bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brain and Neural Reserve Mediate Associations Between Lifestyle and Cognition: The Maastricht Study
Authors: De Jong Nathan R. , Jansen Jacobus F.A. , van Boxtel Martin P.J. , Schram Miranda T. , Stehouwer Coen D.A. , van Greevenbroek Marleen M.J. , van der Kallen Carla J.H. , Koster Annemarie , Eussen Simone J.P.M. , de Galan Bastiaan B.E. , Backes Walter H. , Köhler Sebastian 
Published: December 2023

AbstractBackgroundLife‐course exposure to risk and protective factors impacts brain macro‐ and micro‐structure, which in turn affects cognition. Brain Age Gap (BAG) directly measures brain reserve by comparing an individual’s neuroimaging‐based predicted age with their calendar age. Higher BAG implies accelerated brain aging and lower brain reserve. In this study, we comprehensively modeled mutual associations between brain risk and protective factors, reserve, and cognition in a large, middle‐aged population using structural equation modelling (SEM).MethodCognitive test scores, lifestyle, and 3T MRI data for n = 4881 participants (mean age(±SD) = 59.2(±8.6), 50.1% male) were available from The Maastricht Study, a population‐based cohort study with extensive phenotyping. Whole brain volumes (gray matter (GM), cerebrospinal fluid (CSF), white matter hyperintensity (WMH)), cerebral microbleeds (CMB), and structural white matter connectivity were calculated. Lifestyle factors were combined into the Lifestyle for BRAin health (LIBRA) weighted sum score, with higher score indicating greater dementia risk (Figure 1). Cognition was calculated by averaging z‐scores across three cognitive domains (executive function and attention, memory, information processing speed). BAG was calculated by comparing calendar age to predictions from a neuroimaging‐based multivariable regression model. Paths between LIBRA, BAG and cognitive function were tested using linear regression and SEM, adjusting for confounders. LIBRA was split into tertiles for SEM analysis.ResultCSF, GM, WMH and CMB best predicted BAG (R2 = 0.455, RMSE = 6.44). In regression analysis higher LIBRA scores (greater dementia risk) were associated with higher BAG (standardized regression coefficient ß = 0.127, p<0.001), lower connectivity (ß = ‐0.071, p = 0.002) and worse cognition (ßlinear = ‐0.044, p = 0.017, ßquadratic = ‐0.047, p = 0.018). Higher BAG was associated with lower connectivity (ß = ‐0.418, p<0.001) and worse cognition (ß = ‐0.165, p<0.001). Lower connectivity was associated with worse cognition (ß = 0.086, p<0.001). In mediation analysis (Figure 2), 21% of the total differences between the highest and lowest LIBRA tertile on cognition was mediated by BAG (ßindirect = ‐0.019, p<0.001; ßtotal = ‐0.092, p<0.001), and an additional 4.1% was mediated via connectivity (ßindirect = ‐0.003, p<0.001; ßtotal = ‐0.073, p<0.001).ConclusionAssociations between health‐ and lifestyle‐based risk/protective factors (LIBRA) and cognition is partially explained by maintenance of brain reserve (BAG) and neural reserve (connectivity). Lifestyle interventions promoting brain and neural reserve may help maintain cognitive function into older age.
The effect of local versus remote Beta‐Amyloid on functional connectivity mediated Tau‐Spread in Alzheimer’s Disease
Authors: Wanger Fabian , Steward Anna , Biel Davina , Dewenter Anna , Roemer Sebastian Niclas , Ewers Michael , Dehsarvi Amir , Franzmeier Nicolai 
Published: December 2023

AbstractBackgroundIn Alzheimer’s disease (AD), Amyloid‐beta deposition (Ab) is associated with tau spreading from epicenters to connected regions. Yet, Ab and tau accumulate in strikingly different patterns, and it is unclear how Abdrives tau spreading. We envision two mechanisms, i.e. i) that cortical Ab exerts remote effects and “pulls” tau out of connected epicenters or ii) that tau is pushed out of epicenters, once Ab has reached the tau epicenters (Fig.1A). We tested both mechanisms in AD spectrum patients and determined the predominant mechanism by which Ab drives tau spreading.MethodWe included 313 ADNI participants (n = 110 CN Ab‐, n = 98 CN‐Ab+, n = 70 MCI Ab+, n = 35 Dementia Ab+), with baseline amyloid‐ and longitudinal tau‐PET, based on which we calculated tau‐PET rates of change (ROC) for 200 cortical ROIs (Schaefer‐atlas; Fig.2). Using the same atlas, a resting‐state fMRI‐connectivity template was obtained in 42 healthy controls. For each subject, we determined tau epicenters as 5% of ROIs with highest baseline tau‐PET and assessed epicenter connectivity. Using ROI‐wise regression, we tested whether faster tau‐PET increase was explained by the simultaneous occurrence of i) higher tau‐PET in the epicenter, ii) stronger connectivity to the tau epicenter and iii) higher Ab in the target region (i.e. pull‐effect) or the tau epicenter (i.e. push‐effect). Lastly, we assessed ROI‐wise main effects of local Ab vs. Ab in the tau epicenter on tau‐PET changes, to determine which mechanism predominates.ResultHigher tau and Ab in the epicenter explained faster tau‐PET increase in connected temporo‐parietal and frontal regions (Ab‐push‐effect, Fig.1B). In contrast, Ab‐pull‐effects were found also for late‐Braak regions (Fig.1C). Congruently, we found that Ab push effects dominated over Ab‐pull effects in temporo‐parietal brain regions, whereas Ab‐pull effects dominated in late‐Braak regions (Fig.1D‐F). Lastly, patients in which Ab and tau converged locally showed fastest subsequent tau accumulation (Fig.3).ConclusionAb exerts local and remote effects on tau spreading, yet local convergence of Ab and tau in the epicenter may predominantly drive tau spreading. Thus, local convergence of Ab and tau is a key event in AD that gives rise to accelerated tau spreading, previously referred to as cataustrophy.
ADPrep ‐ A fully automated Alzheimer’s Disease Neuroimaging Preprocessing Pipeline for MRI and multi‐tracer PET data
Authors: Dehsarvi Amir , Denecke Jannis , Biel Davina , Dewenter Anna , Roemer Sebastian Niclas , Steward Anna , Wanger Fabian , Brendel Matthias , Franzmeier Nicolai 
Published: December 2023

AbstractBackgroundState‐of‐the‐art preprocessing of MRI and PET data is crucial for Alzheimer’s disease (AD) neuroimaging research. Therefore, standardization and harmonization of neuroimaging preprocessing across sites is key to generate comparable and sharable datasets and to reduce potential bias introduced by different preprocessing strategies. Further, neuroimaging preprocessing requires strong expertise in different programming languages (R/MATLAB/Python/Bash) and software packages (SPM/FSL/AFNI/FreeSurfer), i.e., a major barrier for non‐expert users. Therefore, we developed a docker‐based, state‐of‐the‐art, fully automated, neuroimaging toolbox that integrates robust preprocessing of structural/functional MRI, and multi‐tracer PET (amyloid/tau/FDG/TSPO), generating easy to use spreadsheet and nifti outputs. The toolbox requires no programming expertise and can facilitate neuroimaging analyses, data harmonization and sharing across the AD neuroimaging community.MethodThe pipeline works on standardized bids‐formatted data and was fully developed in nipype (see Figure 1). Preprocessing for structural MRI includes volumetric and cortical thickness assessments for widely used brain atlases (Desikan‐Killiany/Schaefer/LPBA/Hammers/Neuromorphometrics/Cobra/Destrieux), plus spatially normalized and smoothed grey/white‐matter segments for voxel‐based morphometry and generation of subject‐specific PET‐reference regions. Functional MRI processing includes slice‐timing and motion correction, nuisance regression, spatial normalization, and functional connectivity assessments for the above‐mentioned atlases. PET processing includes generation of spatially normalized SUVR images for different tracer‐specific reference regions, as well as extraction of atlas‐based SUVRs (PVE and non‐PVE‐corrected). Standardized imaging outputs in FreeSurfer space were benchmarked against available ADNI data to illustrate comparability with existing pipelines (see Figure 2). Cluster implementation is provided to ensure large‐scale data processing.ResultThe toolbox was tested successfully on large‐scale multimodal datasets, including several thousand scans from ADNI, ADNI‐DOD, and A4 with an overall processing failure rate of <4%. Using ADNI data as a benchmark, our data closely reproduce global amyloid‐PET (r = 0.99, p<0.001, Fig.2A) and temporal meta tau‐PET SUVRs (r = 0.98, p<0.001, Fig.2B), suggesting that PET‐thresholds can be applied. Runtime is ∼1h for a structural MRI and ∼5hrs for a PET including PVE‐correction.ConclusionWe propose a user‐friendly multimodal robust neuroimaging preprocessing toolbox, that can be applied to different neuroimaging datasets by non‐expert users. The toolbox provides outputs that can be directly used for statistical analyses. Future work involves implementing other imaging modalities, e.g., diffusion tensor imaging.
GABA transporters gene expression associates with [18F]FDG‐PET signal in regions typically hypometabolic in Alzheimer’s disease
Authors: Carello‐Collar Giovanna , Ferrari‐Souza João Pedro , Machado Luiza Santos , Povala Guilherme , Limberger Christian , Pascoal Tharick A. , Zimmer Eduardo R 
Published: December 2023

AbstractBackgroundIt is suggested that the [18F]fluorodeoxyglucose(FDG)‐PET signal reflects astrocyte and neuronal activity, with the excitatory glutamate uptake by astrocytes as the trigger for brain glucose metabolism. The inhibitory neurotransmitter γ‐aminobutyric acid (GABA) is taken up by neurons and astrocytes through the GABA transporters (GAT) 1 and 3. However, its role in the [18F]FDG‐PET signal in healthy aging and Alzheimer’s disease (AD) remains poorly investigated. Thus, we aimed to explore associations between neuronal and astrocytic GABA transporters gene expression with [18F]FDG‐PET in cognitively unimpaired (CU) and AD individuals.MethodWe obtained [18F]FDG‐PET imaging data of CU and AD from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Table 1). GAT1 and GAT3 gene expression in the brain was obtained from the Allen Brain Atlas (Table 1, Figure 1). We extracted SUVr (pons as reference region) and mRNA values using the RMINC package and performed Pearson’s correlation to test the association between [18F]FDG‐PET and GABA transporters mRNA (p‐value < 0.05).ResultIn brain regions typically hypometabolic in AD, we observed a strong positive association of [18F]FDG‐PET SUVr with GAT1 and GAT3 gene expression in CU (GAT1: r = 0.93; p < 0.0001; Figure 2A, GAT3: r = 0.94; p < 0.0001; Figure 2B) and AD individuals (GAT1: r = 0.9; p < 0.0001; Figure 2A, GAT3: r = 0.92; p < 0.0001; Figure 2B). By contrast, in brain regions typically resilient, we found a weak correlation with CU individuals (GAT1: r = 0.32; p = 0.016; Figure 2C, GAT3: r = 0.26; p = 0.04; Figure 2D) and no significant correlation in AD patients (GAT1: r = 0.24; p = 0.07; Figure 2C, GAT3: r = 0.24; p = 0.059; Figure 2D).ConclusionOur results demonstrate a significant strong positive association between [18F]FDG‐PET SUVr and GABA transporters gene expression in CU and AD in hypometabolic regions. These findings suggest that GABA transporters are involved with brain glucose metabolism in regions classically vulnerable to AD pathology. ”
APOEε4 potentiates the effects of Aβ pathology on the deposition of neurofibrillary tangles via tau phosphorylation
Authors: Ferrari‐Souza João Pedro , Bellaver Bruna , Ferreira Pamela C.L. , Benedet Andrea Lessa , Povala Guilherme , Lussier Firoza Z , Leffa Douglas Teixeira , Therriault Joseph , Tissot Cécile , Soares Carolina , Wang Yi‐Ting , Chamoun Mira , Servaes Stijn , Macedo Arthur C. , Vermeiren Marie , Bezgin Gleb , Kang Min Su , Stevenson Jenna , Rahmouni Nesrine , Pallen Vanessa , Poltronetti Nina Margherita , Cohen Annie , Lopez Oscar L. , Klunk William E , Soucy Jean‐Paul , Gauthier Serge , Souza Diogo O. , Triana‐Baltzer Gallen , Saad Ziad S. , Kolb Hartmuth C. , Karikari Thomas K , Villemagne Victor L , Tudorascu Dana , Ashton Nicholas J. , Zetterberg Henrik , Blennow Kaj , Zimmer Eduardo R , Rosa‐Neto Pedro , Pascoal Tharick A. 
Published: December 2023

AbstractBackgroundThe mechanisms by which the apolipoprotein E e4 (APOEe4) allele influences Alzheimer’s disease (AD) pathophysiological progression are poorly understood. Here, we tested the association of APOEe4 carriership and amyloid‐ß (Aß) burden with longitudinal tau pathology progression.MethodWe studied 104 individuals across the aging and AD clinical spectrum from the McGill TRIAD cohort. Study participants underwent clinical assessments, APOE genotyping, magnetic resonance imaging, positron emission tomography (PET) for Aß ([18F]AZD4694) and tau ([18F]MK6240) at baseline, as well as an additional follow‐up tau‐PET scan (mean follow‐up, 2.4 years). We further assessed longitudinal changes in tau phosphorylation (plasma phosphorylated tau at threonine 217 [p‐tau217+]), brain atrophy (gray matter density), and clinical function (clinical dementia rating scale sum of boxes).ResultWe found that APOEe4 carriership potentiates Aß effects on longitudinal tau tangle accumulation over two years (Figure 1). Interestingly, the APOEe4‐potentiated Aß effects on tangles were mediated by longitudinal plasma p‐tau217+ increase (Figure 2). In addition, this longitudinal tau accumulation as measured by PET was accompanied by brain atrophy and clinical decline during the follow‐up period (Figure 3).ConclusionOur results support a model in which the APOEe4 allele plays a key role in Aß downstream effects on the aggregation of phosphorylated tau in the form of neurofibrillary tangles in the living human brain, which is a key factor in the development of dementia. These observations have important implications for the design of future trials by suggesting that the combination of therapies targeting both ApoeE4 and Aß pathology might have the potential to synergistically halt tau progression in AD.
Trajectory of MRI and FDG‐PET biomarkers in Alzheimer’s disease
Authors: Heeman Fiona , Moscoso Alexis , Dunås Tora , Schöll Michael 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) has a long preclinical phase, which is hypothesised to start with the accumulation of ß‐amyloid (Aß) plaques, followed by tau neurofibrillary tangles and neurodegeneration. Despite presenting quite different types of information, MRI‐derived volumetric measures and FDG‐PET‐derived metabolism are both commonly used as proxy of neurodegeneration in AD. The aim of this work is to assess the trajectory of both of these biomarkers in AD, relative to time to amyloid‐positivity.Method3289 MRI scans (682 participants) and 2472 FDG‐PET scans (1296 participants) were included from the ADNIGO, 1,2 and 3 cohorts. For MRI scans, total volume was calculated for a meta‐ROI consisting of entorhinal, inferior, middle temporal and fusiform gyri. For FDG‐PET, standardized uptake value ratios (SUVR) were calculated from 30‐60 min post injection for a meta‐ROI consisting of the angular, posterior cingulate and inferior temporal gyri, normalised to the pons. All metrices were transformed into z‐scores using the references values of the Aß‐negative subjects. Age of amyloid‐positivity was determined by fitting a nonlinear mixed effects model to 3607 Centloid values corresponding to 1690 participants. Next, time to amyloid‐positivity was calculated for each scan. The relationship between time to amyloid‐positivity and (z‐scored) SUVR or cortical volume values was modelled using a non‐linear mixed effects model with spline function and trajectories from both modalities were compared.ResultBaseline demographics can be found in Table 1. Figure 1 shows the trajectory of amyloid deposition that was used to determine age of amyloid‐positivity. Cortical volume and SUVR showed similar trajectories over time, with steeper decline in both measures after the event of amyloid‐positivity (Figure 2). The overlay of both biomarker trajectories shows that for the meta‐ROI, there is no difference in when these biomarkers become abnormal.ConclusionThe present results suggest that measures of neurodegeneration from MRI and FDG‐PET scans corresponding to a meta‐ROI, follow very similar trajectories. Future work will investigate if this also holds true for smaller regions of interest.
Investigation of neighborhood disadvantage in the context of APOE‐associated amyloid onset age risk
Authors: Irizarry‐Pagan Erica E , Teague Jordan P , Buckingham William R. , Cody Karly Alex , Langhough Rebecca E , De Chavez Elena Ruiz , Navaratna Ruvini , Okonkwo Ozioma , Asthana Sanjay , Johnson Sterling C. , Kind Amy J. , Bendlin Barbara B , Powell W. Ryan , Zuelsdorff Megan , Betthauser Tobey J 
Published: December 2023

AbstractBackgroundExposome social determinants of health (SDoH), such as neighborhood disadvantage, have shown associations with accelerated cognitive decline, brain volume loss, and postmortem AD pathology. However, associations between neighborhood disadvantage and amyloid biomarker positivity (A+) onset remain understudied. This study uses estimated A+ onset age (EAOA) and the Area Deprivation Index (ADI; an exposome SDoH) to investigate associations between neighborhood disadvantage and A+ risk.MethodParticipants (n = 599, Table 1) from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) and the Wisconsin Alzheimer’s Disease Research Center (WADRC) with available amyloid PET, address, and APOE genotype were included. EAOA was estimated using sampled iterative local approximation. Neighborhood disadvantage was categorized by national‐rank 2020 ADI (most disadvantaged = 80th percentile; least disadvantaged <80th percentile). APOE groups were established based on the number of e4 alleles. Cox proportional hazard models assessed A+ risk associated with APOE, ADI, and their interaction. Secondary Cox models used continuous ADI and a lower 70th ADI percentile for neighborhood groups. Kaplan‐Meier curves were generated to depict A‐ probability vs. age for combinations of APOE and ADI groups. A‐ were right censored.ResultA significant interaction (p = 0.015) between ADI and APOE groups was observed such that APOE‐e4 homozygotes who resided within a highly disadvantaged neighborhood had higher risk of becoming A+, but this group was limited to two participants. Secondary analyses indicated no significant effect of ADI or interaction with APOE on A+ risk (p>0.49). Plots of A+/‐ and EAOA by ADI and APOE group (Figure 1) and Kaplan‐Meier EAOA curves indicated EAOA overlap of ADI groups within APOE strata. A+ risk was significantly higher for APOE‐e4 homozygotes, followed by APOE‐e4 heterozygotes, and then APOE‐e4 non‐carriers.ConclusionIn this mostly unimpaired, highly educated sample, higher number of APOE‐e4 alleles was associated with higher A+ risk and earlier A+ onset. APOE‐associated A+ risk was similar for all ADI groups, but representation across APOE genotypes for individuals residing within highly disadvantaged neighborhoods was limited. Further work is needed in more diverse samples to investigate the possible influence of ADI and other exposome SDoH factors on AD biomarker timing and trajectories.
Relationship of Atrophy and Tau Imaging Subtypes in Prodromal and Clinical AD
Authors: Risacher Shannon L , Deardorff Rachael , Saykin Andrew J. 
Published: December 2023

AbstractBackgroundAmyloid‐positive MCI and AD participants show heterogeneity in patterns of atrophy and tau‐deposition. We sought to directly compare the previously‐defined subtypes (hippocampal sparing (HpSp), limbic predominate (LP), typical AD (tAD), and minimal atrophy) derived from [18F]flortaucipir PET (T) and structural MRI (N) in the context of positive amyloid (A). In addition, we examined assessed associations between demographic factors and subtype group for atrophy or tau.Method243 amyloid‐positive participants from ADNI with MRI and tau PET scans at the same visit were included (160 MCI, 83 AD). Brain volumes were extracted using Freesurfer version 6 and tau PET SUVR values were extracted using standard techniques. Hippocampal to cortical ratios for grey matter volume and tau SUVR were calculated as previously described (Risacher et al. 2017 Neurology). Minimal pathology subjects showed similar levels of atrophy relative to cognitive normal participants. Relationships between atrophy‐ and tau‐based subtypes, as well as their relationship to sex and race/ethnicity were assessed using a Chi‐square test. An ANOVA model was used to compare age and education between atrophy‐ and tau‐based subtypes.ResultA significant association between tau‐based and atrophy‐based subtypes was observed, driven by consistency in minimal atrophy, HpSp, and tAD groupsp = 0.024). In addition, differences by sex and race/ethnicity were also observed for atrophy but not tau subtypes with non‐Hispanic Whites most likely to be either minimal atrophy or tAD, while African Americans were more likely to be HpSp. Finally, significant differences in age and education were observed across the atrophy subtypes, with the HpSp group being younger and less educated, while the LP were older and more educated.ConclusionOverall, we observed consistency between atrophy‐based and tau‐based subtypes, especially minimal pathology, tAD, and HpSp forms. Interesting associations between demographics and atrophy but not tau subtypes were also observed. Future studies in larger and more diverse samples are needed. These results underscore the importance of examining heterogeneity in amyloid‐positive MCI and AD across multiple modalities to better understand patterns of A/T/N pathophysiology that may have implications for risk of progression and personalized therapeutic strategies.