bluuebunny/crossref_metadata_2025_split huggingface dataset:
Latinx brain health disparities in cognitive aging: The role of acculturation on brain integrity and cognition among older HIV+ adults
Authors: Mindt Monica Rivera , Savin Micah J. , Morris Emily P. , Summers Angela , Aghvinian Maral N. , Crook Cara L. , Byrd Desiree , Clark Uraina S. , Guzman Vanessa A. , Stiver Jordan , Fidaleo Kaleigh L. , Breen Elizabeth L. , Slaughter Alex L. , Robinson‐Papp Jessica 
Published: December 2020

AbstractBackgroundThe US Latinx population is the largest ethnoracial minority group and the fastest growing sector of the aging population (Escarce et al., 2006; US Census Bureau, 2008). Compared to non‐Latinx whites (NLW), the Latinx population bears a disproportionate dementia burden secondary to multiple etiologies (e.g., Alzheimer’s, HIV‐infection; Babulal et al., 2018; Rivera Mindt et al., 2014). Yet, little is understood regarding the sociocultural factors underlying these significant brain health disparities. This aim of cross‐sectional study was to investigate indices of brain integrity, cognition, and acculturation in older HIV+ Latinx and NLW adults.MethodParticipants included 75 older HIV+ adults (Age: M=60 yrs [SD=6.9]; Education: M=13.6 yrs [SD=3.1]; 47% Latinx [primarily Caribbean heritage] & 53% NLW; and 71% male) who completed multimodal neuroimaging (structural MRI and resting state fMRI [rs‐fMRI]), cognitive (seven domains; e.g., leaning, memory, executive function), neuromedical (HIV clinical indices [HIV viral load]), and sociocultural (e.g., acculturation [Abbreviated Multidimensional Acculturation Scale, AMAS]) evaluations. Global and domain‐specific Average T‐scores were computed on individual tests based on demographically‐adjusted norms.ResultCompared to the NLW group, the Latinx group had greater white matter lesion volumes ([mL], FLAIR; Cohen’s d=1.13, p<.01) and lower hippocampal and posterior cingulate intrinsic activity (rs‐fMRI fALFF values; d’s =.61 ‐ .67), and worse global cognition, learning, memory, and processing speed (ds=.53 ‐ .83, ps< .05), with the largest effects in learning and memory. Lower acculturation to US majority culture (i.e., AMAS US Total Score) was related to reduced intrinsic activity of the right hippocampus and posterior cingulate cortex at the trend level (rs‐fMRI fALFF values; rs=.61 ‐ .63, ps<.10) and worse global cognition, learning, memory, and processing speed (rs = .39 ‐ .51, ps<.05).ConclusionThese findings highlight marked disparities in brain integrity and cognition in older HIV+ Latinx adults. Acculturation may be an important factor for understanding these disparities. Future longitudinal research is needed with HIV‐ controls, larger sample sizes, and the inclusion of other Latinx subpopulations (i.e., Mexican‐American, South American) to extend the current findings and inform culturally‐targeted interventions to promote Latinx brain health. Acknowledgements: The authors thank the Alzheimer’s Association for their support of this study (AARGD‐16‐446038).
Anterolateral entorhinal cortical thinning as a biomarker for Alzheimer’s disease in Down syndrome
Authors: Sathishkumar Mithra , Janecek John , Smith Anna , Phelan Michael , Tustison Nick , Keator David , Doran Eric , Hom Christy , Nguyen Dana , Petersen Melissa , Rosas H. Diana , Lai Florence , Brickman Adam M. , Schupf Nicole , Silverman Wayne , Lott Ira T. , O’Bryant Sid , Yassa Michael A. 
Published: December 2020

AbstractBackgroundVirtually all adults with Down syndrome (DS) develop Alzheimer's disease (AD) associated neuropathology by the age of 40 and the majority of them develop dementia by age 60. Atrophy of the anterolateral entorhinal cortex (aLEC) has been previously associated with cognitive decline. Here, we sought to determine whether aLEC thinning is associated with clinical impairment in older adults with DS as well as whether it is associated with blood neurofilament light chain (NfL), another biomarker of neurodegeneration in AD.Method103 older adults with DS enrolled in the Alzheimer’s Disease in Down Syndrome (ADDS) study were included in this analysis. Scans were classified into 3 groups based on consensus diagnosis: 14 (6F) possible/definite dementia (DEM), 19 (5F) mild cognitive impairment (MCI‐DS) and 70 (28F) cognitively stable (CS). Images were processed through the joint label fusion (JLF) pipeline in ANTs to obtain medial temporal lobe subregional volumes and thickness. Cortical reconstruction was performed using Freesurfer 6.0 to obtain total intracranial volume(ICV). We used a multiple linear regression model to fit left aLEC thickness using diagnostic group as the independent variable, correcting for age, gender, ICV and scan site differences. For a subset of these data (N=89), plasma NfL was quantified using a Simoa platform and we and tested whether left aLEC thickness was associated with NfL.ResultCortical thickness in left aLEC was a significant predictor of clinical status. Thickness values were highest in the CS group, followed by MCI‐DS, followed by DEM. Groupwise differences were significant in DEM vs. CS and DEM vs. MCI‐DS comparisons. We also found a significant correlation between NfL and left aLEC thickness specifically in the DEM group. Finally, random forest classification showed that both NfL and left aLEC thickness were important features in predicting clinical diagnosis DEM vs MCI‐DS. Their estimates provided a diagnosis ROC curve with sensitivity of 0.79 and specificity of 0.92.ConclusionIn older adults with DS, left aLEC cortical thickness atrophy may be a primary feature of structural neurodegeneration that is associated with MCI‐DS and a blood‐based measurement of NfL may be informative as to this structural degeneration.
Functional fitness assessments predict cognitive outcomes
Authors: Gills Joshua L , Gray Michelle , Glenn Jordan M , Madero Erica N , Fuseya Nami , Hall Aidan , Bott Nicholas T 
Published: December 2020

AbstractBackgroundThe rate of Alzheimer’s disease (AD) is anticipated to triple by 2050, affecting 131 million people globally. To combat this dramatic increase, it is imperative to detect early cognitive decline in order to provide timely interventions. As a result, the ability to predict cognitive status through ubiquitous functional fitness assessment would provide a cost‐effective method for identifying cognitively at‐risk older adults. This study sought to determine whether functional fitness measures could accurately predict cognitive outcomes.Method85 older adults (age: 80.93 ± 5.4) participated in the current study. Each participant completed demographic questionnaires; completed three cognitive tasks: Montreal Cognitive Assessment (MoCA), digit coding symbol test (DCS), and visual paired comparison target foil accuracy (VPCTFA) assessment; and completed six functional fitness assessments : 10‐meter maximal speed walk, dual‐task maximal speed (DTMS), dual‐task habitual speed (DTHS), sit‐to‐stand power, timed up‐and‐go (TUG), and the short physical performance battery (SPPB). Results were analyzed through three multiple linear regressions with MoCA, DCS and VPCTFA test each as the dependent variables, and age, sex, education, DTMS, DTHS, sit‐to‐stand power, TUG, and SPPB as predictor variables.ResultThe first model explained 55% of the variance of the MoCA (p < .001) with DTMS (45%; p < .001) representing the only significant predictor. The second model explained 13% of the variance of the DCS (p = .13) with age (28%; p = .01) and DTMS (23%; p = .04), representing significant predictors. The last model explained 18% of the variance of the VPCTFA (p = .007) with DTMS (21%; p = .05), TUG (21%; p = .05), 10‐meter maximal walking speed (21%; p = .05) and 4‐meter walk (SPPB variable; 22%; p = .04) representing significant predictors.ConclusionThese results suggest functional fitness assessments may predict cognitive outcomes. Functional fitness assessments accounted for more variance of MOCA scores than DCS and VPCTFA outcomes. This suggests functional fitness assessments may be better predictors of global cognition performance than performance on assessments of processing speed or working memory. Future research will investigate whether functional fitness parameters may be cost‐effective evaluation tools for predicting global cognitive performance over time.
Functional molecular network models for the genetic risk factors of Alzheimer’s disease
Authors: Ming Chen , Marcora Edoardo , Wang Minghui , Renton Alan E. , Wang Erming , Goate Alison , Zhang Bin 
Published: December 2020

AbstractBackgroundGenome‐wide association studies (GWAS) have pinpointed a limited number of risk loci in Alzheimer’s disease (AD). However, the biological functions of these AD risk loci remain mostly elusive. Network analysis of functional genomic data provides an excellent opportunity to understand AD systematically. In this study, we leverage a cohort of large‐scale transcriptomic data in AD to develop functional network models for AD risk genes.MethodWe integrated all sumstats of AD risk loci from four previous AD GWAS studies and generated putative AD‐risk genes(PADRG) using MAGMA. We determined the +/‐ 1 Mb cis‐regulome interval for the promoter of each protein‐coding gene, and applied MAGMA in top SNP mode to all AD GWAS summary statistics using cis‐regulome intervals as the unit of aggregation, then retained genes whose interval achieved both p < 5e‐8 in any GWAS and p < 1 in all four GWAS. Based on the Mount Sinai Brain Bank AD cohort comprised of multi‐Omics data from multiple brain regions, we performed the multiscale embedded gene coexpression network (MEGEN) analysis to delineate gene coexpression relationships. Subnetworks centering around each PADRG were then extracted and evaluated for relevance with AD‐related clinical and pathological traits.ResultThe 751 PADRGs are enriched in the immune response‐related modules in the MEGENs from four brain regions, consistent with the known prominent role of immune response in AD. The subnetwork analysis further identifies the functional contexts of PADRGs. All the top thirty‐nine PADRG‐centered subnetworks are from the parahippocampal gyrus region and have a variety of biological functions such as synaptic signaling, protein‐complex subunit organization, and RAS protein signal transduction. The top two subnetworks are centered around FAM81A and RCAN2 and are involved in γ‐aminobutyric acid (GABA) signaling. Three well‐known AD risk genes CLU, MAPKAPK2 and STX3, are predicted to function in the cellular response to organic substance, inflammatory response, synaptic signaling pathways, respectively.ConclusionOur multiscale network analysis systematically identifies the functional contexts of the PADRGs. The subnetworks around the PADRGs and the comprehensive characterization of those subnetworks lay down a solid foundation for not only prioritizing AD‐associated genes but also investigating their biological functions.
Associations between lifestyle risk, β‐amyloid, tau, and cognition in late mid‐life
Authors: Cody Karly Alex , Koscik Rebecca L. , Erickson Claire M , Birdsill Alex C , Berman Sara E , Chin Nathaniel A. , Clark Lindsay R , Christian Bradley T , Betthauser Tobey J , Johnson Sterling C 
Published: December 2020

AbstractBackgroundModifiable lifestyle risk (LR) factors are increasingly viewed as preventive strategies for AD and dementia; however, the impact of LR factors on AD biomarkers or AD biomarker‐related cognitive decline remains unknown. We utilized the Lifestyle for BRAin health (LIBRA) index 1)to investigate whether baseline LR predicts incident cognitive impairment and/or β‐amyloid onset; 2)to examine the effect of longitudinal LR on cognitive decline in the presence of β‐amyloid; 3)to preliminarily examine the cross‐sectional relationship between late‐midlife LR and β‐amyloid and tau biomarker groups.MethodParticipants (n=1244, Table 1) from the Wisconsin Registry for Alzheimer’s Prevention completed serial health and neuropsychological assessments with a subset undergoing PiB PET imaging (n=238) and MK‐6240 (n=186) PET imaging. Cox regressions assessed the predictive utility of baseline LIBRA for incident cognitive impairment (1.5 SD below covariate adjusted 3‐test Preclinical Alzheimer’s Cognitive Composite, PACC‐3) and β‐amyloid duration (i.e. age at assessment – estimated age PiB(+)). Linear mixed models examined the main effects and age interactions of LIBRA and β‐amyloid duration on longitudinal cognitive performance. Group differences in LIBRA were assessed across biomarker groups, where amyloid (A+) and tau (T+) positivity were defined as a global PiB DVR>1.19 and entorhinal cortex MK‐6240 SUVR>1.27, respectively.ResultIn the survival analysis, baseline LIBRA predicted risk for incident cognitive impairment of subjects unimpaired at baseline (HR=1.18; Figure 1). In the amyloid imaging subset, baseline LIBRA did not predict β‐amyloid onset (HR=0.85; Figure 1). Linear mixed effects models indicated a significant main effect of LIBRA (higher LIBRA risk = worse PACC‐3) and a significant interaction of β‐amyloid duration×age on PACC‐3 (longer β‐amyloid duration = faster PACC‐3 decline; Figure 2). In the combined imaging subset (n=186), there were no differences in LIBRA across biomarker groups (Kruskal‐Wallis H: 5.08, p=.166; Figure 3).ConclusionThese findings suggest that a healthy lifestyle is associated with overall cognitive performance but not the rate of cognitive decline or biomarker status. In this sample, LR did not influence the onset of β‐amyloid or hasten AD‐related cognitive decline. Replication of this finding and future work is necessary to better understand the relationships between LR factors, cognitive decline and AD.
CD33, MEF2C, and SORL1 are associated with variability in macroscale functional brain architecture in AD
Authors: Ramanan Vijay K , Botha Hugo , Radford Jonathan Graff , Wiste Heather J. , Senjem Matthew L. , Gunter Jeffrey L. , Boeve Bradley F. , Knopman David S. , Petersen Ronald C. , Jack Clifford R. , Lowe Val J. , Jones David T. 
Published: December 2020

AbstractBackgroundAlzheimer’s disease (AD) is marked by selective disruptions to anatomically‐distinct brain regions and their connectivity which collectively support cognition. Based on a variation of pattern‐recognition analysis of FDG‐PET images, we previously developed a framework, the global functional state space (GFSS), which captures this macroscale architecture by assigning each individual a series of weightings on 10 independent patterns (eigenbrains=EBs). In this study, we applied the GFSS as a quantitative endophenotype to test whether known AD genetic risk factors were associated with variability in large‐scale neurodynamics represented by the GFSS.MethodThe study sample included 410 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with evidence of cognitive impairment (CDR‐global>0) and amyloidosis (positive amyloid‐PET). The GFSS was estimated for each individual using the set of 10 EBs to describe global patterns of variation in glucose metabolism. AD genetic risk variants identified in prior large consortium case‐control studies were included for analysis if the variant (or a proxy by linkage disequilibrium r
2>0.7) was assayed on both genotyping platforms used in the sample and if the minor allele frequency exceeded 1%. Genetic associations with the collective set of EBs were tested using multivariate linear regression under a dominant genetic model.ResultSignificant multivariate associations were identified for CD33 (p=7.04x10‐4), MEF2C (p=0.001), and SORL1 (p=0.02). These findings remained significant after covarying for age and sex. The strongest component loadings for CD33 (p=9.09x10‐4; std. β=‐0.16) and MEF2C (p=1.59x10‐4; std. β=‐0.18) were for EB1, with the minor allele in each case associated with a negative axis deviation in the direction of the GFSS area representing episodic memory dysfunction. SORL1 displayed its strongest loading onto EB4 (p=0.03, std. β=‐0.11). The association of APOE ɛ4 with the GFSS was marginally nonsignificant (p=0.09).ConclusionWe identified associations of risk variants in CD33, MEF2C, and SORL1 with differences in patterns of FDG hypometabolism in persons with symptomatic AD. These findings establish the GFSS as a useful endophenotype for genetic analyses, particularly as an avenue toward a mechanistic understanding of how heritable factors may influence inter‐individual variability and syndromic diversity in AD. Further investigation in larger and independent cohorts is warranted.
Neurocognitive mechanisms of spatial pattern separation in older adults with and without subclinical memory impairment
Authors: Márquez Freddie , Larson Myra S , Smith Anna , Miranda Blake A , McMillan Liv , Sathishkumar Mithra , Yassa Michael A 
Published: December 2020

AbstractBackgroundDecline in episodic memory is a hallmark feature of Alzheimer’s disease (AD). Episodic memories can be operationalized as detailed conjunctions of items and their spatiotemporal context. Our past work has uncovered deficits in pattern separation of similar object memories in nondemented older adults. However, separation of similar spatial locations was only slightly impaired in this population. We hypothesized that these deficits may be more readily observable in individuals with subclinical memory impairment (assessed by verbal list recall). We also sought to determine the neural mechanisms by which spatial pattern separation deficits may manifest in older adults, focusing on the posterior parietal network which is involved in spatial processing and implicated in AD.MethodsTwo cohorts were included in this analysis. The first is a group of older adults with subclinical memory impairment (aged impaired – AI, age range: 61‐91; mean MMSE: 28.3 ± 1.50; mean RAVLT delayed recall: 9.66 ± 4.05). The second is a group of non‐demented older adults without memory impairment (aged unimpaired – AU; age range: 61‐86; mean MMSE: 28.1 ± 1.59; RAVLT delayed recall: 11.4 ± 3.01). Participants underwent whole brain high‐resolution resting state functional magnetic resonance imaging (1.5 x 1.5 x 2 mm resolution). They were also administered a battery of neuropsychological tests including tests of object and spatial pattern separation. We used performance on the RAVLT as the outcome predicted by performance on the discrimination tests. Specifically, we used delayed recall (DR), retroactive interference (RI), and percent forgetting (PF).ResultsBehaviorally, spatial, but not item, pattern separation performance predicted RAVLT DR, RI and PF scores in the AI group, but not the AU group. Functional connectivity analyses revealed that the connectivity of regions involved in spatial processing was significantly related to spatial pattern separation performance in the AI group, but not the AU group.ConclusionThese results validate the neurobiological basis of spatial pattern separation and suggest that such tasks can be used to detect early stages of AD, which has important implications for future diagnostics and treatment approaches.
Medial temporal neuroinflammation unleashes tau spreading over the neocortex
Authors: Pascoal Tharick A. , Kang Min Su , Therriault Joseph , Benedet Andréa Lessa , Chamoun Mira , Lussier Firoza Z. , Tissot Cécile , Mathotaarachchi Sulantha , Stevenson Jenna , Massarweh Gassan , Soucy Jean‐Paul , Gauthier Serge , Rosa‐Neto Pedro 
Published: December 2020

AbstractBackgroundTransentorhinal tau neurofibrillary tangles deposition is a typical finding in cognitively unimpaired (CU) elderly. Thus, how tau tangle spreads over the neocortex, leading to disease progression, is a pressing issue in Alzheimer's disease (AD). Although amyloid is postulated to trigger this spread, the fact that amyloid deposition is present in low concentrations in temporal structures challenges this framework. Here, we tested the hypothesis that brain amyloid burden interacts with medial temporal neuroinflammation unleashing tau spread over the neocortex. To assess neuroinflammation, we used [11C]PBR28 PET, a ligand that binds to the 18‐kDa translocator protein(TSPO), considerate a proxy of inflammation.MethodsTSPO polymorphism (rs6971) was genotyped in 678 participants from the Montreal catchment area (48% presented rs6971 polymorphism of high TSPO binders). Following inclusion/exclusion criteria, we studied 131 high TSPO binders (22 CU young, 63 CU elderly, 28 MCI, 18 AD). All individuals had PET; amyloid [18F]NAV4694, tau [18F]MK6240, and [11C]PBR28,as well as MRI, cognitive and genetic assessments. Structural equation modeling (SEM) tested associations between markers.Results[11C]PBR28 uptake was progressively higher from CU to AD(Fig.1). [18F]NAV4694,[18F]MK6240, and [11C]PBR28 were correlated (Fig.2); however, analysis of variance suggested that an interaction between [18F]NAV4694and[11C]PBR28 on [18F]MK6240 best explain the association between these markers(P<0.0001). Voxel‐wise analysis, taking into consideration global and local tracer effects, suggests an interaction between global [18F]NAV4694 and medial temporal [11C]PBR28 on medial temporal [18F]MK6240(Fig.3). Stratified analysis revealed that individuals with abnormal amyloid, tau, and neuroinflammation (A+/T+/NI+) had elevated cognitive impairment and hippocampus atrophy(Fig.4). SEM suggests that global amyloid interacts with transentorhinal inflammation leading to tau spread over Braak II‐III(Fig.5). Subsequently, tau hierarchically spread from Braak III to VI. Models inverting the variables mentioned above or the order of Braak regions did not fit the data. Accumulation of tau only in Braak IV‐VI was associated with cognitive symptoms (Fig.6).ConclusionOur results suggest that an interaction between global amyloid and medial temporal neuroinflammation triggers tau spread over the neocortex. Global amyloid may represent distant amyloid load or amyloid oligomers. Our results suggest individuals with tau deposition confined to transentorhinal cortex as targets for the use of anti‐inflammatory drugs in AD.
How often are patients with Alzheimer disease given that diagnosis?
Authors: Pippenger Mark A 
Published: December 2020

AbstractBackgroundAlzheimer Disease (AD) is the most common cause for dementia, but outside the research setting the majority of people who meet diagnostic criteria for AD do not receive the diagnosis of AD, or in some cases do not even receive a diagnosis of dementia. Much of the data on this subject come from research studies, however, and relatively less is drawn from community practice. The author began a Memory Clinic within a large health system in the city of Charlotte, NC, in February 2019. All referrals for cognitive complaints within that system, came to the author's clinic. This affords a "real world" environment to assess the diagnoses assigned by clinicians, to patients who meet diagnostic criteria for AD.MethodAll new patient evaluations for a one‐year period were examined. For those patients determined to meet diagnostic criteria for "probable" AD, the specialty of the referring clinician, and the diagnosis assigned by the referring clinician, was assessed. Severity of dementia was estimated using the Mini‐Mental State Exam (MMSE), as Mild (≥20), Moderate (10‐19), or Severe (≤9).ResultOf the patients diagnosed with "probable" AD during the first year of clinic, 9% had previously received no cognitive diagnosis, 48% had been given a diagnosis simply of "memory loss," 12% had been given a nonspecific diagnosis of "dementia," a few some other diagnosis, and 25% had been given the diagnosis of AD. These percentages didn't vary a lot between levels of severity, except to note there were few "severe" patients, and interestingly, the majority of patients who carried no cognitive diagnosis at all, had "moderate" dementia.ConclusionThese data provide further evidence that most people who meet diagnostic criteria for Alzheimer Disease, do not receive a specific etiologic diagnosis for their dementia.