bluuebunny/crossref_metadata_2025_split huggingface dataset:
Distinguishing Frontotemporal Lobar Degeneration Tau from TDP‐43 using white matter microstructure
Authors: Radhakrishnan Hamsanandini , Cook Philip A. , Phillips Jeffrey S , Olm Christopher A , Brown Christopher D. , Gee James C. , Lee Eddie B , Deerlin Vivianna M Van , Irwin David J , Massimo Lauren , McMillan Corey T 
Published: January 2025

AbstractBackgroundFrontotemporal lobar degeneration (FTLD) is defined neuropathologically by misfolded tau (FTLD‐tau) or TAR DNA‐binding protein of 43 kDa (FTLD‐TDP). However, we lack biomarkers that can distinguish them in vivo which is a major barrier to effective disease‐modifying treatment trials. Based on neuropathological evidence of distinct patterns of cellular degeneration, more prominent in white matter (WM) for FTLD‐tau relative to FTLD‐TDP, we hypothesized that diffusion MRI (dMRI) measures of white matter microstructure would help dissociate FTLD‐tau and FTLD‐TDP during life.MethodWe evaluated dMRI for 157 individuals with FTLD‐Tau (n=66, autopsy‐confirmed sporadic=51, MAPT=15) and FTLD‐TDP (n=91, autopsy‐confirmed sporadic=25, GRN=19, C9orf72=47) defined by a pathogenic genetic mutation or autopsy confirmation. Images were preprocessed using QSIprep and reconstructed using Generalized Q‐sampling Imaging. Tensor‐based scalar maps of fractional anisotropy (FA) and mean diffusivity (MD) were then calculated. Deterministic tractography was run and white matter bundles of pathogenic interest based on literature were segmented with DSIstudio using template‐based tracking.ResultOverall, FTLD‐Tau individuals showed worse white matter integrity compared to those with FTLD‐TDP with lower FA in the superior longitudinal fasciculus (SLF), uncinate fasciculus and fronto‐parietal cingulum; and higher MD in the uncinate fasciculus. Even within sporadic autopsy‐confirmed individuals, FTLD‐Tau subtypes had significantly lower FA in the SLF, fronto‐parietal cingulum, and left corticostriatal tract relative to FTLD‐TDP.ConclusionOur results demonstrate that white matter microstructure can distinguish between FTLD‐Tau and FTLD‐TDP pathologies in a tract‐specific manner with greater evidence of white matter degeneration in disease‐associated regions in FTLD‐Tau. Future work examining longitudinal associations with disease progression and white matter microstructure could aid in non‐invasive differentiation of these subtypes enabling more sensitive treatment monitoring.
Goldilocks Framework: semi‐automated model configuration for democratising data‐driven analyses of Alzheimer's disease progression
Authors: Oxtoby Neil P 
Published: January 2025

AbstractBackgroundData‐driven models are at the mercy of the data upon which they are built. Poorly configured models can lead to misleading conclusions. Here we introduce a framework called Goldilocks for carefully configuring data‐driven modelling analyses to remain in the Goldilocks Zone — features that are not too weak (in terms of disease signal), and model (hyper)parameters that are not too strong (with respect to available training data).MethodGoldilocks has three key steps: 1. Normalise data. Transform data from cases (e.g., patients) into “disease signal”, e.g., normative z‐scores relative to controls. 2. Feature selection. Identify candidate input features having the necessary minimum signal. 3. Model configuration (hyperparameters). Remain within the available maximum signal. Experiments: we synthesize ADNI‐like biomarker data, define the Goldilocks Zone to lie between two event horizons (minimum and maximum disease signal), and demonstrate Goldilocks for configuring a Subtype and Stage Inference (SuStaIn) analysis. Minimum disease signal zmin is determined by the tipping point between case/control data, where the half‐normal cumulative density function HN‐CDF(zmin)==0.5. Maximum disease signal zmax is defined where the empirical distribution function EDF(zmax)==0.9, such that 10% of the biomarker’s data (from cases, not controls) exceeds zmax.ResultWe synthesized z‐scored ADNI‐like synthetic CSF biomarkers (ABETA, TAU), cognitive test scores (ADAS13, MMSE), and a brain volume (Ventricles normalised by intracranial volume). Figure 1 shows biomarker empirical distributions as a function of z‐score, and the Goldilocks Zone between event horizons at zmin==0.67 and EDF(zmax)==0.9. Since EDF(zmin) < 0.5 for all biomarkers, they each contain sufficient disease signal to be included in a data‐driven modelling analysis like SuStaIn. The maximum disease signal for each biomarker ranged between approximately 2 to 6. Figure 2 compares default parameters in a Z‐Score SuStaIn analysis (using the official pySuStaIn software) to Goldilocks‐informed parameters.ConclusionGoldilocks has the potential to improve validity and interpretability of data‐driven discoveries in general while also making data‐driven modelling accessible to non‐experts. This promotes democratization of data‐driven analyses, unlocking previously untapped data responsibly. This is particularly important in fields such as computational medicine, where data‐driven decisions can relate to life‐or‐death outcomes.
CSF sTREM2 is associated with neuroprotective microglial states early in Alzheimer's disease and deleterious effects later in the disease trajectory
Authors: Crook Harry , Wahdan Malak , Tuil Marie Emilie , Livingston Nicholas R , Raza Sanara , Nowell Joseph , Edison Paul 
Published: January 2025

AbstractBackgroundNeuroinflammation is a key component of Alzheimer’s Disease (AD) pathology. Triggering receptor expressed on myeloid cells 2 (TREM2) is crucial to microglial involvement in AD, mediating trem2‐dependent activation and Disease‐Associated Microglia (DAM) polarization. However, GWAS revealed that loss‐of‐function mutations of its encoding gene are an important risk factor for AD. There is mounting evidence supporting a biphasic microglial response in AD trajectory, and such contrasting effects would support this. We evaluated the influence of cerebrospinal fluid soluble TREM2 (CSF sTREM2) on AD pathology in healthy, Mild Cognitive Impairment (MCI), and AD patients.MethodPatients were grouped by diagnosis and each group segmented according to amyloid and tau, into amyloid‐positive (Aß+) and negative (Aß‐), and tau‐positive (tau+) and negative (tau‐). We used a combination of CSF biomarkers and positron emission tomography (PET) scans to assess neuropathological substrates of AD: CSF p‐tau181 for tau pathology, CSF Amyloid‐ß 42 (Aß42) and 18F‐florbetapir (18F‐AV45) PET scans for amyloid deposition, and magnetic resonance imaging (MRI) scans for neurodegeneration.ResultWe observed higher sTREM2 levels in Aß+/tau+ AD compared with Aß+/tau‐ AD and Aß‐/tau‐ MCI patients. Likewise, Aß+/tau+ MCI patients showed higher sTREM2 than Aß‐/tau‐MCI patients. Furthermore, sTREM2 levels were negatively associated with cortical volume in MCI and AD, and with MMSE scores in AD. sTREM2 was positively associated with CSF p‐tau181 in all groups except Aß+ controls. However, a positive association with CSF Aß42 was only observed in the Aß‐ control and the Aß+ and Aß‐ MCI groups. Similarly, sTREM2 was positively correlated with amyloid load (18F‐AV45 uptake) in controls and MCI patients yet negatively correlated in the AD group.ConclusionThe MCI and AD stages of the disease showed contrasting associations between sTREM2 and neuroimaging markers for AD pathology. These suggest diverging effects of the microglial response along AD trajectory, with a neuroprotective phase in early AD and a neurotoxic one later on. This could indicate that microglia polarize first towards an anti‐inflammatory phenotype before switching to a pro‐inflammatory one as neuropathology progresses.
Longitudinal neurodegeneration in relation to amyloid pathology among cognitively unimpaired individuals
Authors: Stephenson Henry Gilreath , Betthauser Tobey J. , Hulle Carol A. Van , Du Lianlian , Langhough Rebecca E. , Jonaitis Erin M. , Kollmorgen Gwendlyn , Quijano‐Rubio Clara , Chin Nathaniel A. , Okonkwo Ozioma C , Carlsson Cynthia M. , Asthana Sanjay , Christian Bradley T. , Johnson Sterling C. , Blennow Kaj , Zetterberg Henrik , Bendlin Barbara B. 
Published: January 2025

AbstractBackgroundThe timing of neurodegeneration in relation to the onset of Alzheimer’s disease pathology is not fully known. This study examined the association of longitudinal atrophy derived from T1‐weighted MRI with 1) cerebrospinal fluid (CSF) amyloid‐tau (AT) groupings and 2) Pittsburgh compound B (PiB) PET‐derived estimates of amyloid duration among cognitively unimpaired (CU) individuals.MethodCU participants in the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center (N = 297) underwent longitudinal MRI, APOE genotyping, and lumbar puncture to determine CSF Aß42/40 (A) and pTau181 (T) concentration at baseline using in‐house cutoffs. CSF measurements were performed using the NeuroToolKit, a panel of robust prototype biomarker assays (Roche Diagnostics International Ltd). Image segmentation was performed using a longitudinal pipeline in SPM12. Grey matter volumes from AD‐associated regions (adjusted for intracranial volume) were z‐scored according to age‐associated predictions from a robust longitudinal A‐T‐/APOE4‐/CU control group. Differences in longitudinal atrophy relative to controls were tested in linear mixed‐effects models using contrasts between A+T‐ and A+T+ groups and A‐T‐ controls and interactions with time (years from baseline). In the PiB PET subset, sampled iterative local approximation was used to estimate time‐to‐positivity at baseline relative to a PiB DVR threshold of 1.16. CSF A+ individuals were grouped as CSF A+ only, early PiB+ (PiB+ <= 5 years), and established PiB+ (PiB+ > 5 years). Differences in longitudinal atrophy were tested with A+ group/A‐T‐ contrasts and interactions with time. All analyses were FDR‐corrected. See Table 1 and Figure 1 for participant characteristics and model details.ResultBased on CSF assays, both A+ groups showed longitudinal atrophy relative to controls in a majority of ROIs, suggesting that A+, regardless of pTau+, is associated with neurodegeneration. Further analysis incorporating PET showed that CSF A+ in the absence of PiB+ was not associated with atrophy, but early PiB+ was associated with atrophy in medial temporal ROIs and precuneus. The largest and most widespread atrophy was observed in the established PiB+ group (Figure 2).ConclusionNeurodegeneration appears to begin soon after the onset of PET‐measurable amyloid‐positivity. Clinical intervention at the earliest signs of amyloid deposition may be needed to delay neurodegeneration.
Renal Atrophy Relates to Brain Volume Loss on 7,149 MRI Scans
Authors: Meysami Somayeh , Raji Cyrus A. , Hashemi Sam , Garg Saurabh , Akbari Nasrin , Gouda Ahmed , Chodakiewitz Yosef Gavriel , Nguyen Thanh Duc , Niotis Kellyann , Merrill David A. , Attariwala Rajpaul 
Published: January 2025

AbstractBackgroundRenal atrophy may reflect an end organ consequence of chronic vascular disease. Renal volume loss may therefore provide a window into brain aging and Alzheimer disease risk.MethodWe obtained whole‐body 1.5T MRI scans on 7,149 healthy individuals across four sites. The participants had a mean age of 53.06 ± 12.95 years (age range: 18‐97 years), with a gender distribution of 48% women and 52% men; 38% were non‐white. Deep learning with FastSurfer on 3D T1 volumetric MPRAGE trained on 134 participants aged 27‐66 and segmented 96 brain regions. Whole body sequences included coronal T1 that allowed for separate segmentation and quantification of renal volumes. Partial correlation analysis controlling for sex and total intracranial volume was done on kidney volumes and the following brain regions: tissue types, lobar structures and Alzheimer disease risk regions – hippocampus, precuneus, and posterior cingulate gyrus. Renal volumes were normalized to the aggregate abdominal organ volumes and tissues such as the liver, spleen and abdominal fat. Multiple comparisons were corrected for with the Bonferroni method.ResultLower renal volumes were correlated with lower gray matter volumes (rp = ‐0.229, p = 5.18×10‐85) as well as total white matter volume (rp = ‐0.204, p = 2.51×10‐67). The cerebral ventricles, demonstrated a moderate positive correlation with lower renal volumes (rp = 0.099, p = 5.28×10‐16) reflective of a generalized parenchymal volume loss. Lower renal volumes also predicted lower lobar volumes in: frontal lobe (rp = ‐0.204, p = 3.34×10‐67), temporal lobe (rp = ‐0.236, p = 9.76×10‐90), parietal lobe (rp = ‐0.215, p = 2.69×10‐74), and occipital lobe (rp = ‐0.207, p = 8.29×10‐69), all showed significant negative correlations with lower renal volumes. Lower renal volumes also correlated to lower volumes in AD risk regions: the Hippocampus (rp = ‐0.203, p = 2.89×10‐66) and posterior cingulate (rp = ‐0.18, p = 3.06×10‐52), precuneus (rp = ‐0.171, p = 8.00×10‐47).ConclusionThis study demonstrates a strong relationship in a large sample between lower renal volumes and reduced volumes in various brain regions, including gray matter, white matter, and specific lobar structures, as well as Alzheimer's disease risk regions.
Impact of anchoring cutoff point of tau positivity on CU young or older adults using Flortaucipir and MK‐6240 – Head Study
Authors: Ruppert Emma Patrice , Povala Guilherme , Bauer‐Negrini Guilherme , Lussier Firoza Z , Ferreira Pamela C.L. , Bellaver Bruna , Amaral Livia , Zalzale Hussein , Oliveira Markley , Saha Pampa , Rodrigues Matheus Scarpatto , Abbas Sarah , Soares Carolina , Felix Cynthia , Madeiros Marina Scop , de Paula Elisa , Resende França , Caramelli Paulo , Klunk William E , Tudorascu Dana , Jagust William J. , Pascual Belen , Gordon Brian A. , Lowe Val J. , Oh Hwamee , Soleimani‐Meigooni David N. , Rosa‐Neto Pedro , Baker Suzanne L. , Pascoal Tharick Ali 
Published: January 2025

AbstractBackgroundTau PET provides continuous measurements of tau tangle pathology in the human brain. However, establishing cutoffs is crucial for selecting individuals for treatment in clinical trials or practice. In the absence of postmortem data, PET cutoffs must be established using statistical methods based on what is considered normal tracer uptake. In this study, we tested the impact of various methods to determine tau positivity using two different tau PET tracers in individuals scanned head‐to‐head.MethodsWe studied 147 individuals from Head‐to‐Head Harmonization of Tau Tracers in Alzheimer's Disease (HEAD) study with tau tangle PET scans with [18F]Flortaucipir and [18F]MK‐6240, and amyloid‐ß (Aß) PET. Tau deposition was measured with the standardized uptake value ratio (SUVR) of each agent in the Medial Temporal Lobe (MTL) and the Entorhinal Cortex (EC). To determine Tau positivity three different methods were used: >2.5 standard deviations (SD) than the mean of the young, >1.5 SD mean of the cognitively unimpaired (CU) and >1.5 SD mean of CUAß‐.ResultsDemographic characteristics of the study population are reported in Table 1. Using the cutoff >2.5 SD mean of young, [18F]Flortaucipir was positive in 35 (23.8%) and 69 (46.9%) individuals in the EC and MTL, respectively. [18F]MK‐6240 was positive in 49 (33.3%) and 58 (39.5%) individuals in EC and in MTL. Using >1.5 SD mean of CUAß‐, [18F]Flortaucipir was positive in 38 (25.9%) and 45 (30.6%) individuals in EC and MTL, while [18F]MK‐6240 was positive in 51 (34.7%) and 50 (34.0%) in EC and in MTL (Figure 1).ConclusionsOur findings indicate variations in tau positivity when employing different methods based on either the young or CUAß‐. [18F]Flortaucipir exhibited a higher rate of positive results when the method based on young individuals was applied in the MTL. Conversely, [18F]MK‐6240 showed more consistent and generally higher positivity when other methods were used for cutoff determination and/or in the EC region. Further research with a larger sample size is required to gain a better understanding of the optimal cutoff determination methods for these tracers.
Single nucleotide polimorphisms of the main glutamate transporter in the brain are associated with lower regional brain glucose metabolism in cognitively impaired individuals
Authors: de Oliveira Roberta dos Santos , Limberger Christian , Machado Luiza Santos , de Bastiani Marco Antônio , Schlickmann Thomas Hugentobler , Carello‐Collar Giovanna , Povala Guilherme , Pascoal Tharick A. , Zimmer Eduardo R. 
Published: January 2025

AbstractBackgroundAstrocytes play a main role in brain energy metabolism, primarily through the metabolic cooperation with neurons. The use of [18F]fluorodeoxyglucose(FDG)‐PET has become a valuable indicator of neurodegeneration in Alzheimer’s disease (AD), revealing a brain hypometabolic signature, but it is sensitive to changes in astrocyte metabolism. It is postulated that the activation of the excitatory amino acid transporter 2 (EAAT2) is the main trigger of FDG‐PET uptake in astrocytes. However, the potential relationship between single nucleotide polymorphisms (SNPs) in the gene that encodes for EAAT2 (SLC1A2) and the AD hypometabolic signature remains unknown. Here, we investigated whether SNPs in SLC1A2 affect brain glucose metabolism in cognitively impaired (CI) individuals.MethodWe assessed 331 CI individuals from the ADNI cohort with available [18F]FDG‐PET imaging data, CSF Aß1‐42 and p‐tau181 measures, and genotyping data of SLC1A2 (Table 1). After the genetic data processing, 100 SNPs from the SLC1A2 gene were identified. In this study, we focused on the 10 SNPs more associated with [18F]FDG SUVR in an exploratory ROI‐wise analysis. Then, we performed voxel‐wise correlation analysis with RMINC, testing the association between the SLC1A2 SNP carriership and [18F]FDG‐PET, adjusting for age, sex, APOE4 status, and CSF Aß42 and p‐tau181 levels (corrected p‐value < 0.05).ResultOur findings revealed a negative association between four SLC1A2 SNPs and [18F]FDG‐PET in specific brain regions. This indicates that SLC1A2 SNP carriership is associated with lower regional glucose metabolism. Notably, rs57339940 carriers presented a significant cluster in the cingulate region (tmax = 3.04, p < 0.01, Figure 1A). Carriers of rs11033098 and rs11033099 exhibited bilateral clusters in areas of the lingual gyrus (tmax = 2.4806 and tmax = 2.8736, respectively, p < 0.01, Figure 1B‐C). Additionally, individuals with rs10768132 showed a substantial bilateral impact on the posterior areas of the cuneus (tmax = 2.9821, p < 0.01, Figure 1D).ConclusionThese preliminary results show that SLC1A2 SNP carriership had a significant influence on regional glucose hypometabolism, specifically in areas comprising the occipital lobe and the cingulate region. Metabolic alterations may influence the susceptibility of those regions to accumulate amyloid in later stages of AD.
Characterization of plasma biomarkers of individuals with amyloid‐independent increased tau positron‐emission tomography uptake
Authors: Chan Tevy , Therriault Joseph , Wang Yi‐Ting , Aumont Etienne , Arias Jaime Fernandez , Macedo Arthur C. , Rahmouni Nesrine , Servaes Stijn , Trudel Lydia , Hosseini Seyyed Ali , Zheng Yansheng , Socualaya Kely Monica Quispialaya , Mathotaarachchi Sulantha , Stevenson Jenna , Jia Wan Lu , Matsudaira Takashi , Hall Brandon J , Lussier Firoza Z , Gauthier Serge , Triana‐Baltzer Gallen , Kolb Hartmuth C. , Karikari Thomas K , Ashton Nicholas J. , Benedet Andrea L. , Zetterberg Henrik , Blennow Kaj , Pascoal Tharick Ali , Rosa‐Neto Pedro 
Published: January 2025

AbstractBackgroundIncreased uptake on Tau positron‐emission tomography (PET) is sometimes observed in the absence of amyloid ß accumulation. This A‐T+ PET profile might represent primary age‐related tauopathy (PART), an amyloid ß‐independent 3R/4R tauopathy observed in aging brains. Although A‐T+ individuals have been shown to follow a different cognitive trajectory compared to A‐T‐ and A+T+ individuals, it remains unknown how they differ in terms of plasma biomarkers. Here, we aim to characterize the plasma biomarkers of A‐T+ individuals.MethodThis is a cross‐sectional study with data from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort. Participants were classified into four categories based on their amyloid ß ([18F]AZD4694) and Tau ([18F]MK6240) PET status (A‐T‐, A+T‐, A+T+ and A‐T+). Plasma biomarkers phosphorylated tau (p‐tau)181, p‐tau217, p‐tau231, total tau (t‐tau), neurofilament light (NfL) and GFAP were compared using the Kruskal‐Wallis test with post hoc Benjamini‐Hochberg (BH) correction. Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUROC).ResultAmong the total 468 participants, 64 (13.7%) had A‐T+ PET status. The A‐T+ PET participants had a mean age 66.9 years (SD: 12.8), with 62.5% women and 39.1% showing cognitive impairment. Plasma p‐tau181, p‐tau217, p‐tau231 and GFAP were significantly higher in A+T+ individuals compared to A‐T+ (all p < 0.001). On the other hand, plasma p‐tau181 and 217 were significantly different between A‐T‐ vs A‐T+ (p<0.05), but not p‐tau231 and plasma GFAP. No significant differences were found in t‐tau or NfL regarding A‐T‐. Plasma p‐tau217 and plasma GFAP had the highest discriminative accuracies for A‐T+ vs A+T+ (AUROC: 0.860 and 0.836 respectively).ConclusionOur data suggest that individuals with an A‐T+ PET status exhibit a different plasma biomarker profile than those with A+T+ and A‐T‐ status. Further characterization of fluid biomarkers could help identify this group of individuals and facilitate the differential diagnosis of adults with cognitive impairment. Furthermore, our results lend support to the use of plasma biomarkers for identifying amyloid ß PET positivity.