bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of performance structures of activities of daily living affecting the sleep using social network analysis
Authors: Son Sungmin , Hwang Mose , Shim Aeri , Lee Hyeyoung , Jeong Kyuho , Nam Yeonjoo , Kim Taehui 
Published: January 2025

AbstractBackgroundSleep directly affects daily life, and lack of sleep affects cognitive function and mental health. So, this study analyzed the performance structures of daily activities affecting sleep using social network analysis.MethodsThe subjects were 313 people over 50 years old. The performance structures of daily activities were analyzed by social network analysis, and network characteristics, centrality, and cohesion structures were analyzed. Daily activities were analyzed by categorizing into pre‐sleep daytime activities, sleep preparation activities, and post‐sleep daytime activities. Data collection utilized a structured survey. Subjects were allowed to describe up to five activities to affect sleep. Intensity was assessed using a 5‐point scale.ResultsThe analysis of network characteristics showed that the number of activities performed in the daytime was the most performed. However, during sleep preparation, due to the decrease in the number of activities, the density increased, and the values of degree and eigenvector centrality showed high. The results of network centrality showed that, before sleep, bathing/showering, physical activities, and job performance and maintenance influence sleep. During sleep preparation, turning off lights was the most affecting activity on sleep. After sleep, eating and swallowing was the most affected activity by the results of sleep. The results of cohesion analysis showed that, before sleep, job performance and maintenance formed the structures associated with socializing with others. Eating and swallowing, and physical activities formed the structures associated with the self‐management activities performed. In preparing for sleep, turning off lights formed the structure associated with organizing the environment for sleep. After sleep, eating and swallowing formed the structures associated with the basic daily activities related to self‐management.ConclusionsThis study analyzed the interaction between sleep and daily activities. Based on these, key interventions for optimizing sleep include reinforcing bathing/showering and physical activities, as well as regulating job performance and maintenance before sleep. Organizing the environmental factors affecting to sleep and encouraging basic daily activities post‐sleep are needed. The findings can be used as important information to enhance sleep‐related activities of daily living.
Subclinical thyroid dysfunctions and the risk of dementia in the oldest old: the Monzino 80‐plus population‐based study
Authors: Lucca Ugo , Tettamanti Mauro , Forloni Gianluigi , Galbussera Alessia A. 
Published: January 2025

AbstractBackgroundOvert hyperthyroidism and hypothyroidism are regarded as possible causes of reversible dementia. Evidence on the risk of dementia associated with subclinical thyroid dysfunctions is limited and barely existent in the very old, while findings are conflicting. Objective: To cross‐sectionally and longitudinally investigate the association of subclinical‐hyperthyroidism and subclinical‐hypothyroidism with dementia in a prospective population‐based study of 80‐years or older residents in Varese province, Italy (Monzino 80‐plus Study).MethodTSH, FT4, and FT3 concentrations were determined by immunofluorescence ELFA. According to laboratory reference values of concentration, euthyroidism was defined as a THS between 0.25 and 5.00 mUI/mL, subclinical‐hypothyroidism as a TSH >5.00 mUI/mL and an FT4 within reference range, subclinical‐hyperthyroidism as a TSH <0.25 mUI/mL and an FT4 within reference range. Diagnosis of dementia fulfilled DSM‐IV criteria. Covariates entered in the adjusted logistic regression and Cox proportional hazard models: age, sex, education, alcohol consumption, current smoking, diabetes, hypertension, heart failure, myocardial infarction, COPD, ictus, TIA, parkinsonism.ResultConsent to blood sampling was obtained from 815 participants, 804 of whom had TSH, FT4 and FT3 measurements available (mean age 90.6 years [SD: 5.3]; 598 women [74.4%]; mean education 5.0 years [SD: 2.4]; 312 with dementia [38.8%]; 710 euthyroid individuals, 33 with subclinical‐hyperthyroidism; 41 with subclinical‐hypothyroidism). At the initial evaluation, neither subclinical‐hyperthyroidism (OR: 1.47, 95%CI: 0.72‐2.97; p = 0.28) nor subclinical‐hypothyroidism (OR: 0.65, 95%CI: 0.31‐1.26; p = 0.21) were significantly associated with dementia. The association remained non‐significant when adjusted for confounders (subclinical‐hyperthyroidism, fully adjusted OR: 1.31, 95%CI: 0.56‐3.02; p = 0.53; subclinical‐hypothyroidism, fully adjusted OR: 0.53, 95%CI: 0.22‐1.18; p = 0.13). At blood sample 418/432 participants with at least one follow‐up available were without dementia. In the following 6 years (median: 2.8 years), euthyroid individuals (N = 403) had similar occurrence of dementia (40.7%) as the 15 with subclinical‐hyperthyroidism (40.0%; fully adjusted HR: 1.08, 95% CI: 0.46‐2.53, p = 0.85) and the 29 with subclinical‐hypothyroidism (41.4%; fully adjusted HR: 0.98, 95% CI: 0.53‐1.84, p = 0.96).ConclusionIn the present population‐based study, neither subclinical‐hyperthyroidism nor subclinical‐hypothyroidism were significantly associated with the risk of dementia in the very old. Although congruous with previous prevalence estimates, the low frequency of subclinical cases without dementia limited the longitudinal analyses.
Neural Imaging for Alzheimer’s Prediction using AI: Exploring CNNs and LIME Explanations
Authors: Varghese Abraham , Sherimon Vinu , Raja Xavier C. , Ephrem Ben George , Gouda Prasanth 
Published: January 2025

AbstractBackgroundThis study explores Alzheimer’s prediction through brain MRI images, utilizing Convolutional Neural Networks (CNNs) and Lime interpretability. Based on an extensive ADNI MRI dataset, we demonstrate promising results in predicting Alzheimer’s disease. Local Interpretable Model Agnostic Explanations (LIME) shed light on decision‐making processes, enhancing transparency. Using artificial intelligence to predict Alzheimer’s disease, this work emphasizes its importance in improving accuracy and interpretability, thereby advancing the field and improving diagnostics.MethodAlzheimer’s Disease Neuroimaging Initiative (ADNI) brain MRI images undergo rigorous preprocessing, which includes focusing on brain regions, thresholding and morphological operations to improve image quality. The CNN model was constructed and trained to identify the features of diverse classes of AD, CN, EMCI, LMCI, and MCI in the dataset (Figure 1). With two convolutional layers (25 and 75 filters), maximum pooling, flattening, and dense layers (500, 250, 100 units), the CNN model was trained on a variety of ADNI classes (AD, CN, EMCI, LMCI, MCI). With 375,168,555 trainable parameters, the model is evaluated on split datasets, emphasizing interpretability through LIME analysis for transparent decision insight in neural imaging analysis. This comprehensive methodology ensures robust Alzheimer’s prediction, transparency, and interpretability in neural imaging analysis.ResultThe training procedure resulted in an impressive 98.75% training accuracy after 20 epochs. For the evaluation, 65 diverse images from various classes were used, and all images were accurately classified (Figure 2). Focusing specifically on the CN class data, LIME explanation was applied to identify and emphasize regions that significantly influenced the model’s predicted class. LIME delineates positive and negative regions in its output according to features that support or counter the predicted class (Figure 3). This thorough analysis contributes to a clearer understanding of prediction and promotes transparency in decision rationale.ConclusionAI is helpful for predicting AD using MRI, providing accurate diagnostics and allowing for the interpretation of complex imaging data to identify subtle patterns indicative of the progression of AD. By interpreting the LIME representation, we can gain a deeper understanding of how the neural network makes its predictions, by identifying key regions in the brain image that influence the model’s predictions.
Compound intervention affects pathology in a novel humanized APP Knock‐In mouse model (APPSAA), strengthening its relevance as a tool in preclinical research
Authors: Dejonckheere Wannes , Carmans Sofie , Cornelissen Tom 
Published: January 2025

AbstractBackgroundTo improve clinical translatability of non‐clinical in‐vivo Alzheimer’s disease (AD) models, a humanized APP knock‐in mouse model (APPSAA) was recently created (Xia, D. et al., 2022). The genetic modifications lead to increased Aβ42/40 ratios in AD relevant tissues, resulting in an age‐dependent amyloid deposition. Here we assess the value of this model for non‐clinical efficacy studies of experimental treatments with diverse mechanisms of action to facilitate the development of novel AD therapeutics.MethodAPPSAA mice and WT controls were aged and sacrificed at various time points. In addition, APPSAA mice were treated for 3 months from the age of 3 months onward with an NLRP3‐inhibitor, an in‐house developed calcium homeostasis modulator or vehicle. The major pathological hallmarks were investigated with biochemical and immunohistological assays. Additionally a preliminary screen for AD biomarkers was conducted in CSF samples. Since APPSAA mice lack an obvious behavioral phenotype, synaptic plasticity was investigated to serve as a functional readout.ResultCortical Aβ40, Aβ42 and pyroglutamate modified Aβ42 (N3pE‐42) levels were shown to be affected with age. In addition, cortical amyloid plaques, surrounded by activated microglia, were shown to be present already at an age of 3 months, albeit at a low level. With age, the number of cortical plaques increased. In addition, we were able to confirm the presence of endogenous phospho‐Tau positive dystrophic neurites within these plaques, clearly demonstrating the translational relevance of this model. Interestingly, several of the observed hallmarks were counteracted by compound intervention, including phospho‐Tau pathology. Finally, we show that several CNS biomarkers can be detected in mouse CSF.ConclusionThese data lead us to conclude that this knock‐in model is a broadly applicable tool to investigate efficacy of disease‐modifying drugs with diverse mechanisms of action even without directly targeting protein aggregates.
Association Between in vivo Tau Deposition in the Pedunculopontine Nucleus and REM Sleep Alterations in Non‐demented Older Adults
Authors: Jeon So Yeon , Yi Dahyun , Byun Min Soo , Kong Nayeong , Jung Joon Hyung , Chang Yoon Young , Keum Musung , Ahn Hyejin , Lee Jun‐Young , Lee Yun‐Sang , Kim Yu Kyeong , Kang Koung Mi , Sohn Chul‐Ho , Lee Yu Jin , Lee Dong Young ,   KBASE Research Group
Published: January 2025

AbstractBackgroundRapid eye movement(REM) sleep alterations are increasingly recognized as an early manifestation of Alzheimer's disease as well as Lewy body disease. The pedunculopontine nucleus(PPN), the main brain source of cholinergic innervation, is known to play an important role in the regulation of REM sleep and has been reported to be one of the early brainstem regions where hyperphosphorylated tau accumulates. Nevertheless, little information is available about the relationship between tau pathology in the PPN(PPN‐tau) and REM sleep disturbance. Therefore, we aimed to investigate the relationship between Tau‐PPN and REM sleep characteristics, alongside the modulating role of amyloid pathology.MethodA total of 22 non‐demented older adults from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer’s Disease were included. All participants underwent [11C]‐PiB PET and [18F]‐AV1451‐PET. Sleep variables were measured through nocturnal polysomnography(PSG) over two consecutive nights at a center. Tau deposition in the PPN was measured in the ROI masks for each participant, prepared in native space using the Harvard arousal network atlas for the PPN. Linear regression analyses were performed with sleep variables as dependent variables and tau deposition levels as independent variables after controlling for age, sex, education, and apolipoprotein‐E ε4 positivity. In an exploratory analysis, sleep variables demonstrating significance at a threshold of p < 0.1 were further subjected to subgroup analysis based on amyloid positivity.ResultPPN‐tau was significantly correlated with increased REM latency (p<0.001; Figure 1) and showed a trend‐level association with REM sleep percentage(REM %) (p=0.079). Even after controlling for global tau deposition and amyloid positivity, the significance did not change (p = 0.014). In addition, subgroup analyses according to Aβ positivity revealed that higher PPN‐tau was significantly related to increased REM latency (p = 0.001) and decreased REM % (p=0.011) in Aβ‐positive group but not in Aβ‐negative group (p=0.123 for REM latency and p=0.690 for REM %) (Figure 2).ConclusionOur findings support the possibility that tau pathology in the PPN underlies REM sleep alteration, more specifically, increased REM latency and decreased REM sleep in non‐demented older adults. Additionally, this relationship appears to be more prominent in individuals with brain Aβ pathology.
Association of Air Pollution with Memory Decline Later in Life in the United States
Authors: Zhang Boya , Weuve Jennifer , Langa Kenneth M , D'Souza Jennifer , Szpiro Adam , Faul Jessica D , de Leon Carlos Mendes , Gao Jiaqi , Kaufman Joel D. , Sheppard Lianne , Lee Jinkook , Hirth Richard A. , Adar Sara 
Published: January 2025

AbstractBackgroundEmerging evidence suggests that air pollution exposure might diminish the cognitive health of older adults. Although many studies have reported that air pollution is associated with increased dementia risk, associations with the process of cognitive decline have been more heterogeneous.MethodWe used biennial data between 2000 to 2016 from respondents>65 years in the Health and Retirement Study (HRS), a national, population‐based cohort in the United States, to study associations of air pollution with cognitive decline. We assessed episodic memory as a composite score of performance on immediate and delayed recall tests combined with proxy‐reported memory function. We standardized the memory score at baseline and during follow‐up based on its baseline mean (0.66) and standard deviation (0.69). We used spatiotemporal models to predict 10‐year average particulate matter (PM2.5, PM10‐2.5), nitrogen dioxide (NO2) and ozone (O3) at participant residences before each interview. Associations with rates of memory decline were estimated by generalized estimating equation regression, adjusting for individual demographics, area‐level characteristics, time, and spatial trends.ResultAmong the 19,063 participants during an average of 7.4±5.3 years of follow‐up time, the mean age at baseline was 71.0±7.3 years. The mean (interquartile range, IQR) PM2.5, PM10‐2.5, NO2 and O3 concentrations were 12.2 (3.6) µg/m3, 9.4 (4.6) µg/m3, 11.2 (7.8) ppb and 26.6 (3.7) ppb respectively. In single‐pollution models, we observed consistently steeper decline in episodic memory scores with higher PM2.5, PM10‐2.5 and NO2 long‐term concentrations (all approximately ‐0.003 standard units/year per IQR, 95%CI: ‐0.006, ‐0.0004), whereas the reverse was observed with higher exposures to O3 (0.003 standard units/year per IQR, 95%CI: 0.001, 0.005). Associations were changed to null for PM2.5 and NO2, and slightly attenuated for O3 after adjustment for other pollutants in the multi‐pollutant model.ConclusionThis study suggests that long‐term exposure to air pollution might be associated with cognitive decline among older adults though the results differed by pollutant.
Metabolic glutamate receptor 5 (mGluR5) is associated with tau pathology in mild Alzheimer's disease
Authors: Wang Yan , Xie Fang , Guan Yihui 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) is characterized by the deposition of amyloid plaques and tau neurofibrillary tangles in the brain with continuous cognitive impairment. Although the mechanism underlying AD pathogenesis remains unclear, more evidence suggests that synaptic dysfunction and loss may be an early event in disease progression. Metabotropic glutamate receptor 5 (mGluR5), a kind of G protein–coupled receptor, is involved in AD pathology through modulating synaptic transmission and plasticity and thus exhibits therapeutic effects. However, research on the associations of mGluR5 with the neuropathological hallmarks of AD needs to be completed.MethodIn this single‐center, cross‐sectional study, fifteen patients with mild AD and thirteen normal controls (CN) underwent a PET/MRI scan with [18F] PSS232 (mGluR5 availability) and trouble PET/CT scan with [18F] florbetapir (β‐amyloid) and [18F] MK6240 (tau deposition). The associations of [18F] PSS232 and [18F] MK6240 binding were investigated. We also investigated the correlations of tau deposition and mGluR5 availability with cognitive performance. Voxel‐wise and volume‐of‐interest (VOI) analyses were performed on PET data.ResultCompared to CN, individuals with mild AD showed smaller clusters of [18F] PSS232 binding in the hippocampus and increased tau deposition in the hippocampus, lateral temporal, posterior cingulate, frontal, occipital, and parietal (Figure 1). Based on Voxel‐wise and VOI analyses, tau deposition in the hippocampus was negatively associated with [18F] PSS232 binding in the hippocampus (r = 0.56, p = 0.002, Figure 2). Lower neuropsychological test scores were associated with decreased [18F] PSS232 and increased [18F] MK6240 binding in the hippocampus (Figure 3).ConclusionPatients with mild AD have significant reductions in mGluR5 availability and have high tau deposition in the hippocampus, which is considered the critical region involved in early cognitive impairment. That means mGluR5 and tau were interrelated in the hippocampus and may apply in tau spreading. The correlation of mGluR5 availability and tau deposition in severe AD patients and more longitudinal data are needed to illustrate the role of mGluR5 in AD pathology.
Examining the Associations of Social Support on Later‐life Cognition in the Health and Aging Brain Study‐Health Disparities (HABS‐HD) Cohort
Authors: Lee Jillian K. , Johnson Leigh A. , O'Bryant Sid E. , Mielke Michelle M. ,   HABS‐HD Study Team
Published: January 2025

AbstractBackgroundSocial support has been associated with a reduced risk of heart disease and stroke, higher cognitive function, less cognitive decline, and resilience to stress. Notably, social support differs by gender and race/ethnicity. However, it is not clear whether these factors modify the relationship between social support and cognition. In a large diverse cohort, we cross‐sectionally examined the association between social support and cognition and whether gender or race/ethnicity modified the associations.MethodParticipants included 2,831 older adults (mean [SD] age, 64.98 [8.53] years; 63% women, 39% Non‐Hispanic White, 39% Mexican American, 22% African American) enrolled in the Health and Aging Brain Study‐Health Disparities (HABS‐HD). Perceived social support was measured using the Interpersonal Support and Evaluation List. Comprehensive cognitive testing including multiple tests across the following domains: memory, language, processing speed, and attention/executive function. Linear mixed‐effect models evaluated the associations between social support and cognitive performance, adjusting for age, education, gender, and race/ethnicity, the Geriatric Depression Scale, and the Penn State Worrisome questionnaire. Interactions between social support and gender or race/ethnicity on cognition were assessed.ResultHigher social support was significantly associated with most tests across the cognitive domains. With increasing social support, women performed significantly better than man on Digit Span Forward (b = 0.0391, p = 0.0019) and Backward (b = 0.0335, p = 0.0055), Digit Symbol Substitution (b = 0.1259, p = 0.03780), and Trails A (b = ‐0.2569, p = 0.0198). Increasing social support was more strongly associated with cognitive performance on the Digit Symbol Substitution for African Americans compared to non‐Hispanic whites (b = 0.1652, p = 0.0346). Race/ethnicity did not modify the association between social support and any other cognitive test.ConclusionIn the cross‐sectional study, higher social support was associated with better cognitive performance across all domains. Associations appeared to be stronger for women versus men but generally did not differ by race/ethnicity. Future research is needed to evaluate the longitudinal associations of social support and changes in cognitive trajectories overtime.
Quantitative dissection of healthy aging using protein and peptide signatures from paired CSF and plasma
Authors: Shichkova Polina , Kamalian Aida , Tognetti Marco , Below Christopher , Bruderer Roland , Feng Yuehan , Lutz Michael W , Moghekar Abhay 
Published: January 2025

AbstractBackgroundDespite age being the primary risk factor for Alzheimer's disease (AD), there remains a necessity for a thorough understanding of the distinct biological pathways affected in the course of healthy aging as opposed to the pathological aging that leads to neurodegeneration. As the genome remains constant throughout one's lifespan, it becomes crucial to unravel the impact of aging on the proteome. Proteins, being key players in various cellular functions, mediate the effects of environmental stimuli and epigenetic alterations. Moreover, despite the brain's protection by the blood‐brain barrier, emerging evidence suggests that systemic events like inflammation and metabolic dysfunction can significantly influence the risk of developing AD.MethodMatched CSF and plasma samples were obtained from individuals at the same visit from the Neurology Clinic and Alzheimer’s Disease Research Center at Johns Hopkins. Samples were collected from young control subjects (n= 53), and healthy elderly subjects (n = 40). The plasma samples were depleted in 96‐well format using an automated MARS‐14 depletion system. The depleted plasma and neat CSF samples were subsequently processed to tryptic peptides and analyzed using data‐independent acquisition (DIA)‐mass spectrometry (MS). Data processing and analysis were performed using Biognosys’ Spectronaut software.ResultUsing our optimized discovery workflow, we analyzed the above‐described cohort of 93 matched plasma and CSF sample pairs. This resulted in more than 73254 quantified peptides (associated with > 4000 proteins) in CSF and 62750 quantified peptides (associated with > 2500 proteins) in plasma. The depth and breadth of protein quantification cover numerous pathological mechanisms such as AB and Tau pathology, synaptic dysfunction, neuronal injury, iron toxicity and inflammation. Harnessing the unique feature of MS‐based proteomics, we focused on the identification of differentially regulated peptides that are located in protein regions where known events such as alternative splicing, truncation or protease cleavage occur.ConclusionThe peptide‐centric nature of our approach allowed us to assess a variety of post‐translational modifications, truncations and isoforms. We envision this unbiased profiling of proteoforms as a powerful tool to elucidate complex disease mechanisms.