bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using neuroimaging biomarkers to define plasma thresholds in a community dwelling cohort
Authors: Rudolph Marc D. , Sutphen Courtney L. , Register Thomas C. , Whitlow Christopher T. , Sai Kiran K. Solingapuram , Hughes Timothy M. , Bateman James R. , Dage Jeff L. , Russ Kristen A. , Mielke Michelle M. , Craft Suzanne , Lockhart Samuel N. 
Published: December 2023

AbstractBackgroundAD Imaging biomarkers (IBM) include measures of amyloid (A) on PET, and neurodegeneration (N) on MRI, with thresholds for classification of imaging A/N positivity commonly used at research centers. Novel AD plasma biomarkers (PBM) can potentially enhance neuropathological characterization; however, knowledge regarding which thresholds to use to characterize PBM abnormalities is limited.MethodWe evaluated a range of PBM thresholds based on (1) plasma data alone (gaussian mixture models [GMM]), and (2) when classifying A/N IBM using both existing and data‐driven cutoffs (ROC analyses). Baseline plasma and imaging data were obtained in a community‐dwelling cohort enrolled in the Wake Forest ADRC, including cognitively normal participants (N = 300) and individuals with consensus diagnosis of mild cognitive impairment (N = 192) or dementia (N = 64; Table 1). We examined PBMs (Quanterix SIMOA HD‐X from NCRAD: Aß42/40, GFAP, NfL, p‐tau181) and IBM measures of A (global PiB PET; A+>1.21 SUVR) and N (FreeSurfer temporal cortical thickness; MAYOTHCK), N+ = 2.68mm; FreeSurfer hippocampal volume, N+ = 0.454% head size; HPC‐VOL).ResultOptimal/low/high GMM‐derived thresholds based on PBM distributions (Figure 1a) are provided in Figure 1b. ROC analysis using existing IBM cutoffs are in Figure 1c, with details (sensitivity/specificity/AUC) in Figure 1d. GMM‐ and ROC‐derived p‐tau181 thresholds were highly aligned (range 3.698‐4.071 pg/ml). For NfL and GFAP, imaging ROC‐derived thresholds were lower than GMM‐derived thresholds. AUCs were highest when PBM classification was used to predict A‐PET using existing PET‐positivity cutoffs, with PBM performing more poorly for MRI‐based cutoffs. A/N IBM histograms and current/optimal/low/high GMM‐derived thresholds for each IBM are in Figure 2a. Figure 2b represents how different data‐driven IBM cutoffs impact PBM thresholds in ROC analyses, with AUC statistics for these ROC analyses in Figures 2c‐e. Notably, GMM‐optimal IBM cutoffs performed similarly to current cutoffs for defining PBM thresholds, especially when using A‐PET (AUCs for current and GMM‐optimal >0.747).ConclusionWhile use of existing and data‐driven IBM cutoffs to define PBM thresholds in a community‐dwelling cohort is feasible, some plasma assays (e.g., p‐tau181) are more consistent than others, and caution is warranted against selecting a single threshold. To facilitate clinical use, future work will focus on establishing confidence intervals for PBM abnormalities.
The role of cerebrovascular pathology in the association between amyloid‐β and tau in cognitively unimpaired and impaired individuals
Authors: Coomans Emma M. , van Westen Danielle , Binette Alexa Pichet , Strandberg Olof , Spotorno Nicola , Palmqvist Sebastian , Stomrud Erik , Ossenkoppele Rik , Hansson Oskar 
Published: December 2023

AbstractBackgroundAlthough cerebrovascular pathology often co‐exists with Alzheimer’s disease pathology, it remains incompletely understood to what extent it can contribute to the progression of Alzheimer’s disease pathology. We investigated whether cerebrovascular pathology (white matter lesions [WML] and microbleeds) modified the association between amyloid‐ß (Aß) and both cross‐sectional and longitudinal tau in cognitively unimpaired (CU) and cognitively impaired (CI) individuals.MethodWe included 1240 participants from the Swedish BioFINDER‐2 cohort including 402 Aß‐negative CU, 147 Aß‐positive CU, 257 Aß‐negative CI (MCI and non‐AD dementia) and 434 Aß‐positive CI participants (MCI and AD dementia) (Table 1). All underwent baseline [18F]RO948 (tau)‐PET, and a subset (n = 678) underwent longitudinal tau‐PET (2.6±1.0‐years). We extracted tau‐PET SUVr in a temporal meta‐region‐of‐interest. For Aß, we used global [18F]flutemetamol‐PET SUVr for CU and Aß‐status (+/‐) for CI participants. Global WML volume was calculated using the SPM lesion segmentation tool, corrected for intra‐cranial volume and log‐transformed. Presence of microbleeds was dichotomized into absent or present ( = 1) by visual read. Primary analyses included age‐, sex‐ and APOE e4‐corrected linear mixed models between tau‐PET (outcome) and interactions between time, Aß and WML volume/microbleeds (predictors).ResultUpon correcting for age, sex and e4‐carriership, Aß‐positive CU and CI participants showed significantly more microbleeds compared to Aß‐negative participants of the same cognitive stage (both p<0.05), but no differences in WML volume was observed (both p>0.05). In CU, we observed a significant interaction effect between = 1 microbleed and higher Aß‐load with both greater cross‐sectional tau (ß = 0.67 [CI: 0.45‐0.89], p<0.001) and longitudinal tau (ß = 0.11 [CI: 0.07‐0.16], p<0.001) (Fig‐1, Table‐2). In CU, we also observed a significant interaction effect between larger WML volumes and higher Aß‐load with greater cross‐sectional tau (ß = 0.14 [CI: 0.06‐0.22], p = 0.001). When including both microbleeds and WML volume in the same model, only effects of microbleeds remained significant (Table‐2). No significant effects were observed in CI participants.ConclusionIn CU (but not CI) individuals, cerebrovascular pathology may modify the association between Aß and tau by accelerating tau accumulation in the presence of Aß. These results might suggest an early effect of cerebrovascular pathology on the interplay between Aß and tau.
In vivo data‐driven patterns of Tau accumulation associated with AD progression using 18F‐MK‐6240 PET
Authors: Fonov Vladimir S , Nazneen Tahnia , Rosa‐Neto Pedro , Collins D Louis 
Published: December 2023

AbstractBackgroundAlzheimer’s disease is a neurodegenerative disease associated with accumulation of amyloid beta and tau neurofibrillary tangles following a pattern known as Braak stages (Braak 1995,2011). Recent papers (Pascoal 2020) indicate possibility to recapitulate Braak histo‐pathological stages in vivo using tau tangles PET tracer 18F‐MK‐6240, using manually defined regions of interest. This study analyzes patterns of Tau accumulation associated with AD in a completely data‐driven fashion.MethodWe used T1w MRI and 18F‐MK‐6240 Pet scans from healthy controls in the community or outpatients at the McGill University Research Centre for Studies in Aging. The following number of datasets from each diagnostic group [n scans total (m unique participants)]: cognitively normal: 347(194), mild cognitive impairment: 163(99), Alzheimer’s disease dementia: 114(77). T1w MRIs were pre‐processed (non‐uniformity correction, intensity normalization, stereotaxic registration, brain masking, tissue classification); FALCON (Fonov 2020) was used to extract the mid‐cortical surface. PET scans were linearly registered to the T1w MRI and the standardized uptake value ratio (SUVR) was calculated using whole cerebellum grey matter as the reference. SUVR values were sampled along cortical mid‐surfaces and geodesically smoothed with a 10mm gaussian and resampled to the cortical surface of MNI2009c template (Fonov 2011).ResultFigure 1 shows Mean SUVR values on top of Figure 1, and latent factors at the bottom. Overall low values of tau in the temporal, partial and occipital region are associated with normal cognition (F1). Tau changes in the temporal lobe (F2, similar to stage 1&2) are associated with a slight cognitive change. Change in parietal and posterior temporal areas (F3, similar to stage 3&4) are associated with even more cognitive loss. Finally changes in the motor strip and ventral portion of the occipital lobe and remaining cortex (F4 and F5) are associated with the very advanced stages (5&6) of cognitive decline.ConclusionWe discovered patterns of tau accumulation associated with progression of cognitive decline in a completely data‐driven fashion.
Effect of SUVR reference region selection on sampled iterative local approximation estimates of amyloid onset age
Authors: Teague Jordan P , De Chavez Elena Ruiz , Irizarry‐Pagan Erica E , Cody Karly Alex , Asthana Sanjay , Johnson Sterling C , Christian Bradley T. , Langhough Rebecca E , Betthauser Tobey J 
Published: December 2023

AbstractBackgroundAmyloid PET imaging quantifies Aß plaques informing longitudinal modeling methods such as sampled iterative local approximation (SILA) that provide individualized estimated amyloid onset age (EAOA). EAOA is defined as the transition to abnormal Aß accumulation that occurs in Alzheimer’s disease. This work investigates the effect of SUVR reference region selection on EAOA by comparing DVR‐trained versus SUVR‐trained SILA models with three different SUVR reference regions.MethodN = 585 participants (mean (SD) baseline age = 66.40 (7.89) years, 512 unimpaired, 44 MCI, 28 dementia, 1 unknown) from the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center underwent T1‐weighted MRI and dynamic 11C‐Pittsburgh Compound B (PiB) amyloid PET imaging (0‐70 minutes after bolus injection). Reconstructed dynamic PET time series were processed and quantified using MR‐guided imaging pipelines optimized for DVR (Logan, k2’ = 0.149 min‐1, cerebellum gray matter (CBLM‐GM) reference region) and SUVR (50‐70 min frames) quantification with CBLM‐GM, whole cerebellum (WHOLE‐CBLM), or subcortical white matter (WM) as a reference region. Mean cortical PiB DVR and SUVR were each calculated by averaging across eight bilateral ROIs. Group‐based trajectory modeling was applied to longitudinal DVR data to establish an amyloid positivity (A+) threshold (DVR>1.14; equivalent to 14 centiloids). LME was used to define the relationship between DVR and SUVR and translate the DVR threshold to SUVR (SUVRCBLM GM>1.28; SUVRWHOLE CBLM>1.07; SUVRWM>0.68). Within‐person paired differences between EAOADVR and EAOASUVR_REFERENCE_REGION are reported as (mean; [95% CI]).ResultEAOA derived from DVR and SUVR were compared for n = 198 (34%) DVR A+ participants. Amyloid vs. age data are shown for each quantification approach in Figure 1. EAOASUVR‐WHOLE‐CBLM (‐1.29 years; [‐1.51, ‐1.08]) had the highest agreement with EAOADVR compared to EAOASUVR‐CBLM‐GM (‐1.44 years; [‐1.75, ‐1.12]) and EAOASUVR‐WM (‐0.67 years; [‐2.13, 0.79]; Figure 2). EAOA variability was highest for DVR>1.8 where fewer longitudinal observations were available for model training.ConclusionSUVR with whole cerebellum as a reference region resulted in the smallest difference in EAOA compared to DVR from the SILA model. Future work is ongoing to define SUVR thresholds that will optimize agreement with DVR‐trained EAOA results for harmonization of temporal estimates.
Diffusion MRI free water in hippocampal subfields CA3 and CA1 tracks progression in prodromal and overt synucleinopathy
Authors: Huddleston Daniel , Langley Jason , Chen Xiangchuan , Tucker Kelsey , Littrell Kevin , Maan Saad , Hyland Sierra , Bliwise Donald , Hu Xiaoping 
Published: December 2023

AbstractBackgroundPostmortem study of the hippocampal subfields in the Lewy body dementias has revealed selective involvement of CA3 with synuclein pathology and CA1 with amyloid and tau, i.e., Alzheimer’s disease (AD) co‐pathology (Coughlin et al., 2020, PMID:32892355). However, new neuroimaging methods are needed to study the hippocampal subfields in vivo in synucleinopathies. Diffusion MRI free water (FW) measurement detects increased hippocampal FW in AD (Ofori et al., 2019, PMID:31470214). However, typical diffusion MRI protocols have voxels = 2 mm3 making hippocampal subfield imaging infeasible due to volume averaging effects. To address this, we developed a high‐resolution diffusion MRI protocol capable of imaging the hippocampal subfields. We applied this approach to study a prodromal synucleinopathy group – idiopathic REM sleep behavior disorder (iRBD), as well as PD with normal cognition (PD‐NC), PD with cognitive impairment (PD‐CI), and dementia with Lewy bodies (DLB).MethodParticipants with PD‐NC, PD‐CI, DLB, and polysomnographically confirmed iRBD were recruited at Emory University. T1 MPRAGE (0.8mm3 voxels) and high‐resolution diffusion MRI (b = 0, 500mm2/sec; 30 diffusion directions; 1.1mm3 voxels) data were acquired with a Siemens 3T Prisma‐fit system. FreeSurfer v7 was used to segment hippocampal subfields. FW was measured as described in Ofori, et al (2017, PMID:28714593). Groups were compared using ANCOVA followed by post‐hoc tests, and clinical and MRI features were correlated.ResultTable 1 shows group demographic and clinical characteristics. A progressive increase in CA3 and CA1 FW was observed from iRBD to PD‐NC to PD‐CI and DLB, and the ANCOVA model, controlling for age, found a statistically significant difference between groups for CA3 FW (Figure 1). Figure 2 shows statistically significant negative correlations between MoCA and hippocampal FW measures and between years of education and FW in CA3 and CA1.ConclusionCA3 and CA1 FW are significantly increased in advanced synucleinopathy (DLB, PD‐CI) as compared to iRBD, and PD‐NC has intermediate values. Strong negative correlations between CA3 and CA1 FW and cognition further indicate that these measures may track disease progression. The negative correlations between CA3 and CA1 FW and education also suggest that these measures may be biological correlates of cognitive reserve.
Topographical pattern of blood‐brain barrier impairment in preclinical and clinical Alzheimer’s disease
Authors: Yi Dahyun , Byun Min Soo , Park Joon Sik , Byeon Gihwan , Ahn Hyejin , Jung Gijung , Kang Koung Mi , Sohn Chul‐Ho , Park Jaeseok , Lee Dong Young 
Published: December 2023

AbstractBackgroundWhile blood‐brain barrier (BBB) damage in some brain regions has been suggested in Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI), the topographical pattern of BBB impairment associated with AD process in whole brain level is still poorly understood. Using high‐resolution 3‐dimensional (3D) dynamic contrast‐enhanced (DCE) scans of BBB permeability (Ktrans) for the whole brain, we aimed to reveal topographical pattern of BBB impairment related to Alzheimer’s disease (AD) including preclinical as well as clinically impaired state.MethodNinety‐one participants were included in the study [45 cognitively normal (CN) older adults, 24 individuals with mild cognitive impairment (MCI), and 22 patients with AD dementia]. Comprehensive clinical and neuropsychological assessments were administered to all participants. High‐resolution DCE‐ as well as T1‐weighted‐MRI scans were obtained in addition to amyloid‐PET scans. Amyloid‐beta (Ab) positivity was obtained using previously established cut‐offs. Preprocessing of Ktrans maps incorporated FreeSurfer‐based (v6.0) segmentation to ensure exclusion of the voxels within cerebrospinal fluid (CSF) spaces. Voxel‐wise analyses were conducted using SPM12 implemented in MATLAB 2018b.ResultSubtle BBB impairment, as measured by Ktrans map images based on high‐resolution 3D DCE‐MRI, was observed at multiple brain regions even in preclinical AD (i.e., Ab‐positive CN) as well as AD with cognitive impairment (i.e., Ab‐positive MCI and AD). The brain regions with impaired Ktrans closely corresponded to the AD‐signature regions (Figure). Compared to Ab‐negative CN, Ab‐positive CN showed increased breakdown of BBB in the precuneus, posterior‐cingulate cortex, and medial temporal lobe. Moreover, compared to Ab‐negative CN, Ab‐positive MCI and AD patients showed increased breakdown of BBB in more diffuse regions including bilateral frontal, parietal and inferior temporal areas. In contrast, when using low‐resolution BBB images for the same participants, BBB damage was not detected in preclinical AD and observed at only limited brain regions in AD with cognitive impairment.ConclusionWe demonstrated topographical pattern of BBB impairment associated with preclinical AD as well as AD with cognitive impairment through technically advanced high‐resolution DCE‐MRI. The findings suggest that subtle BBB changes detectable from high‐resolution DCE‐MRI has potential as a biomarkers for detection and monitoring of BBB impairment in whole AD continuum.
Tau PET: utility as a biomarker for chronic traumatic encephalopathy
Authors: Krishnadas Natasha , Dore Vincent , Lamb Fiona , Guzman Rodney , Ponsford Jennie L , Hicks Amelia J , Williams Robert , Feizpour Azadeh , Villemagne Victor L , Rowe Christopher C 
Published: December 2023

AbstractBackgroundChronic traumatic encephalopathy (CTE) is a neurodegenerative disease characterized by tau deposition in the depths of the sulci associated with exposure to repetitive head impacts (RHI). It is a post‐mortem diagnosis. We previously reported a frontotemporal predominant tau 18F‐MK6240 PET pattern resembling the distribution of CTE in a retired Australian Rules Football player in the context of a moderate amyloid‐beta plaque burden.1 This study aimed to investigate the utility of 18F‐MK6240 PET as a potential biomarker for CTE in contact sports players with exposure to RHI (sRHI).MethodsRHI (n = 33) and age‐matched healthy controls (HC) (n = 32) completed amyloid (18F‐NAV4694) and tau (18F‐MK6240) PET scans. Amyloid PET was quantified in Centiloids. 18F‐MK240 standardized uptake value ratios (SUVRs) were generated for the dorsolateral prefrontal cortex and composite regions of interest (ROI) (frontal; mesial temporal; temporoparietal).ResultFor sRHI, the primary contact sport was Australian Rules Football (n = 17), boxing/kickboxing/martial arts (n = 11), rugby (n = 4) and soccer (n = 1), with 36.4% participating at a professional level. sRHI had a mean age of 54.2 (±9.2) (vs HC 53.0±9.5, p = 0.61), and 94% were male (vs HC 78%, p = 0.08). sRHI did not differ from HC in years of education (p = 0.46) but had more impaired MMSE (28.1±1.9 vs 29.3±0.8, p = 0.006, d = ‐0.80) and Clinical Dementia Rating scores (0.21±0.3 vs 0±0, p<0.001, d = 1.25). sRHI and HC did not differ in mean Centiloid values (2.9±8.4 vs 3.0±8.2). sRHI and HC did not differ in 18F‐MK6240 SUVR in the regions examined, and no differences were observed between professional and amateur sRHI.ConclusionContact sports players with exposure to repetitive head impacts did not differ from age‐matched healthy controls on 18F‐MK6240 SUVR in frontal, mesial temporal, and temporoparietal brain regions. Study limitations include the small sample size, heterogeneity in sports type and highest level of participation, and participants with relatively mild cognitive and functional impairments. Additionally, while 18F‐MK6240 has high affinity for 3R/4R tau in Alzheimer’s disease, its affinity in CTE, particularly important at early stages, remains unclear. References: 1 Krishnadas N et al. Case report: 18F‐MK6240 tau positron emission tomography pattern resembling chronic traumatic encephalopathy in a retired Australian Rules Football player. https://doi.org/10.3389/fneu.2020.598980.
Cluster analysis of the longitudinal progression of plasma biomarkers and amyloid imaging in the Wisconsin Registry for Alzheimer’s Prevention
Authors: Wells Michael , Bilgel Murat , Jonaitis Erin M. , Langhough Rebecca E , Du Lianlian , Betthauser Tobey J , Blennow Kaj , Ashton Nicholas J. , Zetterberg Henrik , Johnson Sterling C , Jedynak Bruno Michel 
Published: December 2023

AbstractBackgroundThe analysis of the longitudinal progression of plasma biomarkers and amyloid imaging might lead to new insights in the early pattern of biomarker changes.MethodPlasma Aß42/Aß40, pTau231/Aß42, pTau181/Aß42, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NFL) were measured for n = 223 subjects and 1673 total visits in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) using Quanterix Simoa assays at the University of Gothenburg. Global distribution volume ratio (DVR) was computed from 11C‐Pittsburgh compound B PET scans. Inclusion criteria included Amyloid or CSF Amyloid positive status, and at least one plasma serial value. Trajectories spanning age 46.9 to 80.3 years old for the DVR and plasma biomarkers were estimated by fitting a vector field to the longitudinal data (https://arxiv.org/abs/2206.15215). A Louvain community clustering analysis was performed. The time from Amyloid positivity (DVR = 1.19; centiloid of 22) was also computed for all individual trajectories that crossed this threshold.ResultThe Louvain community clustering algorithm resulted in three distinct communities (Fig 1): no increase in DVR or plasma biomarkers (Green, n = 88(39.5%)); increase in DVR and all plasma biomarkers except NFL (Red, 41(18.4%)); and mixed DVR plus GFAP and NFL increase (Blue, 94(42.2%)). Time since Amyloid positivity (TSAP) was computed for the subset of subjects (n = 84) expected to cross the Amyloid positivity threshold. The Louvain communities as a function of TSAP are shown in Fig 2.ConclusionWe identified three communities regarding the longitudinal progression of amyloid PET and plasma biomarker levels. Plasma biomarkers were dynamic up to 30 years before Amyloid positivity.
Sex‐associated differences in plasma and cerebrospinal fluid biomarkers in Early‐onset Alzheimer’s disease
Authors: Nemes Sára , Logan Paige E. , Dage Jeff L. , Hammers Dustin B. , Fagan Anne M. , Manchella Mohit K. , Eloyan Ani , Foroud Tatiana M. , Zetterberg Henrik , Koeppe Robert , Aisen Paul S. S , Carrillo Maria C. , Blennow Kaj , Schindler Suzanne E. , Gray Julia D , Dickerson Brad C. , Rabinovici Gil D. , Apostolova Liana G. 
Published: December 2023

AbstractBackgroundFemale sex is associated with greater atrophy, amyloid and tau burden in Early‐onset Alzheimer’s Disease (EOAD) in the Longitudinal EOAD Study (LEADS). APOE‐e4 non‐carrier‐status was found to be a further predictor of EOAD pathology. We expanded the analyses by examining the impact of sex and APOE‐e4 on plasma and cerebrospinal fluid (CSF) biomarkers of AD. Plasma markers included: neurofilament light chain (NfL), plasma glial fibrillary acidic protein (GFAP), Aß 42/40, and pTau231. CSF markers included: Aß 42/40, neurogranin, tTau, pTau181, synaptosomal‐associated protein‐25 (SNAP25), YKL‐40, and visinin‐like protein‐1 (VILIP1).MethodWe included 201 amyloid‐positive EOAD, 64 amyloid‐negative Early‐onset cognitively impaired (EOnonAD), and 86 Cognitively Normal (CN) LEADS participants with plasma biomarker data. Of these 100 EOAD, 35 EOnonAD, and 38 CN participants also had CSF data. Participants were stratified by sex and APOE‐e4 genotype. Demographics (age, education, APOE‐e4, MMSE) and biomarker differences were compared using ANOVA within each diagnostic group. ANCOVAs were run to control for the effects of age and education on biomarkers.ResultCompared to men, EOAD women showed greater levels of plasma NfL (p = .03), and GFAP (p<.001) while Aß 42/40, and pTau231 were comparable. In CSF, EOAD women showed higher levels of neurogranin (p = .01), tTau (p = .01), pTau181 (p = .01) and VILIP1 (p = .02). EOnonAD women showed significantly greater plasma GFAP levels (p = .02). In the CN cohort, women had greater CSF SNAP25 levels (p = .03) while men showed a trend for higher plasma pTau231 levels (p = .05). APOE‐e4 carrier‐status was not associated with differences in levels of plasma or CSF biomarkers in either sex.ConclusionAs highly selective and specific AD fluid biomarkers emerge, understanding sex‐based differences in CSF and plasma is imperative. Female sex is associated with higher levels of neurodegeneration (NfL, tTau, pTau181, neurogranin, VILIP1) in EOAD and astrogliosis (GFAP) in both EOAD and EOnonAD. This data supports our previous findings that greater pathology burden is associated with female sex. This paves the way for further examination of sex and APOE‐e4 genotype‐based differences in imaging and fluid biomarkers, their associations, and utility in early diagnosis.