bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ultra‐high‐resolution quantitative MRI at the grey/white interface in Alzheimer’s disease and posterior cortical atrophy
Authors: Veale Thomas , Ocal Dilek , Edwards Luke J , Pine Kerrin J , Fox Nick C , Weiskopf Nikolaus , Cash David M 
Published: December 2023

AbstractBackgroundCortical grey matter (CGM) and superficial white matter (SWM) undergo pathological changes, but standard MRI spatial resolutions make interpreting disease‐related effects at their interface challenging. We present findings from a study investigating the grey/white interface in typical amnestic Alzheimer’s disease (tAD), posterior cortical atrophy (PCA) and healthy controls using ultra‐high‐resolution quantitative MRI measures sensitive to microstructural properties.MethodEleven participants (Table 1) were scanned at 7T using multi‐echo FLASH (0.5mm isotropic resolution with prospective motion correction) and diffusion MRI (dMRI) (1.25mm isotropic resolution). The hMRI toolbox produced multi‐parametric mapping (MPM) measures: R1, R2* and Proton Density (PD) (Vaculciakova et al., 2021https://doi.org/10.1002/mrm.29253). dMRI processing included denoising and correcting for Gibbs ringing, eddy currents, susceptibility artifacts and motion. Metrics from dMRI were fractional anisotropy (FA), and mean diffusivity (MD), as well as tissue fraction (TF), neurite density index (NDI) and orientation dispersion index (ODI) from Neurite Orientation Dispersion and Density Imaging (NODDI). A deep learning segmentation approach estimated the grey/white boundary (Billot et al., 2021, arXiv:2107.09559). MPM and dMRI measures were sampled from the CGM into the SWM at 0.5mm increments in regions of interest (Harvard‐Oxford cortical atlas). Tissue‐weighted (tw) averages were calculated for NDI and ODI. Metrics were visualized across the grey/white interface using boxplots.ResultFigures 1‐2 show MPM and dMRI measures in the precuneus (dashed lines indicate group medians). Qualitatively, most tAD and PCA participants had lower R1 and R2* than controls, most prominently 0.5mm into the SWM (Figure 1). PD appeared higher in tAD and PCA patients across the grey/white interface. Qualitatively, most tAD and PCA participants had lower TF and higher MD in the CGM, where twNDI and FA appeared lower than controls across grey/white interface (Figure 2). Most tAD and PCA had qualitatively lower twODI in CGM and a flatter slope across the grey/white interface compared to controls.ConclusionMacromolecular (R1, R2* and twNDI) and iron content (R2*) MR measures at 7T appear reduced in tAD and PCA across the grey/white interface, whereas neurite organisational complexity (twODI) may become more homogeneous. Ongoing study will assess if these findings are consistent with further participants.
Altered BOLD Signal Fluctuations in precuneus relate to inflammatory markers and vascular risk in the AD spectrum
Authors: Maass Anne , Behrenbruch Niklas , Incesoy Enise I , Brosseron Frederic , Menze Inga , Bernal Jose , Hayek Dayana , Kleineidam Luca , Bürger Katharina , Fliessbach Klaus , Laske Christoph , Perneczky Robert , Peters Oliver , Priller Josef , Schneider Anja , Spottke Annika , Teipel Stefan , Wiltfang Jens , Wolfsgruber Steffen , Yakupov Renat , Wagner Michael , Jessen Frank , Duzel Emrah , Schreiber Stefanie , Heneka Michael T. , Ziegler Gabriel 
Published: December 2023

AbstractBackgroundInflammation, vascular disease and Alzheimer’s Disease (AD) appear to be closely linked. We recently showed that increased vascular risk relates to certain inflammatory profiles in CSF in the DELCODE cohort (Hayek et al, AAIC 2022). Moreover, we found cerebrovascular alterations in posterior‐midline regions including precuneus in subjects at risk for AD and with cognitive impairment relative to controls (Behrenbruch et al). Here, we examined the contributions of vascular risk and related inflammatory markers to alterations in resting‐state fMRI BOLD fluctuations that are sensitive to cerebrovascular reactivity (CVR)—a proposed marker of cerebrovascular dysfunction.MethodLeveraging resting‐state fMRI BOLD signal, we mapped voxel‐wise relative CVR (rCVR), using mean global signal as regressor, and the relative coefficient of variation (rCV). See Figure 1 for methodological details. We derived average rCV/rCVR from the precuneus as region of interest. Inflammation‐related CSF markers and AD markers (ptau181 and Aß42/40) from CSF were available in 205 subjects with rCVR/rCV maps. A Principal component (PC) analysis on 16 inflammation‐related CSF markers previously revealed 4 PCs. PC2 and PC3 were related to higher vascular risk scores (covering hypertension, diabetes and abnormal fat metabolism based on medical reports). We used linear regression models to assess how inflammation PC scores and AD biomarkers related to precuneus rCVR or rCV (controlling for age, sex, site, education and mean framewise displacement). We also tested if rCVR/rCV was related to vascular risk or memory.ResultLower precuneus rCVR was predicted by higher values of the inflammatory component PC2 (mainly driven by markers CRP, C1q and FactorB), with no additional effect of other PCs or AD markers. Lower precuneus rCV was predicted by higher values of the inflammatory component PC3 (mainly driven by IP10, MCP1, MIF, CRP, C4) and by higher ptau. Lower precuneus rCVR was also related to higher vascular risk. Lower rCVR and rCV related to worse memory. Figure 2 shows supplementary correlations of individual inflammation markers.ConclusionLower rCVR and rCV in the precuneus that are thought to reflect vascular dysfunction, relate to higher vascular‐risk related inflammatory markers beyond AD pathology. Their concurrent dynamics requires further investigation to understand their unique contributions to microvascular pathology.
Biological and methodological factors underlying a continuous amyloid CSF/PET imbalance model and its association with longitudinal cognition
Authors: Mastenbroek Sophie E , Sala Arianna , Gispert Juan Domingo , García David Vállez , Schmidt Mark E , Stephens Andrew W. , Gismondi Rossella , Farrar Gill , Frisoni Giovanni B , Boada Mercè , Ritchie Craig W , Visser Pieter Jelle , Bucci Marco , Hansson Oskar , Barkhof Frederik , Ossenkoppele Rik , Nordberg Agneta K , Rodriguez‐Vieitez Elena , Collij Lyduine E. ,   the AMYPAD consortium
Published: December 2023

AbstractBackgroundDiscordance between cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers of amyloid‐ß (Aß) occurs in up to 20% of individuals. Generally, Aß discordance is defined using binary cut‐offs. Given the methodological limitations of this approach, we propose a continuous Aß CSF/PET imbalance model. We investigated biological and methodological factors underlying discrepancy in these amyloid biomarkers and associations with longitudinal cognition.MethodWe included 383 non‐demented subjects (Nunimpaired = 326, Nmildly‐impaired = 57) from the AMYPAD‐PNHS cohort. Z‐scored CSF Aß42 and amyloid‐PET (Centiloids [CL]) units were fitted to a hyperbolic regression model by minimizing the Euclidian distance of the observed datapoints to the fitted line. Subject‐specific standardized residuals were derived as a measure of continuous imbalance (Zimbalance), with Zimbalance<0 reflecting greater Aß abnormality in CSF relative to PET and Zimbalance>0 greater Aß abnormality in PET relative to CSF (Figure‐1). Linear models adjusted for relevant covariates (Figure‐2) were run between Zimbalance and (1) methodological factors (CSF‐PET interval, CSF kit, PET tracer, and PET injected dose); (2) demographics (age, sex, education, APOE genotype) and CSF p‐tau biomarker; (3) vascular burden (white matter hyperintensities measured as Fazekas scores). Linear mixed models were used to investigate the predictive value of Zimbalance on longitudinal cognition in several domains.ResultSample characteristics are shown in Table‐1. Methodological factors were not significantly associated with Zimbalance. Increased CSF p‐tau levels were significantly associated with a higher Zimbalance (i.e. greater Aß abnormality in PET relative to CSF) (ß = 0.06±0.03, p = .033) (Figure‐2). In addition, a lower Zimbalance (i.e. greater Aß abnormality in CSF relative to PET) was associated with a higher Fazekas score (n = 189, ß = ‐0.55±0.26, p = .038) and predictive of faster cognitive decline on a language categorical fluency test (ß = ‐0.03±0.02, p = .038) (Figure‐2).ConclusionWe found that methodological factors did not contribute to a continuous model of Aß CSF/PET imbalance, suggesting that this imbalance reflects distinct biological profiles. Indeed, continuous CSF/PET imbalance showed differential associations, with greater pathological levels of aggregated Aß relative to soluble Aß being related to increased CSF p‐tau levels and excess pathological levels of soluble Aß relative to aggregated Aß to increased white matter hyperintensities and steeper cognitive decline.
CSF Biomarkers and [18F]flutemetamol PET‐Derived Centiloids in Patients with SCD or MCI
Authors: Battle Mark R , Buckley Chris J , Farrar Gill , Janelidze Shorena , Palmqvist Sebastian , Ossenkoppele Rik , Smith Ruben , Hansson Oskar 
Published: December 2023

AbstractBackgroundCSF biomarkers are well characterised as surrogate markers for amyloid deposition and neurofibrillary tau tangles reflecting pathological features of Alzheimer’s disease. Amyloid PET images provide a visual and quantitative assessment of brain amyloid burden. In diagnostic workup these biomarkers are considered interchangeable, however their relationship is not fully understood.MethodCentiloid (CL) values for 387 [18F]flutemetamol images from the Swedish BioFINDER I study were derived following the methods of Klunk et.al. (Alzheimer’s & Dementia, 2015). Subjects, comprising Controls, SCD and MCI (Table 1), were ranked by Centiloid values (lowest to highest) and the average for each consecutive group of 30 subjects calculated. Corresponding CSF biomarker values (Aß42/40 and pTau181) were plotted for each group.ResultSubjects in groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) Aß 42/40 ratio of 0.144 (±0.033, range 0.051 to 0.236, n = 240) with no statistically significant differences between groups (Fig. 1A). A statistically significant fall in CSF Aß42/40 ratio between the 29.0 CL and 47.3 CL groups (from 0.134 (+0.041, 0.066 to 0.236) to 0.088(+0.031, 0.045 to 0.183), p<0.0001) was observed. The average CSF Aß42/40 ratio value for all subjects ranging from 66.3 Cl to129.8 CL was 0.072 (±0.017, 0.030 to 0.127, n = 117) with no statistically significant difference in CSF levels across these CL bands. Subjects in the groups averaging ‐7.7 CL to 29.0 CL had a mean(±SD) pTau181 value of 48.9 pg/mL (±15.9, 22 to 138) with no statistically significant difference between groups (Fig. 1B). A statistically significant (p<0.05) increase between 29.0 CL (47.3±19.3, 24 to 116 pg/mL) and 47.3 CL groups (67.8±29.4, 24 to 175 pg/mL) was observed. For all Subjects from 66.3 CL to129.8 CL mean pTau181 levels were 79.6 (±30.3, 32 to 209 pg/mL) with no statistically significant difference across these CL bands.ConclusionComparison of [18F]flutemetamol image quantitation and CSF biomarkers indicates CSF Aß42/40 and pTau181 starts to show amyloid positivity at approximately 30‐40 CL units. Recent comparisons between visual rating and Centiloids from PET imaging indicate visual positivity from approximately 20 CL units indicating sensitivity of amyloid PET imaging for the detection of early amyloid deposition.
Clinical and biomarker characteristics of amnestic MCI patients exhibiting a temporo‐limbic FDG‐PET pattern suggestive of LATE
Authors: Grothe Michel J. , Silva‐Rodríguez Jesús , Moscoso Alexis , Schöll Michael 
Published: December 2023

AbstractBackgroundWe have recently described a specific temporo‐limbic FDG‐PET signature that characterizes autopsy‐confirmed cases with limbic age‐related TDP‐43 encephalopathy (LATE), and have demonstrated its clinical utility for differential diagnosis of amnestic dementia patients. Here we assessed the utility of this pattern for patient stratification at a predementia disease stage, and further used tau‐PET data to study the degree to which this “LATE‐specific” FDG‐PET signature may correspond to a limbic‐predominant AD subtype.MethodIndividual FDG‐PET scans of 909 amnestic MCI (aMCI) patients from the ADNI cohort were screened for hypometabolic patterns being suggestive of LATE (sLATE) or typical AD (sAD) using automated pattern‐matching to autopsy‐confirmed pathology‐specific FDG‐PET signatures. sLATE and sAD patients were compared on clinical features, APOE4 genotype, and CSF biomarkers. Tau‐PET scans ([18F]Flortaucipir) were available in a smaller subsample (30%) and were analyzed for tau‐positivity using an FDA‐approved visual interpretation method as well as entorhinal SUVR (positivity‐threshold: SUVR = 2.1).Result734 aMCI patients showed evidence of significant regional hypometabolism, and of these 92 had a pronounced temporo‐limbic hypometabolic pattern classified as sLATE, whereas 68 had a classical temporo‐parietal pattern classified as sAD(Fig‐1). Compared to sAD, sLATE patients had similar MMSE and memory scores but more preserved executive function scores, and they showed generally slower cognitive decline over follow‐up (p<0.001)(Fig‐2). They were also significantly older (d = 0.83, p = 0.01), less often APOE4 carriers (39% vs 64%, p<0.001), and had less abnormal CSF Aß42 (d = 0.60, p = 0.001) and p‐tau181 levels (d = ‐0.40, p = 0.01). With regards to tau‐PET, 9/30 sLATE (30%) and 9/10 sAD (90%) patients were visually positive and similar proportions were seen for entorhinal SUVR (40% vs 90%).ConclusionThe majority of amnestic MCI patients exhibiting a LATE‐typical temporo‐limbic FDG‐PET pattern show clinical and biomarker features that deviate from prodromal AD. A smaller subset of these patients shows elevated tau‐PET signal and could thus represent a limbic‐predominant AD subtype.
Association of FDG‐PET with co‐pathologies in autopsy‐proven AD
Authors: Chapleau Marianne , Mundada Nidhi , Spina Salvatore , Seeley William W. , Grinberg Lea T. , Schonhaut Daniel , Rosen Howard J. , Kramer Joel H. , Miller Bruce L , La Joie Renaud , Rabinovici Gil D. 
Published: December 2023

AbstractBackgroundThe presence of multiple pathologies is the rule and not the exception in Alzheimer’s disease (AD). The goal of this study was to assess whether 18F fluorodeoxyglucose (FDG) PET can provide information about the presence of non‐AD pathologies in autopsy‐proven AD patients.MethodOur cohort included 55 patients with antemortem FDG‐PET and autopsy‐proven intermediate/high ADNC from the UCSF Neurodegenerative Diseases Brain Bank. All patients underwent FDG‐PET. Standardized uptake value ratio (SUVR) images were computed using the pons as reference region. W‐maps (adjusted Z‐score maps) were created, comparing each patient to a group of 187 controls (age range: 20‐96, 40% males). Voxel‐wise correlations were computed to verify the unique contribution of each neuropathology to cerebral hypometabolism, while controlling for age, sex, time between FDG‐PET scan and death, scanner type, Thal and Braak staging, and the presence of all other co‐pathologies. Associations between SUVR values in specific region of interests (ROIs) were also assessed. The ROIs included were the following: Landau AD ROI for AD pathology, Cingulate Island Sign (CIS) for Lewy body disease, Inferior/Medial Temporal Lobe (IMT) and IMT/Frontal Supraorbital (IMT/FSO) ratios for limbic‐predominant age‐related TDP43 encephalopathy neuropathologic change (LATE‐NC).ResultMean age was 63.8 ± 10.5, mean time between FDG and death was 6.2 ± 2.8, and mean MMSE was 21.1 ± 7.1 at the time of PET (Table 1). Group averaged W‐map showed the typical temporo‐parietal AD pattern; hypometabolism in these regions was associated with higher Thal phase and Braak stage (Figure 1). Lewy body disease (LBD), argyrophilic grain disease (AGD), aging‐related tau astrogliopathy (ARTAG), cerebral amyloid angiopathy (CAA) and vascular brain injury (VBI) were associated with hypometabolism in small discrete regional clusters (Figure 2). The Landau AD ROI was associated with both Thal (rs = ‐0.325, p = .015) and Braak stages (rs = ‐0.447, p<.001). The CIS sign was not associated with the presence of LBD (t(53) = 1.01, p = 0.319). Both the IMT (t(53) = ‐2.25, p = .028) and IMT/FSO (t(53) = ‐3.40, p = .001) ratios were associated with the presence of LATE‐NC.ConclusionCo‐pathologies make a relatively small contribution to hypometabolism in patients with intermediate‐high ADNC. Novel biomarkers are needed for precise detection of co‐pathologies.
Explainable AI and its instantiation in Computational Pathology for a better understanding of Alzheimer’s disease
Authors: Racoceanu Daniel 
Published: December 2023

AbstractBackgroundEXplainable Artificial Intelligence (XAI) has an essential role to accelerate the AI adoption by empowering biomedical experts and catalyzing modern research through the emergence of a new generation of virtual actors, enabling traceable knowledge‐based quantification, as a guided qualification support. XAI instantiation is shown through our study, hypothesizing that rapid Alzheimer’s Disease (rAD) brains display subtle histological changes that would be undercovered by high‐throughput automated microscopic analysis. We study the topography and morphology of tau lesions’ aggregates to better understand the morphological substratum of Alzheimer’s Disease (AD) heterogeneity.MethodTo address this question at a large scale, we designed, tested and implemented a software for automatic segmentation, annotation and quantitation of brain lesions in histopathological whole slide images (WSI). A dataset of 15 whole slide images of postmortem human brain tissue is used in this study. These are fully annotated by neuropathologists yielding a set of more than 30,000 annotated plaques and tangle objects. A deep learning pipeline consisting of an attention‐UNet model with explainability features is trained for segmentation of tau aggregates in WSIs. The attention maps corresponding to segmentation predictions from the model are used by pathologists as a comparable reference to assist their decision making process.ResultThe DL‐based WSI analysis framework allows detecting a majority of the tau aggregates and refining tau aggregate boundaries with higher accuracy compared to manual annotations. Besides, with such a DL model based tau identification, the pathologist needs to annotate only a few samples for the DL model training. Overall, DL assisted analysis of tau aggregates could ease the effort of pathologists and facilitate analysis of hundreds of WSIs which are vital in AD research areas such as identifying different AD forms and AD patient stratification.ConclusionXAI is not only providing traceability by opening the way to an effective adoption, but also allows optimizing the ML/DL design, by keeping high performances. The green footprint is, therefore, considered, as well as the GDPR (European General Data Protection Regulation), towards Responsible Artificial Intelligence.
Containerizing neuroimaging workflows for scalable and reproducible analyses
Authors: Keefe Sarah J. , McKay Nicole S. , LaMontagne Pamela J. , Flores Shaney , Gordon Brian A. , Benzinger Tammie L.S. 
Published: December 2023

AbstractBackgroundLarge multi‐center, longitudinal neuroimaging projects aiming to understand neurodegenerative disorders, such as Alzheimer disease (AD), frequently generate large datasets for use by external researchers. However, maintaining a replicable data processing workflow in an on‐going study can be difficult due to changes in personnel, computer or software upgrades, and disparate computing resources across collaborating institutions. Replicating workflows from prior studies is also important for validating and building scientific knowledge but can present similar challenges. Containerization is an increasingly popular solution to these issues. This process allows users to package their code, software, and any dependencies together, ensuring stable and consistent output across computing platforms. The main goal of this project is to guide researchers through the steps of custom container development for their own needs.MethodWe demonstrate the process of containerization using a neuroimaging workflow that quantifies edge density (ED), an emerging metric of white matter integrity. To calculate ED, structural and diffusion magnetic resonance images were processed using standard FreeSurfer (5.3) and FSL (6.0) procedures. Whole‐brain probabilistic tractography, ED metrics, and diffusion tensor indices were computed. A template for building a container image for neuroimaging use was developed. Scripts used for analyses were incorporated into the template and compiled as reusable container images using Docker and Apptainer. Additional scripts and documentation were released to facilitate custom implementation.ResultContainerized tractography, ED metrics, and tensor indices were identical across multiple computing systems and in‐line with prior research showing white matter integrity begins to decline prior to outward cognitive symptoms. The provided code and documentation outlines the containerization process for our workflow (Figure 1) and describes how to run the resulting container image using data from the recently updated Open Access Series of Imaging Studies 3 dataset (OASIS‐3) as an example.ConclusionContainerizing a neuroimaging processing workflow ensures stable results across the lifespan of a study and facilitates replication of prior workflows in new datasets. The containerized ED pipeline provided enables other researchers to replicate or extend upon our analyses in this cohort. The provided documentation and templates can also be adapted for use with other datasets that use common neuroimaging file formats.