bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using image‐text similarity scores from picture descriptions for differentiating the logopenic variant of progressive aphasia from amnestic Alzheimer’s dementia
Authors: Pugalenthi Lokesha , Dial Heather R , Li J. Jessy , Schmitz Suzanne , Henry Maya L. , Wauters Lisa , Keller Jeffrey N. , Largent Avery , Chang Patrick , Chang Joshua , Foil Heather , Lester‐Smith Rosemary A , Toprac Paul , Hilsabeck Robin C. 
Published: January 2025

AbstractBackgroundPrimary progressive aphasia (PPA) is a language‐led dementia associated with underlying Alzheimer’s disease (AD) or frontotemporal lobar degeneration pathology. As part of the Alzheimer’s spectrum, logopenic (lv) PPA may be particularly difficult to distinguish from amnestic AD, due to overlapping clinical features. Analysis of linguistic and acoustic variables derived from connected speech has shown promise as a diagnostic tool for differentiating dementia subtypes. Here we investigate the utility of a machine learning (ML)‐based classifier for differentiating lvPPA from amnestic AD using an image‐text similarity metric derived from a picture description task.MethodMonolingual English speakers diagnosed with lvPPA (n = 18) and amnestic AD (n = 14) were asked to describe a picture (Cat Rescue). A similarity score between the image and patient’s transcription was derived with each model of the Vision‐Language encoder CLIP. This involved computing the cosine similarity between the image‐ and transcription‐based representation vectors. These image‐text similarity scores were fed into 10 ML classification algorithms to differentiate lvPPA from amnestic AD.ResultFor differentiating lvPPA from amnestic AD, our CLIP‐based feedforward neural network classifier achieved F1, AUC, and Accuracy scores of 81%, 80%, and 81% respectively. This outperformed our baselines of stratified random sampling and uniform random sampling by 30%.ConclusionThe current study shows promising differentiation of lvPPA from amnestic AD through the single feature of image‐text similarity. This approach holds potential as an automated connected speech analysis tool for differential diagnosis of dementia subtypes. Future directions include evaluating performance in a larger dataset and comparing the classification performance between image‐text similarity and acoustic‐linguistic features.
The ARTS Marker of Arteriolosclerosis: Instrumental and Clinical Validation
Authors: Arfanakis Konstantions , Evia Arnold M , Leurgans Sue E. , Fornage Myriam , Lu Hanzhang , Arvind Caprihan , Decarli Charles , Gold Brian T , Maillard Pauline , Satizabal Claudia L , Stables Lara , Wang Danny JJ , Helmer Karl G , Fischl Bruce , Singh Herpreet , Schwab Kristin , Greenberg Steven M. , Schneider Julie A. 
Published: January 2025

AbstractBackgroundThe ARTS biomarker is a fully automated software container that predicts the presence of arteriolosclerosis based on in‐vivo MRI data and demographic features. The present study describes findings from the instrumental and clinical validation of ARTS conducted by the MarkVCID consortium.MethodInstrumental validation of ARTS involved assessment of inter‐rater reliability, test‐retest repeatability, and inter‐scanner reproducibility. Inter‐rater reliability was assessed by means of the intraclass correlation (ICC) between ARTS scores generated from all MarkVCID sites using data on 20 older adults. Test‐retest repeatability was assessed by means of the ICC between test and retest ARTS scores from 41 older adults imaged twice. Inter‐scanner reproducibility was assessed by means of the ICC between ARTS scores on 20 older adults imaged on 4 different scanners. Clinical validation of ARTS included two groups of non‐demented older adults: N = 156 participating in community cohort studies at Rush Alzheimer’s Disease Center; and N = 906 participating in the Atherosclerosis Risk in Communities (ARIC) study. The primary prespecified hypothesis was that change in Trails B score (or perceptual speed score for Rush data) two years after baseline MRI is associated with baseline ARTS score, controlling for years of education. The secondary prespecified hypothesis was that baseline Trails B (or perceptual speed) is associated with baseline ARTS score, controlling for years of education.ResultsARTS exhibited excellent inter‐rater reliability (ICC = 0.9999, p<10‐200, CI:[0.99987, 0.99997]), test‐retest repeatability (ICC = 0.996, p<10‐42, CI:[0.993, 0.998]), and inter‐scanner reproducibility (ICC = 0.955, p = 0.00018, CI:[0.622,0.989]). In Rush data, a higher baseline ARTS score was associated with a faster two‐year decline in perceptual speed (beta = ‐0.32, p = 0.04, CI:[‐0.63, ‐0.01]) (Fig.1A) and a lower baseline level of perceptual speed (beta = ‐1.04, p<10‐4, CI:[‐1.48, ‐0.60]) (Fig.1B). In ARIC data, a higher baseline ARTS score was associated with a faster annual increase in Trails B (beta = 3.7, SE = 1.7, p = 0.03) (Fig.2A), and a higher baseline Trails B (beta = 45.5, SE = 5.8, p<10‐4) (Fig.2B).ConclusionARTS exhibited outstanding technical performance and baseline ARTS score was associated with subsequent decline in cognitive abilities known to be affected by small vessel disease. The above evidence supports ARTS as a marker of the risk of vascular contributions to cognitive impairment and dementia (VCID).
Association between education and multimodal neuroimaging in men and women across the lifespan
Authors: Gonneaud Julie , TURPIN Anne‐Laure , Vivien Denis , la Sayette Vincent De , Chételat Gael 
Published: January 2025

AbstractBackgroundEducation has been associated with reserve mechanisms and lower dementia risk, but the literature shows inconsistent results on the association between education and brain outcomes across the lifespan. Considering that both dementia risk and education are likely to differ between sexes, our study aims at understanding the association between education and brain outcomes across the lifespan and whether it differs by sex.MethodIn 207 healthy individuals (110women) aged 19‐84 years old (47.98±18.75), we investigated the association between years of education and multimodal neuroimaging (structural‐MRI, FDG‐PET, Florbetapir‐PET) and how this association was modulated by age and sex. Analyses were restricted to regions involved in Alzheimer’s disease and/or reserve mechanisms (hippocampal volume, temporoparietal metabolism, neocortical amyloid, anterior and posterior cingulate cortices [ACC/PCC] for each modality).ResultThere was no main effect of years of education on neuroimaging (ps>.09) nor interaction between education and the other variables. However, exploratory interactions conducted within young (18‐40), middle‐aged (40‐60) and older (60+yo) groups separately showed that, only in the older group, sex interacted with education on ACC volume, revealing that education was associated with greater volume only in older women (Figure; pinteraction = .006). Complementary analyses conducted in an independent and larger sample of older adults (n = 135, 83women, >65yo) in whom lifestyle was assessed retrospectively, suggest that midlife and late‐late‐life mental engagement, more than early‐life engagement (reflecting mainly education), are related to greater brain outcomes in late‐life (ACC volume, PCC amyloid burden, ACC metabolism). We assessed the moderative effect of sex on these associations and found that the association between grey matter volume and occupation (midlife engagement) was mainly driven by women (pinteraction = .02). The interaction between early‐life engagement and sex on ACC volume highlighted in the main cohort was replicated, yet at a trend‐level (pinteraction = .08).ConclusionOur study suggests that education is not strongly associated with age‐related differences in structure, metabolism or amyloid burden. Older women with higher levels of education, however, showed higher ACC volumes. These results suggest that education could promote brain reserve in late life in a sex‐dependent manner. Future studies should investigate the mechanisms behind these differences in brain reserve.
Improving Memory and Cognition by Enhancing Mitochondrial function and Biogenesis
Authors: Nour Dalia , Mostafa Mai , Ismail Fatma , Hanafi Rasha , AbdelKader Reham M. 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) treatment remains to be a challenge; this is due to the fact that the pathogenesis of the disease is very complicated and not well understood. Unquestionable is the fact that the pathological hallmarks of AD are accumulation of amyloid plaques, neurofibrillary tangles and mitochondrial dysfunction all leading to neuronal cell death. Mitochondrial dysfunction plays an important role in AD. Previous studies demonstrated reduced mitochondria number in hippocampal neurons in brains from AD patients, emphasizing the role of mitochondrial biogenesis in AD. They showed significant changes in proteins levels in AD brain tissues compared to controls, demonstrating reduced mitochondrial biogenesis leading to mitochondrial dysfunction. TIn this study our aim was test natural isolated compounds (Licochalcone(LicoA) and pyrroloquinoline quinone(PQQ)) with potential effect on mitochondrial biogenesis on memory and cognition in a neuroinflammatory mouse model.MethodA new GC/MS analysis method was developed and validated for detection of PQQ and LicoA in brain tissue. Mice were treated with either PQQ or LicoA in a neuroinflammatory mouse model. Memory was tested using object recognition and Y‐Maze tests. Brains were extracted from treated mice and levels of PQQ and LicoA were determined. Furthermore mRNA levels and proteins involved in mitochondrial biogenesis and function were determined using RT‐qPCR and ELISA.ResultFor the first time we were able to develop a method for determination of PQQ and LicoA in brain tissue of mice. Interstingly both PQQ and LicoA were found to cross the blood brain barrier. Both PQQ and LicoA enhanced memory and cognition. Moreover, both PQQ an and LicoA increased the mRNA levels and the levels of proteins involved in the mitochondrial biogenesis pathway such as PGC‐1α, NRF‐1, ND1 and ATP levels.ConclusionIsolated natural phenolic compounds such as PQQ and LicoA levels were determined in brain tissues of mice after peripheral adminstration. These compounds enhanced memory and cognitive function and thier mechanism of action involves enhancing mitochondrial biogenesis and function. Therefore, enhancing mitochondrial biogenesis may represent a new era for treatment of AD.
Exploring sex‐specific accelerated hippocampal atrophy across AD stages beyond APOE‐ε4 assessment in genetic risk evaluation
Authors: Evans Tavia E , Vilor‐Tejedor Natalia , Rodrigo Albert , Genius Patricia , Rodríguez‐Fernández Blanca , Anastasi Federica , Navarro Arcadi , Adams Hieab H.H. , Gispert Juan Domingo 
Published: January 2025

AbstractBackgroundCurrent evidence suggests that hippocampal subfields have partially different genetic architecture and may improve the sensitivity of the detection of Alzheimer’s disease (AD). In this study, we investigated whether genetic predisposition to AD contributes to the accelerated rate of hippocampal volume atrophy across sex and AD stages and how this contribution is specifically driven by functional variants located in the APOE gene.MethodsThe study comprised 1,051 participants from ADNI cohort (75.2 yo; 42.3% women), with complete demographic, genetic, and magnetic resonance imaging scans at baseline and every 6 months for a total of 96 months (Controls (amyloid negative), CUN = 363; mild cognitive impairment, MCIN = 474, AD patients, ADN = 214). Hippocampal subfields were extracted using the longitudinal processing method within FreeSurfer (v6.0). Genetic predisposition to AD was assessed through polygenic scoring with and without considering the APOE región. Effects of polygenicity of plasma APOE, functional APOE brain eQTLs and CSF pQTL were also considered. Linear mixed‐effect models with random‐ time slope and intercept for individuals were used to investigate the association between genetic predisposition to AD and hippocampal subfields volumetric trajectories over time. Models were adjusted by age, sex, AD disease status, years of education and total hippocampal volume. Disease and sex status‐specific trajectories dependent on genetics predisposition were assessed by including an interaction term.ResultsSignificant hippocampal subfield volume reductions were observed, with more pronounced atrophy in MCI and AD participants [Figure 1A]. High genetic predisposition to AD was associated with accelerated atrophy in various subfields [Figure 1B]. MCI individuals with high genetic predisposition showed more severe atrophy in several subfields, with sex‐related differences observed, particularly in women [Figure 2]. Notably, these effects were non‐significant when excluding APOE variants. Further functional analyses pinpointed CSF pQTLS of the APOE gene and APOE‐e4 carriership status as primary drivers [Figure 3].ConclusionsOur findings underscore the significance of genetic predisposition, especially functional APOE variants, in understanding AD progression and sex‐specific trajectories. The results reveal varying atrophy patterns across disease stages, offering crucial insights for refining AD detection and tailoring interventions for more effective management.
Predicting Individual’s Cognitive Performance Through Multi‐Omics Blood Data Using Hierarchical Input Neural Network ‐ HINN
Authors: Vashishath Yashu , Beaver Sarah , Bozdag Serdar , Saeed Fahad 
Published: January 2025

AbstractIntroductionDetecting declines in cognitive function is a critical global health concern, highlighting the need for timely identification to implement effective intervention strategies. This study investigates the potential of blood‐based biomarkers as accurate and non‐invasive measures of cognitive function. We developed a novel deep learning architecture that integrates multi‐omics data by considering their relationship. Specifically, we integrated single nucleotide polymorphisms (SNPs), gene expression, and DNA methylation data to predict cognitive test scores.MethodsA novel computational architecture called Hierarchical Input Neural Network (HINN) (Figure 1) was developed to integrate multi‐omics datasets. In this study, HINN had three input layers receiving SNPs, DNA Methylation, and gene expression data, respectively. First, we conducted multiple GWAS runs to identify significant SNPs associated with cognitive scores based on different cognitive tests and found 373 SNPs across these runs. We identified 13037 DNA methylation sites proximal to these SNPs and 455 genes associated with these probes.ResultsWe observed that MAE of the HINN model for MMSE, MoCA, ADAS11, and RAVLT‐I was 2.28, 2.98, 4.54, and 8.88, respectively. Similarly, MSE of the same model for MMSE, MoCA, ADAS11, and RAVLT‐I was 10.41, 17.16, 45.30, and 135.59, respectively. We compared our model with the other baseline models, namely L1‐regularized, support vector machine, random forest, and deep neural network. We also compared HINN with the pathway guided deep neural network in which all omics data were fed to single layer, which was connected based on biological processes relationship. The HINN model showed improvement of 2.98% to 9.81% for MAE and 3.99% to 12.3% for MSE in comparison to all other models (Figure 2).ConclusionThe findings derived from the HINN model highlight its efficacy in detecting novel biomarkers for cognitive assessment. Through the hierarchical input approach, we outperformed baseline methods to predict cognitive score. Notably, our method is characterized by its independence from assumptions and subjective scoring, ensuring the robustness and reliability of predictions across diverse cognitive measures. The versatility of our model, capable of predicting multiple cognitive metrics, amplifies its utility and confidence in biological studies targeting cognitive assessment.
Development, Validation, and Clinical Utility of the Janssen plasma p‐tau 217+tau Simoa assay
Authors: Triana‐Baltzer Gallen 
Published: January 2025

AbstractBackgroundOver the last two decades the Alzheimer’s biomarker field has progressed dramatically, beginning with the identification of specific analytes in CSF that track with autopsy‐confirmed AD, progressing to development of specific PET tracers and CSF assays to monitor brain amyloid and tau burden up to 20 years before symptoms, and now delivery of highly accurate, sensitive, and precise assays for measuring specific Abeta and pTau species in plasma.MethodHere we present on the development, validation, and clinical utility of Janssen’s plasma p217+tau Simoa assay. The assay was initially created for CSF in 2017 and used as a pharmacodynamic marker in the Ph1 posdinemab trials, demonstrating substantial dose dependent reduction in CSF p217+tau. Translation of the assay to plasma required several key modifications, to enable quantification of the much lower levels in the periphery (∼1% of CSF levels) and avoid substantial negative interference in plasma (Triana‐Baltzer 2021).ResultOver the past four years the Janssen plasma p217+tau assay has been evaluated in >10 large clinical cohorts, where it has consistently demonstrated high accuracy in MCI/AD populations for identifying brain amyloid or tau status (by CSF or PET; AUCs 0.85‐0.95; e.g. Dore 2022) as well as cognitive decline (Fig. 1, Feizpour 2023; Saloner 2023). Cutoff concentrations and specificity/sensitivity parameters appear conserved when transitioning from symptomatic to cognitively unimpaired populations (Dore 2022), however more work is needed to confirm this. Preliminary data indicates age, creatinine, BMI, sex and/or race may impact p217 concentrations (Fig. 2, Pichet‐Binette 2023), however more work is needed.In late 2023 the assay was validated at Quanterix as a Lab Developed Test (LDT) in CAP/CLIA environment for global diagnostic availability. This test, marketed as Lucent AD p217, was evaluated in two large cohorts (n = 352 and 521, MCI/AD) against amyloid status (PET or CSF). The assay captured 100% of samples in the linear range and employs a 2‐cutoff approach for identifying subjects with low/intermediate/high risk of amyloid positivity (ala Brum 2023), with observed 90.3% sensitivity, 91.3% specificity for identifying low and high‐risk groupsConclusionTo accelerate clinical trials the assay is currently being used as a prescreening tool in multiple Janssen‐sponsored Ph2 trials.
Neurodegeneration and acute neuronal injury drastically skew the p‐tau217/non‐p217 ratio in both CSF and plasma regardless of analytical technique – a mass spectrometry and Simoa study
Authors: Blennow Kaj 
Published: January 2025

AbstractBackgroundPlasma p‐tau217 shows high promise as an AD biomarker. In some mass spectrometry (MS) studies, the ratio between p‐tau217 and the corresponding non‐phospho peptide tau212‐221 (p‐tau217/T217) was suggested to increase accuracy to detect AD‐type tau phosphorylation. However, disorders with rapid neurodegeneration or acute neuronal injury, are known to have increased neurodegeneration biomarkers e.g., total tau [T‐tau] and brain‐derived tau [BD‐tau]. Since both T‐tau and BD‐tau contain the T217 peptide sequence, it is likely that T217 also would increase, and the ratio thus be affected, but CSF/plasma p‐tau217/T217 has not been studied in these conditions.MethodCSF and plasma p‐tau217, T217 and p‐tau217/T217 measured by Simoa and/or MS; T‐tau and BD‐tau by Simoa. Neurodegeneration cohorts (n>300) included CSF and plasma from controls, AD and sporadic Creutzfeldt‐Jakob disease (CJD). Plasma samples from acute neuronal injury cohorts (n>500) included traumatic brain injury (TBI) and cardiac arrest with brain hypoxia (Simoa analyses) plus ischemic stroke (MS and Simoa).ResultBoth p‐tau217 and the p‐tau217/T217 ratio markedly increased in AD, in CSF around 400% and in plasma around 280% of mean control levels. All neurodegeneration biomarkers increased dramatically in CJD (e.g. 1400% for CSF T217 by MS) and with acute brain injury (e.g. 600% increase in TBI for plasma BD‐tau). Changes were similar when measured by MS or Simoa, and CSF T217 by MS also correlated tightly with T‐tau by Lumipulse (r = 0.99). Consequently, in both CSF and plasma, the p‐tau217/T217 ratio was drastically skewed in rapidly progressive dementias (e.g., 16% of control levels in CSF in CJD) and in acute brain conditions, with marked reductions in p‐tau217 ratios in CSF and plasma, regardless of analytical technique.ConclusionNon‐phosphorylated tau variants (T‐tau, BD‐tau and T217) all markedly increase in dementias with rapid neurodegeneration and with acute brain injury and thereby drastically skew the p‐tau217/T217 ratio. This will be an important consideration for applications in clinical routine where patients with mixed pathologies are common. Therefore, plasma p‐tau217 alone may be preferred, as also supported by recent studies (Montoliu‐Gaya, Acta Neuropathol 2024;147:5) showing similar performance to identify AD for plasma p‐tau217/T217 and p‐tau217 by itself.
Computational Modeling and Simulation of the Cerebrospinal Flow and Drug Delivery
Authors: Montoya Gustavo , Teli Jaykumar Vishwanath , Kadamus Christopher , Lin Kathleen , Eddy Kaitlyn , Curry Eli , Blankenship Keith , Anumba Nmachi , Sharma Rakesh , Sharma Vijay Kumar , Ghosh Ujjal Bhanu , R Prasanth , Corrales Carlos , Sheikh Imran , Mariano Dean , DeMaria Christopher , Roy Kaushambi , Sirkar Rhea , Katuin Joseph , Subramony Anand 
Published: January 2025

AbstractBackgroundThe spinal intrathecal space, characterized by a complex three‐dimensional (3D) fluid‐filled geometry with varying levels of anatomic intricacy, plays a pivotal role in drug administration strategies targeting the central nervous system (CNS). Lumbar injections into this space represent a clinically used approach for delivering therapeutics directly to the brain, bypassing critical barriers such as the blood‐brain and blood‐cerebrospinal fluid barriers. A nuanced understanding of CSF dynamics is vital for comprehending physiological states of the CNS for drug delivery.MethodThe CSF’s dynamic nature enables the dispersion of solutes throughout the brain and ventricular system. In order to optimize CSF drug delivery strategies, a profound comprehension of the intrathecal anatomy coupled with insights into the physiology of CSF generation, absorption, and flow, is imperative. The application of quantitative modeling tools, such as Computational Fluid Dynamics (CFD), yields critical insights into the impact of CSF fluid dynamics on drug dispersion and efficiency. CFD modeling provides a granular analysis of CSF flow field, surpassing the capabilities of Magnetic Resonance Imaging (MRI) or invasive methodologies.ResultEli Lilly’s initiative in developing a CSF model aimed to predict drug distribution patterns mediated by cerebrospinal flow within the intrathecal space. This model sought to simulate prototypical CSF flow to ascertain the time required for a specified drug mass to reach the brain following lumbar intrathecal injection. Additionally, it aimed to assess the subsequent distribution of the drug within the brain, across a diverse patient demographic. This approach is instrumental in fine‐tuning drug delivery protocols, customizing delivery methods to particular drugs, avoiding off‐target drug effects, and ensuring maximum efficiency and efficacy for direct CNS treatments.ConclusionThrough this computational methodology, we have demonstrated the feasibility of obtaining validation data for the CFD simulations using several imaging methodologies and techniques. Furthermore, we have been able to build these complex models and use them to inform in vivo studies. We have also been able to demonstrate that it is possible to build comparable NHP and human models from the fluid dynamics perspective.