bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating quality of routine vs. tape‐transfer histology sections for 3D reconstruction of paraffin embedded brain tissue and correlation with 7T MRI
Authors: Ho William Hai Dang , Wang Yixin , Taghavi Hossein Moein , DiGiacomo Phillip , Nirschl Jeffrey , Chu Pauline , Georgiadis Marios , Zeineh Michael 
Published: January 2025

AbstractBackgroundSpecimen analysis is crucial for identifying imaging and neuropathological signatures. Histology is the gold‐standard, but sample preparation and sectioning induce tissue deformations which hinder quantitative analysis or registration of histology to 3D MRI providing a challenge to the development of MRI biomarkers. Overall, we aim to develop a workflow to correlate histology with high‐resolution MRI at a microscopic level (Fig. 1), Here, we evaluate a critical step in this process ‐ the section quality from tissue mounting techniques, comparing: A) traditional water bath (Fig. 1F), and B) tape transfer (Fig. 1G), for the purpose of image segmentation and correlation with high‐resolution MRI.MethodsFixed human brain specimens were MRI scanned, dehydrated with graded ethanol, xylene‐cleared, and paraffin‐embedded. Before sectioning, the specimen surface was captured by a brightfield microscope (Fig. 2, top left, “Blockface image”). Embedded tissue was sectioned at 7 µm using a Leica 100 microtome and mounted on slides using either A) traditional water bath capture onto standard charged slides (Fig. 1F), or B) tape‐transfer capture onto adhesive‐coated slides (Alphametrix) (Fig. 1G). Slides were hematoxylin and eosin stained followed by imaging using the same microscope settings as the blockface image, and total stained tissue area was calculated. Area of expansion was determined by change from the blockface image (Fig. 2, 3rd column). Internal tissue distortions were calculated by subtracting the tissue section mask from the original blockface mask (Fig. 2, 2nd column).ResultsThe tissue area measurements show that traditional capture resulted in average expansion of 16.7% relative to the blockface image, whereas tape transfer had 0.9% expansion (Fig. 3). Both methods had similar amounts of average total tissue damage (approximately 3%), representing regions with tissue in the blockface image where no tissue was present on the slide. Subjectively, tissue distortion was concentrated at the periphery on traditional capture, whereas there were linear striations within internal structures on tape transfer (red arrows). Tissue yield for tape transfer was subjectively near 100% compared to traditional at approximately 60‐70%.ConclusionTape transfer had a more consistent yield and better edge preservation with blockface image but had more internal microfractures.
Machine‐learning derived proxy phenotypes for Alzheimer’s disease genome‐wide association study by proxy
Authors: Goudey Benjamin , Saint‐Jalmes Martin , Liu Shu , Laws Simon M. , Masters Colin L 
Published: January 2025

AbstractBackgroundFor large genomic studies of middle‐aged individuals, the prevalence of Alzheimer’s disease (AD) is extremely low, making it difficult to conduct genomic analysis of the condition. To enable genome‐wide association studies of AD in such datasets, an approach called Genome‐wide association by proxy (GWAX) uses family history of disease as a proxy for disease status. Borrowing from the machine learning (ML) literature, we treat the development of proxy phenotypes as a pseudo‐labelling task, where an ideal proxy label accurately predicts the lifetime risk of AD. Given this, this work develops ML‐derived proxy phenotypes of AD for downstream GWAX based on survival models of conversion to AD and demonstrates their predictive performance and impact on downstream GWAX.MethodUsing cognitively normal (CN) and mild cognitive impaired (MCI) individuals with at least 2 timepoints in AIBL (n = 426), ADNI (n = 283) and UK Biobank (n = 56,105), we derived two proxy phenotypes of AD: i) a ML‐derived boosting model with age, sex, years of education and AD family history, i.e. number of recorded parents with a history of Alzheimer’s disease and ii) family history alone. The prognostic ability for future AD conversion was measured using concordance index (C‐index) in 10‐fold cross‐validation. The two models were applied to the subset of UK Biobank with white British ancestry (n = 408,165) and a GWAX was conducted on the predicted labels. From this GWAX, PRS was derived and evaluated using area under the ROC curve (AUC).ResultsIn 10‐fold cross‐validation, the ML‐derived phenotypes were more predictive of conversion to AD than family history alone (median C‐index: 0.7 vs 0.5, Mann Whitney p‐value <0.001). GWAX of UKB from these proxy phenotypes indicates that the multivariate model detects a higher number of significant regions compared to family history alone. PRS constructed from these ML‐derived phenotypes GWAX show greater power to separate true AD case/control labels when externally validated on ADNI.ConclusionPreliminary analysis of ML‐derived proxy phenotypes of AD indicates may be a promising approach to improve AD genomic studies in middle age cohorts. Further analysis of the results and potential biases of this approach are needed to fully characterise this method.
A multiple cohort study exploring the influence of partial volume correction on age and sex effects on tau PET imaging
Authors: Hobbs Diana A , Flores Shaney , Keefe Sarah J. , Morris John C. , Benzinger Tammie L.S. , Gordon Brian A. 
Published: January 2025

AbstractBackgroundTau PET imaging has become pivotal in understanding the pathophysiological processes underlying Alzheimer disease (AD). In individuals without amyloid pathology, there is evidence tau levels are elevated with increase age and that females show greater levels of binding. An unknown question is how consistent these effects are, and whether they are susceptible to methodological choices impacting PET quantification. We explored the impact of partial volume correction (PVC) on tau regional values in data from the Charles F. and Joanne Knight Alzheimer Disease Research Center (ADRC) and the Alzheimer Disease Neuroimaging Initiative (ADNI).MethodData came from 323 participants (age: 67.89±8.57) at the Knight ADRC and 274 individuals (age: 70.14±6.61) from ADNI. Forty‐two regional values of tau PET binding were measured using flortaucipir with and without partial‐volume correction as implemented using study specific geometric transfer matrix approaches. Participants were selected to be amyloid‐negative. Similarity in regional estimates was determined using spatial permutation testing.ResultWithin the ADRC cohort the value of the regression coefficients changed with PVC by the spatial pattern of the effects remained highly similar for both age (Fig. 1A: r = 0.66, p<0.001) and sex (Fig. 1C: r = 0.85, p<0.001). In ADNI there were moderate association between the maps of age‐related effects (Fig. 1B: r = 0.36, p<0.05) and sex (Fig. 1D: r = 0.31, p<0.05). When comparing between the ADRC and ADNI cohorts using non‐PVC data there were highly significant associations for both age (Fig. 2A: r = 0.83, p<0.001) and sex (Fig. 2C: r = 0.95, p<0.001). When using PVC, data no significant associations were found for age (Fig. 2B: r = 0.32, p = 0.06) or sex (Fig. 2D: r = 0.02, p = 0.92).ConclusionThe implementation of, and choice of PVC approach can drastically impact the detection of PET signals. This is a particular concern in the context of cohorts where there may be low to moderate levels of underlying pathology and where non‐specific binding may dominate the signal. These results emphasize the importance of considering PVC in tau PET analyses and highlight the reliability of observed age‐ and sex‐related effects across different cohorts.
Brain Age Gap Correlates with 18F‐NAV‐4694 and 18F‐MK‐6240 Standardized Uptake Value Ratio
Authors: Rajabli Reza , Soltaninejad Mahdie , Shafiee Neda , Fonov Vladimir S , Rahmouni Nesrine , Servaes Stijn , Therriault Joseph , Gauthier Serge , Rosa‐Neto Pedro , Collins D Louis 
Published: January 2025

AbstractBackgroundIt is feasible to train a model on a healthy cohort to estimate the chronological age from a T1‐weighted (T1w) MRI. This model can be used to estimate the apparent brain age of subjects with Alzheimer's Disease (AD). The difference between the true chronological age and the apparent brain age, called Brain Age Gap (BAG), is a potential feature to estimate the level of pathology and neurodegeneration of an individual patient with AD. To further study this, here we examined the linear relationship between BAG and the standardized uptake value ratio (SUVR) of the amyloid‐beta tracer (18F‐NAV‐4694) and tau tangle tracer (18F‐MK‐6240).MethodWe used ∼40K T1w MRIs from the UK Biobank dataset, with improved preprocessing and more extensive data augmentation, to train an SFCN‐reg model (Leonardsen 2022). Our implementation achieved a generalization gap error of less than 1 year, surpassing the performance reported in the original study. Then, using the trained model, we estimated BAG for 245 T1w images from the Translational Biomarkers in Aging and Dementia (TRIAD) dataset (Therriault 2022) consisting of 146 normal controls (NC), 46 with Mild Cognitive Impairment (MCI) due to AD, and 53 with AD dementia. Subsequently, We compared the estimated BAG values with the SUVR values for neocortical amyloid and tau PET meta‐ROI (Jack Jr. 2016).ResultFigure 1 illustrates the linear relationship between the 18F‐NAV‐4694 SUVR for all subjects (at baseline scan) vs age (no correlation), apparent brain age (r = 0.31, p << 0.001), and BAG (r = 0.43, p << 0.001). Figure 2 illustrates the linear relationship between the 18F‐MK‐6240 SUVR vs age (r = ‐0.27, p << 0.001), apparent brain age (r = 0.11, p < 0.08), and BAG for the same subjects (r = 0.58, p << 0.001).ConclusionWe demonstrated a meaningful relationship between 18F‐NAV‐4694 and 18F‐MK‐6240 SUVR values and BAG, stronger than with age or apparent age, reinforcing the notion that BAG can serve as a feature to estimate the amount of neurodegeneration due to accumulation of amyloid and tau when more precise data, such as PET scans, are not available.
Association of CSF cortisol with cortical thickness in Alzheimer's disease continuum
Authors: Souza Laura Willers , Ros Lucas Uglione Da , Bieger Andrei , Ferrari‐Souza João Pedro , Borelli Wyllians Vendramini , de Bastiani Marco Antônio , Bauer‐Negrini Guilherme , DuBois Jonathan M. , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundMultiple studies have linked high cortisol levels, a frequently used biomarker of stress, with the Alzheimer's disease (AD) pathophysiology. However, the relationship between cerebrospinal fluid (CSF) cortisol levels and AD‐related brain atrophy is not fully understood. This study sought to investigate the cross‐sectional and longitudinal association between CSF cortisol levels and brain cortical thickness in patients across the biological and clinical continuum of AD.MethodsWe evaluated 310 individuals from the ADNI cohort with available baseline CSF cortisol concentrations, structural MRI and CSF Elecsys biomarkers (Aß1‐42 and p‐tau181). Cross‐sectional analysis was conducted in Freesurfer (v7.1.1) through vertex‐wise analysis using general linear models (GLMs) corrected for multiple comparisons through cluster formation (p<0.01) and permutation (Monte Carlo simulation of 10,000 iterations). Longitudinal measures of an AD signature meta‐ROI were estimated from the surface area‐weighted average of the mean cortical thickness of the following ROIs: entorhinal, inferior temporal, middle temporal, and fusiform. Linear mixed‐effects (LME) models corrected for confounders were performed to evaluate cortical thickness longitudinal trajectories. The following models were used to assess the association between cortical thickness and cortisol levels: (1) cortisol as an independent variable; (2) cortisol, clinical diagnoses, and interaction; (3) cortisol, AD biomarkers positivity, and interaction.ResultsDemographics are depicted in Table 1. Cross‐sectional analysis revealed that higher cortisol levels were associated with higher brain atrophy in several brain regions, as depicted by cortical brain thickness measures (Figure 1). These analyses also showed that cortisol does not interact with clinical diagnoses or AD biomarkers to decrease cortical thickness. In the longitudinal analyses, the association between cortisol and cortical thickness and the interaction with clinical diagnoses were not significant. However, we found that cortisol significantly interacts with AD biomarkers positivity to reduce cortical thickness values over time (ß=0.002, p=0.03; Figure 2).ConclusionsAltogether, our results support that cortisol levels potentiate AD pathophysiology effects on AD‐related brain atrophy. These findings suggest that CSF cortisol levels may affect brain vulnerability to AD pathophysiology in the long term through neurodegeneration. Thus, our results have potential implications for understating AD‐related brain atrophy and developing innovative therapeutic strategies.
Differential Patterns of Brain and Body Aging on MR Imaging
Authors: Raji Cyrus A. , Meysami Somayeh , Hashemi Sam , Garg Saurabh , Akbari Nasrin , Gouda Ahmed , Chodakiewitz Yosef Gavriel , Nguyen Thanh Duc , Niotis Kellyann , Merrill David A. , Attariwala Rajpaul 
Published: January 2025

AbstractBackgroundComparative information on how whole‐body organs are linked with age and the brain is lacking.MethodOverall, 7,149 healthy participants from four sites (Mean age 53.06 ± 12.95 years, 18‐97 years; 48% women; 52% men; 38% non‐white) were scanned on 1.5T MR machines with a whole‐body protocol. Whole body sequences utilized in the quantitative analyses were coronal T1 for organ, fat and muscle segmentation. Deep learning with FastSurfer on MPRAGE trained on 134 participants aged 27‐66 and segmented 96 brain regions. Partial correlation analysis was done controlling for sex and total intracranial volume for brain regions and total abdominal organ volume for abdominal organs. Multiple comparisons were accounted for using the Bonferroni method.ResultAge negatively correlated with gray matter (rp = ‐0.38, p = 6.31×10‐244) and white matter (rp = ‐0.263, p = 4.03×10‐112). Age positively correlated to cerebral ventricle volume (rp = 0.493, p < .001). Increasing age was also inversely correlated with the lobar structural volumes: frontal lobe (rp = ‐0.420 p = 2.70×10‐302), temporal lobe (rp = ‐0.378 p = 4.50×10‐240), parietal lobe (rp ‐0.373 with p = 2.22×10‐233), occipital lobe (rp = ‐0.259; p = 1.58×10‐108). Age negatively correlated with AD risk regions: hippocampus (rp = ‐0.288 and p = 9.79×10‐136), posterior cingulate (rp = ‐0.338, p = 4.37×10‐189), precuneus (rp = ‐0.321, p= 1.44×10‐169). Beyond the brain, the kidney and psoas muscle showed significant negative correlations with age (kidney; rp= ‐0.114, p = 1.23×10‐211) and the psoas muscle (rp = ‐0.353, p= 1.13×10‐208). Strikingly, visceral fat (vfat) was found to have a strong positive correlation with age, (rp = 0.416, p= 8.81×10‐297). Subcutaneous fat (sfat), by contrast, showed a null correlation (rp = ‐0.01, p=1.0). Increasing age showed negative correlations with liver volumes (rp = ‐0.226, p = 2.21×10‐82), spleen (rp = ‐0.262, 5.46×10‐112) and total muscle volume (rp = ‐0.317, 3.17×10‐165).ConclusionDifferential patterns of brain and body organ aging may lend insight into how age can increase the risk for common brain disorders in the elderly such as Alzheimer’s.
Associations of gray matter volume and white mater hyperintensity mean diffusivity and volume with PiB status in mild cognitive impairment
Authors: Santini Tales , Li Jinghang , Emanuel James E , Liou Jr‐Jiun , Sajewski Andrea , Chu Cong , de Almeida Bruno , Lopresti Brian J , Aizenstein Howard J , Wu Minjie , Gildengers Ariel , Ibrahim Tamer S 
Published: January 2025

AbstractBackgroundWhite matter hyperintensities are associated with vascular diseases, while amyloid deposition serves as an early indicator of Alzheimer's disease (AD). Despite both pathologies contributing to cognitive decline, the ability to distinguish between them is crucial in selecting the appropriate course of treatment or management of these conditions.Method71 mild cognitive impairment (MCI) participants completed the baseline imaging (age 72.6±7.61 years, 38 females) and were included in this study. Pittsburgh Compound B (PiB) PET scans were acquired. 7T MRI images were acquired with a customized radiofrequency coil [1] and included T1w MPRAGE, T2w FLAIR and diffusion sequences. The brain was parcellated using Freesurfer and wmh‐seg [2]. We extracted the baseline (N=63) and longitudinal volumes (N=45, follow up duration = 1.22±0.26 years) of AD signature regions (hippocampus, amygdala, temporal lobes) [3] and the volumes (N=69) and mean diffusivity (N=58) in the white matter hyperintensities and compared the PiB+ (age 75.73±7.97, total SUVR > 1.346, CIRS‐G=8.8±4.1) vs PiB‐ (age 71.5±7.32, CIRS=10.62±3.9) participants. We performed t‐tests adjusting for age, sex, and intracranial volume for baseline variables and adjusting for age at baseline, follow up duration, and sex for the longitudinal variables.ResultWe found a significantly smaller volume in the Hippocampus and amygdala in the PiB+ group and significantly bigger volume of white matter hyperintensities in the PiB‐ group. PiB‐ group also presented higher mean diffusivity inside the white matter hyperintensities, potentially indicating a more advanced stage of the lesion, however partial volume effects need to be evaluated in future work. Longitudinal analysis shows a rapid volume decline in some AD signature gray matter regions in the PiB+ group.ConclusionOur results demonstrate the feasibility of high‐resolution and high‐quality 7T MRI in potentially differentiating between neurodegenerative processes related to AD type dementia versus vascular disease in an MCI population.
Whole genome sequence‐based association of cognitive decline and retinal thickness in the Japanese population
Authors: Taira Makiko , Fuse Nobuo , Saykin Andrew J. , Nagami Fuji , Kinoshita Kengo , Yamamoto Masayuki 
Published: January 2025

AbstractBackgroundDementia is age‐related with a significant genetic contribution, yet genome‐wide association studies have not fully accounted for heritability. This discrepancy may in part be due to reliance on SNPs and small indels. Whole‐genome sequencing (WGS) data in the Japanese population may reveal population‐specific susceptibility loci for dementia. Retinal imaging with optical coherence tomography (OCT) is noninvasive, reproducible, and can detect thinning associated with progressive neurodegeneration. Association of population‐specific genetic susceptibility loci with retinal thinning and cognitive decline may reveal novel aspects of dementia risk and pathophysiology.MethodAmong participants with WGS data from the Tohoku Medical Megabank Organization (ToMMo) Ophthalmology Study ("ToMMo Eye Study"), individuals with adequate quality data on retinal nerve fiber layer and ganglion cell layer thickness from spectral‐domain optical coherence tomography (SD‐OCT) scans were selected. Since retinal thinning also occurs in glaucoma, we performed a GWAS using age, sex, and 10 principal components as covariates using SAGE1.2 to obtain a set of genes responsible for glaucoma and confirm that the genotyping was successful. We then attempted to identify susceptibility loci for cognitive decline by using (1) the Mini‐Mental State Examination, Japanese version (MMSE‐J), (2) the Montreal Cognitive Assessment, Japanese version (MoCA‐J), and (3) the Mini‐COG© (a simple screening for early detection of dementia, Japanese version) scores as associated factors, respectively. Furthermore, these validation results were also compared with those obtained from GWAS using imputation data performed on custom arrays (Japonica ArrayTM, v2 or NEO) for Japanese.Result84 significant (p < 5.0E‐8) genome‐wide susceptibility loci (hg38) of RNFL were detected on 14K WGS‐based study (the top hit locus: Chr14, SIX6 gene, P=4.50E‐46). There were many genetic loci that have already been reported to be associated with glaucoma susceptibility, including the above locus. Among the results of GWAS for cognitive decline combining the three cognitive scores after normalization to z‐scores, several loci have shown significant susceptibility in both of RNFL and cognitive rating scale. Some loci suggested more than a high or moderate effect of altering protein efficacy.ConclusionWe present an initial WGS‐based genetic study of retinal thickness and cognitive decline in the Japanese population.
Advancements in High‐Resolution MRI Postmortem brain imaging: Histological examination on cells exclusively exhibiting White Matter Hyperintensities made possible using a novel, 3D printed, reusable, imaging container and cutting guide
Authors: Berardinelli Jacob , Li Jinghang , Farhat Nadim , Liou Jr‐Jiun , Sajewski Andrea , Santini Tales , Wu Minjie , Mettenburg Joseph , Aizenstein Howard J , Ikonomovic Milos D , Kofler Julia , Ibrahim Tamer S 
Published: January 2025

AbstractBackgroundEmploying a custom 60‐channel single‐transmit/32‐channel receive Tic‐Tac‐Toe (TTT) coil in 7T MRI imaging has enhanced the resolution and contrast for identifying White Matter Hyperintensities (WMHs), which are critical markers in Alzheimer’s disease and related dementias (AD/ADRD).MethodThe method involves resecting the left hemisphere of the brain, excluding the cerebellum, followed by embalming in 10% formalin. 1.5% agar and 30% sucrose by weight are heated and mixed for embedding in the container. The brain is positioned in the cutting guide and inserted into the base (Figure 1). Agar is meticulously poured ensuring minimal air bubble formation in the sulci and ventricles with additional agar pipetted as needed. The lid is then attached, more agar is added until the container is filled, and the cap is secured. The container is sealed in plastic to safeguard the coil from potential liquid damage and isolate the tissue/formalin while scanning. The imaging protocol includes a B1+ map at 3.2mm, MP2RAGE at 0.4mm, GRE at .37mm, and T2‐SPACE at .36mm all isotropic resolutions. The high‐resolution T2‐weighted SPACE is used for WMH segmentation and quantification. The MP2RAGE provides a T1‐weighted image for further data analysis registration to gross anatomy as well as in vivo scans.ResultThis protocol has been applied to 120 brain specimens to date, with representative MR images in Figure 2. The techniques afford pathologists flexibility in slicing the brain into coronal or axial parallel 5 mm slabs at various angles. See Figure 3 for a visual representation of the progression of the container design and of the cutting process.ConclusionThis innovative combination of the cutting guide, container, and the custom 7T TTT coil is reusable and reliable. It accommodates various brain sizes, offers precise cutting options, ensures fast and accurate image registration to in vivo and gross anatomy, and achieves homogeneous B1 fields, culminating in superior research data. The described method is subject to ongoing refinement for enhanced efficiency, with iterative optimization of the container’s shape, size, and function through 3D printing. This work was supported by the National Institutes of Health under award numbers R01MH111265, R01AG063525, T32MH119168, and U19AG068054.