical trial design, illustrating that ApoE4‐carriers may require earlier anti‐Aß intervention to attenuate downstream tau spreading.
Association of resilience with successful ageing and the mediating role of community participation
Authors: Park Sangmi , Kim Jung‐Ran , Lee Chang Dae 
Published: December 2023

AbstractBackgroundHigher level of resilience plays a positive role in successful ageing. Participation was reported as a factor related to resilience and successful ageing. As resilience is developed based on interaction between personal characteristics and environment over time, enhancing resilience level of individuals in later life span is difficult. Finding out modifiable factors which mediate the relationship between resilience and successful ageing can be alternative way to promote successful ageing. The aim of this study was to examine the mediating role of community participation.MethodConvenience sampling was applied to recruit community‐dwelling older adults aged 60 or older who could self‐rate the questionnaire independently. Online and on‐site surveys were conducted using the same questionnaire. Total scores of Korean version of the Connor‐Davidson Resilience Scale (CD‐RISC) and Community Integration Measure (CIM) were used respectively to measure resilience and community participation. Sum score of the satisfaction across four areas‐ subjective health; subjective memory; social relationships; socioeconomic status was used to measure successful ageing. Mediation analysis using SAS 9.4 was conducted to examine the mediating role of community participation in relationship between resilience and successful ageing.ResultData from 284 individuals was analyzed (Mean age: 67.99 ± 7.28; Female: 50.70%). Mean scores of CD‐RISC, CIM, and successful ageing were 60.70, 36.65, 13.05 respectively. A total effect of resilience on successful ageing was statistically significant (β = 0.038, 95% CI = 0.027 to 0.049) after controlling for gender, age, education, living status, employment status, disabilities, depression and chronic disease. Community participation mediated the relationship between resilience and successful ageing (β = 0.006, 95% CI = 0.002 to 0.010). Mediating role of community participation explained 16.44% of total effect of resilience on successful ageing.ConclusionAssisting older adults to increase community participation can be considered as a community‐based health care service to promote successful ageing. Further studies to examine causality among resilience, community participation, and successful ageing is necessary to strengthen the evidence of effect of active community participation on successful ageing.