bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of High‐Resolution Diffusion‐MRI in Neurotrauma Accelerated Alzheimer's Disease
Authors: Hua Ning , Minaeva Olga , Parsons Douglas , Moncaster Juliet A , Jara Hernan , Goldstein Lee E 
Published: January 2025

AbstractBackgroundTraumatic brain injury (TBI) is a risk factor for earlier onset of Alzheimer’s disease (AD)( Graves AB, et al., 1990), and the more severe the injury, the greater the risk of developing AD(Johnson VE, et al.,2010). Given the prevalence of AD in modern society, the possibility that TBI may predispose individuals to develop AD has significant social and economic implications. Therefore, it is important to understand how TBI triggers accelerated AD progression. In this study, we explored how neurotrauma accelerated white matter degeneration in a transgenic mouse model of AD using high‐resolution (60mm3) ex vivo diffusion MRI.Method3xTg‐AD mice were pretreated with a non‐sedating dose of the analgesic buprenorphine (0.2mg/kg, i.p.) prior to TBI. 3xTg‐AD mice were subjected to left‐lateral closed‐head impact injury at 10‐12 weeks of age (Tagge CA, et al.,2018). At six months post‐TBI, mice were sacrificed using transcardial perfusion. The harvested brains were submerged in 10% formalin for 24 hours and stored in Gadavist‐doped PBS (1:400 dilution) until MRI. MRI data were acquired using a 9.4T Bruker scanner and a 2‐element cryoprobe. Key parameters are TR=300ms, TE=27.7ms, b=3000 and 5000 s/mm2 (64 directions), FOV=13.92x10.20x7.20mm3, Matrix=232x170x120, resolution=60mm3. Diffusion MRI was analyzed in DSI Studio (http://dsistudio.labsolver.org). Age‐ and gender‐matched 3xTg‐AD mice without TBI were used as controls.ResultFigure 1 shows the representative quantitative anisotropy (QA) maps obtained from a mouse with TBI. At 6 months post‐TBI, decreased QA values were observed in the brain regions ipsilateral to the injury (arrows) compared to the corresponding contralateral regions. Figure 2 shows the representative QA maps of the regions around the hippocampus in mice with and without TBI. Compared to the control mouse, the mouse with neurotrauma showed decreased QA values in the corpus callosum and bilateral hippocampus, especially in the CA1 subregion.ConclusionOur results demonstrated that ultra‐high resolution diffusion MRI can capture subtle alterations of white matter integrity in different brain sub‐regions. Using this technique, we were able to detect subtle white matter degeneration in a mouse model of Alzheimer’s disease. The observed white matter deterioration may contribute to the worsening of Alzheimer’s disease after neurotrauma.
MMSE and CDR Score Changes and Potential Score Inflation in Multinational Alzheimer’s Disease Trials
Authors: Hackebeil Amanda , Barbati Gila , Machizawa Sayaka , Stenclik Jessica , Appleman Erica R. , Iacob Andrei , Massa Jacqueline 
Published: January 2025

AbstractBackgroundIn Alzheimer’s Disease trials, the Mini‐Mental State Examination (MMSE) and Clinical Dementia Rating (CDR) are commonly utilized as inclusionary criteria at screening. These measures, however, do not always reaffirm inclusionary status at baseline. Score changes between screening and baseline visits may imply potential score inflation at screening leading to inappropriate participant enrollment. This study compared score changes in global AD trials when MMSE and CDR scores were used as inclusionary measures at screening only versus screening and baseline visits.We hypothesized greater score changes would be observed in trials utilizing these inclusionary measures at screening alone.MethodThis study incorporated electronic scale (eScale) data from three global Phase 3 trials comprised of MCI to Mild AD participants, where the MMSE and CDR were used as inclusionary criteria. Two studies applied inclusion scoring criteria at screening, while one study applied inclusion scoring criteria at both screening and baseline. All raters completed Signant Health standardized rater training and certification programs. eScales and data quality monitoring programs increased rater scoring accuracy. Mann‐Whitney U tests and t‐tests were conducted comparing score changes on MMSE total score and CDR Domain and Global Scores.ResultBoth Mann‐Whitney U and t‐tests revealed significant differences in score changes between screening and baseline visits for the two trial designs, including all domain and total scale scores, except for the CDR Personal Care domain (p<0.001), as seen in Table 1. Increased scores for the CDR domain/Global scores and reduced scores on MMSE were observed when inclusionary criteria were required at screening alone.ConclusionThe present study found greater score changes in the CDR and MMSE for participants who met inclusion criteria at screening only compared to both screening and baseline. These findings suggest potential score inflation at inclusionary visits when inclusionary scoring is required only at screening. To reduce inclusionary bias and inappropriate participant enrollment, a data quality surveillance program is recommended at inclusionary and subsequent visits throughout the study. Future studies should explore additional factors contributing to score change.
Perfusion and cerebrovascular reactivity characterization in Alzheimer’s disease and vascular dementia
Authors: Keeling Elizabeth G. , McElvogue Molly M. , Ott Lauren R. , Burke Anna D. , Sabbagh Marwan N. , Bakkar Nadine , Stokes Ashley M. 
Published: January 2025

AbstractBackgroundCerebrovascular changes are often reported in normal aging, Alzheimer’s disease (AD), and vascular dementia (VaD). Cerebral perfusion and cerebrovascular reactivity (CVR) both decrease with dementia compared to healthy aging; as these changes occur prior to symptomatic onset and in distinct brain regions, perfusion and CVR may act as complementary biomarkers of early cerebrovascular changes. These biomarkers can be measured using MRI methods, yielding macrovascular measures of perfusion and CVR. We recently developed a more advanced method capable of measuring microvascular‐specific measures of perfusion and CVR. Here, we characterized the cerebrovascular profiles of AD and VaD using complementary perfusion and CVR biomarkers representing both macrovascular and microvascular regimes.MethodMRI data were acquired at 3T (Ingenia, Philips) in three cohorts: non‐cognitively impaired cohort (HC), AD, and VaD (Table 1). Perfusion data were acquired with a multi‐echo, multi‐contrast (SAGE) acquisition (5 echoes, 7.7/26/56/74/92 ms), before, during, and after injection of gadolinium‐based contrast agent. Acquisition parameters include: repetition time (TR) = 1.5 s, voxel size = 2.75×2.75×5 mm, 200 volumes, acquisition time = 5 min. SAGE functional MRI (fMRI) data were acquired in the same cohort with the same TEs and the following acquisition parameters: TR=3.0 s, voxel size = 3 mm3, 160 volumes, acquisition time = 8 min. SAGE data underwent standard pre‐processing. Macro‐ and microvascular cerebral blood flow (CBF), relative cerebral blood volume (rCBV), and relative CVR (rCVR) were calculated using advanced analysis pipelines. The Montreal Cognitive Assessment (MoCA) was administered prior to MRI acquisition.ResultAs expected, microvascular perfusion and rCVR were lower than the corresponding macrovascular metrics for all groups. Macrovascular and microvascular CBF were lower for AD and VaD compared to HC, while there was no difference in CBF between AD and VaD (Figures 1,2). Similar trends were observed for rCBV. There were no differences in macrovascular rCVR between groups; microvascular rCVR was lower for AD and VaD compared to controls.ConclusionMacrovascular and microvascular perfusion decreases with AD and VaD. Enrollment is ongoing, and future directions include analysis of perfusion metrics within cortical and subcortical regions and correlation of neuroimaging findings with cognitive testing.
Sleep Biomarker‐Based Characterization of Neurodegenerative Disorder Phenotypical Risk
Authors: Levendowski Daniel J , Walsh Christine M , Cho Yeilim , Tsuang Debby W , Lee‐Iannotti Joyce , Berka Chris , Boeve Brad , Neylan Thomas C. , St. Louis Erik K 
Published: January 2025

AbstractBackgroundIn this pilot study, the diagnostic agreement for sleep biomarker‐based neurodegenerative disorder (NDD) risk probabilities was evaluated in patients with Alzheimer’s disease dementia (AD), Lewy body disease (LBD), mild cognitive impairment (MCI), and controls (CG) with a Mini‐Mental State Exam scores ≥28.MethodsSleep biomarkers recorded with the Sleep Profiler (Advanced Brain Monitoring, Inc.) were used as inputs to a 4‐class machine learning algorithm trained to assign NDD risk probabilities to AD=27, LBD=19, isolated REM sleep behavior disorder=15, and CG=58. Input variables included age, time‐REM, non‐REM hypertonia, autonomic‐activation index, spindle‐duration, atypical‐N3, time‐supine, sleep‐efficiency, relative‐theta, and theta/alpha. Distributions of NDD risk were then assigned to AD=37, LBD=19, MCI=71, and CG=106 records.Records with CG output probabilities ≥70% were labeled Probably‐Normal, and from 45‐70% Likely‐Normal with indications of a NDD added when the LBD, AD or prodromal synucleinopathy (pSYN) risks exceeded approximately 25%. Similar thresholds were used to assign Likely‐ or Probably‐ LBD, AD, pSYN, or Mixed based on the group probabilities. Likely‐Normal with indications consistent with the diagnosis trended toward agreement.ResultsThe proportions of each group with classifications consistent with the diagnosis or trended toward agreement were CG=86%, MCI=75%, LBD=79%, and AD=87%.In the CG group, NDD risk assignments were Probably‐Normal=68% and Likely‐Normal with NDD indications=18%, Likely‐NDD=10%, and Probable‐NDD=4%.The MCI group was assigned Probable‐NDD=38% (AD=26%, Mixed=10%, LBD/pSYN=2%), Likely‐NDD=17% (AD=7%, Mixed=6%, LBD=3%, pSYN=1%), Likely‐Normal with indications=20% (AD=9%, pSYN=7%, Mixed=4%) and Probably‐Normal=25%The LBD group’s assigned risks consistent with the diagnoses included Probable‐NDD=74% and Likely‐Normal with pSYN indications=6% but included inconsistencies from Probable‐AD=5%, Likely‐Normal with AD indications=5%, and Probably‐Normal=11%.AD group’s consistent risk classifications included Probable‐NDD=60%, Likely‐NDD=11%, Likely‐Normal with AD/Mixed indications=13% and countered by inconsistencies from Probable‐LBD/pSYN=8%, Likely‐Normal with pSYN indications=3%, and Probably‐Normal=5%.ConclusionsConsistencies between the diagnoses and assigned NDD risks agreements exceeded 75% in all four groups. Studies are underway to confirm the test‐retest reliability and/or evaluate NDD risk probabilities in other patient populations.Support: NIH grants R44AG050326, R44AG054256, RO1AG060477, R33AG064271, ULRR024150, R34AG056639.
Tear Biomarkers as Indicators of Alzheimer's Disease
Authors: Takyar Anshule , Bailey Maisie , Trantow Richard , Coronel Danilo A Sanchez , Garcia Hector Hugo , Gilman Robert H , Diaz Monica M 
Published: January 2025

AbstractBackgroundThe prevalence of Alzheimer’s disease (AD) is increasing worldwide, particularly in low‐ to middle‐income countries (LMICs). Resource limitations and time constraints in many LMICs make AD screening and diagnosis difficult in the clinical setting. Neurodegenerative biomarkers in human tears may be associated with neurodegenerative diseases, but its potential has yet to be investigated in AD. Amyloid‐beta, tau, α‐synuclein, and neurofilament light (NfL) levels in tears were compared in patients with AD (AD+) and age‐matched negative controls (AD‐).MethodsSamples were collected from a cohort of AD+ (N=26) and age‐matched AD‐ (N=9) individuals at a public neurological hospital in Lima, Peru. Tears were collected on Schirmer strips, which were dried and frozen. Proteins were then extracted into a PBS‐based buffer and tested for biomarkers using commercial ELISA kits, Luminex assays, and digital ELISA methods from Quanterix and NanoMosaic. Biomarker burdens were compared between AD+ and AD‐ patients using digital ELISA results. Amyloid‐beta 40, Amyloid‐beta 42, total tau, phosphorylated‐tau181 (pTau181), α‐synuclein, and NfL burdens were evaluated using various ELISA methods, and Human TIMP‐1 was used as a control.ResultsInitial analyses using commercial ELISA and Luminex methods proved inconclusive. NfL burden was evaluated using the Quanterix digital ELISA platform showed a detectable protein burden in 3 AD‐ samples and 1 AD+ samples, but remained statistically insignificant (p = 0.45). A custom digital ELISA assay on the NanoMosaic platform showed AD+ status was associated with elevated Total Tau (p = 0.0023) and pTau181 (p = 0.0016) burden in tear proteome as compared to AD‐ samples, shown in Figures 1 and 2.ConclusionsDue to considerably lower limits of detection, the Quanterix and custom NanoMosaic assays allowed for increased sensitivity of biomarker detection in human tears. Our results indicate a significant association between AD+ status and elevated Total Tau and pTau181 levels in tears, and suggest the potential for tears to be pursued as a potential non‐invasive and accessible addition to AD screening and diagnosis, particularly for LMICs.
Synergistic effects of plasma S100b levels and MRI‐based water exchange rate across the blood‐brain‐barrier on memory performance among older adults
Authors: Pappas Colleen , Zachariou Valentinos , Bauer Christopher E. , Sudduth Tiffany L. , Wilcock Donna M. , Jicha Gregory A , Hartz Anika MS , Shao Xingfeng , Wang Danny JJ , Gold Brian T 
Published: January 2025

AbstractBackgroundNon‐invasive biofluid and MRI measures of blood‐brain‐barrier (BBB) dysfunction may aid early detection of cerebral small vessel disease (cSVD). Plasma markers of astrocytic function and injury, such as S100 calcium‐binding protein B (S100b), have gained increased attention in relation to BBB integrity and cognition. Here we explored the inter‐relationships between plasma S100b levels, an MRI measure of water exchange rate across the BBB (kw), and cognitive performance among older adults.MethodThe participant sample consisted of 74 older adults without dementia recruited from the University of Kentucky Sanders Brown Center on Aging. Relationships between S100b and cognition (memory, executive function) and MRI‐based BBB water exchange rate were tested. Plasma S100b levels (pg/mL) were measured using Meso Scale Discovery R‐PLEX assay at the University of Kentucky’s CCTS Biomarker Analysis Lab. Composite scores were created for memory and executive function. A diffusion‐prepared arterial spin labeling (DP‐ASL) MRI sequence was used to estimate water exchange rate across the BBB (expressed as kw). All data (S100b, cognition, MRI) were collected within 1 year of each other. Multiple linear regression models examined the impact of plasma S100b on memory, executive function, and kw. Covariates included age, gender, and education (for cognition models only). Additionally, kw was tested as moderator of the S100b‐cognition relationships using the PROCESS macro.ResultA negative relationship was observed between S100b and memory, where higher S100b levels were associated with poorer memory performance. A similar relationship was not observed with executive function. S100b was also not associated with kw. However, there was an interaction between S100b levels and kw in the parietal lobe on memory performance such that participants with both lower parietal kw and higher S100b showed the poorest memory performance.ConclusionOur results indicate that S100b levels are negatively associated with memory performance, but not MRI‐based BBB kw. However, higher S100b levels coupled with lower MRI‐based water exchange rate further contributed to the strong negative effects observed for memory performance. This suggests that plasma S100b and BBB kw may be different proxies of BBB function and may have synergistic negative effects on cognition.
Detection of Cognitive Impairment through Spontaneous Speech Acoustic Markers Using Transformer‐based Machine Learning Approach
Authors: Mobtahej Pooyan , Gouron Sam , El‐Khoury Annelisse , Leiby Anne‐Marie C , Kawas Claudia H. , Corrada María M. M. , Sajjadi S. Ahmad 
Published: January 2025

AbstractBackgroundThe increasing prevalence of cognitive impairment and dementia threatens global health, necessitating the development of accessible tools for detection of cognitive impairment. This study explores using a transformer‐based approach to detect cognitive impairment using acoustic markers of spontaneous speech.MethodRecordings of unstructured interviews from baseline visits were obtained from participants of The 90+ Study, a longitudinal study of individuals older than 90 years. For this analysis, participants without dementia at baseline who had stable cognitive diagnoses over the subsequent two assessments were included. Of 120 total participants (Table 1), 78 had normal cognition (NC) and 42 had cognitive impairment with no dementia (CIND). From baseline interviews, 1,273 segmented 20‐second denoised audio samples were generated. Samples from 80% of participants trained our models while the remaining 20%, unseen data, were test data. After filtering from Mel Spectrogram, Mel‐frequency cepstral coefficients (MFCC), spectral centroid (SC), and Chroma representations, 154 acoustic features were extracted. Features were transformed from 2D images into 1D string representations for interpretation by transformer‐based language models, namely Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre‐Trained Transformer‐2 (GPT‐2). Transformer‐generated feature embeddings were inputted into Random Forest (RF) and Logistic Regression (LR) models. Soft voting ensemble approach combined classifier predictions to maximize prediction power for final classification (Figure 1). To reduce the risk of overfitting, 5‐fold cross‐validation determined evaluation metric averages for baseline models, including random forest and logistic regression.ResultOur test data comprised 142, 20‐second samples from 16 NC and 113, 20‐second samples from 9 CIND. Our model achieved 100% AUC with either BERT or GPT‐2 feature embeddings. We also investigated F1 scores: BERT reached 99.56% and GPT‐2 achieved 100% (Table 2).ConclusionOur approach converts acoustic features from 2D images into 1D patterned value representations interpretable by transformer‐based language models. This renders acoustic markers suitable for detection of cognitive impairment based on language model interpretation. Both GPT‐2 and BERT models outperformed baseline models. The model will be validated with external data and increasing sample size in the next step.
Untargeted metabolomics tools to inform diet and drug exposures of cognitively declined cohorts
Authors: Zhao Haoqi Nina , Mohanty Ipsita , Kaddurah‐Daouk Rima F. ,   Alzheimer's Gut Microbiome Project Consortium (AGMP), Dorrestein Pieter 
Published: January 2025

AbstractBackgroundIt is now widely acknowledged that diet, lifestyle, and environmental exposures largely affect an individual’s metabolic state in health and disease, including the brain. Metabolomics has demonstrated its potential to enable exciting discoveries in brain health, facilitated by advances in analytical and informatics techniques. Here, we highlighted the use of MS/MS‐based untargeted metabolomics to study the diet and medication exposure of cognitively declined cohorts through the newly developed FoodMASST and DrugMASST tools.MethodsA food metabolomics reference dataset was created with tandem mass spectrometry data (MS/MS) alongwith detailed and structured metadata for ∼3,500 foods. We used this resource to trace the food origin of important Alzheimer’s disease metabolite biomarkers as well as to investigate the empirical diet patterns of subjects in a Mediterranean diet intervention study (MIND). An MS/MS spectral library for drugs, metabolites, and related molecules was developed by spectral similarity search against ∼1.2 billion public metabolomics spectra. We utilized this resource to perform empirical medication read‐out in 501 brain samples from the Rush Religious Orders and Memory and Aging Project (ROS/MAP) and 637 fecal samples from the Aging and Disability Resource Centers (ADRCs).ResultsWe demonstrated the utility of the FoodMASST and DrugMASST tools through several examples. Using FoodMASST we demonstrated that lentil is an important food source of tryptophan betaine, an important AD marker. FoodMASST‐based diet score separated the metabolomics profiles of MIND subjects in the PCoA cohort. DrugMASST detected 102 drugs in brain samples from the ROS/MAP and revealed that neurology/psychiatry and cardiology drugs were the most important categories. The computationally derived drug metabolite library enabled additional annotation of 15 drug metabolites with high confidence. We observed significant separation in the metabolome of patients with and without antibiotic detection. In the ADRC fecal samples, we detected 190 drugs and the neurology/psychiatry and cardiology categories had the highest number of drugs observed. Carbidopa, a dopamine promoter drug given to treat PD, was detected in 62% of the patients.ConclusionsUntargeted metabolomics tools, such as metadata‐driven metabolomics exemplified by FoodMASST and DrugMASST, create insights into patients' chemical exposure and their role in gut microbiome and Alzheimer’s disease.
Inflammatory role of toll‐like receptors in the hippocampus of 3xTg‐AD animals
Authors: Júnior Carlos Wagner Leal Cordeiro , De‐Paula Vanessa V.J. , de Oliveira Caíque , Couto Portugal 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disease with multifactorial etiology. The toxicity of senile plaques and neurofibrillary tangles is associated with changes in the clearance of tau and beta‐amyloid proteins, which are closely related to inflammatory imbalances. Pro‐inflammatory cytokines are present in abnormal details due to the overactivation of immunological pathways, especially toll‐like receptors. Our objective is to determine the difference between the proteins altered in AD and the inflammatory role of toll‐like receptors in the hippocampus of 3xTg‐AD animals chronically treated with lithium at therapeutic and subtherapeutic doses and how lithium acts in this regulation.Method48 mice (3xTg‐AD;Tg) were treated with lithium for 3 months and divided into 3 groups (control, 1mM Li and 2mM Li). Hippocampal tissue was subjected to proteome. The inflammatory pathways of the identified proteins were compared by the STRING software and the molecular functions were analyzed by Gene Ontology, using the ClueGO software. The Toll‐Likes receptor family genes were analyzed together with Tau and amyloid beta proteins in the STRING bioinformatics program, in order to study the ontological pathways and molecular function. As parameters, we set significance at 0.7 with a limitation of 50 interactions per gene. The project was submitted to the Ethics Committee under protocol (1876/2022).ResultThe genes of the Toll‐Like receptor family were analyzed using the STRING bioinformatics program to study ontological pathways and molecular function. As parameters, we set significance at 0.7 with a limitation of 50 interactions per gene. Thus, the results demonstrate that ontological changes include: signal transduction activating the immune response, activation of the immune response, regulation of defense response, positive regulation of the immune response, regulation of the stress response, immune response, positive regulation of the response to stimulus, and the process of the immune system.ConclusionThe results appear promising for understanding the inflammatory pathways related to toll‐like receptors. Since the identification of positively regulated immune responses suggests that lithium, considering its modulating effect on both proteins involved in neurodegeneration and associated inflammatory processes. However, we emphasize the need for additional clinical studies to validate and extend these findings in animal models.