bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIH Toolbox‐Cognition performance of older persons with normal hearing, cochlear implant candidates, and cochlear implant users
Authors: Perrin Cameron K , Levin Joseph M , Schvartz‐Leyzac Kara C , Watson Gabrielle S , Giordani Bruno , Pfingst Bryan E 
Published: June 2023

AbstractBackgroundA history of hearing loss is associated with lower scores on cognitive tests, as well as with incident Mild Cognitive Impairment and Alzheimer’s disease. In order to compare hearing adaptation in older age, we used iPad‐based testing to assess older healthy controls and older persons with two distinct approaches to hearing correction: one group with cochlear implants and another comprised of individuals with hearings aids awaiting cochlear implants. Speech recognition in noise is a common measure of hearing accuracy and reflects effectiveness of both hearing aids and implants. We expected to find that better speech recognition scores would relate to higher levels of cognitive performance across the groups.MethodParticipants were 54 individuals over 65 years of age (cochlear implants, n = 28; implant candidates using hearing aids n = 12; and normal hearing, n = 14), recruited through the University of Michigan Medical Center. All participants completed a speech in noise task and the NIH Toolbox‐Cognition battery (NIHTB‐CB) adapted for individuals with hearing loss. All scores from the NIHTB‐CB were corrected for education, age, race, and gender.ResultFor speech recognition, all groups performed significantly differently from each other (F = 57.408, p < 0.001), with the normal hearing group performing the best and the pre‐implant group performing the worst. For NIHTB‐CB scores, the only difference found was lower performance for the cochlear implant group compared to the normal‐hearing group on the Picture Vocabulary subtest (F = 3.417, p = 0.041). Duration of hearing loss was not correlated with cognitive performance on any of the tasks.ConclusionOverall, as expected, there were significant differences between groups on speech recognition. However, controls and all groups with hearing loss performed essentially equally well on all aspects of the adapted NIHTB‐CB. This suggests that in cases where hearing is partially corrected with a hearing aid or cochlear implant and tests are adapted for patients with hearing loss, group differences in cognition are not clearly evident, even when groups differed in speech perception. We hypothesized that duration of hearing loss may have driven the differences noted in vocabulary knowledge, however this was not supported in the results.Supported by NIH#R01DC015809‐03S1 and P30AG072931.
Genetic association study of Alzheimer’s disease through whole genome tiling analysis
Authors: Bao Jingxuan , Lee Brian N , Zaranek Sarah Wait , Lee Matthew , Shivakumar Manu , Wen Junhao , Chen Jiong , Wen Zixuan , Yang Shu , Huang Heng , Saykin Andrew J. , Thompson Paul M , Davatzikos Christos , Kim Dokyoon , Zaranek Alexander Wait , Shen Li ,   for the Alzheimer’s Disease Neuroimaging Initiative and the AI4AD Initiative
Published: June 2023

AbstractBackgroundNumerous GWAS studies of Alzheimer’s disease (AD) have identified over 70 AD risk variants using SNP‐based genotyping or sequencing data. Recently, a new whole‐genome tiling (WGT) representation of whole‐genome sequencing (WGS) data has been proposed to enable an innovative definition of an individual’s genome; this WGT representation can support supervised and unsupervised machine learning. In this study, we perform a new AD GWAS study on the WGT representation of the ADNI WGS data.MethodsThe detailed description, genome tiling pipeline, and a publicly available example of WGT data are available at: https://curii.co/su92l‐j7d0g‐swtofxa2rct8495. In our analysis, we first performed quality control, imputation, and one‐hot encoding of tile variants (Fig. 1). Then, for each genome tile, we used the likelihood ratio test to compare two logistic regression models to get a single p‐value, where a full model used the tile variants and covariates to predict disease status, and a null model used only covariates including age, sex, education, APOE4, and first 20 PCs. Participants included 1,504 subjects (1,032 cases and 472 controls). In comparison, set‐based GWAS analysis was performed using PLINK 1.9 on ADNI SNP‐based WGS data.Results8,560,743 tiles passed the QC process and were included in our analysis. The likelihood ratio test yielded 35,582 significant tiles with Bonferroni correction. A set‐based GWAS comparative study among all significant tiles using SNP‐based WGS data identified 1,535 sets with at least one significant SNP variant. Among 1,535 sets, 1,066 sets passed uncorrected p≤0.05; 115 sets passed p≤0.005; and 15 sets passed p≤0.0005 (Fig. 2).ConclusionsOur initial investigation of the tiling data shows that the WGT representation has promising power for identifying significant tiles that cannot be detected using the SNP representation. Complementary to the genotype values examined in traditional SNP analysis, the WGT analysis focuses on examining the haplotype variants within each tile and can capture the interaction pattern among SNPs within the haplotype. This initial AD GWAS study on WGT data demonstrates the promise of the tile representation for revealing novel genetic risk and protective factors in AD.
APOE4 and confluent WMH have a synergistic effect on episodic memory impairment in prodromal dementia
Authors: Kumar Dilip , Yatawara Chathuri , Wang Brian Zhiyang , Wong Benjamin , Tan Yi Jayne , Zailan Fatin Zahra , Ng Kok Pin , Kandiah Nagaendran 
Published: June 2023

AbstractBackgroundWhite matter hyperintensities (WMH) are a known risk factor for cognitive decline (C.Puzo el. al. 2019). While the ε4 allele of apolipoprotein E gene (APOE4) is another risk factor for cognitive decline in concomitance with WMH (D.Tsuang et. al. 2013), it remains unclear how APOE4 affects the relationship between WMH and cognitive decline, specifically in the prodromal stage of dementia. Our objective was thus to determine how APOE4 moderates the relationship between WMH and cognition in prodromal dementia.Method216 participants with prodromal dementia underwent magnetic resonance imaging (MRI), neuropsychological testing (global and domain‐specific), cardiovascular risk factor assessments and APOE genotyping. Visual ratings for WMH as well as total and lobar WMH volumes were quantified. Moderation analysis was performed to determine the influence of APOE4 on the relationship between WMH and performance on global and domain‐specific cognitive measures (executive function, episodic memory, attention, language) in moderation analysis (Figure 1). The role of confluent and non‐confluent WMH subtype on cognition was additionally studied using moderation analysis and logistic regression (Figure 1).ResultAPOE4 carriers (n = 49) had poorer memory and higher global WMH (p = 0.04), temporal WMH (p = 0.01) and occipital WMH (p = 0.02) compared to APOE4 non‐carriers (n = 167). APOE4 strengthened the relationship between higher total WMH burden and worse episodic memory performance (Fig.2a). At the lobar WMH level, APOE4 strengthened associations between frontal, temporal, parietal and occipital WMH and episodic memory performance (Fig.2b, Fig.2c, Fig.2d, and Fig.2e) with large sized effects (β). APOE4 did not interact with either total or lobar WMH to predict worse cognition in other cognitive domains. Moreover, APOE4 strengthened the relationship between frontal WMH with episodic memory in the confluent WMH subgroup (Fig.2f). Logistic regression model showed memory impairment was 4.81 times more likely to develop in patients carrying APOE4 and with confluent WMH compared to APOE4 non‐carriers and those with non‐confluent WMH (OR 4.81, β 1.57, p<0.01).ConclusionThe impact of WMH on memory may be strongest in APOE4 carriers at the prodromal dementia stage. Clinicians targeting WMH would need to consider the APOE4 allele and WMH severity status to strategize cognitive interventions.
NMDA receptor and APOE in Alzheimer’s disease: searching for connections
Authors: Escamilla Sergio 
Published: June 2023

AbstractBackgroundNMDA receptor (NMDAR) malfunctioning has been claimed as a crucial event in Alzheimer’s disease (AD), the most common form of dementia. While synaptic NMDAR activation leads to synaptic plasticity, extrasynaptic NMDAR activation leads to excitotoxicity, mitotoxicity and cell death. In the late onset form of AD, the APOE4 allele is the main genetic risk factor. Our aim is to study the influence of apoE4 on NMDAR function.MethodWe isolated synaptic and extrasynaptic membranes from APOE genotyped frontal cortex samples, from AD and non‐demented subjects. We studied the levels of NMDAR subunits GluN2B and GluN2A. We also evaluated the GluN2B phosphorylation at sites Y1472 and Y1336, associated to synaptic and extrasynaptic membrane location, respectively. We are also developing hiPSC‐derived cortical neurons from APOE genotyped AD and non‐AD subjects.ResultAt synaptic membranes, we found lower levels of GluN2B, Glun2A and pY1472‐GluN2B in AD respect to non‐demented patients. Inside the AD group, APOE4‐frontal cortex showed lower levels of GluN2B respect to non‐APOE4. At extrasynaptic membranes, no changes in GluN2B and Glun2A levels were observed, but phosphorylation at Y1336‐GluN2B was higher in AD respect to that in control. These results suggest that in AD, NMDAR are more likely to be found at the extrasynaptic membranes, where specific phosphorylation retain these subunits to those membranes. The lowest levels of NMDAR at the AD synapsis were found in APOE4 subjects.ConclusionWe found that the APOE4 allele seems to compromise NMDAR levels and phosphorylation in a different manner in synaptic vs extrasynaptic membranes in the AD patients. We try to understand the mechanisms that govern these status using iPSC‐derived neurons. Understansing the relation between the NMDAR and apoE in AD could lead to new therapteutical approaches.
Linguistic metrics of semantic fluency relate to longitudinal cortical thinning in older adults without dementia: The SMART‐MR study
Authors: Vonk Jet M.J. , Beran Magdalena , Zwartbol Maarten H.T. , Ghaznawi Rashid , Hendrikse Jeroen , Geerlings Mirjam I. ,   UCC‐SMART‐Study Group
Published: June 2023

AbstractBackgroundThe need persists for sensitive, low‐cost, and high‐access cognitive markers to complement biomarker information in the earliest stages of Alzheimer’s disease. Sensitive cognitive markers may be derived from information at the item level of neuropsychological tests. We investigated if item‐level metrics add information beyond total score in the relationship of semantic fluency—generating as many words as possible of a category in a given time frame—to cortical thinning across eight years in middle‐aged to older adults without dementia.MethodParticipants were part of the SMART‐MR cohort (n = 251, age = 59.6±8.6, 19.5% women), designed to investigate brain changes in individuals with arterial disease, which puts them at high risk for dementia. Global cortical thickness was derived from MRI scans at two visits approximately eight years apart. Item‐level metrics were derived from linguistic databases: mean log lexical frequency (how often a word occurs in our language) and mean age of acquisition (AoA; at what age a word was learned) of words generated on a 2‐minute semantic (animal) fluency task (Fig 1). We calculated residualized item‐level scores representing the deviation in the item‐level metric from what is expected based on the total score to account for collinearity. Linear regression models—adjusted for age, sex/gender, education, and history of stroke—analyzed the relationship between total score or item‐level metrics as determinants and cortical thickness at follow‐up as the outcome, adjusting for baseline level.ResultTotal score alone was not related to cortical thinning at follow‐up (β = ‐.023[‐.001;.000], p = .466). However, a higher‐than‐expected mean lexical frequency (β = ‐.065[‐.054;‐.002], p = .033) and lower‐than‐expected mean AoA (β = .066[.001;.019], p = .033) were related to more cortical thinning at follow‐up. An alternative analytic approach to capture change, using the difference in cortical thickness between visits without baseline adjustment, yielded similar results.ConclusionAlternative item‐level metrics of semantic fluency, as opposed to the traditional total score, related to future neurodegeneration in high‐risk individuals without a dementia diagnosis. The results seem robust across two different item‐level metrics and two different analytic approaches. Valuable item‐level information could be leveraged for accurate identification of the earliest stages of clinical Alzheimer’s disease, in addition to biomarker evidence, for intervention and timely diagnosis.
Discovery of potential Alzheimer’s disease key biomarkers based on bioinformatics analysis
Authors: Silva‐Lucero Maria del Carmen , Rivera‐Osorio Jared , Lopez‐Toledo Gustavo , Gomez‐Virgilio Laura , Cardenas‐Aguayo Maria del Carmen 
Published: June 2023

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative disorder and the most common cause of dementia in the elderly. AD is characterized by short‐term to long‐term memory loss, confusion, mood changes, and language difficulties. Numerous studies have focused on the dysregulated genes in AD, but the pathogenesis is still unknown. The underlying risk factors remain largely unclear. The discovery of more accurate AD biomarkers will allow early detection and development of new treatments. Recently bioinformatics has become a relevant research tool for biomedicine. In this study, we explored differential expressed genes (DEGs) potentially involved in the pathogenesis of AD.MethodOpen access databases of RNA microarrays of tissue from brain and peripheral blood of AD patient, were analyzed after background correction and data normalization; Limma package was used for Differential expression Analysis (DEA) throw statistical R programming language. Data were corrected with the Benjamini and Hochberg approach, and the genes with p values equal to or less than 0.05 were considered significant. The direction of change in gene expression was determined by its variation in the “log2‐fold change” between controls and patients. We performed a functional enrichment analysis of GO using goana and topGO‐Limma.ResultWith the Brain tissue database, we obtained 17 down regulated genes (DR) and 81 up regulated genes (UR). Functional enrichment analysis of DEGs showed UR pathways: behavior, nervous systems process, post synapses, enzyme binding, while DR were: cellular component organization, RNA metabolic process, protein‐containing complex, nucleoside triphosphatase activity and RNA binding. The blood tissue database showed 850 genes DR and 693 genes UR. The functional enrichment analysis of these DEGs showed UR pathways: regulation of transcription by RNA polymerase‐II and RNA processing, while DR were: cell development and synapses. Finally, the intersection of the DEGs in the two databases showed 3 genes shared between the brain and blood.ConclusionOur in‐silico analysis of brain and blood databases identified several UR and DR genes; the detection of these genes could provide new insight into potential therapies for AD. However, more research is needed to validate these potential new biomarkers and correlate them with the clinical data at different AD stages.
Physical activity and functional status in older adults: Mediating effect of cognitive function
Authors: Jin Yeonju , Hreha Kimberly , Hong Ickpyo 
Published: June 2023

AbstractBackgroundOlder adults who participate in social activities have more active lifestyles which has been shown to improve their quality of life (Acree et al., 2006). Generally instrumental activities of daily living (IADL) require aspects of social participation (e.g., cooking and managing finances) (Guo & Sapra, 2020). A previous study reported that physical and cognitive functions are positively associated with functional status in IADLs (Yam & Marsiske, 2013). In addition, since physical activity is associated with cognitive function, active participation in physical activity is recommended to maintain and improve cognitive function. While a number of studies have investigated the relationship between physical activity, cognitive function, and IADL, it is not clear if cognitive function acts as a mediator between physical activity and functional status equation. The aim of this study was to examine the relationship between physical activity and IADL and to clarify the mediating effect of cognitive function.MethodA retrospective cross‐sectional design that analyzed the association between physical activities and IADL and the mediating effects of cognitive function. We retrieved 3,980 people from the 6 million recipients eligible for the Korean health insurance and benefits, between years 2002 and 2015. The included people in this analysis are 60 years and older and have completed the physical activity questionnaire. We estimated the indirect effect of physical activities through cognitive function on IADL performance.ResultThe average age of the participants was 81.6 years (SD = 5.5), and 72.74% were female. There were a direct effect of high‐intensity exercise on IADL (β = 0.04, 95% CI: 0.01, 0.07) and an indirect effect of high‐intensity exercise through cognitive function (β = 0.05; 95% CI: 0.04, 0.07). In addition, there were a direct effect of moderate‐intensity exercise and IADL (β = 0.04, 95% CI: 0.01, 0.07) and an indirect effect of moderate‐intensity exercise through cognitive function (β = 0.05; 95% CI: 0.04, 0.07).ConclusionThe study indicated that there were positive relationships between physical activities, cognitive function and IADL. In addition, there was a mediating effect of cognitive function on the relationship between physical activities and IADL.
PET‐validated EEG‐machine learning algorithm predicts brain amyloid pathology in pre‐dementia Alzheimer’s disease
Authors: Kim Nam Heon , Park Ukeob , Yang Dong Won , Choi Seong Hye , Youn Young Chul , Kang Seung Wan 
Published: June 2023

AbstractBackgroundCurrently positron emission tomography (PET) is used as the initial or sole biomarker of β‐amyloid (Aβ) brain pathology, which may inhibit Alzheimer’s disease (AD) drug development and clinical use due to cost, access, and tolerability. Machine learning (ML)‐based EEG biomarkers may address these challenges. Previous studies have confirmed their ability to accurately discriminate between normal, mild cognitive impairment (MCI) and Alzheimer’s dementia. However, few quantitative EEG (QEEG) biomarkers have been validated with Aβ PET. We developed a QEEG‐ML algorithm to predict brain Aβ pathology among subjective cognitive decline (SCD) and MCI patients, and validated it using Aβ PET.MethodEEG (19‐channel, eye‐closed, resting‐state) and Aβ PET data were collected from 311 subjects with SCD (Aβ+: N = 36, Aβ‐: N = 160) or MCI (Aβ+: N = 54, Aβ‐: N = 61). We randomly excluded data for 76 subjects (SCD, Aβ+: N = 7, Aβ‐: N = 28; MCI, Aβ+: N = 6, Aβ‐: N = 7) for use in subsequent verification. QEEG absolute power, relative power, power ratio, and connectivity between channels (iCoherence) comprised the input features, from which the most relevant predictive features were identified using several methods (Random Forest Importance (GBM, XGB), ElasticNet, Whitney‐Mann). We then trained four ML algorithms (SVM, Logistic, KNN, Naive Bayes, Random Forest(GBM/XGB)) using each relevant feature set, yielding 24 models (4 sets * 6 algorithms). The 76 validation data sets were input into each model to compare their performance.ResultThe best‐performing model (random forest importance * SVM) showed 82.9% accuracy, 90.9% sensitivity, 76.7% specificity, and 75% positive predictive value in discriminating Aβ+ from Aβ‐, regardless of MCI/SCD. In MCI alone, the model showed 82.1% accuracy, 90.0% sensitivity, 78.9% specificity, and 81.8% positive predictive value. In SCD alone, it showed, 81.1% accuracy, 92.3% sensitivity, 75% specificity, and 66.7% positive predictive value.ConclusionThese findings suggest that our novel ML‐based QEEG biomarker can accurately predict the presence of brain Aβ plaque. Additional benefits of such a biomarker include reduced expense, wide availability, and high‐throughput screening and response monitoring. Future studies will assess utility as primary AD screens, adjunctive use with PET, and as a support in clinical rationales for Aβ PET and treatment choice in AD.
Dynamic functional brain networks in Alzheimer’s disease and healthy ageing
Authors: Rubido Nicolas , Vuksanovic Vesna ,   Swansea University Medical School
Published: June 2023

AbstractBackgroundBrain network analysis from resting‐state functional MRI (fMRI) has helped us to understand cortical activity in dementia. Likewise, dynamic brain networks' analysis, which takes into account temporal fluctuations in the resting‐state fMRI signal, has helped to reveal patterns of activity, usually averaged out by the conventional functional network analysis. These patterns of activity, or dynamic networks, reveal transient (meta‐stable) dynamics. Dynamic functional networks analysis from fMRI data has shown a potential to unveil clinically relevant information.MethodResting‐state fMRI from 187 participants belonging to Alzheimer’s Disease (AD) and cognitively‐normal Healthy Elderly (HE) from the ADNI‐3 database were analysed. Functional network was constructed from fMRI signal of the 120 cortical and sub‐cortical regions of the Jülich Atlas for each participants, and averaged across the two study groups. Dynamic functional connectivity is calculated using sliding window approach and compared to conventional (‘static’) functional connectivity within each study group and between them.ResultSignificant differences when comparing AD with HE individuals were found in the dynamic, but not static functional networks. These differences were restricted to the white matter regions, and to the inferior and superior parietal, and somatosensory cortices.ConclusionOur results demonstrate the existence of common and distinct patterns of static and dynamic brain connectivity in HE and AD, which highlight the importance of including non‐stationary information into the brain network analysis in Alzheimer’s disease.References:Vuksanović V, Hövel P. Dynamic changes in network synchrony reveal resting‐state functional networks. Chaos. 2015 Feb; 25(2):023116 [https://doi.org/10.1063/1.4913526].Moguilner S, García AM, Perl YS, Tagliazucchi E, Piguet O, Kumfor F, Reyes P, Matallana D, Sedeño L, Ibáñez A. Dynamic brain fluctuations outperform connectivity measures and mirror pathophysiological profiles across dementia subtypes: A multicenter study. Neuroimage. 2021 Jan 15; 225:117522 [https://doi.org/10.1016/j.neuroimage.2020.117522].
Acknowledgments: This work was supported by the Roland Sutton Academy Trust (Grant No. 13688)