bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reduced cortical cholinergic innervation correlates with cognitive decline in mild cognitive impairment
Authors: Xia Ying , Eeles Eamonn , Fripp Jurgen , Pinsker Donna , Thomas Paul , Latter Melissa , Dore Vincent , Fazlollahi Amir , Bourgeat Pierrick , Villemagne Victor L , Coulson Elizabeth J , Rose Stephen 
Published: June 2023

AbstractBackgroundDysfunction of the cholinergic basal forebrain (BF) neurotransmitter system, including cholinergic axon denervation of the cortex, plays an important role in cognitive decline and dementia. The association of cortical cholinergic denervation with diverse cognitive profiles in dementia, particularly at early stages, has not yet been fully explored. We assessed the relationship of cholinergic denervation in the cortex measured using 18F‐fluoroethoxybenzovesamicol (FEOBV) with cognitive decline at an early stage of dementia.MethodParticipants (8 with mild cognitive impairment (MCI) and 10 cognitively unimpaired (CU) controls, aged 60‐86, 55.6% female, MMSE = 28.7±1.5) underwent neuropsychological assessment and FEOBV PET imaging. The neuropsychological battery of tests included measures of memory, executive function, attention and language. Domain‐specific composite scores were calculated by averaging z‐scores of the related tests or subtasks within a domain. FEOBV images were co‐registered to the concurrent structural MRI and normalised to the supratentorial white matter reference region resulting in a standardized uptake value ratio (SUVR). Voxel‐wise analysis was performed on normalised FEOBV images to investigate associations with clinical diagnosis and domain‐specific cognitive performance. Voxel‐based morphometry was also performed to rule out differences in brain morphology. All analyses were corrected for age, sex and total intracranial volume and adjusted for multiple comparisons.ResultThe MCI group showed significantly lower global FEOBV SUVR in the cortex although only a focal cluster of reductions near the right middle temporal gyrus was found in the voxel‐wise comparisons. When associations with cognitive performance profiles were assessed in the full cohort, regional positive correlations were found between FEOBV SUVR and cognitive scores across different domains (Figure 1), although these did not include memory. For executive functioning, widespread correlation patterns were observed across both cortical and subcortical regions. Moreover, overlapping profiles were identified for all three cognitive domains of executive function, attention and language, which covered the anterior cingulate gyrus, olfactory cortex, calcarine cortex, middle temporal gyrus and caudate nucleus.ConclusionWe have demonstrated that the topographic profile of FEOBV retention correlated with reduced cognitive performance in different domains, suggesting that reduced cortical cholinergic terminal integrity is an important pathological feature of cognitive decline.
Identifying network hubs from brain anatomical similarity networks
Authors: Mastrou Andreas , Vuksanovic Vesna 
Published: June 2023

AbstractBackgroundBrain hubs are highly connected regions important for integrating cognitive processing in the healthy brain. Brain hubs also play role in neurodegeneration causing dementia. However, it is not clear what brain morphometry is most useful in identifying brain regions critical to their involvement in neurodegeneration.MethodWe examined brain network hubs from four measures of cortical morphometry, derived using anatomical T1‐weigher MRI. From a large cohort of healthy participants (N = 260) from 20 to 85 years of age, we generated 200 networks for each measure of cortical morphometry using bootstrapping. We then tested how two measures of brain network hubness – nodal strength and clustering coefficient – were mapped onto brain network hubs across all 200 networks for each morphometric feature. Regions were identified as brain hubs if ranked two standard deviations away from the corresponding group mean.ResultWe mapped out brain hubs for each four measures (features) of brain morphometry. Nodal strength and clustering coefficient showed similarity in the behaviour across the features: gray volume, regional surface area and its thickness and thickness standard deviation. However, the differences were found in what brain region qualify as a network hub for each feature.ConclusionCortical morphometry extracted from anatomical brain scans can be used as a potential imaging marker for identification of brain hubs potentially involved in early differentiation of dementia subtypes.References: Vuksanović V, Staff RT, Ahearn T, Murray AD, Wischik CM. Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer’s Disease and Behavioral Variant Fronto‐Temporal Dementia. Int J Neural Syst. 2019 Aug;29(6):1850055. https://doi.org/10.1142/S0129065718500557.Evans AC. Networks of anatomical covariance. Neuroimage. 2013 Oct 15;80:489‐504. https://doi.org/10.1016/j.neuroimage.2013.05.054.
Association of the frailty index with functional brain connectivity in the Irish Longitudinal Study on Ageing (TILDA)
Authors: Davis James , Zuniga Raquel Gutierrez , Boyle Rory , Looze Celine De , Meany James F , O’Halloran Aisling M , Whelan Robert , Kenny Rose Anne , Ortuño Roman Romero , Knight Silvin P 
Published: June 2023

AbstractBackgroundFrailty, expressed as accumulation of health deficits in older adults, could contribute to the risk of cognitive decline. We cross‐sectionally studied the brain connectivity signatures of frailty in The Irish Longitudinal Study on Ageing (TILDA).MethodWe included participants from Wave 3 of TILDA who underwent resting‐state functional MRI. Connectivity maps were based on Shen’s parcellation and connectome‐based predictive modeling was used to find associations between connectivity and frailty as measured by Frailty Index (FI) (Table 1).On each iteration of a 10‐fold cross‐validation the training folds were utilized to perform partial Spearman correlations (adjusted for age, sex, and mean framewise displacement) between each brain connection and FI scores. Connections with p<0.001 correlations were selected. Network strength was computed by summing the correlations across all connections selected and a linear model built using the network strength to predict FI score. After 10 folds are complete the performance was measured with linear R values between predicted and true FI scores. This process was repeated 1000 times (with random cross‐validation splits) and mean performance metrics obtained. A permutation test was employed in which the above procedure was repeated 1000 times, with FI scores shuffled each time. Significance of selected connections was calculated as the number of permutations scores that were equal to or greater than the proper mean score, divided by 1000.Result347 participants were included (mean age 67.7 (SD 7.3) and 51.6% men). We obtained a connectivity network that positively correlated with frailty (“frailty network”) (R = 0.169, p = 0.006) and another that negatively correlated with frailty (“robustness network”) (R = ‐0.193, p = 0.006); and are depicted in Figure 1. The “frailty network” was more widespread and showed higher connectivity from right occipital to both prefrontal cortices, subcortical and left temporal cortex, whereas the “robustness network” exhibited higher functional connectivity in subcortical to left parietal and bilateral temporal cortices.ConclusionFunctional brain connectivity could be influenced by frailty. Further research should study the relationship of brain connectivity and different tools for frailty and establish how different patterns of deficit accumulation affect brain health across lifespan.
Brain white matter and resting state eeg markers are abnormal in HIV subjects with relatively good cognitive status
Authors: Noce Giuseppe , Percio Claudio Del , Piervincenzi Claudia , Carducci Filippo , Lizio Roberta , Onorati Paolo , Toma Ginevra , Ferracuti Stefano , Soricelli Andrea , Pennica Alfredo , D'Ettorre Gabriella , Petsas Nikolaos , Andreoni Massimo , Calcagno Andrea , Babiloni Claudio 
Published: June 2023

AbstractBackgroundThis retrospective and exploratory study tested the concept that at the group level, human immunodeficiency virus (HIV) can significantly affect brain structure and function before the manifestation of significant cognitive deficits.MethodFrom a multicenter database, we selected structural 3T magnetic resonance imaging (MRI) and resting state eyes‐closed electroencephalographic (rsEEG) datasets in 30 HIV‐positive (HIV) subjects with relatively good cognitive status and serological markers (i.e., HIV‐RNA copies and CD4 count). Control datasets of matched non‐infected subjects included rsEEG (N = 50) and MRI (N = 30) recordings. MRI markers of interest in HIV were derived from gray matter (GM) volume decrements and white matter (WM) lesion load, while rsEEG markers were cortical source current density estimated by LORETA freeware at relevant delta (< 4 Hz) and alpha (6‐12 Hz) individual frequency bands.ResultIn the HIV group, the subjects exhibited no abnormal score in neuropsychological tests. However, those subjects showed brain GM volume and WM abnormalities correlated with serum CD4 count (Figure 1). They also showed diffuse abnormalities in rsEEG alpha source activities.ConclusionAt the group level, HIV subjects with relatively good cognitive status can show abnormalities in both structural MRI and rsEEG markers. If cross‐validated in longitudinal studies, combined MRI and rsEEG markers may be a useful surrogate endpoint to test antiretroviral therapies with putative neuroprotective function.
Genomewide analysis of Episodic Memory trajectories in The Mexican Health and Aging Study (MHAS)
Authors: Chen Lixian , Arango Silvia Mejia , Obregon Alejandra Michaels , Samper‐Ternent Rafael , Wong Rebeca , Reyes‐Dumeyer Dolly , Renteria Miguel Arce , Tosto Giuseppe , Barral Sandra 
Published: June 2023

AbstractBackgroundGenetic characterization of age‐related memory changes can help identify population subgroups at‐risk of memory decline and dementia. The majority of genetic studies examining memory trajectories in old age have used data from non‐Hispanic White populations. To our knowledge, there are no reports of genetic factors underlying memory function over time in the Mexican population.MethodUsing a previously described latent curve model approach, we estimated episodic memory trajectories in a longitudinal sample from the Mexican Health and Aging Study. We conducted GWAS analyses in a sub‐sample of 6,343 participants. Analyses were stratified by memory stability (Stables, n = 4,437) and APOE status. Three independent cohorts were used for replication purposes: two Non‐Hispanic White samples from the Alzheimer’s Disease Genetic Consortium and the Religious Orders Study, and a Caribbean‐Hispanic sample from the Washington Heights Inwood Community Aging Project.ResultThe strongest genome‐wide significant association was found for an intronic variant in the NR2F1‐AS1 gene (rs555528825, p = 1.8×10−9) among APOE non‐E4 carriers in the “Stables” group. NR2F1 gene is an evolutionarily conserved long non‐coding RNA that enhances neuronal cell maturation and regulates transcription of neuronal genes. SNP variants located less than 50Kb apart from the identified signal showed also nominally significant associations in the three replication datasets (p = 3.9×10−4, p = 1.8×10−4, and p = 0.006 respectively). Additional replication efforts using the UK biobank recourse are ongoing.ConclusionOur study nominates novel genetic variants associated with longitudinal changes in episodic memory performance using data from the Mexican Health and Aging Study, a representative and comprehensive longitudinal study that includes genetic data on a sub‐sample of participants 50 years and older.
Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer’s disease prediction
Authors: Gao Xiaoyi Raymond , Chiariglione Marion P , Nuytemans Karen , Li Yi‐Ju , Martin Eden R. 
Published: June 2023

AbstractBackgroundAlzheimer’s disease (AD) is the most common late‐onset neurodegenerative disease. Identifying individuals at increased risk of developing AD is important for early intervention. Risk prediction models are typically based on a limited number of predictors, possibly with sub‐optimal performance. Here, we explore an explainable machine learning (ML) framework, XGBoost and SHapley Additive exPlanations (SHAP) values, for AD risk prediction, which can handle a large number of predictors and output the impact and importance of each predictor.MethodWe developed an XGBoost model that aggregates polygenic risk scores (PRSs), which include both PRS for AD risk and PRS for age at onset of AD, baseline individual characteristics (e.g., non‐genetic factors), and information from electronic health records for predicting incident AD. The PRSs were derived using summary statistics from genome‐wide association studies in the Alzheimer’s Disease Genetics Consortium (ADGC) dataset (n = 19,918). The model was applied to 457,936 white participants in UK Biobank to predict development of AD within 10 years after the baseline visit (n = 2,177 developed AD). We further used SHAP values to explain the relative information in model predictors.ResultFor participants of age 40 and older, the area under the receiver operating characteristic curve (AUC) for AD risk prediction was over 0.880. PRSs ranked second to age (the best predictor) in feature importance. For subjects of age 65 and above, PRSs for AD were the most important features. Our ML model not only identified traditional risk factors for AD, such as age, education, income, body mass index, diabetes, and blood pressure, but also identified predictors from electronic health records that are not typically considered in traditional prediction models, including urinary tract infection, syncope and collapse, chest pain, disorientation and hypercholesterolaemia, for developing AD. Furthermore, SHAP values aided the ranking of feature importance and model explanation.ConclusionOur ML model improves the accuracy of AD risk prediction by efficiently exploring numerous predictors. PRSs play the most important role in developing AD in individuals of age 65 and older. In application, the model also identified novel feature patterns for AD.
An analysis of factors influencing dementia study enrollment: Focus groups with diverse community members viewing the ACTNOW research registry website
Authors: Tran Leslie , Nunnerley Michael , Gothard Sarah , Golonka Ona , Pierce Aimee , Lindauer Allison 
Published: June 2023

AbstractBackgroundMany barriers discourage individuals from enrolling in dementia research studies, such as lack of information, web accessibility, and transportation. Research stakeholder feedback is necessary to understand these barriers. We used virtual focus groups to learn how to improve the website of our local research registry: ACTNOW (Alzheimer’s Comprehensive Treatment Network of Oregon and Washington). Local community members from diverse backgrounds interacted with the study team to provide insight on some of the roadblocks to research participation. Here, we report on findings from the focus groups and participants’ feedback related to the ACTNOW website and user engagement.MethodThree 90‐minute virtual focus groups (fg) were conducted with 19 participants via videoconferencing. Inclusion criteria were ACTNOW members, African American, and low‐income and rural‐dwelling individuals combined respectively. Participants completed pre‐and post‐focus group surveys about their thoughts on the ACTNOW website and interest in dementia research. Investigators from the research team analyzed the survey data and independently coded the written transcripts from the group discussions using the Dedoose coding software.ResultThere were 19 participants overall (fg_1 = 8, fg_2 = 5, fg_3 = 6). The mean age was 66.9; 89.5% of the sample was female; and 57.9% were white. The analysis revealed that the participants had concerns about the readability of the website and the clarity of purpose of ACTNOW (e.g., “I look at this, and I say, whoa, wait a minute they want me to sign up for what?”). Most of the topics discussed suggested consumers did not understand the purpose of the website or ACTNOW’s promotional materials, resulting in a fear of signing up for the actual program (research registry). Survey data reported a large interest in dementia research but a need for more consumable, relevant information, such as explaining the benefits of research and requirements of being an ACTNOW member.ConclusionFindings demonstrate specific website usability challenges and reasons that prevent community members from signing up for dementia research. Understanding the focus group participants’ feedback is important in fostering greater diversity, appeal, and accessibility in dementia research.
High health coaching adherence increases Alzheimer’s protection in at‐risk inviduals following a 4‐month intervention
Authors: Campitelli Anthony , Paulson Sally , Gills Joshua L. , Jones Megan , Madero Erica N. , Myers Jennifer Rae , Glenn Jordan M. , Gray Michelle 
Published: June 2023

AbstractBackgroundAlzheimer’s disease and related dementias (ADRD) imposed an estimated financial cost to Americans of $355 billion in 2021 and is projected to rise to $1.1 trillion by 2050. ADRD affects more than 6 million Americans and is expected to rise to more than 13 million by 2050. Health coaching (HC) interventions improve many lifestyle factors also known to be protective against ADRD; however, their ability to provide protection in individuals at‐risk for developing cognitive decline is unknown. The purpose of the present investigation was to examine the effect of HC intervention adherence on ADRD protective factor score after 4 months.MethodAdults aged 45‐75 years (n = 104) with at least two ADRD risk factors and a maximum of one protective factor participated in this study. Participants completed the Australian National University Alzheimer’s Disease Risk Index (ANU‐ADRI) both before and after a 4‐month HC protocol focusing on reducing modifiable risk factors for ADRD in six domains (diet, exercise, sleep, stress, social engagement, and cognitive activity). Participants who completed a number of check‐ins with the health coach greater than the group mean (5.6 check‐ins) were placed in the high adherence (HA) group, and individuals with fewer were placed in the low adherence (LA) group. A mixed factorial ANOVA was conducted to determine if there was a difference in pre‐ and post‐ANU‐ADRI overall protective factor score.ResultAfter the 4‐month HC intervention, ANU‐ADRI scores did not change overall in the pooled sample (F(1,102) = 1.45, p = .23). However, a significant adherence*time interaction effect indicated that HA improved overall protective factor score significantly more than LA (F(1,102) = 4.85, p = .03), and pairwise comparisons revealed that protective factor scores improved only in the HA group (p = .02 for HA versus p = .47 for LA).ConclusionThese results indicate an HC intervention may effect behaviors and emotional states understood to be ADRD protective factors when adherence with the protocol is high. This has the potential to improve the quality of life for individuals at‐risk for ADRD. Further investigation will shed light on whether the ADRD protection derived from HC is iterative and durable.
Use of the human reference genome 38 enhances integration of the multi‐tissue proteomics with genetics and disease
Authors: Yi Heng , Yang Qijun , Timsina Jigyasha , Wang Fengxian , Wang Lihua , Cruchaga Carlos , Sung Yun Ju 
Published: June 2023

AbstractBackgroundMost existing protein quantitative trait locus (pQTL) studies focus on a single tissue and use the reference Genome Reference Consortium Human Build 37 (GRCh37/hg19) published in 2009. GRCh38/hg38 was updated in 2013 with correcting thousands of sequencing artifacts that cause false SNPs and updating non‐nuclear genomic sequence. Many papers strongly recommend switching to GRCh38/hg38. The goal of this study is to use multiple tissues (brain, CSF and plasma) to explore the genetic architecture of protein levels in neurologically relevant tissues and to update the human reference genome as hg38 to compare the findings.MethodIn this study, 805 CSF samples and 869 proteins, 529 plasma samples and 953 proteins and 378 brain samples and 1,300 proteins passed quality control process. We performed genome‐wide association analyses of over 8 million autosomal variants (MAF ≥ 0.01) imputed using the most updated hg38 TOPMed imputation panel to identify cis/trans pQTLs. We performed conditional analysis to identify independent pQTLs. After removing pleiotropic loci and including Alzheimer’s disease (AD) variants, Mendelian randomization (MR) was applied to detect the casual associations between proteins and AD risk. To decrease unmeasured pleiotropy effect, we used co‐localization analysis to get more supporting information among multiple traits. Finally, we replicated our findings in much larger studies.ResultIn brain tissue, we identified 4269 pQTLs in hg38 compared to 2418 pQTLs in hg19 with a threshold of 5 × 10−8 for cis‐pQTLs and 5 × 10−8/(number of independent proteins) for trans‐pQTLs. We found the independent SNPs and highlight the complexity of regions with multiple independent local pQTLs. Analysis for CSF and plasma is underway.ConclusionThis study is to apply the hg38 as the human reference genome to detect much more pQTLs and correct possible false findings in the previous hg19 study. With the following analysis, we could get more valuable and confirmed information about the additional GWAS loci and identify the function of certain proteins on the disease risk. The multiple tissue samples and multiple traits could help us detect the complex genetic architecture of protein levels in neurologically relevant tissues.