bluuebunny/crossref_metadata_2025_split huggingface dataset:
Astrocyte specific chromatin accessibility differences in European vs African Alzheimer Disease brains
Authors: Celis Katrina , Griswold Anthony J. , Rajabli Farid , Whitehead Patrice L. , Hamilton‐Nelson Kara L. , Dykxhoorn Derek M. , Nuytemans Karen , Wang Liyong , Weintraub Sandra , Geula Changiz , Gearing Marla , Trojanowski John Q , Bennett David A , Pericak‐Vance Margaret A. , Young Juan J , Vance Jeffery M. , Flanagan Margaret E 
Published: December 2022

AbstractBackgroundThe APOE4 gene, the strongest genetic risk factor for late‐onset Alzheimer disease (AD), is highly expressed in astrocytes. The local ancestry (LA) region surrounding APOE4 (Chr19:44‐46Mb) is associated with increased AD risk for European (EU) compared to African (AF) APOE4 carriers. We previously demonstrated that APOE4 expression is higher in astrocytes from EU LA carrier brains using single nuclei RNA (snRNA) sequencing. We investigate whether chromatin accessibility differences could explain the APOE4 expression differences between ancestries.MethodWe performed snRNA and assays for transposase accessible chromatin sequencing (snATAC) in six EU and six AF APOE4 AD brains. Bioinformatic analysis was performed using Seurat for snRNA and ArchR for snATAC.Result94,411 and 60,306 nuclei were sequenced by snRNA and snATAC, respectively. Following data integration, we resolved 11 distinct cell type clusters. We confirmed that APOE4 expression in astrocytes was higher in EU (Fold change=1.51; p=1.01x10‐113) and that chromatin accessibility is indeed higher in EU astrocytes at the APOE4 promoter and surrounding LA. Genome‐wide, astrocytes had the most differentially accessible peaks between ancestries (N=12,518, 95% increased accessibility in EU) representing 2.1% of all peaks. Interestingly, chr19 had the most differentially accessible peaks between ancestries (N=964) with 98% increased accessibility in EU. (EU:245; AF:4) peaks were in the APOE4 LA region with 60% overlapping promoters. Pathway analysis of genes with increased chromatin accessibility in EU LA showed enrichment for lipoprotein assembly, remodeling, and clearance.ConclusionThis study represents initial efforts to investigate underlying mechanisms that contribute to the differential expression demonstrated between AF and EU LA brains surrounding APOE4. Our results suggest that the increased EU APOE4 expression previously observed in EU AD LA APOE4 homozygotes relative to AF LA APOE4 homozygotes in astrocytes is at least partly due to increased EU astrocyte chromatin accessibility in the LA region.
BLSA Plasma Biomarkers and Prediction of Amyloid PET Status
Authors: An Yang , Bilgel Murat , Walker Keenan A , Moghekar Abhay , Resnick Susan M. 
Published: December 2022

AbstractBackgroundIt’s crucial to quantify and compare the accuracies of plasma biomarkers in predicting amyloid PET status.MethodThe sample included a cross‐sectional sample of 212 participants from Baltimore Longitudinal Study of Aging. Amyloid PET status was determined from Pittsburgh compound B (PiB) PET using a Gaussian mixture model (140 PiB– and 72 PiB+). Using the Quanterix SIMOA Neuro‐4‐plex, three plasma biomarkers (amyloid‐beta 42 to 40 ratio [Aβ42/Aβ40], glial fibrillary acidic protein [GFAP], and neurofilament light chain [NfL]) were evaluated. We used receiver operating characteristic (ROC) curve analysis to estimate area under the curve (AUC) for each of the three biomarkers in predicting amyloid PET status. AUC estimates were cross validated using leave‐one‐out method. Finally, from a set of potential features (i.e., the three plasma biomarkers, age and APOE‐ε4 carrier status), we applied stepwise logistic regression to find the best subset of features to predict amyloid PET status.ResultParticipant characteristics are presented in Table 1. Each of the 3 biomarkers performed better than a random classifier, with GFAP having the highest validated AUC (0.692), followed by Aβ42/Aβ40 (AUC=0.677), and NfL (AUC=0.625) (Figure 1). The only statistically significant AUC difference is between GFAP and NfL (p = 0.029) (Table 2). Based on stepwise logistic regression, the model with Aβ42/Aβ40, GFAP and APOE‐ε4 status was selected as the final model with a moderate AUC of 0.732 (Figure 2). Odds ratios associated with each predictor are reported in Table 3.ConclusionOf the three Quanterix SIMOA plasma biomarkers, Aβ42/Aβ40 and GFAP have similar AUCs and are better at predicting amyloid PET status compared with NfL, although all AUCs are modest. The final multivariable logistic regression model, which included Aβ42/Aβ40, GFAP and APOE‐ε4 status, had a modest AUC of 0.732. Future studies with larger samples and more biomarkers, especially p‐tau measure, will be necessary to develop better prediction models.
Generalized q‐sampling imaging‐based fiber tracking measures of white matter tracts in cognitive impairment
Authors: Duran Tugce , Bateman James R. , Williams Benjamin J. , Hughes Tim M. , Barcus Richard A. , Peavey Jeremy J. , Okonmah‐Obazee Stephanie E. , Whitlow Christopher T. , Craft Suzanne , Lockhart Samuel N. 
Published: December 2022

AbstractBackgroundDiffusion magnetic resonance imaging (dMRI) is a non‐invasive technique for exploring white matter fiber organization of the brain. As traditional diffusion tensor imaging (DTI) methods may provide inaccurate descriptions of local crossing, kissing and diverging fibers, we examined generalized q‐sampling imaging (GQI), a novel method providing better directional and quantitative information using dMRI data from community‐dwelling older adults.MethodWe examined baseline dMRI data from 227 adults aged 71±8 on average from the Wake Forest Alzheimer’s Disease Research Center’s Clinical Core, who had baseline and year‐1 follow‐up global Clinical Dementia Rating (CDR) score of 0 (indicates normal cognitive function) and 0.5 (akin to mild cognitive impairment [MCI]). The primary outcome measure was cognitive migration status determined by CDR increase/decrease between baseline and follow‐up (mean difference=13.9 months), shown in Figure. We performed atlas‐based fiber tracking analysis using GQI in DSI Studio to generate normalized quantitative anisotropy (NQA), DTI fractional anisotropy (DTI_FA) and restricted diffusion (RDI) measures in several AD‐related white matter (WM) tracts: Corpus Callosum (CC), Fornix, Cingulum bundle/Parahippocampal tracts (CFP; CP; CPP), Uncinate Fasciculus (UF), Superior Longitudinal Fasciculus (SLF) and Inferior Fronto‐Occipital Fasciculus (IFOF). We performed multinomial logistic regression (MLR) models adjusted for age, sex, and education, using cognitive migration status as the outcome, and standardized GQI‐based diffusion indices sNQA, sRDI, and sDTI_FA as predictors.ResultTable 1 lists the baseline demographics and biomarker characteristics for cognitive migration groups. MLR results in Table 2 show significant associations between diffusion indices and tracts for outcome 4 (Reversion) in reference to outcome 1 (stability of normal cognition). Decrease in sNQA in bilateral CP and right Fornix was significantly associated with higher odds of Reversion relative to the reference. Increase in sDTI_FA in left IFOF was associated with higher odds of reversion relative to reference. Decrease in sRDI in bilateral CFP, CPP, Fornix and UF and right CP was associated with higher reversion relative to reference.ConclusionIn early trajectories of cognitive impairment, RDI may be a sensitive diffusion marker for cognitive reversion in several AD‐related WM tracts. Future work will validate these markers in other cognitive migration groups (negative migration and MCI stability).
Differential returns on education by ethnicity and nativity in a cohort of older white and Asian Americans
Authors: Hayes‐Larson Eleanor , Ikesu Ryo , Gee Gilbert C , Brookmeyer Ron , Whitmer Rachel A. , Gilsanz Paola , Mayeda Elizabeth Rose 
Published: December 2022

AbstractBackgroundAsian Americans generally have lower risk of dementia than white Americans. Education likely protects against dementia via direct development of cognitive reserve and indirectly via income and material resources. However, the protective effects of education may be weaker among Asian Americans due to lower income returns among the foreign‐born. We hypothesized that: (1) the effects of education may be weaker among Asians than whites; (2) the effects of education are weaker among foreign‐born than US‐born individuals.MethodWe followed a cohort of 5,974 Chinese, 4,601 Filipino, 3,071 Japanese, and 131,762 white Kaiser Permanente Northern California members for incident dementia (2002‐2020). We estimated hazard ratios (HRs) with age‐ and sex‐adjusted Cox proportional hazards models to examine whether estimated effects of college education (versus less than college degree) on dementia incidence differed by race/ethnicity (Asian versus white) and nativity (US‐ versus foreign‐born). Models were run for Asian ethnicities pooled and separately due to potential subgroup heterogeneity.ResultMean baseline age was 71.3 years (standard deviation, SD 7.7), and mean follow‐up time was 9.5 years (SD 4.6). The estimated effect of college education appeared slightly more protective against dementia incidence among Asian Americans than whites (HR 0.84 95% CI [0.76‐0.92] in Asian Americans, 0.89 [0.86‐0.91] in whites; Figure). There was heterogeneity across Asian groups. The estimated effect of college education (versus less than college) was most protective in Chinese individuals (0.73 [0.62‐0.85]) and Filipinos (0.80 [0.67‐0.95]), but null in Japanese (1.02 [0.84‐1.23]). College education also appeared more protective in foreign‐born vs. US‐born Asian Americans but not whites. Nativity results were also driven by strong effect modification among Chinese participants; other Asian ethnic group estimates were imprecise.ConclusionContrary to expectations, college education may confer slightly greater protection against dementia for Asian than white Americans, driven by differences among foreign‐born. Due to devaluing of education among immigrants, it is unlikely that social/material resources conferred by high education explain the greater protective effect in Asian Americans. However, there is substantial heterogeneity by Asian ethnicity, and additional work on the mechanisms driving differences by ethnicity and nativity (e.g. bilingualism, selective migration) is needed.
Multi‐contrast unbiased MRI template of a Parkinson’s disease population
Authors: Madge Victoria , Fonov Vladimir S , Xiao Yiming , Zou Lucy , Jackson Courtney , Postuma Ronald B , Dagher Alain , Fon Edward A , Collins D Louis 
Published: December 2022

AbstractBackgroundParkinson’s disease (PD) is a complex neurodegenerative disorder affecting regions such as the substantia nigra (SN), red nucleus (RN) and locus coeruleus (LC). Processing MRI data from patients with PD requires anatomical structural references for spatial normalization and structural segmentation. Extending our previous work (Xiao et al., 2015/17), we present multi‐contrast unbiased MRI templates of a larger population of 172 subjects using more modalities: T1w, T2*w, T1‐T2* fusion, R2*map, T2w, PDw, fluid‐attenuated inversion recovery (FLAIR), improved susceptibility‐weighted imaging (CLEAR‐SWI) (Eckstein et al., 2021), and neuromelanin‐sensitive MRI (NM).MethodsSubjects were recruited through the Quebec Parkinson’s Network since December 2018 and are included in a database of PD patients, RBD patients, and age‐ and sex‐ matched healthy controls, complete with MRI data, motor, and cognitive evaluation scores. All raw MRI was QC’d, eliminating scans with artefacts, motion, or noise, resulting in 172 subjects (69 female; ages=39‐87). Using our previous methods (Fonov et al., 2011), a non‐linear unbiased average T1‐T2* fusion template was created in stereotaxic space. For the other contrasts, the subject’s linear contrast‐to‐T1‐T2* transformation was concatenated with the subject’s T1‐T2* non‐linear stereotaxic transformation to avoid multiple resamplings before voxel‐wise averaging of all subject data in the common template space.ResultsFigure 1 depicts the whole brain 1mm template. Figure 2 depicts a closeup of the 0.3 mm template of the midbrain. From both figures, the first eight contrasts were created from 172 subjects: 145 PD, 1 RBD, and 26 controls. The NM contrast was created from 121 subjects (59 female; ages=39‐87): 99 PD, 1 RBD, and 21 controls. The T1‐T2* fusion maps provide improved deep grey matter contrast from T2*w images, while maintaining cortical structure information from T1w images. R2* maps, T2w, PD2, FLAIR, and CLEAR‐SWI contrasts highlight deep grey structures including SN, RN, subthalamic nucleus (STN), putamen, and globus pallidum, while the NM contrast highlights NM‐rich regions of SN and LC.ConclusionAnatomical structural references are useful for structural segmentation and morphometric analyses between healthy controls, PD, and RBD patients in regions such as SN, RN, and LC to aid in early diagnosis and better understand disease prognosis.
Literacy, but not memory, is associated with hippocampal connectivity in illiterate adults
Authors: Resende Elisa de Paula França , Lara Vivian Proença , Santiago Ana Luisa Carvalho , Friedlaender Clarisse Vasconcelos , Rosen Howard J. , Grinberg Lea Tenenholz , Brown Jesse A. , Cobigo Yann , Silva Lênio Lúcio Gávio , de Souza Leonardo Cruz , Rincon Luciana Paula , Maciel Francisca I , Caramelli Paulo 
Published: December 2022

AbstractBackgroundThe role of hippocampal connectivity for good memory performance is well known in persons with high educational level. However, it is understudied the role of hippocampal connectivity in illiterate populations.MethodsThirty‐nine illiterate adults from Brazil underwent a literacy assessment, structural and resting state functional MRI and an episodic memory test (Free and Cued Selective Reminding Test). We correlated the hippocampal connectivity at rest with the free recall scores and the literacy scores. Analyzes were done in SPM 12, corrected for head motion and physiological BOLD signal.ResultsParticipants were most female (66%) and black (79%) and the mean age was 49 years‐old (±13.9). The mean score on free recall was 27.2 (±10.7) out of 48 points and the mean literacy score was 34.9 (±21.1) out of 100 points. The median hippocampal gray matter volume was 2.4 [0.9, 2.3]cm3 in the left and 2.1 [2.1, 2.4] cm3 in the right side. There was a significant connectivity between both hippocampi and the precuneus and the ventral medial prefrontal cortex. However, this connectivity did not correlate with episodic memory scores, but the right hippocampal connectivity correlated with the literacy scores (R2 = 0.13, p = 0.014, CI 1.0‐1.0). Furthermore, the hippocampal gray matter volume did not correlate with memory scores.ConclusionsAlthough we did not find a significant correlation between memory and the connectivity between both hippocampi and the precuneus and ventral medial prefrontal cortex, we found that this connectivity correlated with literacy scores on the right side. The lack of association with memory scores might be associated with low brain reserve in illiterate adults. The association between hippocampal connectivity and low literacy scores suggest a room for improvement if those adults learn how to read later in life.
Evaluating the biological variability of blood‐based biomarkers for use in clinical trials
Authors: Trombetta Bianca A. , Allen Shona W. , El‐Mufti Leena H. , Riley Misha M. , Ramirez Christopher E. , Weik Molly , Carlyle Becky C. , Kivisäkk Pia , Arnold Steven E. 
Published: December 2022

AbstractBackgroundBlood‐based biomarkers offer the possibility of inexpensive, minimally invasive, and accessible diagnostic tools for Alzheimer’s Disease (AD). In the context of clinical trials, these biomarkers hold promise for subject stratification and monitoring the effects of therapeutic interventions. The day‐to‐day variability of these measures must be robustly characterized to determine which blood‐based biomarkers would be the most sensitive for detecting treatment‐related responses. In this study we evaluate biomarker performance in plasma and identify the most reliable candidates for clinical trials.MethodEDTA plasma samples were collected from healthy controls (n=20, 50% female, mean age 56.03 ± 9.43 years) at baseline, 2 weeks, and 4 weeks. Singleplex ELISA and multiplex MesoScale Discovery (MSD) immunoassays were used to measure over 25 biomarkers that were selected for their relevance to dysregulated processes in AD and previously validated in cerebrospinal fluid (CSF). Technical performance was assessed by calculating intra‐ and inter‐plate coefficients of variation (CVs), analyzing dilution linearity, and evaluating freeze‐thaw stability. Biological intra‐individual variability was examined by calculating a biotemporal CV for each subject across the three timepoints.ResultsMost biomarkers were quantifiable in plasma and exhibited technical performance within our thresholds (intra‐ and inter‐plate CV < 15%). The biomarkers exhibited a range of freeze‐thaw stability, with mean CVs from 5.2% for Adiponectin, a regulator of glucose metabolism, to 30.2% for GRP78, an ER chaperone protein. Plasma levels were relatively stable across time (biotemporal CV < 20%) for several biomarkers, including 24‐OHC, VEGF‐D, Flt‐1, PlGF, sST2, and sTREM2. Several proteins exhibited significant variability over time (biotemporal CV > 20%), such as 8‐OHdG, GRP78, and YKL‐40.ConclusionKey metabolic, vascular, and inflammatory biomarkers were reliably measurable in the plasma of healthy individuals. Several markers exhibited substantial variability over a 2‐4 week time period, which should be taken into consideration when selecting appropriate markers for assessing drug‐target engagement. Designing future clinical trials with multiple baseline visits will allow us to establish biological variability metrics for each individual’s biomarker levels, providing greater confidence in determining treatment effects. Evaluating biomarker stability in other large‐scale subject populations will be necessary to best inform blood‐based biomarker selection for clinical trials.
Early detection of Alzheimer's disease subtypes in Amsterdam Dementia Cohort
Authors: Venkatraghavan Vikram , Kate Mara ten , Pijnenburg Yolande A.L. , Teunissen Charlotte E. , Barkhof Frederik , van der Flier Wiesje M. , Tijms Betty M. 
Published: December 2022

AbstractBackgroundHeterogeneity in the atrophy profile and clinical presentation of Alzheimer’s disease (AD) is well documented in literature. Defining tangible data‐driven subtypes and detecting these subtypes early in an individual is paramount to devising precise intervention strategies. In this study, we estimated distinct data‐driven AD subtypes and their corresponding atrophy timelines from cross‐sectional data and used it for predicting the subtypes of preclinical and prodromal patients.Method1725 AD (age=66.8±8.4), 856 mild cognitively impaired (MCI, age=67.1±8.0), 672 subjective cognitive decline (SCD, age=61.4±10.2) with unknown or positive amyloid status, and 756 controls or SCD with negative amyloid status (CN, age=58.8±9.0) were selected from the Amsterdam Dementia Cohort. Baseline T1w MRIs were processed using Freesurfer and the volumes were selected after automatic quality check (Rosen, NeuroImage, 2018). AD patients in the cohort were used for identifying distinct subtypes using non‐negative matrix factorization (ten Kate, Brain, 2018). Sequences of regional abnormalities were modelled using discriminative event‐based model (Venkatraghavan, NeuroImage, 2021) to identify early atrophy regions in each subtype. The trained model was used to predict the subtypes of MCI and SCD patients. Lastly, we estimated disease severity scores for each individual in the subtype they belong in, based on their position along the respective atrophy timelines.ResultWe identified four AD subtypes: typical subtype (NAD=453(26.3%)), early frontal cortical atrophy (FCA) (NAD=475(27.5%)), early posterior cortical atrophy (PCA) (NAD=349(20.2%)), and early subcortical atrophy (SA) (NAD=362(21.0%)). 86(5.0%) AD patients did not belong in any of these subtypes. The atrophy timelines of each subtype are shown in Figure 1. The trained model predicted the subtypes in 653 MCI (NTypical=202, NFCA=188, NPCA=86, and NSA=177) and 380 SCD patients (NTypical=83, NFCA=158, NPCA=52, and NSA=87). 203 MCI and 292 SCD patients did not belong to any subtype. Individual severity score increased for worse disease stages in each subtype, as seen in Figure 1, validating both the predicted subtypes and the estimated timelines.ConclusionWe identified four AD subtypes, their atrophy timelines, and used it for early detection of subtypes in MCI and SCD patients. Future work should focus on devising precision intervention strategy for patients in each subtype.
Effects of Acquisition Protocol on Advanced Diffusion MRI Measures
Authors: Mishra Virendra R , Sreenivasan Karthik , Cordes Dietmar , Ritter Aaron 
Published: December 2022

AbstractBackgroundSeveral studies have suggested that advanced diffusion MRI (dMRI) measures derived through the analytic techniques of diffusion kurtosis imaging (DKI) and neurite orientation density and dispersion imaging (NODDI) might provide better sensitivity to predict the progression of Alzheimer’s disease (AD) when compared to conventional single tensor diffusion tensor imaging. Two popular simultaneous multislice (SMS) sequences, namely Alzheimer's Disease Neuroimaging Initiative (ADNI)‐3 and Human Connectome Protocol (HCP) are currently widely used in the acquisition of such advanced dMRI data. Since HCP sequences require a custom connectome scanner, several variants of HCP protocol compatible with clinical MRI are in wide use. Before such techniques are used widely across the globe to understand AD, it is imperative to understand whether the absolute WM measures derived through such advanced dMRI measures are reproducible and whether harmonization of dMRI across sites employing different acquisition protocols is applicable. In this study, we hypothesize that higher spatial and angular resolution dMRI data will result in higher quantification of DKI and NODDI metrics, as compared with comparatively lower spatial resolution advanced ADNI‐3 sequences.MethodA 32‐year‐old healthy male participant was scanned over five weeks, both with Advanced ADNI‐3 protocol (114 directions, 2mm3 isotropic, b‐values=500s/mm2, 1000s/mm2, and 2000s/mm2) and 2 variants of the HCP sequence (1.5mm3 and 2mm3) both with 3 b‐values (500s/mm2, 1000s/mm2, and 2500s/mm2) and 213 directions on a 3T Skyra scanner. Total scan time for the ADNI‐3 sequence was 11:37 minutes, 2mm3 CCF sequence was 18:41 minutes, and 1.5mm3 CCF sequence was 23:13 minutes. Various DKI and NODDI measures were computed on all repetitions in‐house. JHU atlas of WM tracts was used as a mask to extract values along each tract at each run separately.ResultAs shown in Figs 1‐4, dMRI‐derived measures were underestimated for most of the WM tracts for the ADNI‐3 sequence, although they had less variance across different runs. Second, NODDI measures were more reproducible across most WM tracts when compared to DKI measures across protocols.ConclusionAcquisition parameters affect the absolute quantification of advanced dMRI measures. Our data suggest that the pooling of dMRI data across various protocols should be performed with caution