bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cerebrovascular injury markers explain the effect of systemic vascular risk on cognitive decline in older adults with lower amyloid burden
Authors: Shirzadi Zahra , Yau Wai‐Ying Wendy , Rabin Jennifer S. , Buckley Rachel F. , Properzi Michael J , Fu Jessie Fanglu , Hsieh Stephanie , Thibault Emma G. , Mojiri‐Forooshani Parisa , Goubran Maged , MacIntosh Bradley J. , Black Sandra E. , Price Julie C , Johnson Keith A. , Sperling Reisa A. , Chhatwal Jasmeer P. , Schultz Aaron P. 
Published: December 2022

AbstractBackgroundSystemic vascular risk is a well‐established contributor to late‐life cognitive decline, yet the mechanism is not completely understood. We investigated whether neuroimaging‐based measures of vascular injury (white matter hyperintensity (WMH) volume, Peak width of Skeletonized Mean Diffusivity (PSMD), and relative cerebral blood flow (rCBF)) could explain the effect of systemic vascular risk on cognitive decline using longitudinal data from the Harvard Aging Brain Study.MethodWe used the Framingham Heart Study cardiovascular disease risk score (FHS‐CVD) as an index of systemic vascular risk. We extracted WMH from structural MRI (https://hypermapp3r.readthedocs.io/) and PSMD from diffusion MRI (http://www.psmdmarker.com). We performed kinetic modeling on dynamically‐acquired PiB‐PET data to extract 1) amyloid burden as the distribution volume ratio (PiB‐DVR) and 2) relative tracer delivery (PiB‐R1 via MRTM reference‐tissue analysis as a proxy of rCBF). Global cognition was assessed using Preclinical Alzheimer Cognitive Composite (PACC).We considered two linear mixed effect models to examine whether cerebrovascular injury markers could explain the FHS‐CVD effect on PACC change over time: 1) FHS‐CVD on PACC change controlling for age, sex, years of education, and PiB‐DVR; 2) the previous model including WMH, PSMD, and PIB‐R1. Analyses were stratified by high (PiB+) and low (PiB‐) baseline amyloid burden.ResultFigure 1 illustrates the relationships between demographics and neuroimaging measures at baseline. Table 1 shows the demographics and study information. We observed a significant effect of FHS‐CVD on PACC change (PiB‐: t=‐3.9, p<0.001 (Table 2); PiB+: t=‐2.6, p=0.008). When the cerebrovascular injury markers were included in the model, the effect of FHS‐CVD was reduced in the PiB‐ group while PSMD and PiB‐R1 explained PACC change (Table 3; Figure 2). The second model fit was significantly better than the first model (L.ratio= 25.9, p<0.001). The effects remained significant after controlling for gray matter volume. In contrast, cerebrovascular injury measures did not explain the effect of FHS‐CVD on PACC change in the PIB+ group.ConclusionThese results demonstrate that cerebrovascular injury largely explains the effect of systemic vascular risk on cognitive decline in older adults with lower amyloid burden suggesting mechanisms by which a higher systemic vascular risk impacts brain function.
Multi‐modal biomarkers improve prediction of memory function in cognitively unimpaired older adults
Authors: Trelle Alexandra N , Tran Tammy T , Wilson Edward N. , Deutsch Gayle , Sha Sharon , Andreasson Katrin I. , Carr Valerie A , Kerchner Geoffrey A. , Mormino Elizabeth C. , Wagner Anthony D 
Published: December 2022

AbstractBackgroundIdentifying biomarkers that predict current and future cognition may improve estimates of Alzheimer’s disease risk among cognitively unimpaired older adults (CU). In vivo measures of amyloid and tau protein burden and task‐based functional MRI measures of core memory mechanisms, such as the strength of cortical reinstatement during remembering, have each been linked to individual differences in memory in CU. This study assesses whether combining CSF biomarkers with fMRI indices of cortical reinstatement improves estimation of memory function in CU, assayed using three unique tests of hippocampal‐dependent memory.MethodParticipants were 157 CU (90F, aged 60‐88 years, CDR=0) enrolled in the Stanford Aging and Memory Study (SAMS). Cortical reinstatement was quantified using multivoxel pattern analysis of fMRI data collected during completion of a paired associate cued recall task. Memory was assayed by associative cued recall, a delayed recall composite, and a mnemonic discrimination task that involved discrimination between studied ‘target’ objects, novel ‘foil’ objects, and perceptually similar ‘lure’ objects. CSF Aβ42, Aβ40, and p‐tau181 were measured with the automated Lumipulse G system (N=115). Regression analyses examined cross‐sectional relationships between memory performance in each task and a) the strength of cortical reinstatement in the Default Network (comprised of posterior medial, medial frontal, and lateral parietal regions) during associative cued recall and b) CSF Aβ42/Aβ40 and p‐tau181, controlling for age, sex, and education. For mnemonic discrimination, linear mixed effects models were used to examine the relationship between discrimination (d’) and each predictor as a function of target‐lure similarity.ResultStronger cortical reinstatement was associated with better performance across all three memory assays. Age and higher CSF p‐tau181 were each associated with poorer associative memory and a diminished improvement in mnemonic discrimination as target‐lure similarity decreased. When combined in a single model, CSF p‐tau181 and Default Network reinstatement strength, but not age, explained unique variance in associative memory and mnemonic discrimination performance, outperforming the single‐modality models.ConclusionCombining fMRI measures of core memory functions with protein biomarkers of Alzheimer’s disease significantly improved prediction of individual differences in memory performance in CU. Leveraging multimodal biomarkers may enhance future prediction of risk for cognitive decline.
Amyloid PET in the Lewy Body disease continuum
Authors: Diaz‐Galvan Patricia , Przybelski Scott A. , Lesnick Timothy G. , Schwarz Christopher G. , Senjem Matthew L. , Gunter Jeffrey L. , Jack Clifford R. , Min Paul H , Jain Manoj K. , Miyagawa Toji , Forsberg Leah K. , Fields Julie A. , Savica Rodolfo , Graff‐Radford Jonathan , St Erik K , Knopman David S. , Graff‐Radford Neill R. , Ferman Tanis J , Petersen Ronald C. , Lowe Val J. , Boeve Bradley F. , Kantarci Kejal 
Published: December 2022

AbstractBackgroundβ‐amyloid plaques, a pathological hallmark of Alzheimer’s disease, are common in dementia with Lewy bodies (DLB). However, little is known about when β‐amyloid levels increase throughout the Lewy body disease (LBD) continuum, from an early prodromal stage of isolated REM sleep behavior disorder (iRBD), to a stage of mild cognitive impairment‐Lewy bodies (MCI‐LB), and finally DLB. Our objective was to investigate β‐amyloid deposition throughout the LBD continuum, as well as the influence of sex and APOE ε4 status, as potential factors contributing to Alzheimer’s disease pathological changes.MethodPatients with iRBD (n=24), MCI‐LB (n=62), and DLB (n=82) from the Mayo Clinic Alzheimer’s Disease Research Center and Center for Sleep Medicine were included. β‐amyloid levels were measured by Pittsburgh compound B (PiB) PET and global cortical standard uptake value ratio (SUVR) was calculated. Global cortical PiB SUVR values from each clinical group were compared to each other and to cognitively unimpaired individuals (CU; n=100) balanced on age and sex from the Mayo Clinic Study of Aging (MCSA). Clinical groups were compared using ANCOVA after adjusting by age.ResultMCI‐LB and DLB patients had significantly higher global cortical PiB SUVR than CU participants, while PiB SUVR in iRBD patients was comparable to that of CU participants (Figure 1). DLB patients also had significantly higher global cortical PiB SUVR compared to iRBD patients (p=0.004). In MCI‐LB and DLB, global cortical PiB SUVR was higher in APOE ε4 carriers compared to APOE ε4 non‐carriers (p<0.001), and women had higher global cortical PiB SUVR compared to men (p=0.024).ConclusionIn this cross‐sectional study, β‐amyloid pathology gradually increased throughout the clinically defined LBD continuum. Whereas β‐amyloid levels are comparable to cognitively unimpaired individuals in iRBD, a significant elevation in β‐amyloid levels coincides with cognitive impairment observed in MCI‐LB and DLB. Specifically, women and APOE ε4 carriers tend to have higher β‐amyloid levels than men and APOE ε4 non‐carriers. These findings have important implications for targeting patients within the LBD continuum, who may benefit from amyloid modifying therapies.
Problems using structural MRIs from the oldest‐old, and some solutions: Lessons learned from The 90+ Study and ADNI
Authors: Woodworth Davis C. , Nguyen Hannah L. , Li Janice X. , Nguyen Katelynn M. , Greenia Dana E. , Kawas Claudia H. , Corrada Maria M. , Sajjadi S. Ahmad 
Published: December 2022

AbstractBackgroundThe oldest‐old (≥85 years) have the highest rates of dementia. However, because of pronounced brain atrophy, standard neuroimaging programs face unique challenges in this cohort. Here, using data from both The 90+ Study and Alzheimer’s Disease Neuroimaging Initiative (ADNI), we report the prevalence of errors when processing brain MRIs from oldest‐old participants using standard approaches and present some strategies for ameliorating these issues.MethodFirst, we analyzed image quality issues and segmentation errors (FreeSurfer) for the last available scans by age‐groups (oldest‐old, ≥85 years, vs younger‐old, <85 years) in ADNI. Second, we evaluated the utility of an age‐specific template (the 90+ T1w Template) for realigning MRIs from the oldest‐old compared to a standard template (Montreal Neurological Institute T1w template, MNI) using linear and nonlinear registration (FSL) and evaluating alignment quality using visual and deformation (Jacobian) measures for oldest‐old participants in both The 90+ Study and ADNI. Lastly, we evaluated whether stringent preprocessing (bias field removal, denoising, brain extraction, intensity normalization using Computational Anatomy Toolbox (CAT12)) helped reduce FreeSurfer segmentation errors in frontal, parietal, and temporal regions in MRIs from the oldest‐old. Comparisons were performed using Chi‐square tests.ResultIn ADNI data (Table‐1), MRIs from oldest‐old participants (∼10% of cohort) were around twice as likely to have image quality issues (20% vs 13%, P<0.001) and brain segmentation errors (45% vs 29%, P<0.001) compared to younger‐old participants. Figure‐1 shows a histogram of image quality issues and segmentation errors, which become more common with greater age. Compared to the MNI template, MRIs from participants aged 90 and older from both The 90+ Study (N=163, Table‐2) and ADNI (N=75) had fewer registration errors (∼5% vs ∼23%, P<0.001 for all) and less severe deformation when using the 90+ T1w Template (Figure‐2). Our proposed preprocessing pipeline (see Figure‐3 for example) roughly halved segmentation errors in the oldest‐old (P<0.001 for all regions, Figure‐4).ConclusionOur results show that adapting pipelines specifically for processing brain MRIs of oldest‐old participants minimizes errors, enabling more effective use of neuroimaging to study neural correlates of neurodegeneration and dementia in this important portion of our aging population.
Imaging and fluid biomarkers predictive of progression from mild cognitive impairment to dementia
Authors: Jannati Ali , Gomes‐Osman Joyce Rios , Tobyne Sean , Bates David , Pascual‐Leone Alvaro 
Published: December 2022

AbstractBackgroundDementia is a growing socioeconomic and medical problem that affects increasing portions of the population, with a prevalence that doubles every 5 years after the age of 65. Alzheimer’s disease (AD; 60–80%) and frontotemporal lobar degeneration (FTLD; 5–10%) constitute two of the most common causes of dementia. The first symptoms appear during an initial clinical phase referred to as mild cognitive impairment (MCI). Each year, 8–15% of patients with MCI develop clinical dementia. Identifying biomarkers predictive of progression from MCI to dementia will enable initiation of disease‐modifying treatments at a stage where clinical symptoms of dementia are still limited. We investigated the predictive utility of imaging and fluid biomarkers of AD and FTLD including ß‐amyloid plaques and neurofibrillary tangles composed of hyperphosphorylated tau in positron emission tomography (PET), cerebrospinal fluid (CSF), and MRI for predicting progression of MCI to dementia.MethodMixed‐effects logistic regressions were conducted in the National Alzheimer's Coordinating Center (NACC) dataset (n=222–2,462) to investigate the predictive utility of abnormally elevated amyloid or tau on PET (PET‐amyloid, PET‐tau), abnormally low amyloid in CSF (CSF‐amyloid), abnormally elevated CSF tau (CSF‐tau), AD or FTLD pattern in FDG‐PET (FDG‐AD, FDG‐FTLD), hippocampal atrophy in MRI, and MRI evidence for FTLD (MRI‐FTLD). Potential covariates including age at baseline, gender, education, apolipoprotein E genotype, and number of follow‐up visits were included in the regression models.ResultsPET‐amyloid (OR=5.80), MRI‐FTLD (OR=4.46), hippocampal atrophy (OR=4.22), CSF‐tau (OR=3.49), and FDG‐AD (OR=2.91) were the strongest predictors (p‐values<=0.001), followed by FDG‐FTLD (OR=2.71), and CSF‐amyloid (OR=2.21) (p‐values<0.027). Among patients with available imaging AD biomarkers (n=500), PET‐amyloid (OR = 3.31) and hippocampal atrophy (OR = 3.12) were independent and comparably strong predictors. Among patients with available fluid AD biomarkers (n=214), CSF‐tau was a significant predictor of MCI progression (OR=2.48, p=0.035), whereas CSF‐amyloid was not (OR=1.71, p=0.21).ConclusionPET‐amyloid, hippocampal atrophy, and CSF‐tau were the strongest predictors of progression of MCI to AD. MRI evidence was a stronger predictor than FDG‐PET for progression of MCI to FTLD. Imaging and fluid biomarkers provide complementary prognostic factors for forecasting the trajectory of clinical outcome among patients with MCI.
Microglial activation might protect against accumulation of Aβ and tau aggregates over time in non‐demented Aβ‐positive individuals
Authors: Pereira Joana B , Janelidze Shorena , Binette Alexa Pichet , Strandberg Olof , Palmqvist Sebastian , Mattsson‐Carlgren Niklas , Stomrud Erik , Hansson Oskar 
Published: December 2022

AbstractBackgroundThe past years have seen a turning point in the view of the impact of microglia in Alzheimer’s disease (AD). In particular, the activation of soluble TAM receptors and the TREM‐2 dependent transition from a homeostatic to a disease‐associated microglia stage 2 state (DAM2) seem to play relevant roles in AD. However, a comprehensive analysis of microglial‐associated biomarkers and their associations with longitudinal accumulation of amyloid‐β (Aβ) and tau aggregates in humans has not been performed yet.MethodWe determined the baseline levels of DAM2‐associated biomarkers (sTREM2, LPL, CST7, SPP1, CSF1), TAM receptors (AXL, Mer) and their ligand (Gas6) in the cerebrospinal fluid (CSF) of non‐demented individuals from the Swedish BioFINDER‐2 cohort. All subjects underwent longitudinal Aβ (18F‐flutemetamol) and tau (18F‐RO948) positron emission tomography (PET). Linear mixed effects models were employed to test whether the microglial markers predicted longitudinal amyloid and tau PET changes, while adjusting for relevant covariates and multiple comparisons.ResultHigher levels of DAM2 markers, TAM receptors and their ligand were associated with slower global Aβ‐PET accumulation over time in Aβ‐positive participants (p<0.05), in contrast to SPP1, which was associated with faster Aβ‐PET deposition (p=0.012). Moreover, higher levels markers associated with TAM receptors and DAM2 microglia were associated with slower longitudinal tau‐PET accumulation in the neocortex (p<0.01) in Aβ‐positive subjects with evidence of tau pathology. The associations with longitudinal tau remained significant after adjusting for Aβ‐PET changes indicating they were independent of amyloid.ConclusionThese findings suggest that markers associated with DAM2 and TAM might mitigate the accumulation of amyloid and tau aggregates in individuals with Aβ as well as both Aβ and tau pathology, respectively. These findings have important implications for future clinical trials using microglial drugs in isolation or in combination with anti‐Aβ and anti‐tau therapies for AD.
Developing early vulnerable area aggregates for PET detection of beta‐amyloid based on young and middle‐aged adults from the Framingham Heart Study
Authors: Thibault Emma G. , Farrell Michelle E. , Beiser Alexa S , Becker Alex , Satizabal Claudia L , Jacobs Heidi I.L. , DeCarli Charles S. , Hanseeuw Bernard , Killiany Ronald J , Sperling Reisa A. , Seshadri Sudha , Johnson Keith A. 
Published: December 2022

AbstractBackgroundAs Alzheimer’s clinical trials shift earlier and earlier in the disease process, current global PET measures of beta‐amyloid (Aβ) positivity may be insufficient for detecting the earliest Aβ deposits. Regional PET measures may better detect the earliest deposits. Previous efforts to identify early‐accumulating regions have inferred which regions may be most vulnerable based on older adults. Our aim was to identify early vulnerable areas by looking earlier in the lifespan.Method235 clinically‐normal adults ages 33‐74 from the Framingham Heart Study (FHS) underwent one time‐point of dynamic Pittsburgh Compound B (PIB) PET (Tab.1). PIB was regionally quantified in Desikan regions using distribution volume ratio (DVR, cerebellar reference). Linear and quadratic (Age + Age2) models were run to determine association of age with region of interest (ROI) PIB DVR, and ROIs with significantly increasing DVRs were selected for the generation of candidate early vulnerable area (EVA) aggregates. Multiple aggregate EVA DVRs (EVA2‐EVA11) were generated by sequential addition of regions in rank order of descending Age2 estimate. EVA positivity was derived from both full‐sample GMM and <45 sample mean+2SD thresholds. Finally, EVA aggregates were tested using an independent cohort of older adults from the Harvard Aging Brain Study (HABS) that were imaged under an identical protocol (n=250, ages 50‐92), computing sensitivity and specificity of each aggregate at baseline to predict progression to global PIB positivity 3 years later.ResultOf 35 Desikan regions each evaluated on the left and right, 11 emerged as quadratic age relationships (p<0.05; Fig.1) and were used to generate EVA aggregates (Fig.2). Independent validation in baseline global PIB‐ individuals from HABS indicated EVA9 and EVA10 best predicted future accumulation (Fig.3). GMM‐based thresholds provided excellent specificity (SP=1.0) but weak sensitivity (SE=0.41) to predict progression to global PIB positivity 3 years later, while the more liberal mean+2SD thresholds improved sensitivity but decreased specificity (SE=0.88, SP=0.88).ConclusionUtilizing lifespan data from FHS, we identified a set of early vulnerable regions that are predictive of future accumulation in an independent sample of older adults. These findings are potentially useful in identifying the earliest deposits of Aβ for use in clinical trial design.
Identifying lifestyle factors that promote brain resilience in ApoE4 carriers
Authors: Haddad Elizabeth , Javid Shayan , Zhu Alyssa H , Nir Talia M , Gadewar Shruti , Gari Iyad Ba , Lam Pradeep , Gupta Arpana , Thompson Paul M , Jahanshad Neda 
Published: December 2022

AbstractBackgroundApolipoprotein (ApoE) E4 (e4) carriers are at increased risk for Alzheimer’s disease (AD) (Strittmatter, 2012), which is often characterized by accelerated hippocampal atrophy and cognitive decline after 65 (Reinvang, 2013). However, not all e4 carriers develop AD. In those at heightened genetic risk, it is important to evaluate lifestyle factors that might slow brain aging, and delay dementia onset. Poorer sleep quality, physical activity, and diet have been associated with several AD‐related processes ‐ including increased beta‐amyloid burden, decline in vascular integrity, reduced glucose metabolism, and neuronal dysfunction (Mander 2017, Radak 2010, Sezgin 2014). Smoking (Durazzo, 2014) and excessive alcohol consumption (Venkataraman, 2017) have also been shown to promote AD pathology and risk. Moreover, social contact and education may enhance cognitive reserve (Livingston, 2020). Here, we investigated the role of these factors in promoting structural brain resilience in a population susceptible to AD from the UK Biobank (UKB; Miller, 2016).MethodUKB participants over age 65 with e3e4 or e4e4 genotype were included (1415M, 1355F). A novel deep learning method (Lam, 2020) was used to calculate predicted brain age (a machine‐learning estimate of a person’s age from their brain MRI). Participants were labeled as resilient if their predicted age was 3+ years younger than their actual age. Lifestyle factors evaluated may be found in Table 1. Association rule learning (Agrawal, 1993) using the mlxtend python library was used to identify sets of factors most often associated with resiliency.Result604/2770 of the e4 carriers were labeled as resilient (Figure 1). The antecedent set with the highest likelihood (42%) of being associated with resiliency included following the ideal recommendations for physical activity, infrequent alcohol consumption, averaging at least 7 hours of sleep/night, participating in leisure/social activities, and never having smoked. These measures also appeared in other likely antecedent sets (Table 2).ConclusionWe identified several lifestyle factors that may promote resilience to brain aging in those at elevated risk for AD. We aim to replicate our findings using a larger dataset with brain age calculated, and train sex‐specific models as well as testing them in people of more diverse ancestry.