bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and Preliminary Feasibility of a Translatable Volumetric Brain Age Metric
Authors: Kress Gavin T , Iglesias Juan Eugenio , Cole James H. , Popa Emily S. , Siddarth Prabha , Bramen Jennifer E. 
Published: January 2025

AbstractBackgroundBrain aging (BA) involves the gradual deterioration of brain systems and is associated with chronological age (CA). Measures of BA have been validated and adopted in aging and neurological disease research (Biondo, 2022; Eickhoff, 2021) and could be a useful clinical tool. BA predicts CA in healthy adults (Cole, 2017) and accelerated BA precede Alzheimer’s disease (AD) symptoms (Elliott, 2021). Current BA estimations rely on the acquisition of high‐quality neuroimaging data and complex, computationally expensive analysis workflows, creating obstacles for clinical translation. This study aims to develop an easily implementable workflow for BA where inputs are clinically relevant features derived from heterogeneously acquired magnetic resonance imaging (MRI) using the SynthSeg tool.MethodThirty‐two regional volumes from T1‐weighted MRIs from 56,505 participants (mean age=63.6; 44‐82; SD=7.52) were segmented using the SynthSeg tool. This tool analyzes clinically acquired, heterogenous inputs and requires minimal processing power, memory, and time. Only z‐transformed SynthSeg‐derived measures were included to allow investigations into confounds. Performance evaluation included three filter‐based feature selection approaches (Fischer’s Score, Information Gain, and Correlation Coefficient) combined with seven machine learning (ML) algorithms including linear regression (LR), 2nd‐order polynomial regression (2PR), Gaussian process regression (GPR), Decision Tree (DT), Random Forest (RF), a Convolutional Neural Network (CNN) treating age as a continuous variable, and as a categorical measure. Ability to predict CA was assessed through 10‐fold cross validation, each with an independent 90‐10 training‐testing split, and regression analysis between predicted and CA using an alpha=0.001.ResultFeature selection demonstrated that each structural feature contained unique information for predicting CA. All seven ML algorithms significantly predicted CA (Table 1). 2PR performed the best overall with LR and GPR following closely behind (Figure 1). Both the categorical and numerical CNN performed slightly worse, but still significantly better than the RF and DTConclusionThe association between CA and volumetric‐BA estimates derived from SynthSeg segmented data supports the potential validity of this approach. However, its simplicity may increase translatability. Future directions of this work are to improve performance. The long‐term goal of this work is to develop and validate local volumetric‐BA (as opposed to global volumetric‐BA) metrics.
Free water is a sensitive predictor of future white matter lesions
Authors: Maillard Pauline , Fletcher Evan , Martinez Oliver , Singh Baljeet , Sandoval Francisco , Fan Audrey P. , Farias Sarah Tomaszewski , Decarli Charles 
Published: January 2025

AbstractBackgroundCerebrovascular disease features as the most common comorbidity in older individuals with dementia. White matter (WM) injury identified through white matter hyperintensities (WMH) on FLAIR represents evident late‐stage pathophysiological manifestations of CVD. This study aims to assess the role of baseline microstructural WM integrity, using free water (FW), a measure from diffusion tensor imaging (DTI), in predicting WMH growth dynamic.MethodsOur study includes 271 individuals (age: 76±7, 60% female) from the UCD ADRC longitudinal cohort who underwent an MRI exam at two different date (mean time±SD: 2.7±1.2 years). We tracked individual lesions between baseline and follow‐up. Briefly, for each participant, distinct WMH region were labeled on their baseline and follow‐up scans, coregistered into the FSL template space. All lesions were then placed into three categories: 1) baseline WMH that do not get larger over time were labeled stagnant WMH, 2) baseline WMH that grow larger over time were labeled growing WMH, and (3) WMH identified only on follow‐up scans were labeled noncontiguous incident WMH. DTI‐derived mean signal of Free Water (FW) was computed for each individual WMH region at baseline, as well as within the normal appearing WM (NAWM). Using a linear mixed‐effect model, we investigated differences in baseline FW according each WMH tissue category and NAWM. Analyses were adjusted for age and sex.ResultsAt baseline, FW signal was found to be lower within NAWM as compared with growing WMH (b=‐0.11, pvalue< 0.001, see Figure 1) and stagnant WMH (b=‐0.13, pvalue< 0.001), and also with incident WMH (b=‐0.096, pvalue< 0.001) suggesting that WM tissue where incident WMH are to emerge over time is already injured as compared to normal WM. Baseline FW for stagnant WMH was significantly higher than for growing (b=0.027, pvalue< 0.001) and incident WMH (b=0.038, pvalue< 0.001). Baseline FW for incident WMH was not found to be significantly different from baseline FW in growing WMH (b=0.011, pvalue=0.078).ConclusionOur results suggest that local FW measures predict the future incidence as well as the growth dynamic of lesions over time and emphasize the sensitivity of FW as a biomarker of early CVD‐related WM injury.
Association between blood biomarkers of vascular endothelial dysfunction and brain glucose metabolism in long COVID
Authors: da Silva Ana Paula Bornes , Machado Luiza Santos , Siebert Marina , Silveira Maiele Dornelles , Borelli Wyllians Vendramini , Senger Joana Emilia , da Ros João Pedro Uglione , Jotz Arthur Viana , de Mello Guilherme Bastos , Kadan Matheus Fakhri , Radaelli Graciane , Ferrari‐Souza João Pedro , de Bastiani Marco Antônio , Povala Guilherme , Matushita Cristina Sebastião , Soder Ricardo Benardi , Schumacher‐Schuh Artur Francisco , Pascoal Tharick A. , Souza Diogo O. , Lourenco Mychael V , de Paula Faria Daniele de Paula , Coutinho Artur Martins , daCosta Jaderson Costa , de Souza Débora Guerini , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundCOVID‐19, identified as the greatest health concern of the century, is associated with vascular inflammation and endothelial activation, resulting in multisystemic damage, including to the central nervous system (CNS). Recent investigations indicate a link between endothelial dysfunction, neurological changes, and the development of the so‐called long‐COVID. Molecules expressed in the endothelium such as P‐selectin, E‐selectin, and VEGF‐A, increased under inflammatory injury, may be associated with conditions like brain injuries and neurodegenerative diseases. These markers may remain altered in the organism for months after the acute episode of COVID and may be related to long‐term neurological disorders. This study aims to identify associations between biomarkers of endothelial dysfunction and brain glucose metabolism in individuals with long‐COVID.MethodsA total of 39 individuals presenting with long‐COVID and 10 healthy controls (HC) underwent brain [18F]FDG‐PET imaging. Standardized uptake value ratio maps were generated using the [18F]FDG‐PET global mean. Using an ELISA Multiplex assay, we measured P‐selectin, E‐selectin, and VEGF‐A in plasma samples. Voxel‐wise linear regressions models accounting for age, gender, and years of education were used to evaluate the association between the interaction biomarker*group and the brain glucose metabolism.ResultsThe analyses showed that the interaction between P‐selectin and group presents positive association clusters in the right isthmus of the corpus callosum, thalamus, and left lingual gyrus (Figure 1). In this model, we also observed negative association clusters in the lingual gyrus bilaterally, the left inferior occipital gyrus, and the flocculonodular lobe (posterior cerebellum) (Figure 2). The linear regression analysis with the interaction between VEGFA or E‐selectin and group did not present a significant association with [18F]FDG‐PET signal.ConclusionThe results provide preliminary insights into the complex interactions between endothelial dysfunction and cerebral glucose metabolism in long‐COVID. The regions presenting significant associations are involved in cognition, memory, sensory and motor function, language and visual processing, indicating a potential widespread brain vascular response. Additional studies are needed for a comprehensive understanding of these relationships in long‐COVID.
Early Identification of Alzheimer’s Disease with a Machine Learning‐Enabled Digital Cognitive Assessment: Concurrent Detection of Cognitive Impairment and Amyloid‐Beta PET Status
Authors: Jannati Ali , Thompson Karl , Toro‐Serey Claudio , Higgins Connor , Banks Russell , Showalter John , Bates David , Tobyne Sean , Pascual‐Leone Alvaro 
Published: January 2025

AbstractBackgroundMaximizing the benefits of disease‐modifying treatments (DMTs) for Alzheimer’s disease (AD) requires early identification of cognitive impairment and abnormal brain amyloid‐beta (Aβ) status. Either one alone is insufficient. Additionally, clinical trials of DMTs are impeded by high screen failure rates and costly prescreening. Thus, an efficient and cost‐effective solution to streamline the process of early AD identification is urgently needed. This study aimed to assess the accuracy of a brief digital cognitive assessment, the Linus Health Digital Clock and Recall (DCR), to identify cognitive impairment and predict brain Aβ status.Method930 participants (mean age 72.0±6.7; 56.8% female; 23% minorities) in the Bio‐Hermes‐001 study were classified as cognitively unimpaired, mild cognitive impairment, or probable Alzheimer’s dementia, and 35.1% were Aβ+ on 18F‐florbetapir PET scan. A DCR‐based algorithm (LinusAD) used age, APOE status, drawing metrics, speech and acoustic features, and temporal‐spatial features of stylus manipulation. LinusAD was compared with MMSE and blood‐based biomarkers (BBMs) Lilly pTau‐217, Quanterix pTau‐181, C2N Aβ42/40, and C2N Amyloid Probability Score (APS). Superiority or non‐inferiority was established if 95% confidence interval of the bootstrapped AUC difference between two tests was higher than or within ΔAUC±0.1.ResultCognitive‐impairment identification by the DCR (AUC=0.85) was superior to Aβ42/40, pTau‐181, and pTau‐217 (AUCs=0.63, 0.66, 0.72) and non‐inferior to RAVLT and MMSE (AUCs=0.89, 0.82). Aβ‐status prediction by LinusAD (AUC=0.882) was equivalent to BBMs (AUC range: 0.775–0.887; average AUC=0.82) and superior to MMSE (AUC=0.71) (see Table and Figure). Combining the DCR with Aβ42/40, pTau‐181, and pTau‐217 improved their Aβ‐status prediction performance to AUCs of 0.882, 0.835, and 0.905, respectively.ConclusionThe 3‐minute DCR was superior to BBMs in detecting cognitive impairment and boosted their Aβ‐PET prediction ability to levels comparable to CSF biomarkers of AD. Digital cognitive assessments that leverage AI process metrics, such as the DCR, enable cost‐effective integration into multi‐step clinical workflows to prioritize the most suitable patients for BBM testing, DMTs, and decrease high screen‐failure rates in AD clinical trials.
Psoas Muscle Sarcopenia Predicts Brain Volume Loss on MRI in 7,149 Individuals
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

AbstractBackgroundSarcopenia has been linked to brain atrophy and there is lack of information on specific muscle groups that may contribute to this link. The psoas muscles are sensitive to sarcopenia and thus may sensitively relate to brain aging and Alzheimer disease risk.MethodThis study utilized 7,149 healthy individuals across four sites (Mean age 53.06 ± 12.95 years, age range 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 included coronal T1 for segmentation of psoas muscle volumes. Deep learning with FastSurfer on 3D T1 volumetric MPRAGE trained on 134 participants aged 27‐66 segmented 96 brain regions. Partial correlation analysis was done on psoas muscle volumes and brain regions controlling for sex and total intracranial volume to determine if lower psoas muscle volumes correlated to brain atrophy. Multiple comparisons were accounted for using the Bonferroni Method.ResultLower psoas muscle volumes demonstrated a statistically partial correlation to lower gray matter (rp = ‐0.164, p = 2.66×10−43) and white matter volumes (rp = ‐0.171, p = 4.39×10−47). The frontal lobe exhibited a negative correlation (rp = ‐0.172, p = 1.30×10−47), identical to the temporal lobe (rp = ‐0.172, p = 1.21×10−47). Negative correlations were also noted in the parietal lobe (rp = ‐0.119, p = 8.72×10−23), and occipital lobe (rp = ‐0.139; p = 2.89×10−31). The hippocampus also showed a statistically significant negative correlation (rp = ‐0.145, p = 7.65×10−34), as did the posterior cingulate (rp = ‐0.162, p = 5.20×10−42) and the precuneus (rp = ‐0.101, p = 1.34×10−16). The cerebral ventricles showed a non‐significant negative correlation (rp = ‐0.025, p = 0.345).ConclusionWe demonstrate a significant correlation between lower psoas muscle volume and brain volume loss, including AD risk regions such as the hippocampus and precuneus. These MRI findings underscore the importance of muscle health in relation to brain integrity. The volume of the psoas, which is involved in basic leg flexion activities such as walking, could be a crucial indicator of brain health and Alzheimer risk.
Immunomagnetic Separation and Magnetic‐Susceptibility‐Based Quantification of AD Biomarkers Using Ultra‐Magnetic Iron Oxide Nanorod Agent Magfiniti®
Authors: Li Yuancheng , Mao Hui , Padelford Jonathan , Ma Hedi 
Published: January 2025

AbstractBackgroundEnriching and detecting Alzheimer’s disease (AD) biomarkers in cerebral spinal fluid (CSF) or blood samples are increasingly applied in the AD diagnosis and monitoring of disease progression and treatment response. The accuracy of these processes is dependent on the sensitivity and specificity of capturing and quantifying AD biomarkers, e.g., Aβ40 and Aβ42, in the samples. This report introduces a new immunomagnetic separation based biomarker detection system, which employs AD‐biomarker‐targeting peptide functionalized ultra‐magnetic iron oxide nanorods Magfiniti® to rapidly capture and separate AD biomarkers, coupled with measurement of change of the magnetic susceptibility upon capturing AD biomarkers.MethodThe colloidal stability over 24 hours and magnetic separation efficiency of Magfiniti® were determined using UV‐Vis spectroscopy. The capacity of Magfiniti® in avoiding non‐specific protein adsorption, with Dynabeads® as a control, was examined by incubating with human serum followed by quantifying adsorbed proteins. Synthetic γ‐AApeptides with proved targeting effect for Amyloid beta peptide 1‐40 (Aβ40) and 1‐42 (Aβ42) were conjugated to Magfiniti® for detecting Aβ40 and Aβ42 spiked in human serum (100 to 2000 pg/mL). After a 30‐min incubation with serum samples, γ‐AApeptide‐conjugated Magfiniti® with captured biomarkers were magnetically separated (10 min), washed (2X) with and re‐dispersed in PBS. Biomarker‐bound Magfiniti® were then measured for the AC magnetic susceptibility (χ’) using a XacQuan II AC susceptometer, with blank Magfiniti® with the same concentration measured as the baseline. The linear correlations between the difference of magnetic susceptibility (Δχ’) and Aβ concentrations were evaluated by the coefficient of determination (R2).ResultMagfiniti® were stably dispersed in water, with >95% separated magnetically in 5 min. Magfiniti® exhibited minimal adsorptions by serum proteins (∼5 µg/mg Fe) with the incubation time varying from 5 to 60 min, whereas proteins on Dynabeads® ranged from 61.3 ± 12.7 to 223.3 ± 22.5 µg/mg Fe, as the incubation time increased. Excellent linear correlations (R2=0.9999) between Δχ’ and Aβ concentrations with standard deviations <1% were achieved, when Δχ’ were measured at the AC frequencies of 500, 1000, and 2000 Hz.ConclusionThe Magfiniti®‐based AD biomarker measurement may provide an alternative approach for liquid biopsy of AD using routinely collected blood samples.
Delayed blood arrival partially mediates the association between arterial dilatation and cerebral small vessel disease
Authors: Robb W Hudson , Zhang Panpan , Shah Krish U , Houston Michelle L. , Shashikumar Niranjana , Shashikumar Niranjana , Pechman Kimberly R. , Liu Dandan , Davis L. Taylor , Jefferson Angela L. , Hohman Timothy J. 
Published: January 2025

AbstractBackgroundCerebral arterial dilatation, signifying outward vascular remodeling, is linked to a higher risk of Alzheimer’s disease and a higher burden of white matter hyperintensities (WMH). Arterial dilatation may disrupt cerebral hemodynamics and lead to delayed blood arrival to the brain, which is itself linked to an increased burden of WMH. We examined if arterial dilatation was associated with blood arrival timing and if blood arrival timing mediated the effect of arterial dilatation on WMH burden.MethodsVanderbilt Memory and Aging Project participants free of clinical dementia or stroke at enrollment (n=249, 73±8 years, 85% cognitively unimpaired, 41% female) underwent multimodal 3T brain magnetic resonance imaging. Internal carotid artery (ICA) and basilar artery (BA) arterial inner diameters were quantified by an expert rater using vessel wall imaging. An established marker of blood arrival timing, the arterial spin labeling spatial coefficient of variation (ASL‐sCoV), was quantified using pseudo‐continuous ASL. Higher ASL‐sCoV indicates slower blood arrival. WMH burden was quantified using semi‐automated methods. Linear regressions related ICA and BA inner diameters to ASL‐sCoV and log‐transformed WMH burden in anterior and posterior arterial territories, individually. Models were adjusted for age, sex, race/ethnicity, cognitive status, Framingham Stroke Risk Profile, intracranial volume, and apolipoprotein E‐ε4 status. We also assessed if ASL‐sCoV mediated associations between arterial diameters and WMH burden.ResultsLarger ICA inner diameter was associated with higher anterior ASL‐sCoV (p=1.7x10‐8) and higher anterior WMH burden (p=9.6x10‐5). Anterior ASL‐sCoV partially mediated the association between ICA inner diameter and anterior WMH burden (proportion mediated=15%, p=0.04). Larger BA inner diameter was associated with higher posterior ASL‐sCoV (p=1.3x10‐8) but not with higher posterior WMH burden (p=0.06).ConclusionsOur study illustrates novel links between cerebral vascular structure, cerebrovascular hemodynamics, and vascular brain pathology. Because most of the effect of arterial dilatation on WMH burden was not mediated through blood arrival timing, future work will consider other potential mediators, such as arterial stiffness.
Microvascular physiology differs in older adults with varying vascular risk and white matter lesion burden
Authors: Gassner Gabriele M , Damestani Nikou L , Yadav Shrikanth M , Wheeler Natalie S , Jacoby John R , Mellen Sarah F , Maina Katherine N , Salat David H , Juttukonda Meher R 
Published: January 2025

AbstractBackgroundWhite matter lesions (WMLs) are common with aging and are prevalent in AD, but the underlying physiology as well as associations with conventional vascular risk factors are not yet fully understood. In this study, we investigated the relationship between vascular risk factors and microvascular physiology (i.e., oxygen supply and oxygen extraction fraction), and their association with WML burden in older adults.MethodStudy design: Typically‐aging older adults between 60–80 years (n=42) were enrolled and categorized as ‘high’ (VRF+; n=27) or ‘low’ vascular risk (VRF−; n=15) based on medication use, clinical diagnosis, and disease‐specific markers of four modifiable conditions: hypertension, diabetes, hyperlipidemia, and overweight. Participants were further subdivided into ‘high’ or ‘low’ WML burden based on positive or negative residuals from a regression of WML burden against age.Hemodynamic imaging: Magnetic resonance imaging (MRI) data was acquired using pseudo‐continuous arterial spin labeling (ASL) for cerebral blood flow (CBF) and T2‐relaxation‐under‐spin‐tagging (TRUST) MRI for oxygen extraction fraction (OEF). Cerebral blood blow (CBF) was computed using a two‐compartment model and with accounting for arterial transit time. Cerebral oxygen supply was computed as the product of CBF, arterial oxygen saturation, and the oxygen transport capacity of blood. Venous oxygenation was derived from TRUST, and OEF was computed as the relative ratio between arterial oxygen saturation (from pulse oximetry) and venous oxygenation.ResultParticipants did not differ in oxygen supply, but OEF was significantly higher in VRF+ (39.22%) versus VRF− (35.2%) individuals (p = 0.02; Figure 1). Within the VRF+ group, no differences were observed in oxygen supply or OEF were found when comparing high versus low lesion burden individuals. Within the VRF− group, no differences in oxygen supply were observed, but OEF was significantly lower in the high lesion burden (30.4%) compared to the low lesion burden (37.6%) subgroup (p = 0.01; Figure 2).ConclusionOur findings suggest that lower OEF may represent a marker of impaired physiology associated with WML burden in the absence of conventional vascular risk factors. Such impairment could arise from disturbed capillary transit patterns, and future work will investigate these mechanisms.
Dynamic Resting State Functional Connectivity in Cognitive Resilience of Alzheimer's Disease
Authors: Liu Lin , Zhu Yalin , Li Anqi , Fang Lili , Ma Shaohua , Guo Tengfei 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) is characterized by a decline in cognitive abilities, with cognitive resilience (CR) denoting the capacity of AD patients to withstand such declines. Prior studies have linked the segregation of functional networks with cognitive resilience in AD. The emergence of dynamic functional connectivity (dFC) is a notable advancement in the assessment of brain network dynamics of CR features in AD.MethodsOur present study adopted a novel metric (temporal variability), which has been successfully applied to many clinical diseases, to examine the dynamic functional connectivity of CR features in 164 AD participants into reference group of 96 with cognitive unimpairments (CU) and CR group of 68 with high CR. The specific calculation formula is in Figure 2, because the formula cannot be added to the text.ResultsFigure 1 A‐B illustrates the flow chart used to calculate the separation coefficient within and between brain networks. Upon comparing the high cognitive elasticity group with the control group, we observed a significant decrease in the separation coefficient within and between the SMN, DMN, SUB, and CN brain networks in the high cognitive elasticity group (Figure 1C‐D).ConclusionsThrough dFC features pertinent to CR can be obtained and have been corroborated in different clinical diagnosis groups and cognitive function scores.