bluuebunny/crossref_metadata_2025_split huggingface dataset:
Harmonization of amyloid and tau PET tracers in a multi‐center cohort from NIA Alzheimer’s disease research centers in the United States
Authors: Harrison Theresa M. , Landau Susan M. , Baker Suzanne L. , Lee JiaQie , Ward Tyler J. , Murphy Alice , Chadwick Trevor , Mormino Elizabeth C. , Johnson Sterling C , Rogalski Emily J , Koeppe Robert A. , Decarli Charles , Toga Arthur W. , Kukull Walter A. , Biber Sarah , Jack Clifford R. , Jagust William J. 
Published: December 2023

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aß)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aß‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aß‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aß‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aß‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aß‐PET data across 4 tracers in CLs, there were expected differences in Aß burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.
Cortical and substantia nigra 18F‐PI‐2620 tau PET signal in Lewy body disease
Authors: Winer Joseph R. , Vossler Hillary , Young Christina B. , Romero America , Shahid Marian , Abdelnour Carla , Anders David , Shen Bin , Morales Aimara Pacheco , Davidzon Guido , Mormino Elizabeth C. , Poston Kathleen L. 
Published: December 2023

AbstractBackgroundTau PET imaging is a potential tool for measuring comorbid tau pathology in patients with Lewy body disease. Furthermore, tau PET ligands are known to bind to neuromelanin within substantia nigra (SN), with reduced binding in Parkinson’s disease relative to healthy individuals. We sought to characterize 18F‐PI‐2620, a next generation PET tracer, in patients from across the Lewy body disease spectrum.MethodA total of 126 participants from the Stanford Alzheimer’s Disease Center (ADRC) and the Stanford Aging and Memory Study (SAMS) were recruited for PET scanning with 18F‐PI‐2620. We compared 18F‐PI‐2620 uptake within medial temporal (entorhinal cortex, amygdala) and occipital (precuneus, lingual gyrus) regions as well as SN across patients with Parkinson’s disease (PD, n = 26), dementia with Lewy bodies (DLB, n = 6), and Alzheimer’s disease spectrum (AD, n = 21), in addition to cognitively normal older adults (healthy controls, HC, n = 73). Mean bilateral signal was extracted from cortical regions of interest in native space 18F‐PI‐2620 SUVRs (inferior cerebellar gray reference). Amyloid status was determined either by 18F‐florbetaben PET or CSF. To quantify SN, SUVRs were normalized to template space and an anatomical mask was used to extract mean signal. MDS‐UPDRS‐III motor exam scores, collected in an off‐medication state for all patients, were used as a measure of motor impairment.ResultEntorhinal and amygdala SUVR were significantly elevated in the AD group relative to all other groups, and in amyloid‐positive relative to amyloid‐negative HC (Fig 1). Tau burden in all regions was low across PD and DLB groups and did not differ based on amyloid status. SN SUVR was significantly lower in the PD group compared to the HC and AD group (Fig 2A). In patients with Lewy body disease (PD & DLB), MDS‐UPDRS‐III scores were negatively associated with SN SUVR (Fig 2B).ConclusionThese findings suggest that cortical tau is low in Lewy body disease. Neuromelanin‐related 18F‐PI‐2620 signal within SN carries information about disease severity. Cortical tau and SN binding represent two unique signals in the same tau PET image that may be informative in the context of Lewy body disease.
The association between 73 Alzheimer’s disease risk variants and white matter microstructural decline in aging
Authors: Archer Derek B , Shashikumar Niranjana , Jasodanand Varuna , Moore Elizabeth E. , Murray Sydney , Pechman Kimberly R. , Bilgel Murat , Beason‐Held Lori L , An Yang , Risacher Shannon L , Landman Bennett A. , Jefferson Angela L. , Saykin Andrew J. , Resnick Susan M. , Hohman Timothy J. 
Published: December 2023

AbstractBackgroundAlthough several prior diffusion MRI studies have indicated a role of medial temporal lobe (MTL) white matter microstructural decline in Alzheimer’s disease (AD), it is unclear which AD risk genes are associated with this decline. The goal of this study is to leverage multi‐site harmonized diffusion MRI data to investigate the relationship between 73 AD risk variants and microstructural decline in MTL tracts.MethodThe dataset used in this study was collated from three longitudinal cohorts of aging [Alzheimer’s Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)]. In total, this dataset included 1,308 participants (70% cognitively unimpaired, 27% MCI, 3% AD) aged 50+ who had both diffusion MRI and genotype data and includes a total of 3,442 imaging sessions (mean number of visits: 2.68 ± 1.66, interval range: 1 ‐ 12 years). Genotype data was preprocessed using a standardized approach, and the dosage of the 73 AD risk variants was extracted. Diffusion MRI data was preprocessed using the PreQual pipeline and free‐water (FW) correction was conducted to obtain FW and FW‐corrected fractional anisotropy (FAFWcorr) maps. FW and FAFWcorr were quantified within 5 MTL tracts, including the cingulum, fornix, uncinate fasciculus, inferior longitudinal fasciculus, which were subsequently harmonized using the Longitudinal ComBat package. Linear regression was conducted to investigate the association between AD risk variants and MTL microstructure at baseline, while linear mixed effects regression was used for longitudinal analysis. All models were corrected for age at baseline and sex and corrected for multiple comparisons using the FDR approach.ResultAt baseline, APOE‐e4 dosage was strongly associated with MTL FW in all tracts, with additional associations for TMEM106B and CR1 dosages with fornix FW (Table 1). Longitudinally, APOE‐e4 dosage was a strong predictor of MTL FW decline in all tracts, and KAT8, CR1, and TMEM106B dosages exhibited significant associations with fornix microstructural decline (Table 2 and Figure 1).ConclusionThis study suggests that although APOE‐e4 is the strongest predictor of white matter microstructural decline, several other AD risk genes (i.e., CR1, SPI1, TMEM106B, KAT8) contribute to decline, particularly in the fornix.
A comparison of two novel visual read‐based methods for high tau identification from flortaucipir‐PET
Authors: Iaccarino Leonardo , Tunali Ilke , Kim Min Jung , Lu Ming , DiFabbio Kelly , Kowaleski Stacey , Ducker Courtney , Patel Sneha , Arora Anupa , Southekal Sudeepti , Kotari Vikas , Shcherbinin Sergey , Pontecorvo Michael J. 
Published: December 2023

AbstractBackgroundTo compare the performances of two novel visual read‐based methods for flortaucipir‐PET in patients with Alzheimer’s Disease (AD) to identify high tau accumulation, defined as AD‐signature weighted neocortical Standardized Uptake Value ratio (SUVr)>1.46.MethodBaseline flortaucipir‐PET scans displaying advanced‐AD tau‐patterns (increased neocortical activity beyond posterior lateral temporal and occipital regions with bindings 1.65‐times higher [1.65x‐visual threshold] than cerebellar average) were utilized from the confirmatory phase of A05 study (N = 78, NCT02016560). Four expert human‐readers conducted reads using two novel visual read‐based methods while being blinded to each other and all quantitative results. The first approach (SUVrmax‐method) requires a reader to visually identify and hand‐draw focal regions‐of‐interest on frontal areas with the highest bindings and calculate an SUVr between the identified frontal maximum and average cerebellum binding. The second approach (Enhanced Read‐method) requires the reader to evaluate binding elevation compared to cerebellum with an enhanced visual threshold, either across gray matter (global, 3.795x‐visual threshold) or only in the frontal lobe (2.805x‐visual threshold), compared to the standard 1.65x‐visual threshold specified in the FDA‐approved Tauvid™ label. Methods were compared to the standard quantitative high tau determination as standard‐of‐truth.ResultBased on a previously found ROC‐determined cut point using computer‐generated SUVrmax values, the SUVrmax‐method showed median 88% Positive Percent Agreement (PPA), 87% Negative Percent Agreement (NPA) and 87% overall accuracy across raters. The Enhanced Read‐method showed median 75% PPA, 88% NPA and 85% overall accuracy and median 81% PPA, 91% NPA and 88% overall accuracy for global and frontal approaches, respectively.ConclusionBoth novel flortaucipir‐PET visual read‐based methods showed comparable and promising performances in identifying AD with high tau, do not require computationally intensive approaches and can be implemented with different imaging software.
The impact of subjective cognitive decline on aging effects in the brain as measured by diffusion tensor imaging
Authors: Flaherty Ryn , Sui Yu Veronica , Masurkar Arjun V. , Rusinek Henry , Lazar Mariana 
Published: December 2023

AbstractBackgroundDiffusion tensor imaging (DTI) can separate subjective cognitive decline (SCD) participants who progress to mild cognitive impairment and those who do not 1. We explored the effect age and memory performance has on DTI measures in SCD and controls in the hippocampal cingulum bundle, which is typically affected early in the course of Alzheimer’s disease2.Method325 participants Cam‐CAN 3 (127 SCD) older than 55 were included in the analysis. Diffusion imaging data was collected on a 3T Siemens TIM Trio scanner with a 32‐channel head coil for 0, 1000 and 2000 s/mm2 b‐values and 30 gradient directions. Preprocessing and calculation of the DTI metrics fractional anisotropy (FA, mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AxD) were completed using MRTrix3, FSL and MATLAB. The hippocampal cingulum bundle regions of interest (ROIs) were extracted from the ICBM‐DTI‐81 atlas. Memory was assessed using the delayed story recall (DSR) task from WMS‐III. Linear regression models were completed in R. Gender was included as a nuisance variable.ResultThere were no differences in gender or age distribution between cohorts, however participants with SCD had a 14% lower DSR score (Table 1). There was a significant interaction effect between age and cohort in MD, AxD, and RD (Figure 1), as well as between memory and age in AxD (Figure 2). In post‐hoc analyses, compared to controls, the SCD group showed a stronger correlation with age in MD, AxD, and RD (Figure 1) while participants with worse memory showed a stronger correlation with age in MD (Figure 2), indicating an accelerated aging effect.ConclusionParticipants with SCD and worse memory present with an accelerated aging effect, showing steeper variations in diffusion microstructure with age. Since SCD is likely to be a preclinical stage of Alzheimer’s disease, future work will analyze this effect with more advanced diffusion microstructure models to further elucidate the underlying pathophysiology. • Selnes P, Aarsland D, Bjornerud A, et al. J Alzheimers Dis. 2013;33(3):723‐36. • Jack Jr CR, Knopman DS, Jagust WJ, et al. The Lancet Neurology. 2013;12(2):207‐16. • Shafto MA, Tyler LK, Dixon M, et al. BMC neurology. 2014;14(1):1‐25.
Sequencing cortical microstructural changes along the Alzheimer’s disease continuum
Authors: Nir Talia M , Javid Shayan , Villalon‐Reina Julio E , Thomopoulos Sophia I , Salminen Lauren , Thompson Paul M , Jahanshad Neda 
Published: December 2023

AbstractBackgroundMicrostructural abnormalities likely precede macrostructural changes in the Alzheimer’s disease (AD) cascade. Diffusion MRI (dMRI) is sensitive to microstructural properties of brain tissue but few studies have evaluated dMRI measures in cortical gray matter where many early AD histopathological changes occur. Event‐based modeling (EBM) is a data‐driven approach for probabilistically sequencing cross‐sectional biomarkers in the order that they likely become abnormal. Here, we used EBM to examine the sequence of changes in cortical dMRI measures relative to more widely used amyloid, tau, brain volume, and cognitive biomarkers in two independent AD studies.MethodT1w, dMRI, and amyloid‐PET (FBB/FBP) data were analyzed in 461 ADNI3 participants and 188 OASIS3 participants (Figure 1A). Some ADNI3 participants also had tau‐PET (AV‐1451) and CSF pTAU‐181 and Aß1‐42 data. In addition to DTI, novel single‐shell adaptations of multi‐shell models, NODDI‐DTI and MAP‐AMURA, were fit to the dMRI data. 10 mean cortical dMRI measures were extracted using FreeSurfer parcellated T1w‐images (Figure 1B). T1w cortical and hippocampal volumes were also extracted, and all MRI measures harmonized using ComBat. Mean cortical amyloid‐PET SUVRs were converted to centiloids. Associations between each of 17 biomarkers (Figure 1C) and cognitive impairment (CI –MCI+Dementia– vs CN) were tested in ADNI3. Significant biomarkers were included in an ADNI3 EBM to determine event ordering.The EBM was validated in OASIS3 and used to classify clinical diagnosis.ResultAll biomarkers except DTI FA showed significant differences between CN and CI ADNI3 participants (Figure 2A). ADNI3 EBM confirmed existing AD models: CSF amyloid abnormalities preceded PET, followed by CSF pTau, while cognitive and volumetric abnormalities occurred at later stages. Changes in all dMRI biomarkers preceded CSF or PET Tau, cognitive, and volumetric abnormalities (Figure 2B). Estimated disease stages in ADNI3 distinguish dementia from CN with an AUC = 0.96 (10‐fold cross‐validation). OASIS3 disease stages, estimated using ADNI3 biomarker distributions excluding CSF measures and tau‐PET, classified diagnosis with an AUC = 0.85 (Figure 3).ConclusionCortical microstructural measure abnormalities, including greater diffusivity, lower restriction and lower dispersion potentially due to neurite loss and lower dendritic arborization complexity, may precede those detectable with conventional T1w biomarkers.
Microglial activation interacts with amyloid‐β to drive longitudinal tau tangle accumulation and cognitive decline
Authors: Povala Guilherme , Bellaver Bruna , Lussier Firoza Z , Ferreira Pamela C.L. , Ferrari‐Souza João Pedro , Leffa Douglas Teixeira , Aguzzoli Cristiano Schaffer , Zalzale Hussein , Soares Carolina , Abbas Sarah , Rohden Francieli , Tissot Cécile , Therriault Joseph , Bezgin Gleb , Servaes Stijn , Benedet Andrea Lessa , Stevenson Jenna , Rahmouni Nesrine , Cohen Annie , Lopez Oscar L. , Tudorascu Dana , Klunk William E , Villemagne Victor L , Karikari Thomas K , Zimmer Eduardo R , Rosa‐Neto Pedro , Pascoal Tharick Ali 
Published: December 2023

AbstractBackgroundActivation of microglial cells in the brain, more commonly known as neuroinflammation, has often been linked to the pathophysiology of Alzheimer’s disease (AD). However, how microglial activation is associated with longitudinal tau tangle accumulation and consequent cognitive decline is poorly understood. Here, we aimed to investigate whether baseline microglial activation impacts tau tangle deposition and cognitive decline in individuals across the AD continuum.MethodWe assessed 92 individuals from the TRIAD cohort (57 cognitively unimpaired and 35 cognitively impaired) with available baseline [11C]PBR28‐PET, a measure of microglial activation and [18F]NAV4694 Aß‐PET, and longitudinal [18F]MK6240 Tau‐PET (mean follow‐up time = 1.93 years) and Mini‐Mental State Exam (MMSE) (mean follow‐up time = 1.84 years). We performed voxel‐wise associations using linear regressions accounting for age and sex and adjusted for multiple comparisons using Random Field Theory (RFT) (p < 0.05). We used the cuneus and superior temporal cortex as a composite ROI for [11C]PBR28‐PET and Aß‐PET since these regions showed a higher association with longitudinal tau accumulation in the temporal meta‐ROI.ResultVoxel‐wise analysis showed that baseline levels of [11C]PBR28‐PET alone are not sufficient to predict longitudinal tau tangle accumulation (Fig. 1a). However, the interaction between [11C]PBR28‐PET levels and Aß burden predicted an increased accumulation of tau tangle, mainly in the cuneus, inferior frontal and lateral occipital regions (Fig. 1b).Individuals with higher baseline [11C]PBR28‐PET and Aß‐PET levels present higher rates of longitudinal tau accumulation in the temporal meta‐ROI (ß = 0.36, t = 3.46, p = 0.0009; Fig. 1c). Similarly, while [11C]PBR28‐PET levels alone did not correlate with longitudinal changes in MMSE score (ß = ‐0.17, t = ‐1.69, p = 0.10), a significant interaction between [11C]PBR28‐PET and Aß‐PET levels on MMSE annual decline was observed (ß = ‐0.24, t = ‐2.25, p = 0.028; Fig. 1d).ConclusionWe found that baseline levels of microglial activation was associated with longitudinal tau tangle accumulation and cognitive decline in individuals across the AD continuum in the presence of Aß burden. Our results indicate that microglial activation might act potentiating the deleterious effects of Aß on forthcoming tau tangle deposition.
Comparison of Event‐Related Potential Markers in Amyloid Positive vs. Amyloid Negative Patients with Mild Cognitive Impairment (MCI) and Dementia in a general neurological clinic
Authors: Levy Igor 
Published: December 2023

AbstractBackgroundWe used an automated Event Related Potentials (ERP) technique to compare ERP responses in amyloid positive and negative group of 75 patients with Mild Cognitive Impairment (MCI) and Dementia in a general neurological outpatient clinic. Over the years, research labs have repeatedly reported abnormal ERP markers in patients with MCI and dementia. Recent advances in technology have made ERP testing safe, easy to use and low cost, thus rendering it suitable for clinical use. Indeed, ERPs have become more routinely used in clinical assessment of neurological patients with cognitive changes. The possibility that ERP could help identify amyloid status in MCI and dementia patients adds to the potential clinical utility of these biomarkers.MethodPatients with a history of cognitive complains underwent an auditory oddball ERP paradigm in conjunction with routine neurological evaluation and imaging studies. Standard neuropsychological testing were available in most cases. Amyloid PET imaging was done as part of the IDEAS (Imaging Dementia ‐Evidence for Amyloid Scanning) study and provided the readings for amyloid load. ERP responses were analyzed in the context of beta amyloid readings, and in relation to the patients’ degree of cognitive deficits.ResultFinal data analysis will be presented at the time of the conferenceConclusionThe possibility that ERP could help identify amyloid status in MCI and dementia patients adds to the potential clinical utility of these biomarkers. possibility that ERP could help identify amyloid status in MCI and dementia patients adds to the potential clinical utility of these biomarkers.
Functional connectivity trajectories in genetic frontotemporal dementia
Authors: Zhang Liwen , Flagan Taru M. , Staffaroni Adam M. , Häkkinen Suvi , Brown Jesse A. , Mandelli Maria Luisa , Rosen Howard J. , Kantarci Kejal , Ramos Eliana Marisa , Tempini Maria Luisa Gorno , Miller Bruce L , Seeley William W. , Lee Suzee E. 
Published: December 2023

AbstractBackgroundCharacterizing disease trajectories in frontotemporal lobar degeneration (FTLD) is essential to determine the optimal timing for initiating therapeutic interventions, and there is an urgent need for novel and sensitive disease biomarkers. Intrinsic connectivity network (ICN) mapping using task‐free fMRI is a promising biomarker which reveals early abnormalities even before symptomatic onset (Dopper et al. 2014; Lee et al. 2016). Yet, ICN trajectories from the presymptomatic to symptomatic stages in FTLD mutation carriers remain unclear.MethodIn this cross‐sectional study, we used UCSF and ALLFTD Consortia data to study 129 carriers with variants in MAPT (presymptomatic/prodromal/symptomatic: 34/4/7), C9orf72 (30/9/12) and GRN (22/4/7). Using cross‐sectional data to construct ICN trajectories by genotype, we built separate generalized additive models for each genetic group to estimate non‐linear relationships between standardized connectivity changes and disease age across clinical stages. ICN connectivity was based on seed‐based analyses for networks associated with common clinical syndromes for each genetic group (i.e., salience and default mode [MAPT/C9orf72/GRN], corticobasal syndrome [MAPT/GRN], progressive supranuclear palsy syndrome [MAPT], sensorimotor and medial pulvinar‐related [C9orf72], and non‐fluent variant primary progressive aphasia (nfvPPA) syndrome networks [GRN]). We included 127 healthy controls as a reference to calculate ICN z‐scores for each carrier. Each carrier’s disease age was defined as the difference between the carrier’s chronological age and estimated age of onset based on multimodal measures of structural imaging, fluid biomarker and clinical assessments (Staffaroni et al. 2022).ResultIn MAPT+, presymptomatic carriers showed declining salience network connectivity starting approximately 30 years before estimated onset, which plateaued about 20 years before onset. In C9orf72+, salience network hyperconnectivity emerged about 30 years before onset and declined throughout the presymptomatic and symptomatic stages. For GRN+, connectivity changes as a function of disease age were found within the salience and nfvPPA networks, with connectivity stable until around 10 years before estimated onset, followed by decline starting in the late presymptomatic phase and continuing through the prodromal and symptomatic stages.ConclusionThese findings demonstrate that FTLD genes show distinct ICN trajectories throughout the clinical spectrum. These results help pave the way for determining the optimal timing to initiate therapeutic interventions.