bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing PET Intensity Normalization for the Analysis of Cerebral Metabolism: A Study in Simulated and Real Data from an Alzheimer’s Disease Model
Authors: Chamberlain‐Alvarado Helena , Cortes‐Canteli Marta , Casquero‐Veiga Marta 
Published: January 2025

AbstractBackgroundAlzheimer’s Disease (AD) has a long preclinical stage, in which brain metabolic alterations precede the symptoms onset. Therefore, an early and proper diagnosis of AD is essential for prevention and therapeutic evaluation. Current diagnosis and staging procedures rely on neuroimaging techniques, such as positron emission tomography (PET), which require intensity normalization to ensure the correct interpretation of the data. However, the variety of normalization methods and lack of standardization produce a major drawback regarding the proper interpretation of AD data by these techniques.MethodWe formally analyzed the performance of six well‐established intensity normalization methods (proportional scaling and data‐driven approaches) on brain PET images of fluorodeoxyglucose (FDG) obtained from healthy mice (N = 10). The accuracy of each method was determined based on the recovery of different computer‐simulated patterns in the FDG‐PET scans achieved with voxel‐wise analyses. Furthermore, we applied this newly acquired knowledge to evaluate differences in cerebral FDG uptake in 16 and 62 weeks‐old AD mice (N16w = 7; N62w = 10) of the TgCRND8 model, and their wild‐type littermates (N16w = 10; N62w = 5).ResultOur results showed a remarkable ambivalence between and within each normalization method under different simulation scenarios. They supported the use of an iterative histogram‐based normalization (iHN, Martí Fuster et al., 2013) when the data compared is not clearly biased (Fig. 1B). Nonetheless, the reference cluster technique based on preserved regions (Yakushev et al., 2009) stood out for patterns with large and accentuated bias in the data (Fig. 1A). Thus, the iHN method helped us to evaluate AD‐related differences in real FDG‐PET images, identifying genotypic effects that change with age (Fig. 1C).ConclusionThis study evidences the crucial importance of understanding the distribution of the radiotracer uptake in PET studies before selecting an intensity normalization strategy. Furthermore, our results provide simple and accurate guidelines to help to choose a proper normalization method in preclinical PET imaging, demonstrating its applicability in real data from AD mice. References: Martí Fuster et al., 2013. Neuroinformatics 11(1), 77‐89. Yakushev et al., 2009. Neuroimage 44(1), 43‐50. Funding: EU Joint Programme – Neurodegenerative Disease Research (JPND), BrightFocus Foundation, and JDC2022‐048922‐I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR.
Predicting Cognitive Decline: A Comprehensive Study On relative Brain Age, Cognitive Reserve, And Longitudinal Changes
Authors: Motaghi Mahboubeh , Potvin Olivier , Turcotte Valérie , Beheshti Iman , Duchesne Simon 
Published: January 2025

AbstractBackgroundBrain age is a metric that can be determined through anatomical measurements obtained from MRI scans. Relative brain age (RBA) reflects the difference in brain morphometry for an individual relative to a comparison group at a similar chronological age. Increased RBA was found related to poor physical health, neurodegeneration, increase RBA, cognitive decline, and mortality risk. The concept of cognitive reserve (CR), operationalized by merging education, complexity of occupation, and verbal IQ, explains on the other hand how individuals seem able to tolerate the impact of age‐related and/or neurodegenerative changes. This study aimed at exploring the relationship between RBA, CR, and cognitive performance.MethodWe assessed cognitively healthy and MCI individuals from the ADNI study on four cognitive domains (verbal episodic memory; language and semantic memory; attention; executive functions) at each follow‐up visit up to 5 years. First, we employed robust statistical models, including Generalized Linear Models(GLM), to investigate the association between baseline variables and cognitive performance changes over different follow‐up intervals. Secondly, we used support vector regression models to predict cognitive performance over multiple follow‐up periods. We employed 10‐fold cross‐validation to assess the predictive accuracy of the model. Finally, we used GLM to assess the link between RBA and CR with the speed of cognitive changes during a 36‐month period.ResultOur findings reveal that in MCI group, RBA significantly contributes to the variance in verbal episodic memory changes across various time intervals. To predict cognitive performance, our support vector regression model demonstrated robust performance, yielding low mean absolute errors for episodic memory (0.305 to 0.523), attention (0.392 to 0.661), language and semantic memory (0.369 to 0.537), and executive function (0.386 to 0.521) across diverse follow‐up periods. RBA was further associated with a slower decline in verbal episodic memory, attention, language and semantic memory. These findings underscore the significant predictive role of RBA in estimating the speed of decline in cognitive performance within the MCI group.ConclusionWe have shown how RBA interacts with CR, in shaping cognitive performance and aging trajectories. Our findings underscore the pivotal role of RBA in influencing future cognitive decline, offering valuable insights for early clinical detection.
Phosphorylated tau is more closely associated with the spatial extent of tauopathy than with tau load
Authors: Macedo Arthur C. , Rahmouni Nesrine , Therriault Joseph , Wang Yi‐Ting , Servaes Stijn , Tissot Cécile , Hall Brandon J , Arias Jaime Fernandez , Socualaya Kely Monica Quispialaya , Trudel Lydia , Lussier Firoza Z , Brum Wagner Scheeren , Zimmer Eduardo R. , Triana‐Baltzer Gallen , Kolb Hartmuth C. , Ashton Nicholas J. , Benedet Andrea L. , Karikari Thomas K , Blennow Kaj , Zetterberg Henrik , Pascoal Tharick A. , Rosa‐Neto Pedro 
Published: January 2025

AbstractBackgroundRecent evidence indicated that cognitive impairment is more closely associated with the spatial extent of tauopathy (SEOT) than with tau load. It remains unclear whether this is also true for other markers of Alzheimer’s disease (AD) severity, such as fluid levels of phosphorylated tau (pTau). Here, we compared the link between fluid pTau and the SEOT and tau load in the brain, as assessed by PET.MethodWe studied individuals across the aging and AD continuum or with non‐AD neurodegenerative diseases from the TRIAD and ADNI cohorts. TRIAD participants underwent [18F]MK6240 tau‐PET and CSF and plasma pTau 181, 217, and 231. ADNI participants were evaluated with [18F]AV1451 tau‐PET and CSF pTau181. We calculated standard uptake value ratios (SUVR) as a proxy of tau load, and the proportion of abnormal voxels as a measure of the SEOT. Abnormal voxels were those 2.5 SD higher than the mean of a group of young adults. Spearman’s correlations assessed the associations of pTau with SEOT and tau load in a temporal meta‐region of interest (ROI) and Braak‐like cumulative ROIs. Correlation coefficients were compared in R using the cocor package.ResultWe included 325 participants from TRIAD (mean [SD] age, 68 [9.8] years) and 417 from ADNI (mean [SD] age, 71.8 [9.0] years). All pTau biomarkers showed significant correlations with SEOT and tau load in all ROIs (Fig. 1A‐B). All pTau epitopes were more closely associated with SEOT than with tau load in Braak I‐V and I‐VI (Fig. 2). In TRIAD, this was also observed in Braak I‐III and I‐IV for CSF pTau 217 and 231, and in Braak I‐II for plasma pTau 231. In ADNI, this was also seen for CSF pTau181 in Braak I‐II and I‐III. Tau load did not correlate better with pTau in any ROI.ConclusionPTau biomarkers seem to inform better on SEOT than on tau load. There is a trend for better performance of SEOT as an imaging correlate of pTau when studying late Braak stages. Future studies should investigate the relationship between these measures and the clinicopathological progression of AD.
Dissociation of Structural and Functional Changes in Alzheimer’s Disease and Mild Cognitive Impairment
Authors: Dang Annie , Wang Di , Towne Jonathan , Habes Mohamad , Fox Peter T 
Published: January 2025

AbstractBackgroundThe Amyloid‐Tau‐Neurodegeneration (ATN) biomarker framework for Alzheimer’s disease (AD) indicates binary (presence/absence) designations for each type of pathology, without regard for anatomical distribution. Neurodegeneration is designated as positive if atrophy or hypometabolism are found on imaging. However, Clifford Jack et al., 2016 noted that atrophy and hypometabolism were differently distributed and referenced each to different co‐localized pathologies. Thus, there exists a need to further characterize atrophy and hypometabolic changes in AD, with the goal of advancing the application of anatomically‐based biomarkers in the ATN framework.MethodQuery of the BrainMap databases of published, group‐wise neuroimaging, case‐control contrasts was used to identify AD and mild cognitive impairment (MCI) studies for meta‐analysis. The voxel‐based morphometry (VBM) and voxel‐based physiology (VBP) databases were used to identify studies involving atrophy and hypometabolism respectively. 157 VBM contrasts (110 AD, 47 MCI) and 146 VBP contrasts (88 AD, 58 MCI) were identified. Activation likelihood estimation coordinate‐based meta‐analysis was performed separately for VBM and VBP, to identify cross‐study convergence of brain alteration patterns. Mango was then used to visualize results and quantify spatial overlap between VBM and VBP.ResultStructural (atrophy) and functional (hypophysiology) neurodegenerations in AD/MCI exhibit markedly different neuroanatomical distributions (Figure 1). Structural abnormalities chiefly involve the bilateral hippocampus and bilateral temporal lobes; functional abnormalities chiefly involve the bilateral parietal lobes and posterior cingulate. There is a small overlap (2184 mm3) between VBM and VBP, accounting for 10.1% of VBM and 7.1% of VBP.ConclusionVBM and VBP patterns of alteration appear distinct, aligning with the anterior and posterior default mode network respectively. This dissociation may reflect distinct underlying neuropathologies. We suggest that this knowledge can be used to advance the application of anatomically‐based biomarkers in the ATN framework. Network modeling of VBM and VBP data is currently ongoing.
CSF biomarkers of immune activation and Alzheimer's disease for predicting cognitive impairment risk in the elderly
Authors: Shue Francis , White Launia J , Hendrix Rachel , Ulrich Jason D , Henson Rachel L. , Knight William C. , Martens Yuka A , Wang Ni , Roy Bhaskar , Starling Skylar C , Ren Yingxue , Xiong Chengjie , Asmann Yan W. , Syrjanen Jeremy A. , Vassilaki Maria , Mielke Michelle M. , Cruchaga Carlos , Holtzman David M. , Bu Guojun , Petersen Ronald C. , Heckman Michael G. , Kanekiyo Takahisa 
Published: January 2025

AbstractBackgroundThe immune system is substantially involved in the development and progression of age‐related cognitive decline and Alzheimer’s disease (AD).MethodAs genetic and environmental factors interactively impact these conditions, we investigated how risk factors such as APOE genotype, age, and sex influence immune activation markers and AD biomarkers in cerebrospinal fluid (CSF) in elderly individuals enrolled in the Mayo Clinic Study of Aging cohort. Among cognitively unimpaired individuals aged over 65 at the baseline visit (N=298), we measured 365 CSF immune activation markers using the proximity extension assay.ResultWe found that age, sex, and diabetes status are associated with altered CSF levels of immune activation markers independently of other factors. For CSF AD biomarkers, we observed significant positive correlations between age and total tau, phosphorylated tau‐181 (p‐tau181), neurofilament light (NfL), and YKL40. APOE4 was also associated with lower Aß42 and higher SNAP25 in CSF. We further examined whether baseline visit variables can predict cognitive decline, represented by the conversion from CDR=0 to CDR>0. We found that age, Aß42, NfL, and REG4 were independently correlated with CDR conversion risk. When the cohort was dichotomized by their median values, older participants with lower Aß42, higher NfL, and higher REG4 at baseline developed cognitive impairment during the follow up with a c‐index of 0.762 while age alone had a c‐index of 0.699.ConclusionTogether, our results suggest that assessing CSF immune activation markers and AD biomarkers can improve the prediction of cognitive impairment risk in the elderly.
Alzheimer's disease subjects have smaller non‐jugular venous drainage system compared to cognitive normal population
Authors: Prado Keshet , Khasminsky Vadim , Keret Ophir , Beninger Felix , Goldberg Ilan , Auriel Eitan , Glik Amir 
Published: January 2025

AbstractBackgroundThe Internal Jugular Veins (IJVs) and the non‐jugular veins (NJVs) are two pathways responsible for intracranial blood drainage. The NJVs are usually the less prominent drainage system and have been demonstrated to become smaller during aging. This phenomenon may indicate less affective venues drainage and hence less CNS’s waste products clearance as we age. One of the pathological hallmarks in Alzheimer’s disease (AD) is amyloid betta42 (AB42) accumulation. AB42 accumulation may be the result of overproduction or clearance impairment. NJVs narrowing during aging may cause AB42 accumulation due to clearance impairment and hence plays a role in AD pathology. Our aim was to compare the venous cross‐sectional area (CSA) in AD vs. cognitive normal (CN) population.MethodCognitive impaired patients included AD and mild cognitive impairment (MCI) patients that were recruited prospectively as part of the DIASPORA Horizon‐2020 MesCobrad Project. All patients performed comprehensive neurocognitive evaluation and Brain MRI including Time‐of‐flight sequence for venous demonstration. Venous systems CSA were measured at the jugular foramen level and were done by senior neuroradiologist. CN were defined as subjects without CNS’s known pathology. CN Measurements were taken from a previous study of our group. T‐test was used to compare between groups.ResultA total of 39 Cognitive impaired patients and 20 CN were included in the study. Mean ages of 68 years (SD, 5.4) and 66 years (SD, 5.5), respectively. The CSA of the IJVs was similar between groups. However, NJVs CSA was significantly smaller in cognitive impaired patients (mean CSA=41.21 mm2 (SD,21.52) vs. CSA=54.5 mm2 (SD,27.3) P=0.045). Table 1.ConclusionNJVs are significantly smaller in cognitive impaired patients compared to CN population. NJVs narrowing in AD patients may cause impaired venous drainage hence causing AB42 accumulation in this population. New treatment options for AD patients may rise if larger studies replicate our findings.
Whole‐brain Cellome‐Wide Association Study (CWAS) Unveils Ultra‐Early Neurodegeneration Phenomena in a 3D Spatial Cell Atlas
Authors: Mitani Tomoki T , Yamaura Kosei , Matsumoto Katsuhiko , Yamada Rikuhiro , Susaki Etsuo A , Ueda Hiroki R 
Published: January 2025

AbstractBackgroundIn aging societies, neurodegenerative diseases, such as Alzheimer's disease, are receiving attention. These diseases are primary targets for preemptive medicine, emphasizing the importance of early detection and preventive treatment before the onset of severe, treatment‐resistant damages. However, there is a lack of comprehensive investigation of lesions and molecular targets in the entire organ, whereas spatial identification of early‐stage lesions is potentially overlooked at the single‐cell level.MethodHere, we propose a novel approach, CWAS (whole body/organ cellome‐wide association studies), which integrates and analyzes spatio‐temporal cellular information across the entire mouse organ into a comprehensive organ cell atlas using tissue‐clearing imaging technologies [1,2]. [1] Murakami TC, et al. Nat Neurosci. 2018;21(4):625‐637. [2] Matsumoto K*, Mitani TT*, Horiguchi SA, et al*. (*equal contribution) Nat Protoc. 2019;14(12):3506‐3537.ResultIn our initial application to neurodegenerative models, we have been able to identify previously unreported neural degenerations at specific times and locations at the single‐cell level. In addition, our time‐series analysis in a mouse model of Alzheimer's disease revealed the possible simultaneous onset of amyloid deposition and neurodegeneration, challenging the traditional amyloid hypothesis. Furthermore, we were able to identify unique multicellular inflammation networks surrounding neurodegenerative lesions in several disease models.ConclusionOur findings establish the concept of "spatial cellomics" realized through spatially precise organ mapping and integrated multiomics analysis at the single‐cell level. This approach shifts pathology, providing deeper insight into disease mechanisms and preventive strategies.
Demonstration of an open‐source toolbox for network spreading models: regional amyloid burden promotes tau production in Alzheimer's disease
Authors: Thompson Elinor , Schroder Anna , He Tiantian , Legouhy Antoine , Zhao Xin , Cole James H. , Oxtoby Neil P , Alexander Daniel C 
Published: January 2025

AbstractBackgroundConnectome‐based models of disease propagation are used to probe mechanisms of pathology spread in neurodegenerative disease. We present our network spreading model toolbox that allows the user to compare model fits across different models and parameters. We apply the toolbox to assess whether local amyloid levels affect production of pathological tau.MethodsData: Regional tau‐PET and amyloid‐PET SUVRs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study were downloaded in September 2023 (demographics in Figure 1). Subjects were separated into groups based on AT disease stage: 1) amyloid‐negative, tau‐negative; 2) amyloid‐positive, tau‐negative; 3) amyloid‐positive, tau‐positive. Positivity thresholds were defined using Gaussian mixture modelling. Data were averaged within groups to produce stage‐specific maps. Spreading models: We compared three models: the network diffusion model, Fisher‐Kolmogorov‐Petrovsky‐Piscounov (FKPP) and weighted‐FKPP models (Figure 2). Previously we showed that weighting the production rate of FKPP with regional vectors improves the model fit by accounting for different regional characteristics (He et al, MICCAI 2023). Here, we demonstrate this new “weighted‐FKPP” model, using regional amyloid as the fixed production weight. Models were initialised with unity pathology in a bilateral seed region, zero elsewhere. Parameter optimisation: Free parameters were fitted using Gaussian processes optimisation (α and seed region). NDM toolbox: All models are available in our toolbox: https://github.com/ethompson93/network_spreading_models. Null Modelling: we repeated the weighted‐FKPP model optimisation with a distribution of 100 randomly shuffled amyloid maps to demonstrate the spatial significance of the amyloid weighting.ResultAdding amyloid weighting to the production term in the FKPP model improved the model fit to the data for each group. The reduction in squared model residuals with the weighted FKPP model was significant only in the amyloid‐positive tau‐positive group (Figure 3). For all groups, the results from the FKPP weighted with amyloid had significantly improved model fit (Pearson’s R) compared to the null model (all p‐values<1×10‐10).ConclusionUsing our toolbox, we found that incorporating regional amyloid levels improves pathology‐spreading model fit to measured tau‐PET data, particularly at later stages of AD. This supports the hypothesis that amyloid promotes tau aggregation.
Postmortem MRI signature of Hippocampal Sclerosis of Aging
Authors: Rådman Gustaf , Denning Amanda E , Ravikumar Sadhana , Spotorno Nicola , Ittyerah Ranjit , Levorse Lisa M , Bedard Madigan , Chung Eunice , Robinson John L. , Schuck Theresa , Lee Eddie B , Lim Sydney A , Trotman Winifred , Bahena Alejandra , Olsen Rosanna K , Irwin David J , Jimenez Maria del Mar Arroyo , Vela Alicia , Buendia Esther , Martin Maria Mercedes Iniguez de Onzono , Rabal Maria del Pilar Marcos , Munoz Monica , Insausti Ricardo , Yushkevich Paul A. , Wisse Laura E.M. 
Published: January 2025

AbstractBackgroundHippocampal Sclerosis of aging (HS) refers to age‐related selective neuronal loss and gliosis in hippocampal cornu ammonis 1 (CA1) and subiculum that is out of proportion to tau pathology in Alzheimer’s Disease (AD). HS is related to cognitive decline and memory impairments separately from other neurodegenerative pathologies. To date, in vivo imaging biomarkers of HS of aging are non‐existent, and their development would greatly improve diagnosis and prognosis in memory clinics. We therefore aimed to characterize the magnetic resonance imaging (MRI) signature of HS among autopsy confirmed cases of HS.MethodData consisted of same‐subject histological Nissl‐stained slices and postmortem MRI (0.2mm isotropic) acquired from 27 subjects at the University of Pennsylvania and University Castilla‐La Mancha. Based on neuropathological assessment, cases were grouped into HS (N = 5), intermediate to high AD neuropathologic change without HS (N = 10) and limited or no neuropathological burden (N = 12). We manually measured grey matter thickness and GM/WM intensity ratio in select locations across the hippocampal head, body, and tail. We also obtained more global structural measures from hippocampal subfields using an automated postmortem atlas‐based segmentation algorithm.ResultQualitatively, HS is clearly noticeable as cortical thinning and hypointense signal (approximating white matter) on postmortem MRI across the CA1 and subiculum, particularly anteriorly closer to the border between CA1/subiculum (Figure 1). Manual thickness measures showed complete separation between the HS group and the two reference groups across both the hippocampal head and body, and a less pronounced difference in the tail. Global structural measures from automated segmentations corroborated these group differences showing complete separation in CA1 thickness and significant differences in subiculum and the white matter layer of CA1 (Stratum Radiatum Lacunosum Moleculare, SRLM).ConclusionIn MRI, HS is marked by hypointense voxels and cortical thinning across CA1 and subiculum. Both local manual thickness measurements and thickness measures of whole subfields derived from automatic segmentations provided promising results. As a next step, we plan to perform similar measurements on antemortem MRI of the same subjects, with the hopes of making progress toward an in vivo MRI biomarker of HS.