bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gray matter microstructural changes due to cognitive aging in healthy cohort
Authors: Singh Kavita , Barsoum Stephanie , Schilling Kurt , An Yang , Ferrucci Luigi , Benjamini Dan 
Published: January 2025

AbstractBackgroundCognitive impairment with age remains undetected until it interferes daily life activity or presents dementia symptoms. In the US, 61% of dementia population is not diagnosed, which is in part due to limited sensitivity of clinical neuroimaging modalities in assessing early gray matter (GM) changes. Here we look at microstructural changes in GM using mean apparent propagator (MAP‐MRI) in cognitively underperforming (CU) and healthy aging (HA) cohorts, grouped according to their cognitive performance based on the NIH Toolbox.Methods725 subjects aged 36 to 90 years were included in this study using the Human Connectome Project‐Aging (HCP‐A) imaging dataset (T1w, multi‐shell diffusion ‐dMRI). Nine NIH Toolbox measures were used to classify CU and HA population groups (Figure 1). Each test score was z‐normalized and subjects in the lower and upper 50th percentile in all 9 tests were considered as CU and HA, respectively. Diffusion data was processed using TORTOISE dMRI processing package. Corrected DWIs were processed using MAP‐MRI, which yielded the following parameters: return to origin/axis/plane probability (RTOP, RTAP, RTPP), non‐Gaussianity (NG), and propagator anisotropy (PA) (Figure 2a). The T1w images were processed generating 125 SLANT cortical and subcortical regions of interest (Figure 2b). Two‐way ANOVA was used to investigate group differences while accounting for age, sex, education and site. All statistical analysis was done in Matlab2022b.ResultsThe list of tests and respective scores of the two groups is provided in Figure 1. Zero‐displacement probabilities were more sensitive in detecting group differences compared to NG and PA. The two groups displayed notable differences in the following brain regions: the operculum, involved in visuospatial cognition; the precuneus, key to memory retrieval; and the posterior cingulate, which plays a role in processing speed, executive function, and memory. Additionally, the auditory and sensory‐related areas exhibited significant variations, aligning with expectations. These group differences are shown in Figure 3.ConclusionThis study underscores the common occurrence of cognitive aging among the healthy population and demonstrates that MAP‐MRI is a valuable tool for assessing GM microstructural changes, particularly when comparing these changes to those observed in resilient aging populations.
Modeling the influence of presenilin‐1 mutation position on primary outcomes of a gantenerumab trial: findings from the DIAN‐TU‐001 study
Authors: McCullough Austin A. , Cao Yuchen , Chen Charles D. , Gordon Brian A. , Cruchaga Carlos , Goate Alison M. , Hassenstab Jason J. , Ibanez Laura , Li Yan , Perrin Richard J. , Renton Alan E. , Xiong Chengjie , Salloway Stephen , Llibre‐Guerra Jorge J. , Clifford David B. , McDade Eric , Bateman Randall J. , Benzinger Tammie L.S. , Wang Guoqiao , Joseph‐Mathurin Nelly ,   DIAN study team
Published: January 2025

AbstractBackgroundIn autosomal dominant Alzheimer disease (ADAD), the position of a pathogenic genetic variant within the presenilin‐1 (PSEN1) coding sequence influences how amyloid‐beta accumulates and how dementia progresses (Joseph‐Mathurin et al, 2024). In the first trial of anti‐amyloid monoclonal antibodies in individuals at risk for ADAD (Dominantly Inherited Alzheimer Network Trials Unit study 1 [DIAN‐TU‐001]), gantenerumab demonstrated target engagement but did not meet its primary clinical endpoint (Salloway et al, 2021). Whether the distribution of pathogenic genetic variants within the cohort may have influenced the trial results remains unclear. Here, we investigated the primary outcomes from DIAN‐TU‐001 as a function of mutation position.MethodWe evaluated 72 participants with a PSEN1 mutation who completed the DIAN‐TU‐001 study under the placebo (n = 29) and the gantenerumab (n = 43) arms. Both arms were grouped by the PSEN1 mutation position relative to codon 200 as precodon‐200 and postcodon‐200 (Table 1). Mixed models for repeated measures were adjusted for sex, baseline Clinical Dementia Rating® (CDR®) scores, and baseline EYO and estimated the effect of PSEN1 mutation position on the mean change from baseline PiB‐PET composite and baseline cognitive composite, as imaging and clinical outcomes, respectively. Significance of differences in least squares (ls) means on year 4 between pre‐codon 200 and post‐codon 200 mutation groups were calculated.ResultWithin each codon group, compared to placebo, the treated group had significant changes in PiB‐PET but not in cognitive composite (Figure 1). Placebo precodon‐200 group showed significantly faster amyloid accumulation rates (PiB PET composite SUVR) through year 4 than the placebo postcodon‐200 group (ls means diff. P‐value = 0.0065). However, no difference was observed between precodon‐200 and postcodon‐200 gantenerumab groups (ls means diff. P‐value = 0.45) through year 4. Difference in cognitive composite precodon‐200 vs. postcodon‐200 placebo groups was not significant (ls means diff. P‐value = 0.98). Differences in cognitive composite groups between treated groups trended toward significance (ls means diff. P‐value = 0.05).ConclusionThese preliminary results show the potential influence of PSEN1 mutation position on the outcome of a monoclonal antibody targeting amyloid plaque removal. Further investigation into the potential effects of specific mutation type on trial results is warranted.
Attention‐Guided Deep Learning Discovers Brain Morphological Patterns of Alzheimer’s Disease from Structural MRI
Authors: Gu Ran , Li Hongming , Fan Yong 
Published: January 2025

AbstractBackgroundStructural Magnetic Resonance Imaging (sMRI) is an invaluable tool for detecting brain morphological changes to aid in the diagnosis of Alzheimer’s Disease (AD). However, the heterogeneity of AD‐related neurodegeneration poses significant challenges for conventional image analysis techniques to accurately capture discriminative brain changes from whole‐brain sMRI data.MethodWe present a novel attention‐guided deep learning framework to automatically identify and focus on the most discriminative brain regions for robust AD diagnosis from sMRI scans (Figure 1). Our model leverages a 3D U‐Net as an attention generator to learn voxel‐wise attention weights to calibrate the original sMRI data via an attention mechanism, and the calibrated representation is subsequently passed to a classifier for AD diagnosis. This attention‐guided process enables the model to selectively focus on the most informative brain regions for feature learning and decision‐making. Our model has been evaluated on the ADNI dataset.ResultA deep learning model was trained on the ADNI‐1 data and evaluated on the ADNI Go & 2 data. The model achieved an accuracy of 90.0%, precision of 82.3%, recall of 87.3%, and F1‐score of 84.7% in discriminating AD patients from cognitively normal individuals using sMRI data. The accuracy was better than an alternative model without the non‐attention‐guided model (with an accuracy of 87.6%), demonstrating the effectiveness of the attention‐guided mechanism. Visualization of the learned attention maps revealed salient regions localized in the hippocampus, ventricles, temporal lobes, and other areas known to be affected by AD (Figure 2).ConclusionThe proposed attention‐guided deep learning framework demonstrated superior performance compared to traditional methods for AD diagnosis from sMRI scans. By explicitly learning to focus on discriminative brain regions, our model effectively captures AD related brain morphological patterns.
The Role of Education on Brain Pathology and Cognitive Decline in the Language Domain Across Women and Men in the Alzheimer’s Disease Neuroimaging Initiative Sample
Authors: Kim Min Hee , Ying Gelan , Morin Brittany T , Olvera Alice Gavarrete , Gibbons Laura E. , Scollard Phoebe , Lee Michael L. , Klinedinst Brandon S , Nakano Connie , Mez Jesse , Crane Paul K. ,   ADNI, Choi Seo‐Eun 
Published: January 2025

AbstractBackgroundEducation is a strong predictor influencing the dementia progression. With diminished brain integrity, cognitive reserve (CR) is thought to help preserve cognitive function and delay the symptom manifestation. Yet, scholars have not reached consensus on the extent to which education modifies brain integrity‐cognitive decline associations and how it differs by sex/gender.MethodTo test the hypothesis about CR on cognitive decline, we used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) sample and included 785 male and 637 female participants, who were cognitively normal or had mild cognitive impairment at baseline (Table 1). We used three global brain integrity measures (cortical thickness (CT), hippocampal volume (HP), and Reversed‐White Matter Hyperintensities (R‐WMH) as well as the language composite score using the item response theory. We applied sex‐stratified random intercept models including the three‐way interaction term (education x CT x time) against the 5‐year language performance. A robust set of contextual confounders was included; intracranial volume (ICV) difference was accounted for in two ways: covariate adjustment vs. normalization.ResultsThe average age was 72.6 (7.0 SD) and 74.3 years (6.8 SD) for female and males, respectively. Males had high average years of education than females. The linear regression models showed that high levels of brain integrity (HP, CT) were associated with high baseline performance and slower decline; education was only associated with higher baseline performance. The estimation of education modifying the association between brain integrity and language was sensitive to ICV correction methods and differed by brain integrity type and sex. For example, education attenuated the relationship between low normalized‐CT and R‐WMH measures and faster decline (i.e., the lower CT or WMH and decline association was weaker with higher education) only in female sample (Figure 1B); such association was not hold when ICV was included as covariate. In both males and females, lower HP and decline association was stronger with higher education, regardless ICV correction methods (Figure 2).ConclusionIncongruence between theory and empirical evidence on education and cognitive reserve may stem from which brain integrity measures are used, and the different approaches to account for ICV.
Longitudinal Analysis of BCAA Levels and Hypometabolism in ApoE4‐Positive Alzheimer’s Patients Over A 48‐Month Period
Authors: Virdi Jaipal , Silverman Daniel H. 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) is a chronic, incurable neurodegenerative condition characterized by extensive systemic, cellular, and molecular abnormalities. One such aspect recent studies have highlighted is the reduction in plasma branched‐chain amino acid (BCAA) concentrations, identifying them as a potential emerging marker for the disease. Although BCAAs have been implicated in the pathogenesis of AD, their utility in clinical prognosis remains unexplored. This study aims to investigate the role of BCAA levels in AD progression, considering the presence or absence of the APOE4 phenotype over a period of forty‐eight months.MethodBiomarker and clinical diagnosis data for 31 subjects with varying cognitive statuses were obtained from the Alzheimer’s Disease Neuroimaging Initiative (CN, n = 10; LMCI, n = 21). Cognitive status was assessed using the Hypometabolic Convergence Index (HCI), a tool designed to quantify the magnitude and severity of cerebral hypometabolism typical in probable Alzheimer’s Disease patients. BCAA levels were measured through a nuclear magnetic resonance‐based blood biomarker analysis and expressed as concentrations in plasma.ResultA significant correlation was observed between the initial HCI and BCAA measurements and HCI values taken forty‐eight months later, indicating cognitive decline (n = 31, r = 0.362, p‐value = 0.0383). Stratification by APOE4 phenotype revealed more nuanced insights: subjects with zero alleles showed no significant correlation (n = 16, r = 0.165, p‐value = 0.0542), while those with one or two alleles demonstrated a stronger, tighter correlation (n = 15, r = 0.564, p‐value = 0.0183).ConclusionThis study corroborates prior research on the association between diminished plasma BCAA levels and reduced neuronal activity in regions pertinent to AD, as reflected by HCI assessments over a forty‐eight month period. Importantly, our findings reveal a significant correlation between BCAA concentrations and cognitive decline predominantly in individuals carrying one or two APOE4 alleles. This observation highlights the potential influence of the APOE4 allele on the impaired transport of BCAAs to the brain, contributing to cognitive deterioration in these groups. Further research is needed to explore the diagnostic value of BCAA levels in broader AD populations and to understand the mechanistic links in non‐carriers of the APOE4 allele.
Social Isolation and Loneliness in Varied Levels of Cognition Among Racially/Ethnically Diverse Older Americans
Authors: Malatyali Ayse , Cidav Tom , Xie Rui , Thiamwong Ladda , Wiese Lisa Ann Kirk 
Published: January 2025

AbstractBackgroundSocial isolation and loneliness affect 20%‐30% of older Americans, increasing their risk of depression, admission to nursing homes, early disability, and mortality. This study examines the association of social isolation and loneliness with cognitive impairment‐not‐dementia (CIND) and dementia among racially/ethnically diverse older Americans.MethodWe analyzed data from the Health Retirement Study (2020 interviews) using logistic regression analyses with cognition as a response. Our sample consisted of 2,625 participants (age > 64). Measures included the 27‐item HRS cognition scale, five‐item social isolation scale, UCLA loneliness scale, and Center for Epidemiological Studies Depression scale.ResultThe characteristics of our sample were: mean age of 75.5 years, Blacks (16%), Hispanics (11.4%), women (59%), College educated (55%), and residing in rural areas (26.3%). Participants who experienced loneliness were 40% more likely to have cognitive impairment and 20% more likely to have dementia. The likelihood of having cognitive impairment was 20% higher among socially isolated participants than those without social isolation. However, there was no significant association between social isolation and dementia. The relationship of depressive symptoms with CIND or dementia was also non‐significant. Black participants were about seven times more likely to have CIND and two times more likely to have dementia than Whites. Hispanic participants had nearly three times higher risk of CIND than non‐Hispanics, but their risk of dementia was non‐significant. Further interaction analyses showed no statistically significant difference in social isolation or loneliness with CIND or dementia risk by race or ethnicity. The odds of having CIND were significantly higher in men (20%) than women and in rural participants (31%) than urban participants. Conversely, the association of gender groups or rurality with dementia was non‐significant. The risk of CIND and dementia was significantly less in participants with a high school degree and above.ConclusionThese findings support research on the importance of social connectedness in cognitive aging. Further investigation into temporal trends in social isolation and loneliness regarding the changes in cognitive trajectories is needed.
Developing novel supersensitive amyloid‐β 43 and pyroglutamate‐modified amyloid‐β 3‐40 assays for Alzheimer’s disease
Authors: Alawode Deborah O T , Heslegrave Amanda J , Zetterberg Henrik 
Published: January 2025

AbstractBackgroundIn light of the poor fold change in the plasma amyloid‐β (Aβ) 42/amyloid‐β 40 ratio (Aβ42/40) between Alzheimer’s disease (AD) and control (CTRL) cases, novel Aβ biomarkers are needed which will require harnessing the increased sensitivity offered by a novel modification to the Quanterix Corporation Single molecule array (Simoa) technology.MethodSimoa assays for the detection of Aβ43 and pyroglutamate‐modified Aβ 3‐40 (AβpE3‐40) have been developed on the standard HD‐X instrument. Aβ43 and AβpE3‐40 were measured in 8 CSF samples (1 AD, 4 mixed phenotype [MP] and 3 CTRL) on the HD‐X instrument, each of which were measured at four dilution levels – neat, 2x, 4x and 8x dilution.ResultOut of a possible 32 measurements per analyte (four dilution levels per sample), Aβ43 was quantifiable in 68.8% of measurements, 72.7% of which fell below the concentration of calibrator B. In three samples, Aβ43 was measurable at all four dilution levels, and showed a strong but statistically non‐significant dilution linearity. In contrast, AβpE3‐40 was quantifiable in 93.8% samples, 96.6% of which fell below the concentrations of calibrator B. In six samples, AβpE3‐40 was measurable at all four dilution levels. One sample showed a strong but statistically non‐significant dilution linearity, with the remaining five samples showing a poor dilution linearity, suggesting the limits of HD‐X sensitivity had been reached.ConclusionThe increased sensitivity provided by the modification to the Simoa SRx is needed to accurately quantify Aβ43 and AβpE3‐40 in CSF and subsequently plasma. Work is ongoing to transfer these novel assays from the HDx onto the modified SRx instrument.
Vascular Aging Markers and Blood Biomarkers of Neurodegeneration
Authors: Guzman Vanessa A. , Panton Maliyah , Rahman Salwa , Allende Antonio J Spagnolo , Elkind Mitchell S.V. , Rundek Tatjana , Marshall Randolph S. , Cosentino Stephanie , Manly Jennifer J. , Gutierrez Jose 
Published: January 2025

AbstractBackgroundAge‐related changes in the systemic and cerebral vasculature adversely affect brain health and may contribute to neurodegeneration. However, the relationship between markers of systemic (arterial stiffness) and cerebral (flow pulsatility) vascular aging with neurodegeneration remains unclear. This study aimed to evaluate the associations of arterial stiffness and flow pulsatiility with blood biomarkers of neurodegeneration.MethodA subset of 80 participants (Mage = 81+7, 63% female, 78% Latinx) from the Northern Manhattan Study completed carotid ultrasound, transcranial doppler (TCD) ultrasonography, and blood sampling. Arterial stiffness was used as a proxy for systemic vascular aging and measured using pulse wave velocity (PWV) obtained from the Vicorder device or carotid doppler. Middle cerebral artery pulsatility index (MCA PI) was used as a proxy for cerebral vascular aging and measured bilaterally using TCD. Blood biomarkers were derived using Simoa Neurology 4‐Plex E kit for: Aβ40, Aβ42, Ptau181, GFAP, and NfL. The ratio of Aβ42 to Aβ40 (Aβ42/Aβ40) was also calculated and included as an outcome measure. We examined the associations of aortic stiffness, common carotid stiffness, internal carotid stiffness, and MCA PI with each blood biomarker, with and without adjustment for age and sex.ResultMCA PI was associated with increased concentration of Aβ40 (βstandardized = .26, p = .03), p‐tau181 (βstandardized = .31, p = .01), GFAP (βstandardized = .36, p = .002), and NfL (βstandardized = .33, p = .01) but not with Aβ42 (βstandardized = .18, p = .13) or Aβ42/Aβ40 ratio (βstandardized = ‐.05, p = .54). In adjusted models, only the association between MCA PI and ptau181 remained significant (βstandardized = .31, p = .03). There was no association between arterial stiffness measures and blood biomarkers.ConclusionElevated pulsatility in the middle cerebral arteries was associated with increased concentration of blood biomarkers of neurodegeneration; however, associations were attenuated for all blood biomarkers except for p‐tau181 after adjusting for age and sex. These preliminary findings suggest surrogate markers of cerebral vascular aging may be more robust predictors of neurodegeneration. While statistical power was limited by small sample size, future research examining these relationships in larger samples is warranted.
Prediction of regional brain tau levels in early Alzheimer’s disease using plasma pTau217
Authors: Devanarayan Viswanath , Charil Arnaud , Doherty Thomas , Llano Daniel A. , Ye Yuanqing , Andreozzi Erica , Zhou Jin , Sachdev Pallavi , Reyderman Larisa , Hampel Harald , Kramer Lynn D , Dhadda Shobha , Irizarry Michael C. 
Published: January 2025

AbstractBackgroundPET quantification of brain tau pathology aids in Alzheimer’s disease staging and patient screening. This study assesses whether the phosphorylated to nonphosphorylated plasma Tau217 ratio (pTau217R) predicts regional tau PET standardized uptake value ratio (SUVR) and accurately identifies subjects with different levels of tau accumulation.MethodPlasma pTau217 and non‐phosphorylated tau217 concentrations were quantified via immunoprecipitation‐mass spectrometry. Predictive models for MK6240 tau PET SUVR were developed and validated using a 60‐40 random split of a clinical trial cohort comprising 242 amyloid‐β positive early Alzheimer’s disease individuals. The stochastic gradient boosting algorithm was employed to construct models for concurrently predicting SUVR values across various brain regions. Additional analyses explored whether integrating additional predictors (e.g., cognitive assessments, fluid biomarkers, structural MRI, and amyloid PET) into the model could improve the prediction performance of pTau217R. Model performance was cross‐validated within the training set and evaluated further in a hold‐out test set.ResultpTau217R‐based models accurately predicted tau PET uptake across various brain regions, with R2 values ranging from 0.49 to 0.65. The maximum SUVR values reliably predicted across these brain regions fell within the range of 1.87 to 2.3. The area under the receiver operating characteristic curve for detecting tau presence ranged from 84% to 95% across the six Braak stages and cortical regions, maintaining consistent performance at higher tau accumulation levels (Figure 1A‐B; cortical regions). Integrating additional predictors did not improve pTau217R’s performance. Therefore, using pTau217R alone in predicting tau PET SUVR reduced the need for tau PET scans by up to 65%, particularly in identifying low tau concentrations within cortical grey matter, while maintaining a 5% false negative rate.ConclusionOur study demonstrates the robust predictive ability of pTau217R in estimating regional brain tau levels among Aβ+ early Alzheimer’s disease patients, accurately discerning individuals with varying degrees of tau accumulation while notably reducing the need for tau PET scans. The simultaneous prediction of continuous SUVR values across multiple brain regions enables pathological disease staging, enhances practicality, and maximizes accessibility for patients, thereby providing greater flexibility in patient screening and monitoring procedures for both clinical trials and real‐world clinical settings.