bluuebunny/crossref_metadata_2025_split huggingface dataset:
Connectivity to epicenters of tau accumulation contributes to regional synaptic loss in Alzheimer’s disease
Authors: Luan Ying , Wang Weiyi , Guan Yihui , Franzmeier Nicolai , Xie Fang , Ewers Michael 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) is associated with substantial synaptic loss potentially due to synaptotoxicity of fibrillar tau, but the association between tau deposition and synaptic loss remains unclear. Based on previous observations that pathology spreads preferentially between closely connected regions, we tested in the current multi‐PET tracer study the hypothesis that synaptic loss propagates to regions closely connected to epicenters of high tau accumulation.MethodWe assessed 18F‐SynVesT‐1 PET as a measure of synaptic vesicle glycoprotein 2A (SV2A), and 18F‐flortaucipir tau‐PET in fourty‐five 18F‐florbetapir‐PET‐positive (Aß+) subjects with MCI or AD dementia, and 23 cognitivly normal (CN) Aß‐ controls. All PET scans were spatially normalized and parcellated into 200 ROIs. Adopting our previously established connectivity‐based prediction model (Franzmeier et al. Nat Commun 2020), we computed tau‐epicenter connectivity maps by projecting regions of highest tau‐PET SUVR (top 10% of group‐average tau‐PET ROIs) onto a normative resting‐state fMRI derived connectivity template from the Human Connectome Project. The tau‐epicenter connectivity map was projected onto the group‐average synaptic PET ROI w‐scores (standardized SUVR difference between Aß+ vs Aß‐ group) to test in linear regression analysis, epicenter connectivity as a predictor of synaptic PET w‐scores in connected ROIs. As a control, we conducted an additional regression analysis, using this time connectivity to cold spots of tau‐accumulation (10% of ROI with lowest tau‐PET uptake) instead of epicenter‐connectivity as a predictor. We repeated the analyses at the subject level, controlling for the effect of age, gender, clinical diagnosis and global amyloid‐PET uptake.ResultHigher functional connectivity to the tau‐epicenter was associated with lower synaptic PET binding in the connected ROIs (ß = ‐0.380, p < 0.001, Figure 1A). In contrast, functional connectivity to the tau coldspot ROIs was associated with more preserved synaptic PET binding in the connected ROIs (ß = 0.396, p < 0.001, Figure 1B). Subject‐level analysis showed consistent findings (Figure 2 for tau‐epicenters).ConclusionSynaptic loss in AD occurs in a connectivity‐dependent manner, where closer functional connectivity to regions of high tau is associated with stronger loss in synaptic PET. Putative underlying mechanisms are the higher tau in tau‐epicenter connected regions or functional decline in the connected regions.
Resting State Functional Connectivity Assessment of Healthy Aging Trajectories in the Marmoset Brain
Authors: Bhik‐Ghanie Rebecca , Szczupak Diego , Zhang Bei , Papoti Daniel , Campos Vinicius , Dubberley Lauren R , Hitchens T Kevin , Yeh Fang‐Cheng , Schaeffer Lauren K Hayrynen , Carter Gregory W , Rizzo Stacey J Sukoff , Schaeffer David J , Silva Afonso C 
Published: January 2025

AbstractBackgroundThe common marmoset (Callithrix jacchus) is an important animal model in neuroscience and neurological diseases, presenting primate‐specific evolutionary features such as an expanded frontal cortex. We established a new consortium with funding support from the National Institute on Aging to generate, characterize, and validate MArmosets as Research MOdels of AD (MARMO‐AD). This consortium develops and studies gene‐edited marmoset models carrying genetic risk for AD, comparing them against wild‐type aging marmosets from birth throughout their lifespan, using non‐invasive longitudinal assessments. Here, we aim to characterize healthy aging trajectories by investigating their resting state functional connectivity in a population of marmosets.MethodWe imaged a cohort of 25 marmosets (17 males, 8 females) across the lifespan (8 to 150 months) using a dedicated 9.4T/30cm Bruker MRI scanner. The animals were acclimated to restrainers and head‐fixation helmets and imaged awake. EPI images (500 µm isotropic) were acquired for 1 hour and 20 minutes, yielding 2400 whole‐brain volumes. The images were pre‐processed for fMRI using AFNI and FSL. For each run, the first ten time points were removed for magnetization to reach a steady state. The images were despiked, and spatially aligned. Slice timing was corrected, and phase‐encoding distortion was corrected using FSL. Subsequently, brain images were registered to the Marmoset Brain Mapping V3 template, and brain‐wide connectomics were calculated using the GRETNA toolbox.ResultOur group has shown through structural investigations (voxel‐based morphometry and white matter tractography) that several cortical clusters are affected by aging (Figure 1, Top Left). Here, with resting state fMRI, we show that many brain regions that change size with aging also show accompanying changes in their functional connectivity strength (Figure 1, Right). Integrating resting‐state fMRI with structural investigations will allow us to understand how these affected structures work together in a dynamic network.ConclusionOur work is the first to thoroughly describe the changes in resting state functional connectivity in the marmoset brain during normal aging, a valuable model for AD. This research will establish normative baselines for changes in marmoset brain connectivity with aging to evaluate our genetically engineered marmoset models of AD.
Exploring heterogeneity in Motoric Cognitive Risk Syndrome using Volumetric MRI‐guided Clustering
Authors: Nallapu Bhargav Teja , Blumen Helena M , Petersen Kellen K. , Lipton Richard B. , Pradeep V.G. , Srikanth Velandai K , Beare Richard , Beauchet Olivier , Milman Sofiya , Aleksic Sandra , Ezzati Ali , Verghese Joe 
Published: January 2025

AbstractBackgroundThe Motoric Cognitive Risk Syndrome (MCR) is a predementia stage characterized by slow gait speed and subjective cognitive complaints. Defining the heterogeneity of brain volumetrics in individuals with MCR will improve current dementia risk assessments.MethodWe used data from 6 cohorts from the MCR consortium (N=2,007). We used K‐means clustering algorithm guided by volumetric MRI to identify distinct subgroups of participants. We compared the differences in cortical and subcortical volumes, comorbidities, and gait speeds across the identified subgroups using one‐way ANOVA and post‐hoc pairwise group comparisons.ResultThe sample had a mean age of 71.89 (±7.05) years, 48.7% were women, 32.9% were White and 63.2% were Asian (see Table 1). Four subgroups (A to D) were identified through MRI‐based clustering with significant differences in brain region volumes (Figure 1A), gait speeds (Figure 1B) and proportion of individuals with MCR (Figure 2). Subgroups A and C had the least amount of atrophy in all brain regions and had the least proportion of MCR. Subgroup D had the highest proportion of MCR subjects and highest atrophy specifically in hippocampus and cortical regions. The average gait speed of Subgroup D was lower than other subgroups. Subgroup D also had the highest rate of hypertension and diabetes among the subgroups (Figure 2).ConclusionOur results validate the previous findings linking MCR syndrome to MRI evidence of neurodegeneration. Heterogeneity in cortical and subcortical signatures are present in older adults and provides insights into brain substrates of MCR.
Neurobiological correlates of grey matter morphometry changes in preclinical Alzheimer's disease
Authors: Pelkmans Wiesje , Cacciaglia Raffaele , Salvadó Gemma , Fauria Karine , Minguillón Carolina , Kollmorgen Gwendlyn , Quijano‐Rubio Clara , Molinuevo Jose Luis , Grau‐Rivera Oriol , Wilson Rachael E , Jonaitis Erin M. , Langhough Rebecca E. , Blennow Kaj , Zetterberg Henrik , Johnson Sterling C. , Suárez‐Calvet Marc , Gispert Juan Domingo 
Published: January 2025

AbstractBackgroundThe driving mechanisms of structural brain alterations in the earliest stages of Alzheimer's disease (AD) are not well understood. Previous heterogeneous findings in preclinical AD, including subtle atrophy and also increased grey matter (GM) volume, underscore the need for further exploration. This study uses an extensive fluid biomarkers panel to identify pathological drivers behind longitudinal GM changes in cognitively unimpaired (CU) adults.MethodWe investigated 632 CU individuals (age 61.8±6.3, mean±SD) from the ALFA+, Wisconsin ADRC, and WRAP cohorts (Table 1), with available longitudinal MRI and cerebrospinal fluid biomarkers of Aß42/40, p‐tau181, neurogranin, NfL, total‐tau, a‐synuclein, GFAP, YKL‐40, sTREM2, s100B, and IL‐6 measured using the NeuroToolKit panel of robust prototype assays (Roche Diagnostics International Ltd). We used non‐negative matrix factorization to decompose biomarkers into latent components. The per‐subject component weights were associated with voxel‐wise GM volume changes (years 3.5±0.9, mean±SD) and longitudinal performance on the preclinical Alzheimer's cognitive composite (PACC) using mixed model analysis.ResultSix biomarker components (C1‐C6) were identified (Figure 1). High expression of astrocyte biomarkers (C1) was related to GM volume increases in frontotemporal and subcortical regions, and faster cognitive decline in A+ individuals, while axonal damage with glial reactivity (C2) was associated with temporal atrophy. Microglial reactivity (C3) showed subtle associations with both increases and decreases in GM volume in different regions. A component primarily based on Aß pathology (C4) was strongly associated with widespread GM volume loss and faster cognitive decline. IL‐6 (C5) reflected non‐AD related inflammation. Finally, the co‐expression of tau pathology, neuronal injury, and synaptic damage (C6) showed robust associations with frontotemporal atrophy and cognitive decline in A+ individuals.ConclusionIn a large longitudinal sample, we provide in vivo evidence that in the earliest stages of AD, Aß pathology is also a strong contributor to future neuronal loss and cognitive decline besides neurofibrillary tangles and neuroaxonal damage. Astroglial reactivity (i.e. GFAP, s100B) in preclinical AD was associated with an increase in GM volume, which may suggest a neuroinflammatory response, and was associated with worse cognitive performance. These findings underscore the dynamic nature of structural brain changes within preclinical AD, shaped by the contributions from diverse pathophysiological processes.
Prevalence of hypometabolism in AD‐related regions in real‐world FDG‐PET of the brain
Authors: Schwartz Trent , Robb W Hudson , Bishay Steven , Jackson T. Bryan , Jefferson Angela L. , Landman Bennett A. , Smith David Samuel , Hohman Timothy J. , Koran Mary Ellen I. 
Published: January 2025

AbstractBackgroundStandard of care for many cancer workups includes whole‐body FDG PET/CT before, during, and after therapy. At Vanderbilt, these scans include the brain for every patient (>20,000 patients). Brain FDG PET is a validated assessment of neuronal health. We will use these standard of care FDG PET images as incidental screening opportunities to evaluate neuronal health to leverage in future electronic health record (EHR) research.MethodsDemographic data and FDG images were extracted from Vanderbilt’s EHR for the most recently imaged 1000 patients. Brain images were extracted, quality control was performed, and SUVR for an established AD meta‐region of interest (ROI) was calculated (Landau, et.al, 2011) as were SUVR for AD‐related ROIs including parietal and temporal lobes. We evaluated the prevalence of a positive AD metaROI (SUVR <=1.21) at staging and on post‐treatment images as well as change in ROI SUVR over time using a linear mixed effects model with subject specific slopes and intercepts, covarying for baseline age and sex.Results396 patients >=65 years old were included in this analysis. Most common indications for imaging included lymphoma, melanoma, and head and neck and lung cancers. 159 have >=2 FDG, with an average follow up time of 338 days between staging and follow‐up scans. 272 patients (68.6%) had a positive AD metaROI (<=1.21) at their initial staging PET, which increased to 296 patients (74%) on follow up. AD metaROI SUVR decreased over time in these patients (β(ΔSUVR/year)=‐0.013 and p=1.63x10‐4), and in the parietal (β=‐0.014/year and p=2.57x10‐5) and temporal (β(ΔSUVR/year)=‐0.010 and p=1.68x10‐4) lobes (Figure 1).ConclusionIncidental prevalence of a positive AD metaROI on FDG is high compared to expected prevalence of a clinical diagnosis of AD of ∼10%. Thus published cut‐points may not be applicable to PET of the brain captured during whole body imaging; different cut‐points may be required. FDG in AD‐related regions significantly decreased over time, consistent with expected age‐related changes. Incidental evaluation of the cerebral metabolism may hold important information on brain health. Evaluation of the entire cohort (n>20,000) and comparison to age‐matched controls is ongoing.
Dissociable spatial topography of neurodegeneration in Early‐onset and Late‐onset Alzheimer’s Disease: A head‐to‐head comparison of MRI‐derived atrophy measures between the LEADS and ADNI cohorts
Authors: Katsumi Yuta , Touroutoglou Alexandra , Brickhouse Michael , Eckbo Ryan , La Joie Renaud , Eloyan Ani , Nudelman Kelly N. , Foroud Tatiana M. , Dage Jeffrey L. , Carrillo Maria C. , Rabinovici Gil D. , Apostolova Liana G. , Dickerson Bradford C. 
Published: January 2025

AbstractBackgroundUnderstanding how early‐onset Alzheimer’s disease (EOAD) differs from typical late‐onset AD (LOAD) is an important goal of AD research that may help increase the sensitivity of unique biomarkers for each phenotype. Building upon prior work based on small samples, here we leveraged two large, well‐characterized natural history study cohorts of AD patients (LEADS and ADNI3) to test the hypothesis that EOAD patients would show more prominent lateral and medial parietal and lateral temporal cortical atrophy sparing the medial temporal lobe (MTL), whereas LOAD patients would show prominent MTL atrophy.MethodWe investigated differences in the spatial topography of cortical atrophy between EOAD and LOAD patients by analyzing structural MRI data collected from 211 patients with sporadic EOAD and 88 cognitively unimpaired (CU) participants from the LEADS cohort as well as 144 patients with LOAD and 365 CU participants from the ADNI3 cohort. MRI data were processed via FreeSurfer v6.0 to estimate cortical thickness for each participant. A direct comparison of cortical thickness was performed between EOAD and LOAD patients based on W‐scores (i.e., Z‐scores adjusted for age and sex relative to CU participants within each cohort) while controlling for MMSE total scores. All patients underwent amyloid PET with 18F‐Florbetaben or 18F‐Florbetapir and amyloid positivity was centrally determined by quantification‐supported visual read.ResultAs expected, a direct comparison of cortical thickness between patients with EOAD and LOAD revealed a double dissociation between AD clinical phenotype and localization of cortical atrophy: EOAD patients showed greater atrophy in widespread cortical areas including the inferior parietal lobule (EOAD marginal mean W‐score ± SEM = ‐1.33±0.08 vs. LOAD = ‐0.52±0.09, p<.001, η2 = .097), precuneus (‐1.66±0.09 vs. ‐0.59±0.10, p<.001, η2 = .13), and caudal middle frontal gyrus (‐1.65±0.08 vs. ‐0.90±0.10, p<.001, η2 = .074), whereas LOAD patients showed greater atrophy in the entorhinal/perirhinal cortex and temporal pole (‐1.00±0.09 vs. ‐1.41±0.11, p<.008, η2 = .019).ConclusionThese findings demonstrate a clearly dissociable spatial pattern of neurodegeneration between EOAD and LOAD, supporting our previously developed LOAD and EOAD signatures of cortical atrophy, which underlies the distinct episodic memory and other cognitive characteristics of these AD clinical phenotypes.
The Association between the Body Fat Localization, Insulin Resistance, and Amyloid Burden in Midlife
Authors: Dolatshahi Mahsa , Commean Paul K. , Naghashzadeh Mahshid , Nguyen Caitlyn , Lloyd LaKisha , Kasani Sara Hosseinzadeh , McBee‐Kemper Abby , Weeks Jake , Sirlin Claude , Ceriani Lael , Yifei Xu , Mittendorfer Bettina , Liu Jingxia , Benzinger Tammie L.S. , Ippolito Joseph E. , Morris John C. , Raji Cyrus A. 
Published: January 2025

AbstractBackgroundObesity in midlife is a risk factor for developing Alzheimer disease later in life. However, the metabolic and inflammatory effects of body fat varies based on its anatomical localization. In this study, we aimed to investigate the association of MRI‐derived abdominal visceral and subcutaneous adipose tissue (VAT and SAT), liver proton‐density fat fraction (PDFF), thigh fat‐to‐muscle ratio (FMR), and insulin resistance with whole‐brain amyloid burden in cognitively normal midlife individuals.MethodA total of 49 cognitively normal midlife individuals (Age: 50.65±5.77 years, 61.2% female, BMI: 32.0±67.34, 57.1% obese) underwent brain PET scan, body MRI, and metabolic assessment. Homeostatic Model Assessment for Insulin Resistance (HOMAIR) was used for measuring insulin resistance. Dynamic amyloid imaging was performed with a bolus injection of ∼15mCi [11C]PiB, followed by a 60‐min scan. Data from the 30‐60 minute post‐injection window was used for calculating whole‐brain amyloid centiloid. VAT and SAT were semi‐automatically segmented using an in‐house MATLAB‐based software. A trained U‐Net convolutional neural network (CNN) model was used to calculate the hepatic PDFF maps from abdominal T1‐weighted images. After preprocessing and N4ITK bias correction on mid‐thigh slices between the ischial ramus and the medial knee condyle, a MATLAB program was used for segmenting thigh total fat including subcutaneous, inter‐, and intra‐muscular fat, and muscle volumes. Total thigh fat‐to‐muscle ratio (FMR) was calculated. Using Spearman correlation test, the association between whole‐brain amyloid centiloid and BMI, HOMAIR, VAT, SAT, PDFF, and FMR was assessed, with age and sex as covariates.ResultObese individuals had a higher amyloid burden compared to the non‐obese (p = 0.011). Whole‐brain amyloid centiloid values were significantly associated with VAT (rho = 0.62, p<0.0001) and HOMAIR (rho = 0.56, p = 0.013), but not other fat metrics. A mediation analysis showed significant direct effect of VAT (p = 0.02) on amyloid burden, while the indirect VAT effects mediated by HOMAIR were non‐significant (p = 0.28).ConclusionObesity, higher visceral fat and insulin resistance, but not BMI, subcutaneous abdominal fat, liver fat, or thigh fat, are associated with higher whole‐brain amyloid burden in midlife. This highlights the importance of anatomical characterization of body fat for Alzheimer disease risk, where visceral fat shows a strong relationship with amyloid pathology.
Confounding factors to Alzheimer's disease plasma biomarkers in patients from a memory‐clinic cohort
Authors: Rodrigues Matheus Scarpatto , Oliveira Markley , Lussier Firoza Z , Ferreira Pamela C.L. , Bauer‐Negrini Guilherme , Povala Guilherme , Felix Cynthia , Abbas Sarah , Zalzale Hussein , Soares Carolina , Saha Pampa , Madeiros Marina Scop , Hong Chang Hyung , Roh Hyun Woong , Karim Helmet T. , de Oliveira Jade , Zimmer Eduardo R. , Karikari Thomas K , Bellaver Bruna , Son Sang Joon , Pascoal Tharick A. 
Published: January 2025

AbstractBackgroundBlood‐based biomarkers for Alzheimer’s disease (AD) are increasingly prevalent and accessible beyond research environments. Consequently, it is crucial to determine whether confounding factors, particularly highly prevalent comorbidities, influence the levels of these blood biomarkers, thereby refining their clinical interpretation. In this study, we examined the impact of comorbidities on plasma AD biomarker levels within a memory‐clinic cohort.MethodWe analyzed 832 individuals (CU: 101, CI: 731) from the BICWALZS cohort with available information of comorbidities and plasma biomarkers measurements. The effect size of each comorbidity to plasma p‐tau217, neurofilament light chain (NfL) protein, glial fibrillary acidic protein (GFAP) and amyloid‐ß (Aß) 42/40 ratio was accessed by the ß‐estimate and 95% CI values of linear regression models adjusted by clinical diagnosis, age, and sex. Comparison of biomarkers between comorbidities was carried out by ANOVA.ResultAmong the evaluated comorbidities, plasma creatinine levels, BMI index, sleep disorder, and the presence of kidney disease were the primary comorbidities linked to variations in plasma biomarker concentrations (Figure 1). Analyzing the levels of the biomarkers according to the presence of comorbidities, we observed that individuals with increased plasma creatinine exhibited significantly higher plasma levels of p‐tau217, NfL and GFAP in comparison to the individuals with normal levels of creatinine (p < 0.05, Figure 2A, B, C). The mean of p‐tau217 and NfL, but not of GFAP, was elevated between those with kidney disease (p < 0.05, Figure 2A, B). High plasma NfL levels were observed in individuals with diabetes and hypertension (p < 0.05, Figure 2B). On the other hand, high BMI index was associated with lower plasma p‐tau217, NfL and GFAP compared to individual with normal BMI (Figure 2A, B, C). We also observed that individuals with sleep disorder exhibited decreased plasma p‐tau217 levels (Figure 2A). Plasma Aß42/40 ratio was not affected by the analyzed comorbidities (Figure 2D).ConclusionOur findings indicate that elevated creatinine levels, a higher BMI index, and the presence of kidney disease are the most significant confounding variables associated with alterations in AD plasma biomarkers. Together, our results shed light on the impact of different comorbidities to the interpretation of biomarker values in real‐world populations.
Longitudinal changes in amyloid, glucose, and glial reactivity imaging biomarkers in a transgenic rat model of amyloidosis
Authors: Lazzarotto Gabriela , Spelta Lidia Emmanuela Wiazowski , Vera Cleinando Clemente da Silva , da Rocha Andreia Silva , de Souza Débora Guerini , Faria Daniele de Paula de Paula , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundImaging biomarkers have helped to reconceptualize Alzheimer’s disease pathophysiology. More specifically, positron emission tomography (PET) radiopharmaceuticals for non‐invasively assessing amyloid‐beta (Aß) plaques, glucose metabolism, and glial reactivity allow for tracking disease progression in a temporally ordered manner. However, whether transgenic models recapitulate biomarker‐related changes remains elusive. Here, we longitudinally evaluated Aß, glucose metabolism, and glial reactivity using PET in a transgenic amyloid rat model.MethodTGF344‐AD (APP/PS1) and wild‐type rats were scanned at 6, 9, and 12 months with [18F]FDG, [11C]PK11195 and [11C]PIB. Images were manually co‐registered in a rat magnetic resonance template. Standardized uptake values (SUV) were calculated for [18F]FDG and [11C]PK11195, and SUV ratio (SUVR) was calculated for [11C]PIB using the pons as the reference region. The results were normalized using Z‐Score and statistical significance was determined at Z>2 (p < 0.05).ResultAt 6 months, no differences between groups were observed. At 9 months, however, we detected significant amyloid load, glucose hypermetabolism, and increased glial reactivity. At 12 months, animals presented increased Aß load while still presenting signs of brain glucose hypermetabolism. By contrast, glial reactivity did not present alterations compared to 9‐month‐old rats.ConclusionOur findings indicate that in the early phases of detectable Aß deposition, there is a joint increase in brain glucose metabolism and glial reactivity. These findings suggest that amyloid deposition triggers early neuroinflammatory response. We and others have provided evidence that early FDG‐PET hypermetabolism is an indicator of glial reactivity.