bluuebunny/crossref_metadata_2025_split huggingface dataset:
Asymmetric degeneration of the hippocampus predicts risk of Alzheimer’s disease
Authors: Liu Xi , Li Hongming , Fan Yong 
Published: December 2022

AbstractBackgroundAsymmetry of the hippocampus is an important finding in normal aging and Alzheimer disease (AD). However, it is largely unknown how asymmetric degeneration of the hippocampus is associated with an individual's risk of AD.MethodA deep‐learning (DL) classification model was developed to quantify degeneration of the hippocampus based on its magnetic resonance imaging (MRI) scan and binary mask. Different from most extant studies, the DL model was trained to distinguish AD patients from cognitively normal (CN) controls with their bilateral hippocampi as two separate instances for generating two classification scores (positive indicating AD‐like and negative indicating CN‐like), one for each hippocampus. An integration of the two scores of an individual’s MRI data was utilized to classify AD patients and CN controls, to predict individual mild cognitive impairment (MCI) subjects’ progression to AD dementia in a time‐to‐event analysis setting, as well as to identify subgroups of MCI subjects with distinct risk of AD dementia. Particularly, the DL classification model was trained on the ADNI 1 cohort (n=420), and then validated for distinguishing AD subjects from CN controls on the ADNI 2&GO (n=469) and AIBL (n=396) cohorts and for predicting MCI subjects’ progression to AD dementia and identifying MCI subgroups on the ADNI 2&GO cohort (n=439).ResultThe DL model achieved accurate classification performance with classification rates of 0.923 and 0.927 and AUC values of 0.963 and 0.961 on the ADNI‐GO&2 and AIBL cohorts, respectively. The integration of the bilateral hippocampal classification scores achieved a concordance index of 0.765 for predicting individual MCI subjects’ progression to AD dementia. The MCI subgroups with bilateral AD‐like, bilateral CN‐like, and one AD‐like and one CN‐like scores were significantly different in risk of progression to AD dementia and a variety of biomarkers and cognitive scores, including Apoe4, Amyloid, tau, and ADAS‐cog score (p < 0.0004).ConclusionThe present study demonstrated that the asymmetric hippocampal degeneration patterns quantified using a DL model on MRI data could achieve promising classification performance for distinguishing AD patients from CN controls and may aid the clinicians to early prediction of MCI subjects’ progression to AD dementia to facilitate personalized medicine.
Intraindividual variability of cognitive performance and associations with magnetic resonance imaging in aging American Indians: Data from the Strong Heart Study
Authors: Fonseca Luciana Mascarenhas , Chaytor Naomi S , Olufadi Yunusa M , Buchwald Dedra , Galvin James E , Schmitter‐Edgecombe Maureen , Suchy‐Dicey Astrid M 
Published: December 2022

AbstractBackgroundAmerican Indians present with increased risk factors for Alzheimer’s disease and related dementias (ADRDs). Intraindividual cognitive variability (IICV), which measures the variability in performance on neuropsychological tests within a person at a single time, has been proposed as a noninvasive marker of neurodegeneration and early dementia. In this study, we investigated the hypothesis that higher IICV was related to more features of neurodegeneration on magnetic resonance imaging (MRI) measurements among older American Indians.MethodAmerican Indian participants over 64 years of age who underwent MRI and completed a battery of neuropsychological tests were included in the study (N = 750). Data from three neuropsychological tests were used to compute a multidomain IICV measure of cognition along with IICV measures representing the cognitive domains of memory, executive function and processing speed. Regression models estimated IICV associations with total brain volume, hippocampal volume, and graded white matter disease, adjusting for age, sex, education, BMI, and intracranial volume.ResultMemory, executive function and processing speed, and multidomain IICV measures were associated with total brain volume before correction for multiple comparisons. After correction with Bonferroni, only higher IICV of memory was associated with lower total brain volume (Beta = −0.20; 95%CI −0.332, −0.068; p = 0.009). Furthermore, additional adjustment of mean scores from the tests used for the variability measure resulted in loss of significance for all IICV measures, with only mean scores remaining significant. Measures of IICV were not associated with hippocampal volume or white matter disease.ConclusionThese findings suggest that IICV of memory may be associated with imaging measures related to neurodegenerative injury in older American Indians. However, these associations representing variability in cognition are smaller than those representing mean cognition. More research is needed to understand the clinical and etiologic associations for IICV measures.
Dissecting cell‐subtype specific regulatory variants and their target genes underpinning the pathogenesis of late onset Alzheimer’s disease
Authors: Gamache Julia , Gingerich Daniel , Garrett Melanie E , Barrera Julio , Ashley‐Koch Allison , Crawford Gregory , Chiba‐Falek Ornit 
Published: December 2022

AbstractBackgroundThe genetic architecture of late‐onset Alzheimer’s disease (LOAD) is yet to be fully explored. While GWAS discovered numerous LOAD‐associated loci, the causal variants and their target genes remain largely unknown. We aim to advance our understanding of the genetic architecture of LOAD by systematic interrogation of elements and variants underlying gene dysregulation in LOAD in a cell‐subtype resolution.MethodsWe conducted a single‐nucleus (sn)multi‐omics study on the 10X Genomics platform using 24 frozen brain tissues, 12 normal and 12 LOAD. The parallel snRNA‐seq and snATAC‐seq collected from the same nuclei samples and at the same time were used in multimodal analysis to profile in gene expression and chromatin accessibility.ResultssnRNA‐seq data from a total of 202,223 nuclei were grouped into 33 clusters and snATAC‐seq from 79,120 nuclei were separated into 25 clusters. We identified multiple cell subtype specific, LOAD‐associated differential expressed genes (DEGs), differential accessible peaks (DAPs) and cis co‐accessibility networks (CCANs) in LOAD. Integrative analysis focused on 110 LOAD CCANs out of ∼15,000 with at least one peak overlap promoter/intron 1 of DEG that contain >1 DAPs, identified 331 functional candidate cis regulatory elements (cCRE) and 118 linked genes across all cell subtypes. Out of which 12 CCANs encompassing a total of 54 cCREs and their 13 target genes overlap LOAD‐GWAS regions. Finally, we catalogue putative functional SNPs within cell subtype‐specific cCREs that interrupt with transcription factor (TF) motifs. For example, in subtypes of excitatory neurons we identified SNPs in cCREs linked to RPS15, FKBP5 that strongly affect the TF motifs of GSC2 and TBX3; and found in microglia multiple SNPs within APOE region with weaker effects on four TFs.ConclusionTo our knowledge this study represents the most comprehensive integrative single‐cell genomics study in LOAD. We provide new insights into the interactions between the genome, epigenome, and transcriptome in LOAD brains in an unprecedented cell‐subtype specific resolution. The outcomes of this work prioritize CREs, genetic variants and their linked genes for experimental validation in disease model systems using genome editing technologies. In summary, our findings enhance the translation of LOAD‐genetic risk into mechanistic understanding of causation.
Towards Discovering Digital Twins of Dementia Patients: Matching the Phases of Cognitive Decline
Authors: Wickramasinghe Nilmini , Ulapane Nalika , Nguyen Tuan Anh , Andargoli Amir , Ossai Chinedu , Shuakat Nadeem , Zelcer John 
Published: December 2022

AbstractBackgroundDigital twins in healthcare is an emerging area. Twins are created from data of past patients. The objective is to facilitate data‐driven decision support for clinicians for more precise and personalized dementia care. The project has three phases: (1) Twin discovering algorithm development from synthetic data; (2) Algorithm validation with real patient data; (3) Clinical implementation. The current work is related to Phase 1.MethodPast subjects (n=30) showing cognitive decline (based on MMSE scores) mimicking a sample monitored over three years (i.e., 365 x 3 = 1,095 days) were synthetically generated. All past subjects show monotonically decreasing MMSE scores, and have had varying numbers of cognitive tests (between Three and Nine) across three years, unevenly spaced in time. Fig. 1 shows Three examples. Five present subjects (n=5) were also synthetically generated. Present subjects have had only their first two cognitive tests, unevenly spaced in time (i.e., 90 or 180 days—see Fig. 2). Since cognitive decline is traditionally modeled linearly, linear interpolation was used over present and past subjects to fill out missing data between cognitive tests. Post interpolation, an MMSE score is available for every day between a subject’s first and last cognitive assessment. Then, for each present subject, the best matching past subject and their phase of cognitive decline—i.e., a digital twin from history that is closest to the present subject—were determined via Mean Absolute Percentage Error (MAPE) calculation between MMSE scores of a present subject and past subjects.ResultsDigital twin segments showing MAPE < 2%, i.e., 100 ‐ MAPE > 98%, were identifiable for all Five present subjects. Table 1 shows the Five best matching digital twin segments for each present subject. Fig 3 graphically shows a matching.ConclusionResults to discover digital twins via matching phases of cognitive decline are positive. This study looked at only the MMSE score as a one‐dimensional metric, for proof of concept. The validity for such one‐dimensional arrays, indicate generalizability for multi‐dimensional data (i.e., a collection of multiple streams of one‐dimensional data) collected over time, for discovering more comprehensive digital twins.
Sex hormones and Alzheimer’s disease: a Mendelian randomization approach
Authors: Kusters Cynthia Diana Johanna , Ritz Beate R 
Published: December 2022

AbstractBackgroundTo assess the association between sex steroid hormones with Alzheimer’s disease (AD). Sex hormones have been hypothesized to influence AD risk. However, studies of AD have been inconclusive. Here, we used a Mendelian randomization approach to assess the association between sex steroid hormones with Alzheimer’s disease (AD).MethodsWe performed inverse‐variance weighting (IVW) MR analysis using external GWAS summary to retrieve the effect estimates between genetic variants and sex hormone levels and GWAS summary statistics for Alzheimer’s disease from the ADGC, stratified by sex. We included independent variants (linkage disequilibrium R2<0.001) and a P‐value of 5x10‐8 in the GWAS for sex hormones.ResultsOur preliminary findings indicate an association between certain sex hormone concentrations and AD risk. Specifically, an increase in testosterone appears to be associated with a decreased AD risk among men (OR: 0.57; 95%CI: 0.33 – 0.99; P‐value 0.04), a decreased AD risk with an increase in estrone among women (OR: 0.46; 95%CI: 0.27 – 0.79; P‐value 0.005). Among the total population an increase in androstenedione and DHEAS were associated with a decrease in AD risk (OR: 0.83; 95%CI: 0.71 – 0.96; P‐value 0.02; OR: 0.48; 95%CI:0.24 – 0.95; P‐value: 0.03).ConclusionsWe found an indication that various sex hormones may have a protective effect for AD. Some findings indicate sex‐specific associations. Though further analysis should be done, these preliminary findings indicate sex hormones may influence AD risk, further supporting the hypothesis that sex hormones may be neuroprotective.
The SuperAging Research Initiative: A multisite consortium focused on identifying factors promoting extraordinary cognitive aging
Authors: Rogalski Emily J. , Huentelman Matthew J , Roberts Angela C , Maher Amanda Cook , McIlroy Bill , Van Ooteghem Karen , Finger Elizabeth , Lim Andrew , Okonkwo Ozioma C. , Goldstein Felicia C , Hajjar Ihab , Parrish Todd , Scholtens Denise , Gutierrez Stephanie , Weintraub Sandra , Geula Changiz , Mesulam M.‐Marsel 
Published: December 2022

AbstractBackgroundThe designation of SuperAger is reserved for individuals age 80+ who have episodic memory capacity that would be considered at least average for those 2‐3 decades younger. The presence of such outliers raises questions of fundamental importance to the neurobiology of brain aging. Have these superior memory performers resisted age‐related changes, or have they simply started from a much higher baseline? Do they have identifiable peculiarities of genetic background? Is there something special about their brain structure or perhaps their resistance to age‐related processes such as neurofibrillary degeneration and amyloid deposition? These are the questions that were initially addressed by the Northwestern SuperAging Project, which identified unique results encompassing cognitive, psychosocial, molecular, and neuropathologic markers that characterize SuperAgers. Obstacles to further progress have been the relative rarity of this phenotype and, consequently, the barriers to racial diversity in the cohort.MethodsTo address these challenges, we established the SuperAging Research Initiative, a multicenter study focused on increased minority representation, to identify behavioral, health, biologic, genetic, environmental, socioeconomic, psychosocial, neuroanatomic, and neuropathologic factors associated with SuperAging.ResultsHere we provide the organizational structure and progress to date of the SuperAging Research Initiative, which includes three Cores (Administrative/Biostatistics, Clinical/Imaging, and Biospecimen/Neuropathology) and two Research Projects. Enrollment (n = 500) is planned across four US Sites located in Illinois, Wisconsin, Michigan, and Georgia, and a Canadian Site in Southwest Ontario, with a focus on enrollment of Black SuperAgers and Cognitively Average Elderly Controls with similar demographics. Project 1 uses state‐of‐the‐art wearable technology to obtain quantitative everyday measurements of life sleep, physical activity, autonomic responsivity, and social engagement to determine whether SuperAgers have relatively preserved physiologic and behavioral ‘complexity’ compared to Controls. Project 2 focuses on transcriptomic, genetic, and protein profiling to examine central and peripheral immune and inflammatory system parameters of SuperAgers.ConclusionsBy identifying factors contributing to superior memory performance in old age, outcomes may help isolate modifiable factors that promote healthspan and perhaps also prevent age‐related brain diseases such as Alzheimer’s disease.
Head‐to‐Head comparison between Philips Gemini TF64 and Siemens Biograph Vision 600 for brain amyloid Centiloid quantitation
Authors: Li Shenpeng , Bourgeat Pierrick , Bozinovski Svetlana , Huang Kun , Guzman Rodney , Williams Robert , Fripp Jurgen , Villemagne Victor L , Rowe Christopher , Dore Vincent 
Published: December 2022

AbstractBackgroundThe Centiloid (CL) scale calibrates the beta‐amyloid (Aβ) deposition from different PET tracers to a standardised 0‐100 CL unit scale. As imaging sites update their PET cameras, most are switching to digital detector systems with superior resolution and sensitivity that may affect quantitation. This has significant implications for dementia clinical trials. In this study, we examine the impact on CL quantification between Philips Gemini TF64 and Siemens Biograph Vision 600.MethodSeven subjects (76.4±2.2 yo) were imaged with 18F‐NAV4694 on both Gemini TF64 and Biograph Vision consecutively with an average scan interval of 25.1±11.2 weeks. The injected doses were 200MBq and 100MBq, respectively. On the Gemini TF64, the PET images were reconstructed by LOR‐RAMLA algorithm with smoothing parameter setup as ‘SHARP’. On Biograph Vision, the PET images were reconstructed by OSEM‐3D (8 iterations and 5 subsets, TOF enabled) with 3mm post Gaussian smoothing. A T1 MRI image was acquired for each subject. As per the standard Centiloid method the whole cerebellum was used as the reference in SUVR images, and all images were processed using CapAIBL to calculate the CL using both MR‐based and MR‐Less spatial normalisation.ResultFigure 1 shows the CL images of a subject scanned on Gemini TF64 and Biograph Vision within sixteen weeks. The Biograph Vision images have higher contrast and higher spatial resolution despite using half of the dose. Figure 2 shows the linear regression plot of the scanner comparison. Biograph Vision CL are progressively higher than those obtained from the Gemini TF64 as the CL value rises (Table 1). There were no significant differences between the MR‐based and MR‐less results.ConclusionBiograph Vision yields higher SUVR and therefore CL values compared to Gemini TF64 in a head‐to‐head comparison. These results show that the selection of PET camera has a significant impact on CL quantification, which needs to be considered when merging cohorts from different studies or changing cameras during longitudinal studies or trials. These initial results indicate that the CL difference could be corrected by a linear transform.
ADSP Functional Genomics: from gene, to function to mechanisms and targets
Authors: Cruchaga Carlos 
Published: December 2022

AbstractBackgroundGenetic studies have been highly successful in identifying genetic regions associated with Alzheimer’s disease, and we are now at the exciting juncture of applying this knowledge to understanding disease mechanisms. The ability to generate and mine clinical and omicdata is advancing our understanding of neurodegeneration. The Alzheimer’s Disease Sequencing Project (ADSP) Functional Genomics Consortium (FunGen‐AD; https://adsp‐fgc.niagads.org/) aims to apply cutting‐edge genomics technologies, high‐throughput genetic screening and cutting edge disease modeling to understand the functional consequences of genetic susceptibility and resilience to Alzheimer’s Disease, and to identify genetics‐guided targets for the prevention, diagnosis, and treatment of Alzheimer’s disease and related dementias (AD/ADRD).MethodThe investigators of the FunGen‐AD consortium are using multiple approaches, including brain single cell data acquisition, human stem cell modeling and high through put CRISPR‐based screens, to understand how AD variants lead to changes across molecular networks, and understand how specific risk variants affect disease in diverse populations. Multi‐omic data generated in well characterized cohorts are being used to identify systems‐level alterations in and provide insight into the mechanisms underlying genetic variants.ResultThe FunGen‐AD consortium is organizing and facilitating large collaborative projects. Currently we are working on a xQTL project putting together data from multi‐tissue (brain, myeloid cells, CSF and plasma), and multi‐omic layers (transcriptomic in bulk and at single cell level, epigenetic, proteomic, and metabolomic). In order to fully understand the biology of AD we recognize that multiple tissues, and ‐omic layers need to be studied. All the omic data are being processed, harmonized and analyzed using standard pipelines, and mapping on a reference genome and annotation developed by the group. This QTL atlas will be used to perform colocalization and identify the functional genes within AD risk loci, and Mendelian Randomization to identify novel causal genes, proteins and druggable targets. This xQTL project will provide a rich resource of multi‐omics datasets, analysis pipelines, and an xQTL atlas for the research community studying AD/ADRD and other complex neurodegenerative traits.ConclusionsIn summary, the FunGen‐AD consortium is using multidisciplinary and collaborative research approaches to identify genetics‐guided targets for the prevention, diagnosis, and treatment of AD/ADRD.
Neuropsychologically derived errors as predictors of neurocognitive phenotypes: The Framingham Heart Study
Authors: De Anda‐Duran Ileana , Drabick Deborah AG , Hwang Phillip H , Emrani Sheina , Matusz Emily , Wasserman Victor , Swenson Rodney , Andersen Stacy L. , Liu Chunyu , Au Rhoda , Libon David 
Published: December 2022

AbstractBackgroundData‐driven approaches are powerful for identifying impaired neurocognitive constructs and aid in characterizing neurocognitive impairment patterns. There is, however, a significant clinical need for time‐efficient diagnostic tools compared to traditional more time‐consuming neuropsychological (NP) assessments. We evaluated how NP errors can compress the number of tests needed to differentiate among neurocognitive phenotypes.MethodWe studied 1,915 Framingham Heart Study (FHS) Offspring participants (age: 70 ± 8 years, 56% women). Three neurocognitive phenotypes were identified by Latent Profile Analysis (LPA) using nine NP summary scores: Cognitively normal (CN; n = 1060, 88.7%); Moderate‐Mixed (n = 72, 6.0%); and Dysexecutive (n = 63, 5.3%). Receiver operating characteristic (ROC) analysis compared the predictive accuracy of NP errors in identifying the distinct neurocognitive phenotypes. Regression models evaluated associations between significant NP errors (i.e., with an area under the curve (AUC) ≥ 0.64) and neurocognitive phenotypes, adjusting for age, sex, and education. The CN group was used as reference for comparisons.ResultDysexecutive subgroup participants were older (77 ± 8 years) with less education (8 ± 3 years). Individuals producing any of the errors displayed in Figure 1 were 1.3 times more likely to be in the moderate/mixed and dysexecutive group [OR‐range = 1.3 ‐ 3.4, all p’s< 0.001]. Participants generating Trail Making Test‐Part B (TMT‐B) examiner‐corrected non‐perceptual errors were 3 times more likely to be classified into the dysexecutive [OR = 3.44, 95% CI (2.7, 4.4), p< 0.001] and moderate/mixed phenotypes [OR = 3.21, 95% CI (2.13, 4.85), p< 0.001]. Participants were more likely to have a moderate/mixed phenotype with Boston Naming Test (BNT) perceptual [OR = 2.1, 95% CI (1.7, 2.5), p< 0.001] and semantic errors [OR = 1.6, 95% CI (1.4, 21.9), p< 0.001].ConclusionAn analysis of errors derived from the TMT‐B and BNT provided evidence for the presence of two distinct but complementary and impaired neurocognitive constructs: dysexecutive/ inhibition impairment (TMT‐B) and struggling to access lexical information (BNT). Since cognitive screeners (e.g., MoCA) incorporate briefer versions of these tests, these errors may aid in characterizing brain‐behavior relationships and identifying persons at risk for the emergence of dementia‐related syndromes in a less costly and burdensome manner than standard NP protocols.