bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic Analysis of an Amyloid PET‐Negative Autopsy‐Confirmed Alzheimer's Pedigree
Authors: Talozzi Lia , Peña‐Tauber Andrés , Jensen Tanner D. , Wilson Edward N. , Young Christina B. , Stewart Ilaria , Cobos Inma , Vallejo Kristen , Hefti Marco M , Guen Yann Le , Williams Kennedy , Vossler Hillary , Gorzynski John , Andreasson Katrin I. , Mormino Elizabeth , Ashley Euan , Greicius Michael D 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) is the most common form of dementia. Neuropathologically, AD stands out as a mixed proteinopathy. Beta‐amyloid and tau biomarkers can now add in‐vivo support to the AD diagnosis. Rarely, a patient with AD confirmed at autopsy may have a negative amyloid PET. Here we describe a pedigree in whom the index case was amyloid PET‐negative. We performed genetic sequencing of two affected siblings to identify a set of candidate single nucleotide variants (SNVs) and structural variants (SVs) associated with disease and rare in healthy older controls from several large genetic databases.MethodWe performed long‐read sequencing (LRS) to comprehensively evaluate both SNVs and SVs. The index case, an APOE3/E4 male, developed memory trouble at 68 that progressed to probable AD at 72. Notably (Figure 1) his amyloid‐PET scan, which was confirmed to be of high technical quality, was negative. His tau PET scan was positive and his plasma Abeta42/40 was in the expected range for Stanford AD patients. His family history is extensive and suggestive of an autosomal dominant pattern of late‐onset AD (Figure 2). The patient's sister was diagnosed with AD at 62. We filtered their shared heterozygous SNVs keeping those with a minor allele frequency < 0.01 in gnomAD and that were not present in any ADSP healthy controls (HC) over 70y.o. (N=19771;62.1%females;81±6.4y.o). Shared SVs were kept as candidates if they were not seen in any Stanford LRS HC (N=95;59%females;72±1.5y.o.)ResultNeuropathological analysis revealed that both siblings met the A3B3C3 criteria for AD and had cerebral amyloid angiopathy. Among rare mutations on genes expressed in the brain (Table 1), a novel missense in ADNP (Activity‐Dependent‐Neuroprotector‐Homeobox), and a 267bp deletion on TET1 (Tet‐Methylcytosine‐Dioxygenase) were identified. The 3426bp insertion on ATP8A2 (ATPase‐Phospholipid‐Transporting‐8A2) was the longest, rare insertion found. Additionally, a large duplication (∼20Kbp) and inversion (∼160Kbp) were observed.ConclusionThis study highlights an unusual AD pedigree and emphasizes the potential utility of LRS in identifying causal SNVs and SVs. In future work we plan to perform additional amyloid stains to understand why the index case PET was amyloid‐negative. We are also assessing additional family members to improve our ability to identify a causal mutation.
Data‐driven analysis of 10,361 amyloid‐PET scans from the IDEAS study reveals two primary axes of variation
Authors: Giorgio Joseph , Mundada Nidhi S , Blazhenets Ganna , Mejía‐Perez Jhony Alejandro , Schonhaut Daniel R. , Carrillo Maria C. , Hanna Lucy , Gatsonis Constantine , March Andrew , Apgar Charles , Siegel Barry A. , Hillner Bruce E , Whitmer Rachel A. , Jagust William J. , Rabinovici Gil D. , La Joie Renaud 
Published: January 2025

AbstractBackgroundThe variability in the regional distribution of Aß‐PET signal and its relation to clinical features is debated. We used data‐driven approaches to uncover heterogeneity in cortical Aß‐PET signal from a large representative sample collected through the IDEAS study.MethodsWe analysed cross‐sectional Aß‐PET collected from 10,361 patients with MCI or mild dementia scanned in 295 PET facilities using one of the 3 FDA‐approved tracers. Central image processing resulted in template‐space SUVR images (reference: whole cerebellum) and centiloid (CL) values. Spatial independent component analysis was used to decompose SUVR volumes into 40 independent components. After excluding noise components, participants’ scores were extracted for each of the remaining 11 grey matter (GM) components describing cortical and subcortical binding. K‐means clustering was used on these GM component scores to assign each participant to different Aß‐PET clusters based on GM binding (Figure1).ResultsThree informative clusters of PET binding were estimated. Cluster 1: Aß‐(n = 4729, CL mean = 2±23) with low GM binding, and two Aß+ clusters; Cluster 2(n = 2484, CL mean = 76±34) and Cluster 3(n = 3148, CL mean = 86±32). Subtracting average SUVR of Clusters 2 and 3 showed they differed along a posterior‐anterior gradient with Cluster 2 showing an occipital predominant pattern. Principal component analysis conducted on the GM scores confirmed two dominant axes of variation separated the clusters, a Aß‐ to Aß+ axis and, an anterior‐posterior axis (Figure2). Statistically significant but weak differences were observed between the two Aß+ Clusters (2 vs. 3); Visual Read (positive: 95% vs. 92%); Clinical Stage (dementia: 47% vs. 41%); Age (76.9±6.4 vs. 75.9±6.2), however, most clinical variables showed no differences (Figure3a). 48 ADNI participants with Aß‐PET and post‐mortem neuropathology data (11 Female, Age mean = 79.7±7.4, PET‐Death mean = 2.3±1.7years; Aß‐CL mean = 71.2±55.5; APOE4(0/1/2) = 22/21/5; Diagnosis(CN/MCI/AD) = 6/8/32) were applied to the model fit on IDEAS data. Qualitatively, no differences in neuropathology were observed between the two Aß+ Clusters (Figure3b).ConclusionData driven classification of Aß‐PET reveals two primary axes reflecting Aß load and anterior‐posterior binding, with the later not clearly related to clinical or pathological variation. Future work will apply new data to this model and investigate if this spatial variation in Aß‐PET is related to longitudinal changes in pathology.
A clock model of regional tau‐PET accumulation and cognitive decline in sporadic early‐onset Alzheimer's disease
Authors: Schonhaut Daniel R. , Insel Philip S. , Soleimani‐Meigooni David N. , Zeltzer Ehud , Windon Charles , Maiti Piyush , Yballa Claire , Zhang Jiaxiuxiu , Mundada Nidhi S , Shankar Ranjani , Amuiri Alinda , Koeppe Robert A. , Dickerson Bradford C. , Carrillo Maria C. , Apostolova Liana G. , Rabinovici Gil D. , Joie Renaud La 
Published: January 2025

AbstractBackgroundThe timing of tau‐PET accumulation and cognitive decline in sporadic early‐onset Alzheimer’s disease (eoAD, age‐at‐onset<65) has not been established and is needed to optimize tau‐PET as an outcome measure in clinical trials. Here we leverage large‐sample, longitudinal data from the Longitudinal Early‐onset Alzheimer’s Disease Study (LEADS) to model tau‐PET accumulation in three regions relative to cognitive decline.MethodLongitudinal [18F]Flortaucipir‐PET (FTP) and CDR‐SB scores were acquired in 195 amyloid‐PET‐positive, sporadic eoAD patients with MCI or mild dementia due to AD at baseline (Table 1). FTP scans were referenced against inferior cerebellar GM, and mean SUVRs calculated in three meta‐ROIs comprising temporal, parietal, and frontal association cortices (Figure 1A). Generalized additive models estimated nonlinear relations between: (1) baseline SUVRs and annualized rates‐of‐change in each meta‐ROI, and (2) baseline CDR‐SB and annualized rates‐of‐change in cognition. Integrating these baseline‐by‐change functions yielded regional FTP and CDR‐SB trajectories over time. An exhaustive search algorithm identified optimal time‐shifts between each regional tau trajectory and CDR‐SB progression that minimized the error between model‐predicted and observed data at baseline. This time‐shifting procedure was repeated in a held‐out dataset of 184 eoAD patients to assess out‐of‐sample reproducibility (Table 1).ResultFTP SUVRs and CDR‐SB scores increased longitudinally in most patients (Figure 1B, C). Baseline SUVR by rate‐of‐change associations were inverted U‐shaped in each meta‐ROI (Figure 1D), and similar associations were found for CDR‐SB progression (Figure 1E). Time‐shift estimation in the training dataset converged on a single, best‐fitting model in which temporal and parietal FTP accumulation led clinical onset by 4.5 years, while frontal FTP accumulation began 0.5 years before clinical onset (Figure 2A). The replication dataset yielded nearly identical results (Figure 2B). We estimate that the full timecourse of FTP accumulation and cognitive decline occurs over ∼16 years from earliest detectable change in FTP.ConclusionTau‐PET accumulation in the frontal meta‐ROI immediately precedes cognitive decline and tracks closely with clinical progression in eoAD, while temporoparietal tau‐PET is prominent at clinical onset and plateaus as dementia worsens. For clinical trials in symptomatic patients, temporal and parietal tau‐PET may therefore aid staging and inclusion criteria, while frontal tau‐PET is likely superior for monitoring longitudinal change.
Medial temporal lobe subregional atrophy patterns in presymptomatic and symptomatic genetic frontotemporal lobar degeneration
Authors: Gaynor Leslie S , Yballa Claire , Cobigo Yann , Mejía‐Perez Jhony Alejandro , Wuestefeld Anika , Wisse Laura E.M. , Forsberg Leah K. , Heuer Hilary W. , Rosen Howard J. , Boxer Adam L. , Boeve Brad F. , Kantarci Kejal , Staffaroni Adam M. , La Joie Renaud 
Published: January 2025

AbstractBackgroundMedial temporal lobe (MTL) atrophy is an early feature of multiple neurodegenerative diseases. In genetic frontotemporal lobar degeneration (FTLD, i.e. individuals carrying GRN, MAPT, or C9orf72 mutations), MTL atrophy can occur 10‐15 years before symptom onset. We aimed to better characterize MTL atrophy patterns in presymptomatic and symptomatic genetic FTLD using a multi‐atlas segmentation pipeline that allows precise measurement of MTL subregions.MethodParticipants included a diagnostically mixed sample of familial FTLD mutation carriers (MAPT: N = 87; C9orf72: N = 117; GRN: N = 65) and 142 non‐carrier family member controls from the ALLFTD study cohort (Table 1). T1‐weighted MRIs were preprocessed using a multi‐atlas automated pipeline (Automatic Segmentation of Hippocampal Subfields [ASHS]) to segment the amygdala, anterior and posterior hippocampus, and MTL cortices: entorhinal cortex (ERC), perirhinal cortex or Brodmann areas (BA) 35 and 36, and parahippocampal cortex (PHC). Following rigorous quality control and manual correction (Figure 1), we assessed bilateral volume differences in mutation carriers compared to controls, adjusted for age, sex, and total intracranial volume. Analyses were conducted separately for symptomatic (FTLD‐Clinical Dementia Rating Global Score≥0.5) and presymptomatic (Global Score = 0) mutation carriers.ResultOut of 413 scans, 317 (76.8%) segmentations passed quality control while 94 (22.8%) needed some manual edits—this was more common in symptomatic than presymptomatic carriers (32% versus 15%, p = 0.001). Three scans were excluded (<1%; 2 complete scans, 1 for selected subregions) due to insufficient landmark visibility in symptomatic GRN and MAPT carriers. In symptomatic carriers, all MTL subregions demonstrated reduced volume compared to controls, although MAPT carriers exhibited particularly marked atrophy, especially in anterior MTL subregions (Table 2; amygdala, ERC, BA35, hippocampus). Presymptomatic MAPT and C9orf72 carriers also exhibited significant atrophy of primarily anterior MTL cortices, while presymptomatic GRN carriers only exhibited significantly smaller ERC volume compared to controls.ConclusionMTL atrophy is present in genetic FTLD across mutation carriers, even at the presymptomatic stage. Anterior MTL subregions, which are also susceptible to neuropathologic accumulation in early Alzheimer’s disease and Limbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC), tend to be more vulnerable than posterior subregions. Unsurprisingly, MAPT carriers show the greatest MTL atrophy compared to controls across groups.
Plasma phospho‐tau 217 rises with and not before early tau aggregation measured by MK6240 PET
Authors: Feizpour Azadeh , Dore Vincent , Doecke James D. , Masters Colin L , Rowe Christopher C. 
Published: January 2025

AbstractBackgroundRise in plasma phospho‐tau (pTau) is hypothesized to reflect a physiological response to brain Aß plaques, preceding the formation of neurofibrillary tangles (NFT). An alternate explanation is poor sensitivity of tau PET for detection of early NFT formation. The tau tracer MK6240 has very low background “off‐target” binding and may better detect early tau aggregation. Choice of tau PET regions for analysis may also influence early detection of tau.MethodParticipants included 105 cognitively unimpaired (CU) and 178 cognitively impaired (CI), with plasma pTau217 Lumipulse® assay, 18F‐NAV4694 Aß PET and 18F‐MK6240 tau PET data. The entorhinal, amygdala and meta temporal (MT) ROI 18F‐MK6240 SUVR and pTau217 levels were modelled as a function of Centiloid (CL).ResultRise in plasma pTau217 slightly lagged the rise in amygdala MK6240 binding and paralleled the rise in the meta temporal ROI (Figure 1), consistent with our previous report using Janssen p217+Tau measured by Simoa®1. Our findings also suggest that amygdala and meta temporal ROI were more robust for early detection of aggregated tau than the entorhinal cortex possibly related to PART and small ROI volume producing wide variance in the entorhinal normal range in Ab‐ CU.ConclusionThis data suggests that the amygdala is a robust region for detecting early tau aggregation by MK6240 PET and that a rise in plasma pTau217 is associated with early formation of NFT rather than a direct physiological response to brain Aß plaques prior to NFT formation. Figure 1 legend: Modeling of 18F‐MK6240 quantification and pTau217 as a function of Centiloid. Biomarker values Z‐scored using the results from Aß‐ cognitively unimpaired to define the normal range for p217+tau level and each tau PET ROI SUVR. The horizontal line is +2 standard deviations (SD). 1Doré V, Doecke JD, …, Rowe CC. Plasma p217+tau versus NAV4694 amyloid and MK6240 tau PET across the Alzheimer’s continuum. Alzheimers Dement (Amst). 2022 Apr 5;14(1):e12307. https://doi.org/10.1002/dad2.12307. PMID: 35415202; PMCID: PMC8984092.
Data‐driven analysis of Aβ peptide ratios in iPSC models of familial Alzheimer’s disease for diagnosis and prediction of symptom onset
Authors: Saguer Isaac Llorente , Gabriele Rebecca , Wray Selina , Oxtoby Neil P , Arber Charlie 
Published: January 2025

AbstractBackgroundFamilial Alzheimer’s disease (fAD), arising from mutations in amyloid‐precursor‐protein (APP) and presenilin (PSEN1/2) genes, leads to the production of longer, aggregation‐prone amyloid‐beta (Aß) peptides—a hallmark of Alzheimer’s disease. Age‐at‐onset (AAO) varies among carriers of different mutations. Recent evidence challenges the Aß42:40 ratio as the leading and predictor of AAO between different pathogenic variants, prompting exploration of peptide combinations as potential biomarkers for these tasks.MethodData consisted of patient‐derived in‐vitro iPSC models of fAD and Ab measurements via enzyme‐linked immunosorbent assay (ELISA). With novel data of our own (N = 39) and published data from Liu et al. 2022 (N = 134) (table‐1), we performed a data‐driven analysis of Aß37/38/40/42/43 combinations, employing area under the curve (AUC) and coefficient of determination metrics for classification (WT vs AD) and AAO regression. A linear model optimized for these metrics was also explored. Each peptide was divided by the average of the control group of the same assay. Then, we weighted each peptide in accordance with the average profile of the control population in Liu.ResultData transformation using controls proved necessary for to produce comparable ratios (figure‐1). Table‐2 presents diagnostic and predictive performance for each peptide‐ratio biomarker relative to biomarkers from the literature (top 3 rows). Our new data‐driven biomarkers improved upon the literature in both diagnostic AUC (not statistically significant: DeLong’s test p>0.47) and predicting AAO (Spearman’s R^2 significant differences in UCL data; Hittner’s p<0.005; p>0.5 in Liu’s).ConclusionOur results do not recapitulate Liu et al. findings, as Aß37:42 did not outperform Aß42:40 in our data; one explanation could be that our lines represent a physiological model of Ab production that is free from plaque deposition. While no significant differences emerged in AD classification, specific peptide ratios demonstrated varying correlations with AAO: short‐to‐long ratio > Aß42:40, and Aß42:40 > Aß37:42, but other ratios were found to perform better in each set, suggesting mutation‐specific profiles. Data‐driven linear combination can improve the results, and notably, both optimizing for AUC or AAO found the same ratio of short/long, albeit with different weights. Standardization through control‐based data was essential for robust measures.
Longitudinal posterior‐medial functional connectivity changes with age and Alzheimer’s pathology are differentially related to memory performance depending on APOE genotype
Authors: Fischer Larissa , Molloy Eóin N. , Adams Jenna N. , Tremblay‐Mercier Jennifer , Remz Jordana , Binette Alexa Pichet , Villeneuve Sylvia , Maass Anne 
Published: January 2025

AbstractBackgroundThe posterior‐medial network is crucial for episodic memory. However, the medial temporal lobe (MTL) and posteromedial cortex (PMC) regions are vulnerable to aging and early Alzheimer’s disease (AD). Both processes might elicit distinct early functional connectivity (FC) changes which could be detrimental or protective/ compensatory regarding cognition. However, this is not well understood. We hypothesized that resting‐state FC strength between key regions (Figure 1a) would decrease with age and memory decline without AD pathology (A‐T‐) but increase with early AD pathology.MethodWe analysed longitudinal 3‐Tesla resting‐state fMRI data from cognitively unimpaired older adults (OA; PREVENT‐AD cohort). We assessed FC at baseline and after 24 months (FU24) in i) CSF or PET Aß‐ and tau‐negative OA (A‐T‐, N = 96, 63±5years, 70 female, 28 APOE4) and ii) Aß and p‐tau CSF‐characterized OA with available longitudinal p‐tau181/Aß1‐42 ratio (N = 65, 63±5years, 45 female, 22 APOE4). First, we investigated effects of age, APOE genotype and p‐tau181/Aß1‐42 ratio on FC controlling for sex and education. Second, we tested the association between baseline FC or change in FC and change in delayed memory recall in multiple regression analyses.ResultIn A‐T‐ OA, FC decreased mainly between regions within the PMC subnetwork over 24 months (Figure 1b). Higher baseline FCwithin‐PMC was related to increasing memory performance over time (p = 0.047; Figure 2a). Longitudinally, increasing FCMTL‐mPFC was associated with increasing memory in APOE4 non‐carriers and decreasing memory in APOE4 carriers (p = 0.016; Figure 2b). In CSF‐characterized OA, p‐tau181/Aß1‐42 ratio at baseline and FU24 was related to increasing FCMTL‐PMC over time (Figure 3a). Higher baseline FCMTL‐PMC was associated with longitudinally increasing memory in APOE4 non‐carriers and decreasing memory in APOE4 carriers (p = 0.028; Figure 3b).ConclusionOur results provide novel longitudinal evidence incorporating age, APOE, Aß and tau indicating specific memory‐related FC changes in cognitively unimpaired OA. APOE moderated the effects of FC strength on change in episodic memory performance. Higher FCMTL‐PMC and increasing FCMTL‐mPFC seem to be detrimental in APOE4 carriers but beneficial in APOE4 non‐carriers. Importantly, this effect was observed in A‐T‐ OA, hinting that APOE genotype may affect FC earlier than AD‐related pathology.
White‐matter myelin is reduced in monogenic small vessel disease and MCI: evidence from MRI source separation
Authors: Denecke Jannis , Dewenter Anna , Lee Jongho , Pirpamer Lukas , Franzmeier Nicolai , Gesierich Benno , Duering Marco , Ewers Michael 
Published: January 2025

AbstractBackgroundThe myelin sheath around axons is of fundamental importance for signal transduction. Myelin is reduced in white matter hyperintensities (WMH), which occur in both small vessel disease (SVD) and Alzheimer’s disease (AD), giving rise to the question to what extent myelin is reduced in these diseases. Here, we employed an advanced MRI based method to assess myelin independently from a major confounding factor, i.e. iron‐related signal, in monogenic small vessel disease (i.e. CADASIL) and in mild cognitive impairment (MCI) & AD dementia.MethodsWe included 62 CADASIL subjects, 11 with MCI or AD dementia, and 22 elderly controls (HC). Using 3D‐T2‐star‐weighted multi‐echo gradient‐echo MRI, we performed susceptibility source separation of diamagnetic (|χ‐negative|, e.g. myelin) and paramagnetic (χ‐positive, e.g. iron) sources (Shin et al., 2021). Mean diffusivity (MD) was calculated from diffusion tensor imaging. We extracted |χ‐negative| and MD values within WMH, normal appearing white matter (NAWM), and two ROIs including the left anterior thalamic radiation and forceps minor (genu) of the corpus callosum, i.e. two strategic tracts for processing speed. Subject‐level difference‐scores between CADASIL or MCI/dementia patients and group‐averaged HC were derived as abnormality scores. Voxel‐based WMH frequencies were mapped for each group. Group differences in |χ‐negative| and MD values were tested using linear regressions, controlled for χ‐positive scores, age, sex, and education.ResultsVoxel‐wise proportions of WMH are mapped for each group in Figure 1. For CADASIL, |χ‐negative| values were decreased, and MD values increased in each ROI compared to the HC group, with intermediate values for the MCI/dementia group (Figure 2 A&B). Worse |χ‐negative| and MD alterations were observed in WMH compared to NAWM (Figure 2 C‐F) in each disease group. Lower |χ‐negative| values were associated with higher MD in WMH (r = ‐0.57, Figure 3) and NAWM (r = ‐0.56).Conclusion|χ‐negative| values showed a marked decrease in WMH of CADASIL patients, suggesting myelin loss in SVD, with less pronounced myelin reduction present in MCI/AD. |χ‐negative| values were only moderately associated with MD, suggesting that each provides complimentary information. Our results encourage future studies to test the cognitive consequences of myelin loss in SVD and MCI.