bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations of NREM Slow Wave Activity and Regional in‐vivo tau PET deposition in black and white community‐dwelling cognitively normal older‐adults with normal sleep
Authors: Bubu Omonigho Michael M , Umasabor‐Bubu Ogie Queen , Kovbasyuk Zanetta , Valkanova Elena M , Mbah Alfred , Cejudo Jaime Ramos , Turner Arlener , Hu Lu , Williams Natasha , Jean‐Louis Girardin A , de Leon Mony J. , Masurkar Arjun V. , Wisniewski Thomas , Varga Andrew , Osorio Ricardo S 
Published: December 2023

AbstractBackgroundIn this proof‐of‐concept study, we examined whether NREM slow wave activity (SWA) is associated with regional in‐vivo tau deposition; and whether race‐specific differences exist in this association, in clinically normal older adults with normal sleep, in the context of Aß.MethodThis was a cross‐sectional analysis of baseline data from 21 community‐dwelling cognitively normal older‐adults with normal sleep. Participants were matched on age, sex, BMI, and APOE4 carrier status in a 1:3 ratio, blacks: whites. Normal sleep was defined as 6‐9 h of total sleep time and absence of AHI4%>15 and AHI4%>5 with sleepiness. SWA was defined as power between 0.5‐4.0 Hz during NREM with NREM cycles characterized as stages NREM 2‐3 of = 15 min. terminated by rapid eye movement (REM) or wakefulness of = 5 min. Tau burden was assessed using 18F‐PI2620. Linear mixed‐effects models with random intercept and slope were used to assess associations between NREM SWA, and regional in‐vivo tau PET deposition, respectively, controlling for Aß, age, BMI, sex, APOE4‐status, education and other sleep variables.ResultOf the 21 participants, 7 (33.3%) were black, 12 (57.1%) were women and 5 (24%) were APOE4 carriers. The mean (SD) age, and education were 69.1 (5.5), and 17.2 (3.8) years respectively. NREM SWA was associated with tau deposition in the entorhinal cortex, inferior and middle temporal ROIs, including gray matter neocortical ROIs i.e., anterior ventral striatum, orbitofrontal, precuneus, superior frontal, lateral temporal and posterior cingulate gyrus cortices (p <.01 for all). Racial differences existed in the entorhinal cortex, and inferior and middle temporal ROIs, including the lateral temporal, parietal, precuneus and posterior cingulate gyrus cortices (p <.02 for all), with black participants having lower tau levels relative to white participants. Divergent NREM SWA and tau associations were observed in the entorhinal cortex, precuneus, and inferior and middle temporal ROIs with NREM SWA negatively correlated with tau‐PET levels in whites and positively correlated in blacks.ConclusionRacial differences in physiological sleep features, uniquely associated with tau levels in the brain, may reflect one mechanism of how sociocultural factors, often through behavioral processes, become physiologically expressed as increased AD‐risk in blacks.
A multivariate MRI model of Alzheimer’s disease risk is associated with clinical diagnosis, PET imaging, and plasma biomarkers in a mixed dementia sample
Authors: Phillips Jeffrey S , Govindarajan Sindhuja Tirumalai , Hwang Gyujoon , Erus Guray , Cousins Katheryn A Q , Das Sandhitsu R. , Wolk David A. , Irwin David J. , Grossman Murray , Nasrallah Ilya M. , Davatzikos Christos 
Published: December 2023

AbstractBackgroundWe sought to validate two structural MRI‐based brain health models: Spatial Pattern of Atrophy for REcognition of Alzheimer’s disease (SPARE‐AD) and SPARE‐Brain Age Gap (SPARE‐BAG), which estimates the discrepancy between biological brain age and chronological age. These models' generalization to non‐amnestic AD (naAD) syndromes and associations with positron emission tomography (PET) and plasma biomarkers remain underinvestigated. We hypothesized that SPARE‐AD scores in naAD would be lower (less abnormal) than in amnestic AD (aAD) but elevated (more abnormal) relative to controls; and that SPARE‐AD but not SPARE‐BAG scores would be associated with PET and plasma biomarkers of AD.MethodSPARE‐AD and SPARE‐BAG scores were estimated for each of 1607 MRI scans from 277 cognitively normal adults (62.8% female; mean age = 69.5 years) and 902 clinical participants (54.2% female; mean age = 73.1 years), including 585 with amnestic mild cognitive impairment (aMCI) or aAD; 56 with naAD syndromes; 28 with vascular disease; 28 with Lewy body disorders (LBD); 18 with suspected frontotemporal lobar degeneration (FTLD); 56 with non‐amnestic MCI of uncertain etiology; and 131 with other clinical diagnoses. We used mixed effects models adjusting for age, sex, and global cognition to test group differences in SPARE‐AD and SPARE‐BAG. Additionally, we assessed associations with tau (n = 122) and amyloid‐beta (n = 199) PET imaging and plasma‐based phosphorylated tau‐181 (p‐tau181) and glial fibrillary acidic protein (GFAP) (n = 358).ResultSPARE‐AD scores were elevated vs. controls in aMCI/aAD, naAD, vascular disease, FTLD, and naMCI (all p<0.003) but not in LBD or other diagnoses (p>0.1). NaAD patients had lower SPARE‐AD (p<0.02) but higher SPARE‐BAG scores (p<0.02) than aMCI/aAD patients. SPARE‐AD scores were positively associated with mean cortical tau PET uptake, amyloid PET positivity, and plasma p‐tau181 and GFAP (all p<0.001). In contrast, SPARE‐BAG scores were not associated with PET or plasma biomarkers (all p>0.05).ConclusionSPARE‐AD values for naAD syndromes were intermediate between those of controls and aMCI/AD patients, likely reflecting divergent, partially overlapping cortical atrophy relative to aAD. PET and plasma biomarkers of AD neuropathologic change were associated with SPARE‐AD but not with SPARE‐BAG scores, suggesting SPARE‐BAG reflects age‐related atrophy that is largely independent of AD.
Correlations between [18F]flutemetamol PET and region‐matched postmortem integrated density measures of cyano‐flutemetamol signal in Aβ plaques
Authors: Abrahamson Eric E , Buckley Christopher , Pemberton Hugh , Farrar Gill , Smith Adrian , Ikonomovic Milos D 
Published: December 2023

AbstractBackgroundThe binding substrates of [18F]flutemetamol and [11C]PiB PET consist of fibrillar amyloid‐ß (Aß) aggregates in morphologically and structurally distinct diffuse plaques (DP) and cored plaques (CP) which are present at different proportions across brain regions in Alzheimer’s disease (AD). To account for this heterogeneity, an unbiased quantitative measure integrating plaque size and density of amyloid‐ß (Aß) fibrils in plaques could help avoid false positive results, improve imaging‐to‐autopsy correlations, and further advance our understanding of pathological substrates for binding of amyloid PET tracers.MethodSixteen subjects underwent [18F]flutemetamol PET imaging in the pivotal Phase III end‐of‐life study (Study GE067‐007 and GE067‐026) and later came to autopsy (mean imaging‐to‐autopsy interval was 111 ± 87.6 days). [18F]flutemetamol SUVRpons values in four neocortical regions were correlated with region‐matched postmortem measures of cyano‐flutemetamol labeling of all plaques, as well as CP and DP separately, using percent area coverage, mean grayscale intensity of fluorescence, or a combination of the two measures referred to as integrated density.ResultIn the analysis of all cyano‐flutemetamol‐labeled plaques, [18F]flutemetamol SUVRpons values were more closely associated with integrated density measures than percent area coverage and mean grayscale values. Similarly, when CP and DP were analyzed separately, the integrated density values showed the closest association with neocortical region‐matched [18F]flutemetamol SUVRpons values. When area coverage of CP and DP were each expressed as a fraction of total plaque area coverage with or without integrating fluorescence intensity values, [18F]flutemetamol SUVRpons values within each region of interest were associated with the DP, but not the CP, area coverage measure.ConclusionThe association of antemortem [18F]flutemetamol PET and region‐matched postmortem integrated density measurements of cyano‐flutemetamol labeled plaques further supports the hypothesis that amyloid PET signal is influenced both by plaque size and Aß fibril density within DP and CP. Further elucidating the relative contributions of each plaque type to the retention of amyloid PET radioligands could influence how a positive amyloid PET signal is interpreted in relation to postmortem pathology data.
Quantifying associations of four major proteinopathies with grey matter atrophy: an imaging‐pathology study
Authors: Phillips Jeffrey S , Robinson John L. , Cousins Katheryn A Q , Das Sandhitsu R. , Yushkevich Paul A. , Wolk David A. , Lee Eddie B , McMillan Corey T , Trojanowski John Q , Grossman Murray , Irwin David J. 
Published: December 2023

AbstractBackgroundGross atrophy observed on MRI provides a clinically useful marker of possible neurodegenerative disease. However, autopsy studies of polypathology suggest atrophy is likely driven not by a single proteinopathy but also co‐pathologies. We hypothesized that in a clinically heterogeneous autopsy sample, amyloid‐ß, tau, a‐synuclein, and TDP‐43, would co‐occur within regions, and that co‐pathologies would have independent associations with grey matter atrophy.MethodT1‐weighted antemortem MRI was available in 121 patients with postmortem ordinal pathology ratings (0 = none; 1 = mild; 2 = intermediate; 3 = severe). Primary neuropathologic diagnoses included Alzheimer’s disease (AD; n = 52), amyotrophic lateral sclerosis (n = 2), frontotemporal lobar degeneration due to tau (n = 29) and TDP‐43 (n = 22), and Lewy body disease (16). Regional volumes were expressed as W‐scores adjusting for age, sex, and intracranial volume for 10 regions spanning the limbic system, neocortex, and subcortex. We used mixed effects models with a random intercept per patient to regress W‐scores on ordinal pathology ratings.ResultAmyloid‐ß and tau co‐occurred in all regions, reflecting the prevalence of AD neuropathologic change (Fig. 2, top). Alpha‐synuclein and TDP‐43 pathology were highest in amygdala, anterior cingulate, entorhinal cortex, and hippocampus, where they frequently co‐occurred with amyloid‐ß and tau. Neocortical regions including middle frontal, angular, and superior/middle temporal cortex had intermediate rates of co‐pathology, while thalamus, striatum, and globus pallidus had the lowest polypathologic burden (Fig. 2, bottom). Ordinal measures of tau [F(3,1073) = 24.1, p<0.001], TDP‐43 [F(3,1073) = 13.7, p<0.001], and ‐synuclein [F(3,1073) = 4.3, p<0.005] burden were inversely associated with regional volumes, but amyloid‐ [F(3,1073) = 1.8, p>0.15] was not. An increase in ordinal tau scoring by one unit was associated with a mean reduction of ‐0.29 in regional W‐score; for TDP‐43, a decrease of ‐0.51; and for a‐synuclein, a decrease of ‐0.16.ConclusionMixed proteinopathies were common at the regional level in limbic and neocortical structures. Using blinded ordinal pathology scoring, TDP‐43 was associated with greater atrophy than tau; a‐synuclein had a smaller but significant association. Amyloid‐ß was not associated with atrophy when adjusting for tau. Results suggest co‐pathologies have a measurable effect on observed brain atrophy.
A multi‐site study of Alzheimer’s disease neuroimaging and cognitive biomarkers in 2819 cognitively normal individuals
Authors: Xie Long , Das Sandhitsu R. , Wisse Laura EM , Lyu Xueying , McGrew Emily , de Flores Robin , Gibson Eli , Yushkevich Paul A. , Wolk David A. 
Published: December 2023

AbstractBackgroundDeveloping effective Alzheimer’s disease (AD) biomarkers in cognitively normal (CN) individuals is crucial. Prior studies have identified neuroimaging and cognitive measures that are sensitive to preclinical AD. However, the generalizability of these biomarkers in large, multi‐site cohorts and their power to predict disease progression remain unclear. Structural MRI, tau PET and standardized cognitive tests of 2819 CN adults were pooled from three studies to investigate the power of these measures in discriminating ß‐amyloid positivity (A+/‐) and predicting disease progression.MethodT1‐weighted MRI and cognitive data of 2819 CN (A‐/A+: 1180/1540, Table 2) individuals from ADNI, HABS and A4 were included. Baseline medial temporal lobe structural measures were extracted from MRI (complete list in Table 1). For participants with prospective longitudinal MRI or cognitive measures (4.5 years), annualized change rate of each measurement was estimated. ANCOVA and receiver operating characteristic analyses were used to test biomarker differences between A+/A‐. In 563 CN with cross‐sectional tau PET available, stepwise linear mixed effect modeling was performed to identify the subset of baseline cross‐sectional biomarkers (tau, MRI and cognition) that yields the optimal model (smallest Akaike information criterion) in predicting longitudinal atrophy and cognitive decline. The area under the curve (AUC) of each model in discriminating the first (fast) and last (slow progressors) tertiles of each longitudinal measurement was reported.ResultSignificant differences between A+/A‐ (Table 2) were observed in cross‐sectional tau (most significant), cross‐sectional and longitudinal cognitive, as well as longitudinal structural MRI measures. When predicting disease progression with baseline measures (Figure 1‐a), tau (selected in all the models) and cognitive biomarkers were included in the most predictive models while MRI biomarkers did not provide additional information. The selected biomarkers can identify fast versus slow progressors with AUCs ranging from 0.63‐0.78 (Figure 1‐b).ConclusionThe results demonstrated that both cross‐sectional (tau PET and cognition) and longitudinal biomarkers (MRI and cognition) are sensitive to amyloid status in CN. In addition, baseline tau PET and cognitive measures provide complementary information in predicting disease progression regardless of amyloid status. This indicates that these biomarkers may have important utility in preclinical AD clinical trials and normal aging studies.
Impact of florbetaben acquisition timing on continuous and dichotomous measures of amyloid burden: implications for real‐world amyloid PET interpretation
Authors: Johns Emily , Young Christina B. , Vossler Hillary , Younes Kyan , Trelle Alexandra N , Poston Kathleen L. , Davidzon Guido , Mormino Elizabeth C. 
Published: December 2023

AbstractBackgroundThe magnitude of Standardized Uptake Value Ratios (SUVRs) and Centiloid (CL) values increase at later acquisition windows for Florbetaben (FBB). As amyloid PET is anticipated to be utilized for treatment decisions with anti‐amyloid therapeutics (van Dyck, 2022), it is important to understand acquisition factors that influence PET quantification.MethodWe analyzed 120 FBB scans from the Stanford Alzheimer’s Disease Research Center with 5‐minute frame data spanning 70‐110 minutes post‐injection (Table 1). Frame data were motion corrected, summed into 70‐90 and 90‐110‐minute files, and processed through an MRI‐Free pipeline. A global cortical target region and whole cerebellum reference region from the GAAIN atlas were used to create SUVRs and CLs (Klunk, 2014). SUVRs and CLs computed across the two acquisition windows were compared using linear regression to understand the effects of timing acquisition on magnitude. Gaussian Mixture Models (GMM) were used to convert SUVRs to a probability scale separately for 70‐90 and 90‐110 datasets. 10,000 bootstrapped mixed timing datasets were created by randomly selecting either 70‐90 or 90‐110 SUVRs for a given participant, which was then used to understand the impact of GMM‐based classification into A‐ and A+ groups when acquisition time varies within dataset.ResultCLs were systematically lower during the earlier acquisition window (2.01 CL for the CL<10 group, ΔCL range = ‐8.83,9.16; 2.89 CL for the 10‐25 group, ΔCL range = ‐2.18,9.33; 3.62 CL for the 25‐50 group, ΔCL range = ‐0.04,11.34; and 10.94 for the >50 group, ΔCL range = 1.81,26.98) (Fig. 1). GMM classification into A‐ and A+ groups was consistent across the two acquisition windows and in the mixed timing datasets (Table 2).ConclusionGMM is a robust quantitative method that can be used to characterize amyloid PET scans into A‐ and A+ groups when acquisition time varies. However, given the inconsistent impact of acquisition time on CL magnitude, comparison of CL values before and after anti‐amyloid treatments will require adjustment for scan acquisition time. Future work is needed to establish methods that can measure amyloid PET change when acquisition times vary within an individual patient. This is especially important for evaluating the efficacy of currently approved anti‐amyloid therapeutic approaches.
Estimating the time between amyloid and tau‐pet positivity: implications for Alzheimer’s disease prevention trials
Authors: Moscoso Alexis , Heeman Fiona , Dunås Tora , Schöll Michael 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is characterized by a preclinical stage in which abnormal accumulation of amyloid‐ß (Aß) leads to phosphorylated tau aggregation. This sequence of events represents the transition between “AD pathologic change” (A+T‐) to the much riskier full AD neuropathology (A+T+, Jack et al. Alzheimers Dement. 2018). Thus, to understand the clinical relevance of the A+T‐ stage in the long term, it is important to understand the temporal course of Aß and tau. In this study, we used longitudinal Aß‐ and tau‐positron emission tomography (PET) to estimate the expected time gap between Aß‐ and tau‐PET positivity, and explored factors that influenced this gap.MethodWe estimated the natural history of amyloid accumulation in AD using longitudinal Aß‐PET data from 1690 AD‐continuum subjects from the ADNI. Specifically, we fitted a non‐linear mixed effects model (3‐knot spline) and modelled the subject‐specific “age of Aß positivity” as random effects (Figure 1). An interval‐censored time‐to‐event analysis using “time to amyloid positivity” as time variable was performed on 318 Aß+ ADNI participants who had available tau‐PET ([18F]flortaucipir) visual reads. Cox proportional hazards regression was used to assess whether age of onset of amyloid positivity, sex, APOE status, and cognitive impairment at follow‐up influenced the onset of tau‐PET positivity relative to Aß‐positivity.ResultAfter ∼11 years from Aß positivity, 50% of the CU individuals progressed to tau‐PET positivity, while only ∼17% remained tau‐PET negative after 20 years (Figure 2). Among the studied factors, only the presence of cognitive impairment accelerated the onset of visual tau‐PET positivity (HR = 2.52, p<0.001, median tau‐free survival difference ∼6 years) (Figure 3). Both female sex (HR = 1.44, p = 0.16, median tau‐free survival difference∼2.5 years) and presence of the APOE‐e4 allele (HR = 1.44, p = 0.15, median tau‐free survival difference∼2.5 years) were trend‐level associated to faster onset of tau‐PET positivity.ConclusionThe time gap between Aß‐ and tau‐PET positivity typically spans a decade and is highly variable across individuals. This long gap implies that a significant fraction of A+T‐ CU older individuals will never develop tau‐PET positivity and subsequent symptoms in their lifespan, which suggests that these subjects may not benefit from current disease‐modifying therapies for AD.
Interactions of synaptic and inflammatory biomarkers in Alzheimer’s Disease
Authors: Rahmouni Nesrine , Tissot Cécile , Servaes Stijn , Therriault Joseph , Macedo Arthur C. , Stevenson Jenna , Lussier Firoza Z , Machado Luiza Santos , Arias Jaime Fernandez , Wang Yi‐Ting , Stevenson Alyssa , Socualaya Kely Quispialaya , Kunach Peter , Nazneen Tahnia , Hosseini Seyyed Ali , Gauthier Serge , Karikari Thomas K , Benedet Andrea Lessa , Ashton Nicholas J. , Zetterberg Henrik , Pascoal Tharick A. , Blennow Kaj , Zimmer Eduardo R , Rosa‐Neto Pedro 
Published: December 2023

AbstractBackgroundThe sequential model predicts synaptic depletion as a downstream of amyloid, tau and neuroinflammation. However, synaptic toxicity might be consequence of toxic forms of amyloid in the absence of tau. In this study we explore the role of synaptic depletion and neuroinflammation as determinants of tau pathology. GAP‐43 and neurogranin are pre‐ and post‐synaptic biomarkers known to be detected at elevated levels in cerebrospinal fluid (CSF) of patients with Alzheimer’s disease (AD).MethodWe included 126 individuals from TRIAD cohort. Brain inflammation, tau tangle and amyloid‐ß (Aß) deposition were assessed via [11C]PBR28‐PET, [18F]MK6240‐PET and [18F]AZD4694‐PET, respectively. All patients had plasma GFAP quantified, and a subset of 78 individuals had CSF GFAP, CSF Neurogranin and CSF GAP‐43 quantifications available. Voxel and region of interest regression models evaluated the relationship between PET tracers and the fluid biomarkers. Models with [18F]AZD4694‐PET as the outcome were adjusted for [18F]MK6240‐PET voxel‐wise and vice‐versa. A linear regression interaction model evaluated the interaction between CSF GFAP and both synaptic biomarkers with Aß‐ and tau‐PET as the outcome.ResultPositive associations were found between CSF neurogranin and Aß‐, tau‐ and brain inflammation‐PET in AD related regions; the strongest associations were found with tau‐pet and CSF neurogranin in the medial temporal lobe. GAP‐43 was also positively associated with tau‐ and TSPO‐PET, but no associations were found with Aß‐PET. Both plasma and CSF GFAP were associated with Aß‐, tau‐ and TSPO‐PET in AD related regions, with the strongest t‐values in the model including plasma GFAP and amyloid‐PET. A negative interaction was found between CSF GFAP and both synaptic biomarkers with amyloid‐ and tau‐PET as the outcomes. These associations were stronger with tau‐PET and were found in the temporal, occipital and parietal areas.ConclusionThis study supports the role of synaptic dysfunction and neuroinflammation on tau load. As both synaptic biomarkers and GFAP increase as a function of tangles load, tau accumulation is linked to the relationship between synaptic abnormalities and inflammation in early stages of the disease. Our results suggest that synaptic depletion is a phenomenon that might start prior to tau tangles.