bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biological aging is associated with atypical neurodegenerative patterns in Alzheimer’s disease
Authors: Sreepada Lasya P , Brown Christopher , Das Sandhitsu R. , Yushkevich Paul A. , Wolk David A , McMillan Corey T 
Published: January 2025

AbstractBackgroundAtypical presentations of Alzheimer’s disease (AD), which demonstrate more cortical involvement relative to medial temporal lobe (MTL), are generally associated with younger age of onset. Age, defined chronologically, is a primary driver of AD pathology and neurodegeneration. However, some young onset cases are typical, amnestic presentations and some older onset cases present more cortical atrophy. We hypothesize that a biological definition of age, defined using epigenetic clock measures, may be even more tightly linked to atypicality than chronological age.MethodSubjects from ADNI with blood DNA methylation (DNAm) samples and T1 MRI (N=646, 55.7% female, 75.5 +/‐ 7.4 years, Table 1) were selected. Participants were defined as “young” (< 65 years) or “old” (> 80 years) amyloid‐negative controls or amyloid‐positive MCI/AD. DNAm was assayed on Illumina EPIC arrays.Epigenetic age was computed by applying the Shireby cortical epigenetic clock. We then defined epigenetic age acceleration (EAA) by regressing epigenetic age against chronological age and extracting the residual. Subjects were classified as appearing “biologically younger” (EAA < 1 SD below the mean), “neutral” (EAA within 1 SD), or “biologically older” (EAA > 1 SD above the mean).Regional cortical thickness measures were derived from MRI using FreeSurfer 5.1 and harmonized for scanner differences using longitudinal ComBat. We computed composite bilateral thickness z‐scores, adjusted for age and sex relative to controls, in the cortical AD signature anatomical ROIs (Figure 1) and the MTL (entorhinal and parahippocampal cortices). Finally, we defined an AD “mismatch” score as the signed difference of the composite cortical and MTL thickness z‐scores.ResultCortical thickness z‐scores were lower in MCI/AD relative to controls. Lower mismatch scores, reflecting greater cortical neurodegeneration relative to MTL, were observed in young‐onset relative to older‐onset MCI/AD. Similarly, mismatch scores were significantly lower in biologically younger cases relative to biologically older cases (t=2.5589; p = 0.01; Figure 2).ConclusionOur results support the notion that biological age, as measured by epigenetic modifiers, modulates the degree of atypicality in AD patients, akin to chronological age. These findings suggest that biological aspects of aging may influence the topography of AD‐related pathology and spread.
Brain morphometrics and aging with paired 3T and 7T structural MRI: 7T reduces reqiured sample size and improves statistical significance
Authors: Chu Cong , Santini Tales , Marsland Anna , Gianaros Peter , Ibrahim Tamer S 
Published: January 2025

AbstractBackgroundDuring normal aging, it has been observed that brain regional volume and thickness can be altered due to atrophy. While both 3 Tesla (3T) and 7 Tesla (7T) MRI are used to study such changes, it is unknown how individual brain morphology is characterized differently when 7T MRI allows for higher‐resolution and higher tissue contrast images. In this study, we employ a large cohort of healthy subjects that have undergone both 3T and 7T MRI scans to investigate how 7T MRI improves correlation strength between brain morphometrics and aging.MethodPaired 3T (Prisma and Trio Siemens) and 7T (TTT coil [Santini et al Sci. Rep. 2021] with Magnetom Siemens) images (T1‐weighted, 0.5x0.5x1mm3, 1x1x1mm3, 0.75x0.75x0.75mm3 respectively) are acquired with similar acquisition time (∼5 minutes) on 351 healthy participants (age=46.7 +‐ 9.12 years, 192 females). Images are preprocessed with gradient distortion correction, bias correction, and skull stripping. 279 pairs of images passed manual quality assurance without abnormalities. We used Pearson correlation coefficient to assess associations between brain morphometrics and age. Regional volumes are normalized to individual intracranial volume. Multiple tests were corrected using the Bonferroni method.ResultCompared to 3T, the 7T dataset showed more significantly correlated regions, with 28 volume regions in common to 3T (average R3T=‐0.24 and R7T=‐0.38) and 14 more. 7T dataset also consistently exhibits stronger negative and positive (ventricles) correlation with age than 3T for both volume and thickness. The 62% subsampled 7T dataset, averaged from 20 random samples, resembled the significant volume regions of the 3T dataset while still showing stronger correlation (Figure 1). We also show that for all brain regions, volume and thickness, 35% of the 7T sample is sufficient to reach the statistical power of the complete 3T sample (Figure 2).ConclusionOur analysis demonstrated the advantages of 7T MRI at characterizing brain morphometrics, specifically its capacity to measure relatively small changes without needing a large sample size. The higher spatial resolution and signal to noise ratio allow for feasible longitudinal, more importantly with relatively short follow up times, and cross‐sectional analysis with reasonable sample sizes.
Acculturation scales in Dementia in US Latinos/Hispanics: A Systematic Review
Authors: Goytizolo Alicia , Rosselli Monica 
Published: January 2025

AbstractBackgroundAcculturation is a relevant variable in the assessment of dementia. Little is known about the type of acculturation scales and the frequency in which they are used in dementia research among Latinos/Hispanics in the US. The purpose of this paper was to conduct a systematic review of published studies that investigated the use of acculturation measures toward Latinos and Hispanics who are diagnosed with dementia.MethodsDatabases were searched using four key search terms: dementia, acculturation scale, Latinos, and Hispanics. We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) criteria, which included: a sample population of Latinos/Hispanics who were diagnosed with dementia; and the use of an acculturation scale as part of their methods. Review articles, book chapters, and dissertations were excluded. The methodology and findings of all retrieved articles were thoroughly evaluated to determine eligibility. Data were extracted from all eligible articles regarding the study population, study methods, outcomes measured, acculturation measures used, and results.ResultsThe literature search using PubMed and PsychNET databases disclosed sixty‐five articles that were originally reviewed. Twenty‐eight articles were excluded as a result of not meeting the criteria. A total of eight articles were included in the current analysis with a total combined sample of 1323 individuals (Mean age = 67.5, SD; 8.39* female percentage = 0.68). Participants were recruited from California (4 studies), Texas (2 studies), New York (2 studies), Puerto Rico (1 study), Florida (1 study), Illinois (1 study), and Pennsylvania (1 study). The use of five different acculturation scales were identified (Marin Acculturation Scale, Short Acculturation Scale for Hispanics, Bidimensional Acculturation Scale for Hispanics, Abbreviated Multidimensional Acculturation Scale, and the Acculturation Rating Scale for Mexican Americans‐II). 50% of the reviewed articles found a significant association between acculturation and cognition.ConclusionsThe scales and measurements being used to determine the influence of acculturation on dementia in the Latino and Hispanic population continue to be under researched. The limited number of published studies on the topic results in an inability to generalize the conclusions on a larger scale.*Excludes one data set due to unavailable data
Enhancing Neuropsychological MCI Classification through Olfactory Testing
Authors: Karunanayaka Prasanna , Ahmed Biyar , Elyan Rommy , Peiris Senal , Pang Ran , Kanekar Sangam , Eslinger Paul , Yang Qing 
Published: January 2025

AbstractBackgroundAs new therapeutic options emerge, earlier diagnosis is crucial for improving prevention and early intervention. The current standard for screening for MCI and AD involves comprehensive neuropsychological tests prior to performing invasive testing such as lumbar punctures or PET scans. Neuropsychological tests, however, can be variable and confounded by compensatory effects in MCI. Therefore, a more objective means of assessing cognitive deficits would be beneficial. Prominent olfactory deficits have been shown to be prevalent in early MCI and AD and can precede symptoms of memory and cognitive decline. In this study, the relationship between cognition and olfaction was investigated using comprehensive neuropsychological and olfactory testing in cognitively normal (CN) and MCI subjects.Method26 CN (18 females, age 65.15 ± 5.44) and 19 MCI (10 females, age 70.10 ± 7.48) participated in this study. Subjects underwent computerized multiple‐choice olfactory identification testing and forced choice olfactory threshold testing. Normalization tables were used to minimize age as a potential confounding variable. To establish a potential composite score, Principal component analysis (PCA) was performed on neuropsychological test scores which was followed by a logistic regression analysis correlating participant status (CN or MCI) versus the PCs with and without olfactory measures. An ANOVA was performed to assess the significance between these two models.ResultOur findings show significant differences in a wide array of neuropsychological and olfactory measures (Table 1). Measures with a p‐value greater than 0.1 were excluded from the two PCs generated for the PCA. A logistic regression was performed with results and ANOVA findings shown in Table 2.ConclusionOlfactory testing improves the ability to differentiate between CN and MCI (Table 2), suggesting that observed olfactory deficits go above and beyond the cognitive impairments measured by neuropsychological tests in the MCI cohort. Our analysis suggests that olfactory testing can improve the sensitivity of the overall assessment of screening for early MCI diagnosis and can be potentially incorporated in a composite score. Since this is an ongoing longitudinal study, testing will be repeated, enabling further insight into the relationship between olfaction, neurodegeneration, and cognition.
Nationwide Trends in Medical Comorbidity Burden of Patients Newly Diagnosed with Dementia, 2015‐2021
Authors: Lusk Jay B , Ford Cassie B , Blass Beau , Johnson Kim G , Clark Amy G , Soneji Samir , O'Brien Richard J , O'Brien Emily C , Hammill Bradley G 
Published: January 2025

AbstractBackgroundMedical comorbidity burden has a substantial impact on care for patients with dementia and has major impacts on quality of life. No nationwide study has evaluated trends in medical comorbidity burden of patients with a new diagnosis of dementia. We therefore performed a nationwide study of medical claims data to understand the prevalence of comorbid medical conditions at time of dementia diagnosis in real‐world clinical practice.MethodWe studied 100% of nationwide Medicare claims from 2014‐2021 and evaluated the prevalence of medical comorbidities among patients with new diagnoses of dementia. Medical comorbidities were ascertained through searching inpatient and outpatient claims for 1 year prior to dementia diagnosis. We used validated international classification of diseases (ICD) code algorithms to identify both dementia diagnoses and medical comorbidities. We report the number and percentage of patients with each comorbid medical condition.ResultA total of 5,721,711 patients with incident dementia were included in the study. Anxiety disorders were much more common among patients diagnosed in 2021 (29.8%) compared to patients diagnosed in 2015 (23.8%). Depression was also more common among patients diagnosed in 2021 (33.8%) versus 2015 (31.8%). Hypertension remained very common (85.7% in 2021 vs 86.6% in 2015). There was a major decrease in patients with prior history of psychosis (17.8% in 2015 vs 8.1% in 2021). There were modest increases in the rates of comorbid cardiovascular disease (Table 1). Figure 1 shows the percentage of patients with new diagnosis of dementia with various conditions in 2015 and 2021 respectively.ConclusionIn a national database of Medicare claims, there were substantial changes from 2015‐2021 in comorbidity burden among patients newly diagnosed with dementia. There was a dramatic decrease in comorbid psychosis which could be attributable to Medicare initiatives to reduce the inappropriate use of antipsychotic medications among patients with dementia. Our results have implications for health system performance and risk adjustment, given an overall increase in comorbidity burden across many disease states from 2015‐2021. Strategies to address rising multimorbidity may be increasingly important for patients with dementia.
Serum calcium, alkaline phosphotase as risk factors for bone metabolism in Alzheimer's disease
Authors: Chintamanibatla Rupesh RSV , Mehta Preet , Kamarthi Sagar , Suryadevara Vidyani 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) is a neurodegenerative disorder characterised by cognitive decline and progressive deterioration of brain function. Recent research has suggested a complex interplay between AD and bone health, with individuals affected by AD exhibiting an increased propensity for fractures and falls. Our preclinical studies in PSEN, MAPT P301 S and FDD mice have shown sex‐dependent changes in the bone in AD mice, compared to their age‐matched wild type mice. To delve deeper into the molecular underpinnings of this relationship, our study leverages data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate alterations in bone‐specific markers during AD pathology; to unravel potential links between bone metabolism and AD progression.MethodsData from ADNI‐1(n=800), ADNI‐GO (n=700), ADNI‐2 (n=1300) and ADNI‐3 (n=2000) was considered for analysis. Spearman correlation analysis was performed between bone markers i.e. calcium and alkaline phosphatase with AD pathology‐ imaging and cognitive assessment.ResultsCalcium levels and alkaline phosphate levels which are critical for bone metabolism were found to be significantly lower in patients with EMCI, LMCI and AD compared to control patients. A negative correlation was found between Calcium levels and Left Hippocampal Volume (Corr Coeff = ‐0.025, p = 1.38e‐19), Right Hippocampal Volume (Corr Coeff = ‐0.030, p = 1.95e‐26), and Montreal Cognitive Assessment (MOCA) scores (Corr Coeff = ‐0.015, p = 4.34e‐08). Alkaline Phosphatase levels show a strong negative correlation with Left Hippocampal Volume (Corr Coeff = ‐0.075, p = 8.45e‐154) and Right Hippocampal Volume (Corr Coeff = ‐0.078, p = 5.74e‐168) and a negative correlation with MOCA scores (Corr Coeff = ‐0.065, p = 9.02e‐118). The negative correlations between Calcium and Alkaline Phosphatase levels with left and right hippocampal volume suggest that lower bone‐specific markers are associated with reduced hippocampal volume, elucidating brain‐bone crosstalk during ADConclusionThe study reveals correlation between serum bone markers (calcium, alkaline phosphatase) and declining hippocampal volume and cognitive function in Alzheimer's, suggesting a potential link between bone health and AD pathology. This discovery paves the way for deeper understanding and new avenues for diagnosis and treatment.
Leveraging T1 MRI Images for Amyloid Status Prediction in Diverse Cognitive Conditions Using Advanced Deep Learning Models
Authors: Hosseini Seyyed Ali , Servaes Stijn , Rahmouni Nesrine , Therriault Joseph , Tissot Cécile , Macedo Arthur C. , Wang Yi‐Ting , Arias Jaime Fernandez , Socualaya Kely Monica Quispialaya , Zheng Yansheng , Chan Tevy , Trudel Lydia , Gauthier Serge , Rosa‐Neto Pedro 
Published: January 2025

AbstractBackgroundTimely and non‐invasive prediction of amyloid status are pivotal in Alzheimer’s disease (AD) diagnostics. This research leverages T1 MRI images to predict amyloid positivity or negativity, offering an economical and less invasive alternative to amyloid PET scans. Using the comprehensive TRAID dataset from McGill University, the study evaluates a spectrum of cognitive conditions including AD, atypical AD, Cognitively Normal (CN), Mild Cognitive Impairment (MCI), MCI not due to AD, Suspected Non‐Alzheimer's Pathophysiology (SNAP), and Vascular MCI (VMCI).MethodThe study involved 588 subjects, representing a cross‐section of cognitive states. A VGG16‐based feature extraction process was meticulously applied to T1 MRI images to capture complex biomarkers of AD. These high‐dimensional features, alongside demographic data, were harnessed to train two deep learning models: a Long Short‐Term Memory (LSTM) model and a Convolutional Neural Network (CNN) ensemble model. To prevent overfitting and data leakage, the models employ 10‐fold StratifiedKFold validation and early stopping, alongside regularization techniques like dropout and batch normalization. Data preprocessing is contained within StratifiedKFolds, and an independent test set is used for unbiased evaluation. The predictive accuracy was scrutinized using a range of metrics, including accuracy, F1‐score, recall, precision, and ROC AUC on the validation sets.ResultThe LSTM model reported average validation metrics with an accuracy of 70.88%, F1‐score of 70.64%, recall of 78.07%, precision of 70.88%, and a ROC AUC of 71.86%. The CNN ensemble model showed a marked improvement, particularly in its recall of 88.00% and ROC AUC of 82.47%, alongside an accuracy of 76.27% and F1‐score of 75.86% (Figure 1 and 2). However, in another model, it was the CNN ensemble model that demonstrated remarkable predictive power, achieving an ROC AUC of 88% within the AD spectrum (CN, MCI, and AD subjects), indicating a high degree of predictive power in classifying amyloid status.ConclusionThis study underscores the CNN ensemble model's robustness in predicting amyloid status across a broad spectrum of cognitive conditions, with particular emphasis on its application in high‐sensitivity clinical settings. These findings represent a significant advancement in the field of AD diagnostics, contributing to the development of more accessible and cost‐effective prognostic techniques.
Trends in Incidence and Prevalence of Dementia Among United States Medicare Beneficiaries, 2014‐2021, by Race, Sex, and Neighborhood Socioeconomic Status
Authors: Blass Beau , Ford Cassie B , Johnson Kim G , Clark Amy G , Soneji Samir , O'Brien Richard J , Hammill Bradley G , O'Brien Emily C , Lusk Jay B 
Published: January 2025

AbstractBackgroundEstimating the incidence and prevalence of dementia is challenging. Cohort and brain banking studies can provide very precise estimates of incidence and prevalence in specific populations, but it is not clear how generalizable those estimates are to other populations. Furthermore, diagnoses of dementia made in clinical practice may differ from gold‐standard neuropathological diagnoses. Understanding trends in the incidence and prevalence of dementia as determined by routinely collected administrative claims data can provide critical context for clinicians and policymakers. Furthermore, understanding disparities in incidence and prevalence in the population at large can help to ameliorate biases from inequitable representation in survey and cohort designs.MethodWe performed a retrospective study of 100% of Medicare claims from 2013‐2021 (2013 being reserved as a look‐back year). We defined incident dementia diagnoses in each year among patients without a known history of dementia based on validated international classification of diseases (ICD) algorithms. We defined prevalent dementia as patients with any prior history of dementia in each year. We stratified incidence and prevalence by race (classified as Black, non‐Hispanic, Other race/ethnicity, and White, non‐Hispanic), sex, and neighborhood socioeconomic status, measured by the well‐established Area Deprivation Index, which summarizes socioeconomic conditions at the census block group level (roughly 600‐1000 people).ResultFrom 2014‐2021, there were 5,721,711 incident cases of dementia in the United States. The average age at diagnosis was 80.5 years. Patients were 57.7% female, 86.6% White and 7.8% Black, 15.8% dually eligible for Medicare and Medicaid, and 41.5% from the South region. Table 1 shows incidence rates and prevalence of dementia from 2014‐2021. Table 2 shows incidence rates and prevalence stratified by legal sex and race/ethnicity. Figure 1 shows incidence rates of dementia stratified by neighborhood socioeconomic status.ConclusionWe found declining dementia incidence but increasing dementia prevalence in a sample that includes the vast majority of older adults in the United States. Differences in dementia incidence by sex, race/ethnicity, and neighborhood socioeconomic status remained notable in 2021. Our results have implications for health system performance in providing high‐quality and equitable clinical care for a growing population of patients with dementia.
Resting‐state functional connectivity correlates of cognitive function in Mild Cognitive Impairment on functional Near Infrared Spectroscopy
Authors: Rangarajan Subhashini K , Nichenametla Sonika , Sreeraj Vanteemar S , Harbishettar Vijaykumar , Sinha Preeti , Sivakumar Palanimuthu Thangaraju , Venkatasubramanian Ganesan 
Published: January 2025

AbstractBackgroundMild cognitive impairment(MCI) is characterized by an impairment in one or more cognitive domains greater than expected for a person’s age and educational background with preserved functional independence. Functional Near Infra‐Red Spectroscopy (fNIRS) is a modality of non‐invasive neuroimaging that utilizes the optical properties of oxygenated and deoxygenated hemoglobin in the tissue to measure their absolute or relative concentrations following neuronal activity. fNIRS has been used to evaluate neurohemodynamics in MCI. Lower tissue oxygenation in bilateral frontal and parietal regions was found at resting state, which correlated with cognitive functioning in MCI. The objective of this study was to evaluate the correlation between cognitive functioning and resting state functional connectivity on fNIRS in MCI.MethodsThe study was conducted at the Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India (Institutional Ethics Committee approval no.: NIMHANS/34th IEC (BEH.SC.DIV.)/2022) on consenting participants with MCI (N = 38). Detailed cognitive assessment was performed using a validated comprehensive computerised cognitive battery. fNIRS data acquisition was done using the NIRScout device, NIRx medical technologies LLC, CA, USA, operating on continuous wave domain, with two wavelengths of 760nm and 850 nm. 8 sources and 8 detectors were placed in a bilateral prefrontal montage placement resulting in 18 channels (9 on each side). Nodal metrics such as clustering coefficient, degree centrality, shortest path length, local efficiency and a Global metric‐ small worldness was evaluated. Correlation coefficients were computed for the nodal and global metrics with cognitive domains such as reaction time, complex attention, memory, language functioning and perceptuomotor functioning.ResultsMean age of participants was 66.18 ± 6.18 years. The mean duration of illness was 2.63 ± 2.5 years. The results of the resting state nodal and global metrics and their correlation with cognitive functioning will be presented during the conference.ConclusionfNIRS is reported to have a temporal resolution that is comparable to fMRI, at the same time being less expensive, portable and having less interference because of motion artifacts. Findings from this study will provide insights on the functional connectivity correlates of cognitive functioning in MCI.