bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application value of structural MRI combined with ASS1 in early diagnosis of Mild Cognitive Impairment
Authors: Chen Lele , Fan Xiang , Shan Hui , Yu Keyan , Lu Gaigai , Wei Zhuonan , Cheng Guanxun 
Published: January 2025

AbstractBackgroundMild cognitive impairment (MCI) refers to a transitional stage between the cognitively unimpaired (CU) and dementia, which is probable in the Alzheimer’s disease (AD) continuum. Neuropsychological assessments and structural MRI are primary clinical examinations applied for diagnosing MCI. Hippocampal volume (HV) and medial temporal lobe atrophy (MTA) as recognized AD biomarkers are the most frequently used imaging markers in clinical application. However, HV or MTA is not sensitive enough in the early stage of AD. Recently, blood‐based biomarker analysis has demonstrated great potential for early AD diagnosis because it is simple and less invasive. It has been shown that argininosuccinate synthetase (ASS1) expression is increased in Alzheimer’s disease neurons and glial cells, which also correlates with inducible nitric oxide synthase (iNOS) expression, and that there is a link between the increased NO production observed in Alzheimer’s disease brains and iNOS expression. We aim to study whether ASS1 can provide additional value compared with HV in diagnosing MCI and validate the diagnostic utility of the combined model.MethodTwenty MCI patients and 20 CU were recruited and underwent neuropsychological assessment, MRI examinations, and the collection of blood. Protein quantification was analyzed by high‐resolution tandem mass spectrometry (MS/MS), and the ROC curve was plotted for selected biomarkers. The automated structure tool and SPSS 26.0 were used to analyze the HV and ASS1. We investigated the area under curve (AUC) of HV, and the combined model using both ASS1 and HV based on the logistic regression. The Delong test is used to compare the combined model with the single HV.ResultThe AUC of diagnosing MCI by HV was 0.788 (95%CI: 0.629 ‐0.901). When combining HV with ASS1, the AUC increased to 0.938 (95%CI: 0.813‐0.989). The Delong test results indicated a significant difference between the single HV and the combined model. (P = 0.0188).ConclusionCombining plasma ASS1 could provide significantly additional value in detection of MCI, which also means plasma ASS1 and HV may be a potential early biomarker combination in diagnosing early AD.
GENETIC DELETION OF THE PRESINILIN‐LIKE PEPTIDASE SPPL2B SIGNIFICANTLY REDUCES NEUROINFLAMMATION AND HALTS PRODUCTION AND PLAQUE DEPOSITION IN PRE‐CLINICAL MODELS OF Alzheimer's DISEASE
Authors: Badman Jack , Chen Gefei , Garcia Sandra Pérez , Nilsson Per , Tambaro Simone 
Published: January 2025

AbstractBackgroundSignal Peptide Peptidase‐Like 2b (SPPL2b) is relevant for AD, being a brain‐specific intramembrane protein involved in the cleavage of Alzheimer's disease (AD)‐related proteins, such as BRI2, inflammatory‐related proteins like CD74, TNFalpha, and Clec7a, and synaptic proteins Neuregulin‐1 and VAMP 1‐4. SPPL2b is specifically expressed in the hippocampus and cortex. The cleavage of TNFalpha by SPPL2b promotes the inflammatory pathway. Conversely, the SPPL2b substrate BRI2 regulates amyloid precursor protein (APP) cleavage and subsequent Aß production. This work explores the pathophysiological role of SPPL2b in AD pathogenesis and investigates the potential of inhibiting SPPL2b activity as a novel therapeutic strategy.MethodTo characterize the role of SPPL2b in the APP cleavage process and neuroinflammation, we used neurons and glia primary cell cultures from WT and SPPL2b‐deficient mice. Furthermore, we assessed the therapeutic potential of inhibiting SPPL2b by employing a new AD mouse model generated through crossbreeding state‐of‐the‐art AppNL‐G‐F knock‐in mice with SPPL2b‐deficient mice. Cells and mouse brain samples were analyzed through western blotting, immunoprecipitation, and immunofluorescence. Aß levels were quantified using ELISA kits.ResultThe results showed a significant increase in BRI2, followed by a substantial reduction in soluble APP, Aß40, and Aß42 levels in the conditioned media of SPPL2b KO neurons. Immunoprecipitation of BRI2 from the cortex of those mice revealed an increased interaction between BRI2 and APP in SPPL2b KO/AppNL‐G‐F mice. Most importantly, a notable decrease in brain Aß plaque deposition and a significant decrease in both cortical and hippocampal gliosis were observed at 4 months‐of‐age in SPPL2b KO/AppNL‐G‐F mice.ConclusionThe results outlined in this study support a relevant connection between SPPL2b and the development of Aß pathology in AD. In a global scenario characterized by the need to identify novel strategies to prevent and counteract AD progression, this study highlights and strengthens the importance of SPPL2b as a novel therapeutic approach for AD.
Chronic Medical Conditions and Dementia Risk: Brain Age Models for Quantifying Impact and Understanding Mechanisms
Authors: Yu Xi , Przybelski Scott A. , Reid Robert I. , Gebre Robel K , Lesnick Timothy G. , Vassilaki Maria , Graff‐Radford Jonathan , Knopman David S. , Petersen Ronald C. , Jack Clifford R. , Vemuri Prashanthi 
Published: January 2025

AbstractBackgroundChronic medical conditions increase dementia risk, and the mechanism may be captured computationally by measures of accelerated brain aging. We hypothesized that quantifying the impact of chronic conditions on GM and WM aging will aid in measuring the differential impact of each condition on brain aging and shed light on the mechanisms through which they increase dementia risk. We specifically focused on microstructural brain changes with Neurite Orientation Dispersion and Density Imaging (NODDI) based on advanced diffusion MRI ‐ neurite density index (NDI), free water fraction (FWF), and orientation dispersion index (ODI).MethodWe identified 1123 participants [913 cognitively unimpaired (CU), 114 with MCI, 96 with AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with multi‐shell diffusion MRI. MCSA participants had chronic medical conditions’ diagnosis (based on US Dept of HHS) extracted from the electronic health care records. Using the BorutaSHAP algorithm on NDI, ODI, and ISOVF features from GM and WM separately, we constructed two distinct brain age models for GM and WM. First, we evaluated the usefulness of the predicted age gap (PAG), which is the difference between predicted age by model and chronological age, for both WM and GM NODDI models in CU/MCI/AD. We then developed separate GM and WM models for each condition using samples from condition‐negative participants to compare PAG of GM and WM for that condition.ResultThere was greater PAG in GM (avg = ) than WM (avg = 9.15 yrs) in MCI/AD participants (Fig. 1A). Each condition shifted the GM PAG by 0.10 to 3.56 years and WM PAG by 0.38 to 3.72 years with stroke and congestive heart failure showing the greater PAG overall for GM and WM PAD respectively (Fig. 2). COPD, stroke, chronic kidney disease, hypertension had comparatively greater GM PAG than WM PAG; while hyperlipidemia, congestive heart failure and coronary artery disease had greater WM PAG than GM PAG (Fig. 1B).ConclusionChronic conditions are associated with increased GM and WM Brain aging models can aid in quantifying the impact of chronic conditions on dementia risk and understanding mechanisms.
Deep learning‐based estimation of future brain atrophy using baseline MRI and PET
Authors: Le Linh N. N. , Fletcher Evan , Qi Jinyi , Fan Audrey P. 
Published: January 2025

AbstractBackgroundCortical brain atrophy is an excellent marker of clinical decline and can support future clinical course prediction in cognitive impairment. We used a U‐Net image‐generation deep learning network to predict future cortical atrophy rates in elderly populations, with initial T1‐weighted (T1w‐) MRI or baseline amyloid‐PET serving as inputs to the model.MethodMRI and PET data were retrospectively collected from Alzheimer’s Disease Imaging Initiative (ADNI) and all participants had two serial T1w‐MRI scans (Figure 1A,B). The ground truth was an atrophy map (in log‐Jacobian values) generated from Tensor Based Morphometry (TBM) of the two longitudinal MRIs (Figure 1E). A U‐Net architecture was used in this study to predict maps of cortical atrophy. We used mean squared error (MSE) for loss function and five‐fold cross‐validation (Figure 1C,D). The testing set was randomly chosen (n = 30). Separate models were trained for T1w‐MRI data as input (T1‐model) and amyloid‐PET as input (PET‐model). The predicted atrophy rate was evaluated across diagnosis groups in the test set using the whole‐brain structural similarity index measure (SSIM) to the TBM ground truth. Each model’s average atrophy at selected brain regions was compared with TBM using paired t‐tests.ResultFigure 2 shows average predicted atrophy maps and corresponding error for each diagnosis group. In the whole brain, for cognitively normal (CN) and mild‐cognitive‐impaired (MCI) groups, T1 model (with SSIMs of 0.67±0.07 and 0.64±0.06, respectively) slightly outperformed PET model (with SSIMs of 0.70±0.06 and 0.69±0.06, respectively). In selected regions, especially gray matter, we observed a larger rate of atrophy in Alzheimer’s Disease (AD) compared to MCI and in MCI compared to CN groups in both deep learning models, which was consistent with the ground truth. There was no statistical difference in average atrophy predicted by either model and TBM values for all diagnostic groups in all selected regions, with relatively small absolute error and MSE in both models (Table 1).ConclusionA U‐Net deep learning model trained using only baseline T1 weighted MRI or amyloid PET data could be used to predict future atrophy rates. Both models predicted cortical volume loss and ventricle expansion that differed between AD, MCI, and CN.
Harmonized Tractometry Visualizes Alzheimer’s Disease Effects on the Brain’s White Matter Tract Microstructure
Authors: Chandio Bramsh Q , Reina Julio E Villalon , Nir Talia M , Thomopoulos Sophia I , Feng Yixue , Benavidez Sebastian M , Jahanshad Neda , Harezlak Jaroslaw , Garyfallidis Eleftherios , Thompson Paul M. 
Published: January 2025

AbstractBackgroundDiffusion MRI (dMRI) metrics of brain microstructure offer valuable insight into Alzheimer’s disease (AD) pathology; recent reports have identified dMRI metrics that (1) tightly link with CSF or PET measures of amyloid and tau burden; and (2) mediate the relationship between CSF markers of AD and delayed logical memory performance, commonly impaired in early AD [1,2]. To better localize white matter tract disruption in AD, our BUndle ANalytic (BUAN) [3] tractometry pipeline allows principled use of statistical methods to map factors affecting microstructural metrics along the 3D length of the brain’s fiber tracts. Here, we extended BUAN to pool data from multiple scanning protocols/sites ‐ using a new harmonized tractometry approach, based on ComBat [4,5], a widely‐used harmonization method modeling variations in multi‐site datasets due to site‐ and scanner‐specific effects.MethodWe illustrate the effects of integrating ComBat into our BUAN tractometry pipeline. Analyzing Alzheimer’s Disease Neuroimaging Initiative (ADNI3) [6] data, we examined the impact of mild cognitive impairment (MCI) and AD on 38 white matter tracts, comparing results with and without harmonization. We analyzed data from 730 ADNI3 participants, scanned with seven dMRI protocols. After data preprocessing with the ADNI3 protocol [7,8], BUAN extracted 38 bundles and created along‐tract bundle profiles for microstructural metrics: FA, MD, AD, and RD (see Fig. 1 for full names). ComBat was applied to each point in bundle profiles to correct for scanner protocol effects and linear mixed models (LMM) were used to study the group differences in MCI and AD versus cognitively healthy controls (CN).ResultHarmonized BUAN maps reveal AD and MCI effects throughout specific tracts (e.g., the left cingulum and right arcuate fasciculus; Fig. 1, bottom panel). ComBat harmonization enhanced the sensitivity to detect group differences compared to LMM without scanner correction. Adding data ‐ even from different scanner protocols ‐ boosted power (Fig. 2f‐g).ConclusionIn this application of harmonization in neurodegenerative tractometry analysis, we integrated ComBat into BUAN tractometry to merge dMRI data from diverse scanning protocols and sites. Future research will examine various ComBat versions and deep‐learning approaches to track AD pathology effects on the brain’s neural circuitry.
Using a Bayesian workflow approach to define the optimal level of anatomy/functionality voxels clustering for the prediction of tau propagation and cortical loss in Alzheimer's disease
Authors: Soucy Jean‐Paul , Belasso Clyde J , Cai Zhengchen , Bezgin Gleb , Stevenson Jenna , Rahmouni Nesrine , Tissot Cécile , Lussier Firoza Z , Rosa‐Neto Pedro , Rivaz Hassan J , Benali Habib 
Published: January 2025

AbstractBackgroundTau aggregates in Alzheimer’s disease (AD) induce loss of synapses and neurons, leading to cognitive impairment. Predicting tau and neurodegeneration temporal evolution could be used for prognostication and for assessing results of therapeutic trials. Tau PET and MRI volumetry are reliable markers of disease stage, but cost and radiation protection considerations limit research measurement frequency, lowering the accuracy of disease progression modeling. Here, we evaluate, using Bayesian analysis, whether models based on limited numbers of observations can be refined to better predict the temporal trajectory of pathology.MethodImaging data comes from subjects (113; 68 females; 18 AD dementia, 23 MCI and 72 cognitively normal) of the TRIAD cohort (McGill University) who have been evaluated at least twice ( 1 year interval) with both tau PET ([18F]MK‐6240) and structural MRI. Four probability models were evaluated: 1‐ a basic one, assuming that all data points come from 1 data distribution; 2‐ one where subjects' observations are clustered within anatomical ROIs, where an independent distribution is hypothesized; 3‐ data is clustered within known physiological networks, each networks’ distribution parameters having their own specific values ; 4‐ a model assuming that subjects’ observations are described by a distribution of voxel parameters dictated by both the ROI and network(s) in which they lay. Bayesian data analysis was used to compare the predictive accuracy of those models for progression at 1 year from baseline of tau PET and MRI data.ResultModel 4 was the most accurate model for both tau and cortical thickness prediction. We therefore used it to perform posterior predictions across hemispheres, showing that the prediction curves of the left and right hemispheres for the pericalcarine cortex differ. We also noticed a decreasing trend in the CN tau curve for the left hemisphere as the rate of cortical thinning increases. In contrast, there is an increasing trend in the AD tau curve as the rate of cortical thinning increases.ConclusionThe model that incorporated both ROI‐level and network‐level information was the best predictor of progression, and such an approach can reveal underappreciated properties of the disease (i.e., laterality).
Longitudinal stability of extra‐cerebral off‐target binding in [F‐18]MK6240 PET images
Authors: McVea Andrew K , DiFilippo Alexandra H , McLachlan Max , Bettcher Brecca , Johnson Sterling C. , Betthauser Tobey J. , Christian Bradley T. 
Published: January 2025

AbstractBackgroundThe spread of tau in Alzheimer’s Disease (AD) can be tracked in vivo using [F‐18]MK6240, a PET radioligand that binds to tau aggregates in AD with high affinity. However, significant MK6240 signal is also observed in the meninges and sinus and the extra cerebral binding (ECB) signal from these regions can spill into exterior brain regions complicating evaluation of early stage AD tauopathy. This study evaluates the magnitude and variability of ECB in a large imaging cohort to identify trends in this signal.MethodParticipants included in this study were imaged at the University of Wisconsin–Madison on a Biograph mCT (n = 488) or ECAT HR+ (n = 475) PET scanner including 208 with longitudinal data taken an average of 2.0±1.0 years apart. Reconstructed PET images were smoothed using a 6mm3 gaussian kernel then processed using a standardized pipeline to generate SUVR images using the inferior cerebellar grey matter reference region. Meninges and sinus SUVR were compared across demographic groups of interest within the cohort and longitudinal changes in signal were quantified using the absolute change from baseline to follow up scans.ResultsWhile there were not significant differences in ECB signal seen with age (pmeninges= 0.89, psinus= 0.98), amyloid (pmeninges= 0.43, psinus= 0.95) or tau statuses (pmeninges= 0.18, psinus= 0.35) a significantly higher average SUVR in the meninges and sinus for female (pmeninges< 0.001, psinus< 0.001) and APOE4+ participants (pmeninges= 0.007, psinus= 0.05) was observed. Longitudinally there was an average absolute SUVR difference between scans of 0.21±0.34 (12.1±10.9%) in the meninges and 0.24±0.33 (12.9±10.4%) in the sinus. This is more than triple the average longitudinal SUVR variability of 0.06±0.06 (6.5±6.0%) in the entorhinal region in our amyloid negative participants. APOE4+ participants additionally showed a significantly higher absolute change in the meninges (p = 0.05), however, the increase in the sinus was not significant (p = 0.59).ConclusionsMK6240 ECB can be highly variable and has the potential to bias tracer uptake outcomes. While we observed trends related to sex and genetic risk factors, further work to identify the underlying sources and correct for ECB is ongoing.
Integrating Connectomics Constraints in Event‐Based Modeling of Alzheimer's Disease Progression: An Analysis of ADNI PET Data
Authors: Vahdatian Mahdieh , Zhao Yuji , Kurmukov Anvar , Gutman Boris A 
Published: January 2025

AbstractBackgroundAlzheimer's Disease (AD) presents a major health challenge, with complex and variable neurodegenerative progression. Traditional neuroimaging falls short in fully capturing this heterogeneity. Our study addresses this gap by applying an Event‐Based Model (EBM) to Alzheimer's Disease Neuroimaging Initiative (ADNI) Positron Emission Tomography (PET) data, enriched with connectomics data. This innovative approach promises a more individualized understanding of AD progression, combining PET imaging with insights into brain connectivity from connectomics. Our work aims to enhance early diagnosis and personalized treatment strategies in AD research.MethodOur study applied an Event‐Based Model (EBM) to Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets across ADNI1, ADNI2, and ADNI3. We analyzed a diverse cohort including Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Late MCI (LMCI), Early MCI (EMCI), and cognitively normal (CN) groups, using Amyloid (AMY), Fluorodeoxyglucose (FDG), and Tau PET scans. The methodology integrated connectomics data and employed Monte Carlo Markov Chain (MCMC) techniques for enhanced modeling precision. This approach provided an in‐depth understanding of AD progression, combining advanced statistical analysis with diverse neuroimaging data.ResultOptimal order of neurodegenerative events ‐ stages of disease progression as measured by changes in metabolic signature (FDG) or Amyloid accumulation ‐ largely followed previously published results. Earliest regions of Amyloid accumulation mirrored the default mode network (Figure 1). Metabolic changes, notably reduction in FDG SUVR, occurred earliest in the same top 10 regions. Also Z‐test statistic (‐4.78): Indicates a significant difference between the average stages of AD and MCI patients, with AD stages being lower than those of MCI. P‐value (∼0.00000177): Implies an extremely low probability that the observed difference in stages occurred by chance, strongly suggesting a true difference between the groups.ConclusionWe have presented a novel connectome‐informed progression model of amyloid‐beta accumulation and metabolic changes in the brain. The model discriminates well between stages of cognitive decline and suggests that amyloid accumulation and metabolic changes both follow a similar early of pattern conditioned on white‐matter connectivity
Plasma and neuroimaging biomarkers of small vessel disease and Alzheimer’s disease in a diverse cohort
Authors: Lockhart Samuel N. , Sutphen Courtney L. , Tanley Jordan E. , Gonzalez‐Ortiz Fernando , Kac Przemyslaw Radoslaw , Habes Mohamad , Heckbert Susan R. , Ashton Nicholas J. , Mielke Michelle M. , Whitlow Christopher T , Hiatt Kevin , Craft Suzanne , Register Thomas C. , Hayden Kathleen M. , Rapp Stephen R. , Zetterberg Henrik , Blennow Kaj , Karikari Thomas K , Hughes Timothy M. 
Published: January 2025

AbstractBackgroundLittle is known about how plasma Alzheimer’s disease (AD) biomarkers relate to neuroimaging biomarkers of cerebral small vessel disease (cSVD) in the context of neurodegeneration and AD pathology in late life.MethodThis cross‐sectional study included 251 Multi‐Ethnic Study of Atherosclerosis (MESA) Exam 6 participants with plasma AD biomarkers (Aß42/Aß40, GFAP, NfL, p‐tau181, p‐tau217, p‐tau231; Quanterix SIMOA), MRI (neurodegeneration and cSVD), PiB (amyloid) PET, and UDSv3‐based adjudicated cognitive status (69% cognitively normal, 27% MCI, 4% probable dementia) data at the Wake Forest site. Multivariable models examined relationships among cognitive status, plasma, and neuroimaging biomarkers (covariates: age, education, race, gender, smoking status, kidney function [eGFR], APOE‐e4, BMI; significance at p<.05 uncorrected).ResultCognitive status associated with imaging biomarkers and with plasma biomarkers of Aß42/Aß40, GFAP, p‐tau217 and p‐tau231. No NfL or p‐tau181 group differences were observed (Table 1). Lower plasma Aß42/Aß40 (Table 2) was associated with lower gray matter (GM) volume (GMV) and hippocampal volume (HCV); higher GFAP levels were associated with lower HCV. Higher NfL levels were associated with lower GMV and HCV, and with higher GM Free Water (FW). Higher p‐tau217 levels were associated with lower GMV. Plasma p‐tau231 was not associated with neurodegeneration imaging biomarkers. Higher NfL levels were associated with higher white matter (WM) hyperintensity (WMH) volume and WM FW. All plasma tau measurements were positively associated with WM FW but not other cSVD biomarkers. Lower Aß42/Aß40 ratio was significantly associated with greater prevalence of microbleeds. Higher GFAP was associated with higher WMH volume. As anticipated, lower plasma Aß42/Aß40 ratio and higher p‐tau217 and p‐tau231 were associated with higher Aß deposition and odds of Aß positivity (Table 3); a doubling in the level of p‐tau217 or p‐tau231 was associated with a 2‐3 fold increase in the odds of Aß PET positivity.ConclusionWe found that plasma GFAP, NfL, p‐tau181, p‐tau217, and to a lesser extent p‐tau231 are associated with cognitive status, vascular comorbidities and imaging biomarkers of cSVD, in addition to measures of AD‐related pathology. Importantly, we demonstrated novel associations between plasma AD biomarkers of p‐tau and NfL with cSVD measures of WM structural health.