bluuebunny/crossref_metadata_2025_split huggingface dataset:
Examining Complex Relationships among Cerebrovascular, β‐Amyloid, Tau and Cognitive Measures in a Cognitively Unimpaired Late‐Middle Aged Sample
Authors: Du Lianlian , Jonaitis Erin M. , Betthauser Tobey J , Cody Karly Alex , Burghy Cory , Rivera‐Rivera Leonardo A. , Mueller Kimberly D , Chin Nathaniel A. , Hermann Bruce P , Johnson Sterling C , Langhough Rebecca E 
Published: December 2023

AbstractBackgroundRecent research linking vascular risk with promotion of tau pathology in the inferior temporal cortex (ITC) suggests a potential mechanism connecting cerebrovascular risk to AD pathology and cognitive decline among cognitively unimpaired (CU) individuals (Yau et al, 2022).We used MRI White Matter Hyperintensity (WMH), a neuroimaging marker of chronic cerebral small‐vessel disease/cerebrovascular disease, to further elucidate longitudinal relationships between cerebrovascular risk, PET AD pathological burden, and cognition.MethodWe examined baseline CU late middle‐aged adults from the University of Wisconsin’s Alzheimer’s disease research cohorts. β‐Amyloid burden was measured with 11C‐Pittsburgh compound B (“PiB”; DVR derived from a 0‐70 min dynamic PET scan; PET A+ threshold∼1.16 DVR/∼CL 17). Cerebrovascular risk was quantified using WMH volumes (T2‐FLAIR images; SPM Lesion Segmentation Tool). Tau burden was measured with florquinitau PET (MK6240 SUVR from 70‐90 min post‐injection). Tau outcomes included bilateral volume‐weighted averages for ITC, Medial temporal lobe (MTL) and Meta‐temporal (MTC) composites. Biomarker values were z‐transformed relative to CU/PET A‐ values. Random intercept models examined interactive effects of baseline WMH and Aβ levels on longitudinal tau (covarying baseline tau age, sex, APOE ε4 count). We modeled time two ways: years since baseline PiB (TimePiB1; replicating Yau, et al models); and, in parallel models, amyloid duration (years) at tau scans was substituted for years since PiB1 in the A+ subset (TimePiB1/PiB+). We also used LME to examine Aβ, WMH, and time interactions in longitudinal cognition (PACC3 and Digit Symbol Substitution).ResultThe mean(SD) tau baseline age was 67.9(6.8) years (sample details in Table1). The most parsimonious models using TimePiB1 included WMH*Aβ*Time for ITC and MTC and WMH*Aβ for MTL. Patterns showed a synergistic effect of WMH and Aβ on tau accumulation that was attenuated using TimePiB1/PiB+ where the most parsimonious models included Aβ_status*TimePiB1/PiB+ (ITC, MTC) or WMH*Aβ_status*TimePiB1/PiB+ (MTL; Figure1). AICc model fits were substantially better using TimePiB1/PiB+. Both Aβ and WMH contributed to cognitive trajectories (Figure2).ConclusionAlthough cerebrovascular risk’s apparent contribution to tau accumulation varied depending on how time was modeled, results suggest that cerebrovascular risk may complement imaging biomarkers in assessing risk of tau accumulation and cognitive decline in preclinical Alzheimer disease.
The Influence of White Matter Lesion Volume on Hippocampal Shape in Patients Along the Alzheimer’s Disease Continuum
Authors: Kawas Mohammad I , Madi Kedar V , Lockhart Samuel N. , Hudson Jeremy Patton , Barcus Richard A. , Bateman James R. , Craft Suzanne , Wolfe Stacey Q , Whitlow Christopher T. 
Published: December 2023

AbstractBackgroundAlzheimer’s Disease (AD) is a progressive neurocognitive disorder with irreversible hippocampal atrophy and degradation of cognitive function. Hippocampal atrophy can be assessed by volume and shape changes on MRI, and hippocampal volume is a sensitive marker for AD diagnosis and progression; however, it does not reflect regional changes within the hippocampus. Interest in shape analysis of the hippocampus is growing, as it provides more detailed assessment of morphological changes that highlight regional atrophic differences. Cerebral small vessel disease (CSVD) burden quantified as white matter lesion volume from MRI is strongly associated with hippocampal volume loss in AD and mild cognitive impairment (MCI). Our goal was to examine relationships between hippocampal morphometric features and white matter hyperintensity (WMH) volume, a measure of CSVD burden, across the AD continuum.MethodWe analyzed baseline data from 186 participants from the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core cohort whose cognitive diagnosis was adjudicated by an expert panel in accordance with NIA‐AA guidelines (without reference to AD biomarkers). Participants ranged from cognitively normal (CN) (n = 62, 72±8y), to MCI (n = 62, 73±7y), and dementia (n = 62, 73±9y). We focused on the left hippocampus and used T1‐weighted images from 3T brain MRI to generate left hippocampal segmentations and total intracranial volume estimates. Total WMH volume was estimated from FLAIR images using the lesion segmentation toolbox for SPM, it was then normalized to intracranial volume and log transformed to address skewness. Hippocampal shape analysis was performed using SlicerSALT, and a multivariate linear model was implemented to relate WMH volume to hippocampal shape features while adjusting for diagnosis, age, sex, and years of education.ResultOn the superior aspect of the hippocampus, greater WMH volume was associated with more surface contraction of the anterior portions of the CA1, CA2, CA3 and subiculum as shown in Figure 1A. Similar effects were seen on the inferior aspect as shown in Figure 1B.ConclusionThese finding suggest that higher WMH volume is associated with selective hippocampal subfield degeneration in the anterior portions of the hippocampus which may be more vulnerable to CSVD buildup.
A Conceptual Framework of Data Readiness for the Health and Aging Brain Study‐Health Disparities
Authors: Zhang Fan , Petersen Melissa , Johnson Leigh , Hall James , O'Bryant Sid E. 
Published: December 2023

AbstractBackgroundThe Health and Aging Brain Study: Health Disparities (HABS‐HD) seeks to understand the biological, social and environmental factors that impact brain aging among diverse communities. HABS‐HD, like many other NIH funded data‐sharing projects, has important data assets for various uses, including social, environmental and behavioral data, and multiple data flow pathways. Machine learning (ML) develops algorithms and models to continuously improve itself over time, but the determination of data quality and its readiness are needed for these models to operate efficiently. Therefore, developing a data readiness reporting methodology has become a very urgent task for HABS‐HD.MethodIn this study, we developed a conceptual framework of data readiness. First, we analyzed the missing data percentage and used ML‐Based Multiple Imputation (MLMI) for missing data imputation. Then, we performed SVM based on Recursive Feature Elimination and Cross Validation (SVM‐RFE‐CV) for feature elimination and outlier removal. Lastly, we rated the data readiness based on the three metrics: missing data percentage, performance before feature engineering, and performance after feature engineering to rate data readiness. All the three scores were averaged to rate the overall readiness of data.ResultA framework for calculating overall average score for readiness of data was presented (1 stands for completely accessible, 0 for not accessible at all, and 0.5 for neutral). Our results show that the framework of data readiness was straightforward and useful in assessing how ready the HABS‐HD data is for ML.ConclusionThe systematic analysis of readiness of data before building ML models is of utmost importance. And it has a significant impact on biomarker discovery and disease prediction application for Alzheimer’s disease. The conceptual framework of data readiness works well for our Alzheimer’s disease models in HABS‐HD. It can also be applied to other disease data readiness reporting.
Genetic ancestry of human induced pluripotent stem cells banked for neurodegeneration research
Authors: Nudelman Kelly N. H. , Jackson Trever , Ohlemacher Sarah K , White Nicholas RM , O’Neil Olga , Sullen Katharina , Johnson Randal D. , Kirch Vera A. , Schwantes‐An Tae‐Hwi , Abreu Marco , Marshall Jeanine D. , Faber Kelley M. , Mitchell Colleen M. , Edler Michael C. , Meyer Jason S. , Foroud Tatiana M. 
Published: December 2023

AbstractBackgroundThe National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) has established a centralized repository for human induced pluripotent stem cells (iPSCs) and fibroblasts in partnership with the National Institute on Aging (NIA). This is a critical resource for the research community, which can facilitate disease modeling, functional genetic studies, and drug development. However, the utility of this resource is still limited in terms of racial and ethnic diversity, which has the potential to bias neurodegenerative research and drug development unless steps are taken to create and share a more diverse array of cell lines.MethodAt NCRAD, banked iPSCs are subjected to quality control including short‐read whole genome sequencing (WGS). 78 samples have been submitted for sequencing; of these, data for 51 have been returned and analyzed, including multiple isogenic lines from three donors. WGS data was aligned, and quality control performed using Sentieon software, following GATK best practices. Data was combined with sequencing data from 2,548 1000 Genomes participants and pruned in PLINK. R package SNPRelate was used to generate a kinship matrix and perform principal component analysis (PCA). Clustering analysis was performed utilizing K‐means clustering to group samples into 5 clusters. PCs 1‐3 were graphed, color‐coded by cluster, and final ancestry calls reviewed for clustering with reference groups.ResultThe NCRAD 51 iPSC lines with analyzed WGS data were sourced from 28 neurodegenerative cases and 12 controls. In this data set, 83% of samples (89% of cases, 67% of controls) have European ancestry. The cohort also includes one case and one control line of East Asian ancestry, as well as 2 cases and 3 controls showing admixture of European/American ancestry. 17 individuals did not have reported race/ethnicity, while 2 controls showed genetic admixture though reported as White non‐Hispanic, and the remaining individuals’ genetic ancestry matched reported race/ethnicity.ConclusionCentralized banking of iPSCs from individuals with neurodegenerative diseases is creating a key resource to advance research; however, to date the diversity of these samples is limited. We urge researchers to develop and share more diverse cell lines, to promote diversity and equity in neurodegenerative research.
“Taxes, death, and trouble”: Role of structural socioeconomic racism on cognitive function for persons racialized as Black
Authors: Adkins‐Jackson Paris AJ , Kim Boeun , Avila Justina F , Higgins Cesar , Bailey Zinzi D , Ford Tiffany N , Hyun Jinshil , Belsky Daniel W , Seblova Dominika , Turney Indira C , Hill‐Jarrett Tanisha G , Okoye Safiyyah M. , Hardeman Rachel R , Matz Meies‐Amor Blagburn , Gee Gilbert , Szanton Sarah L , Manly Jennifer J. 
Published: December 2023

AbstractBackgroundStructural racism has concentrated Americans racialized as Black in under‐resourced and disinvested neighborhoods. These same Americans experience higher rates of Alzheimer’s disease and related dementias (ADRD). Differences in socioeconomic opportunities structured by racism is a potential contributor to ADRD disparities. To investigate this hypothesis, we tested how life‐course exposure to area‐level measures of structural racism relate to late‐life cognitive test performance.MethodWe computed the Black‐White disparity in land ownership and value, residential and occupational segregation, and income, employment, and poverty at the county‐level using US Census data from 1930‐2010. We aggregated these data to the state‐level then created four unidimensional measures by life‐course period (Before‐Birth 1930‐50, Childhood 1960‐70, Adulthood 1980‐2000, Midlife 2010). We linked state‐ and life‐course‐period‐level data with individual‐level demographic and cognitive data from the US Health and Retirement Study. We included participants racialized as Black and born 1954‐59 (N = 1,172). We measured cognitive performance from repeated measures of the 27‐item global cognitive score from the modified Telephone Interview for Cognitive Status during 2010‐18 (average follow up of 4.1 years). Mixed effects models were used to test associations of life‐course‐period‐specific state‐level structural socioeconomic racism and cognitive change. Covariates were age, gender, education, Living arrangement, and year of death.ResultParticipants racialized as Black who lived in places with higher levels of area‐level structural socioeconomic racism experienced lower levels of cognition at baseline and at varying time points in the six‐year period—compared to those in places with lower structural socioeconomic racism. The effects of the life‐course‐period‐specific state‐level structural socioeconomic racism exposure on the slope were in the expected direction but not statistically different from zero (‐.02, SE 0.25, ‐.51, 4.7).ConclusionAmong participants racialized as Black in the HRS born during 1954‐59, effects of exposure to state‐level structural racism varied according to life course period of exposure. These findings reveal the utility of examining the impact of racist U.S. policies enacted in the past and present that influence health over time. In addition to exploring the epigenetic and in utero exposures that may contribute to ADRD risk later in life.
Microglial Activation Contributes to Neuropsychiatric Dysfunction in Alzheimer’s Disease
Authors: Aguzzoli Cristiano Schaffer , Ferreira Pamela C.L. , Povala Guilherme , Soares Carolina , Ferrari‐Souza João Pedro , Bellaver Bruna , Zalzale Hussein , Lussier Firoza Z , Rohden Francieli , Abbas Sarah , Lemaire Peter Charles , Leffa Douglas Teixeira , Cabrera Arlec , Therriault Joseph , Benedet Andrea Lessa , Tissot Cécile , Servaes Stijn , Macedo Arthur C. , Vermeiren Marie , Bezgin Gleb , Kang Min Su , Stevenson Jenna , Rahmouni Nesrine , Pallen Vanessa , Cohen Annie , Lopez Oscar L. , Tudorascu Dana , Klunk William E , Villemagne Victor L , Poltronetti Nina Margherita , Soucy Jean‐Paul , Wang Yi‐Ting , Gauthier Serge , Souza Diogo O. , Ashton Nicholas J. , Schilling Lucas Porcello , Karikari Thomas K , Blennow Kaj , Zetterberg Henrik , Zimmer Eduardo R , Rosa‐Neto Pedro , Pascoal Tharick A. 
Published: December 2023

AbstractBackgroundPrevious studies have shown that microglial activation (MA) plays a key role in the pathophysiological and clinical progressions of Alzheimer’s disease (AD). However, little is known whether MA is also associated with the development of neuropsychiatric symptoms typically found in patients with AD. Thus, we aim to investigate here the association of MA with neuropsychiatric symptoms (NPS) of individuals across the AD continuum.MethodWe assessed 132 individuals (86 cognitively unimpaired (CU), 28 MCI, and 18 AD dementia) from the TRIAD cohort who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q), and had positron emission tomography (PET) for amyloid‐β (Aβ) ([18F]AZD4694), tau tangles ([18F]MK6240) and MA ([11C]PBR28) at the same visit. Regions were tailored using Desikan‐Killiany (DK) atlas. SUVRs were calculated using the cerebellum gray matter as a reference. Linear regression tested the association between biomarkers accounting for age, sex, and cognitive status.ResultNPI‐Q total score was significantly associated with [11C]PBR28 in the cingulate, inferior temporal, and precuneus accounting for age, sex, and after false discovery rate (FDR) correction for multiple comparisons (Figures 1A, 1B, 1C). This association was independent of Aβ and tau levels (Table 1). Notably, MA predicted neuropsychiatric dysfunction with higher magnitude than Aß or tau using PET values from overlap region (regional SUVR) and if we use a global measure for all tracers (global SUVR) (Figure 2A). When we stratify NPI‐Q domains (agitation, irritability, motor disturbance, disinhibition, elation, delusion, hallucinations, nighttime disturbance, depression, anxiety, apathy, and appetite disturbance) severity score, we found that the hyperactivity subdomain (agitation, irritability, motor disturbance, disinhibition, and elation) showed the larger contribution to the results (Figure 2B).ConclusionOur results suggest MA as a key element associated with neuropsychiatric dysfunction in AD independent of Aβ and tau pathologies. These findings provide additional rationale for the therapeutics targeting glial cells activation in AD patients.
Multi‐ethnic genome‐wide, gene‐based study identifies genes that interact with vascular risk factors in Alzheimer’s Disease (AD)
Authors: Lee Annie J , Reyes‐Dumeyer Dolly , De Jager Philip L , Bennett David A. A , Schneider Julie A , Menon Vilas , Wang Yanling , Lantigua Rafael A. , Medrano Martin , Mejia Diones Rivera , Jiménez‐Velázquez Ivonne Z. , Kukull Walter A. , Brickman Adam M. , Manly Jennifer J. , Tosto Giuseppe , Kizil Caghan , Farrer Lindsay A. , Mez Jesse B. , Chung Jaeyoon , Vardarajan Badri N , Mayeux Richard 
Published: December 2023

AbstractBackgroundCardiovascular risk factors (CVRFs) increase the risk of cerebrovascular disease and AD, and over 30% of the patients with AD coincident cerebrovascular pathology. We previously found that FMNL2 interacts with CVRFs (p = 6.6e‐07) by altering the normal astroglial‐vascular mechanisms that underly amyloid clearance. The goal here was to identify additional genes that contribute to the interaction between CVRFs and AD.MethodA collection of eight multi‐ethnic cohorts was explored to study genome‐wide gene‐CVRF score interaction analysis for AD, in 7,441 AD patients and 10,453 controls. The cardiovascular risk factors score (CVRF score) was created from a first principal component of the four vascular risk factors; self‐reported history of hypertension, diabetes, and heart disease, and measured body mass index. Gene‐based interaction test was performed using the adaptive gene‐environment interaction (aGE) test. Results were summarized using a meta‐analysis. We investigated the association of gene expression with pathological AD and AD phenotype (amyloid‐β, tau, or brain infarcts) in 1,092 samples from the frontal cortex in ROSMAP. The pathway enrichment analysis was performed on the differentially expressed genes. Age and sex were adjusted in the models.ResultThe previous interaction of CVRF score with FMNL2 (p = 3.64e‐07) was strengthened and additional genes were identified including BRINP1 (p = 2.45e‐06), CFAP99 (p = 3.3e‐06), and PRG3 (p = 3.71e‐06). FMNL2 encodes a formin‐related protein important in regulating actin and microtubules. FMNL2 and BRINP1 expressions were higher in the brains of patients with brain infarcts (p = 0.025 and p = 0.006, respectively). The expression level of BRINP1 interacted with brain infarcts on pathological AD (p = 0.02) and with amyloid‐β or tau on brain infarcts (p = 0.02 and p = 0.01, respectively). The pathways were relevant to cellular interactions between astrocytes and endothelia as well as the immune system reaction in the glio‐vascular niche.ConclusionThe four novel genes are likely to be involved in the complex interaction between Alzheimer’s disease pathologies (amyloid and phosphorylated tau deposition) and cerebrovascular pathology, for example, at the glia‐vascular interface during AD progression. Understanding how these genes interfere with the mechanisms underlying the clearance of amyloid and tau increasing their deposition in brain will be essential.
MK‐6240 tau‐PET in an Aβ‐enriched sample from the 1946 British birth cohort
Authors: Coath William , Markiewicz Pawel J , Modat Marc , Scott Catherine J , Malone Ian B , Arstad Erik , Awais Ramla , Sander Kerstin , Weston Philip SJ , Thomas David L , Dickson John , Schöll Michael , Ourselin Sebastien , Richards Marcus , Fox Nick C , Cash David M , Schott Jonathan M 
Published: December 2023

AbstractBackgroundWe investigated imaging biomarkers of Aβ and neurodegeneration in relation to tau‐PET Braak stage in a preclinical birth cohort.MethodCognitively normal individuals enrolled in Insight 46, the neuroimaging sub‐study of the MRC National Survey of Health and Development (1946 British birth cohort), were scanned on combined PET/MR with [18F]florbetapir Aβ‐PET at age ∼70 years and again at ∼73 years. A sub‐sample enriched for Aβ (Aβ+; Centiloid> = 13, hole cerebellum reference) is currently being assessed with [18F]MK‐6240 tau‐PET at age ∼76 years. For this interim analysis, tau‐PET images (90‐110 minutes post‐injection) were co‐registered with T1‐weighted MRI. Anatomical areas were parcellated on the T1 to form Braak regions. Standard uptake value ratios (SUVRs) were calculated using an inferior cerebellar grey reference without partial volume correction. Tau‐PET positivity (Tau+) was defined using Gaussian mixture modelling in each region. Participants were assigned to Tau‐, Braak I‐II, III‐IV or V‐VI groups based on the most advanced Tau+ region. Group differences in baseline Aβ (Centiloids), Aβ accumulation (Centiloids/year) and hippocampal atrophy rate (%/year) were investigated with Mann‐Whitney U tests.ResultAnalysis included 80 individuals with tau‐PET data (Table 1), 45% of the sample were Aβ+ by age 73. Three individuals did not conform to the Braak stage hierarchy (orange triangles, Figure 1). No Aβ‐ individuals were Tau+ beyond Braak I. Figure 2 shows baseline Aβ, Aβ accumulation and hippocampal atrophy rates for Braak stage groups for participants who had data at all timepoints (N = 78). Tau+ individuals (in any Braak region) had significantly higher baseline Aβ than Tau‐ individuals. Rate of annual Aβ accumulation was higher for Braak III‐IV and Braak V‐VI compared to Tau‐ individuals. Hippocampal atrophy rate was elevated for Braak V‐VI compared to Tau‐, and Braak III‐IV was borderline significant.ConclusionIn this preliminary analysis, tau‐PET positivity beyond Braak I was restricted to individuals who were Aβ+ three years prior. Participants with elevated tau aged 76 had increased Aβ at age 70. Increased rates of hippocampal atrophy were occurring at least three years prior to tau scanning in individuals with advanced tau pathology. These findings will be updated as more data is acquired.