bluuebunny/crossref_metadata_2025_split huggingface dataset:
T1 MRI reveals differential hippocampal atrophy in Lewy Body Disorders with and without Alzheimer’s copathology
Authors: Cohen Jesse S , Phillips Jeffrey S , Das Sandhitsu R. , Rhodes Emma , Cousins Katheryn A Q , Yushkevich Paul A. , Wolk David A. , Weintraub Daniel , Irwin David J. , McMillan Corey T 
Published: December 2023

AbstractBackgroundLewy body disorders (LBD) include Parkinson disease (PD), Parkinson disease dementia (PDD), and dementia with Lewy bodies (DLB). LBD are the second leading cause of neurodegenerative dementia after Alzheimer’s disease (AD). An estimated 50% of LBD patients have coexisting AD pathology (LBD+AD), which correlates with shorter time to dementia and death. Autopsy evidence suggests that verbal memory impairment correlates with the severity of hippocampal tau pathology in LBD. However, it is unknown whether greater hippocampal tau burden in LBD+AD causes neurodegeneration detectable by in vivo structural MRI, and whether hippocampal atrophy explains verbal memory impairment.MethodUsing a case‐control design, we studied 87 patients with clinical diagnoses of LBD (PD, n = 52; PDD, n = 12; DLB, n = 23) with available T1 MRI and AD biomarkers or, and cognitively‐normal controls (n = 131). AD copathology was defined hierarchically by: a) AD “intermediate” or “high” by ABC neuropathologic criteria; 2) positive amyloid PET; and 3) CSF total tau:ß‐amyloid1‐42 ratio > 0.3 (autopsy‐validated cutpoint). The Automated Segmentation of Hippocampal Subfields (ASHS) pipeline was applied to subjects’ T1 MRIs. Hippocampal volume (HV) was compared between controls, LBD‐AD, and LBD+AD, and correlated with verbal memory scores. Linear regression was used to test the association of AD copathology (independent variable) and HV (dependent variable), covarying for age, sex, education, and clinical diagnosis.ResultStatistically significant differences (ANOVA p = 0.002) in bilateral HV (expressed as percentage of intracranial volume) were seen between groups (mean 0.456for controls, 0.444±0.045 for LBD‐AD, and 0.416±0.063 for LBD+AD). Tukey’s test showed significant differences between LBD+AD vs controls (p = 0.001) and LBD+AD vs. LBD‐AD (p = 0.047). Linear regression showed statistically significant association of LBD+AD with bilateral HV (p = 0.034). Among all LBD patients, hippocampal volume positively correlated with verbal memory (immediate recall: r2 = 0.24, p = 0.033, delayed recall: r2 = 0.22, p = 0.042, recognition discrimination r2 = 0.58, p<0.001).ConclusionIn patients with LBD, AD copathology is associated with increased hippocampal atrophy as measured by in vivo T1 MRI, which correlates with greater verbal memory deficits. This work supports the that AD copathology impairs cognition in LBD via hippocampal neurodegeneration.
Ante‐mortem magnetic resonance imaging grey‐white matter contrast regional signatures of Alzheimer’s disease neuropathology
Authors: Almeida Francisco , Coelho Ana , Quintas‐Neves Miguel , Teylan Merilee A. , Mock Charles , Kukull Walter A. , Crary John F. , Oliveira Tiago Gil 
Published: December 2023

AbstractBackgroundGrey matter (GM), white matter (WM) contrast (GWC) in T1‐weighted MRI has been shown to decrease with age in healthy populations and cognitive impairment in clinical Alzheimer’s Disease (AD). However, the neuropathological determinants of this signal remain unknown. Here, we aimed to study whether GWC is associated with AD neuropathology and longitudinal hippocampal atrophy.Method157 patients with neuropathological, clinical data and ante‐mortem MRI were selected from the National Alzheimer’s Coordinating Center database. Cortical volume and GWC (WM/GM intensity) in T1/MRI were obtained with Freesurfer 6.0. Hippocampal volume was divided into anterior, intermediate and posterior regions using a lab‐based algorithm. Correlational analysis was conducted for GWC with age, and GWC residuals after linear regression with age for volume, BRAAK stage (neurofibrillary tangles), CERAD score (neuritic plaque density) and Cognitive Dementia Rating Sum‐of‐Boxes (CDR‐SB). APOE genotypes were compared using one‐way‐ANOVA.ResultGWC was negatively correlated with age in widespread brain areas (p < 0.05, FDR), but only the entorhinal cortex showed a positive correlation between volume and GWC. There were no significant correlations between GWC and BRAAK stage, CERAD score, CDR‐SB or differences according to APOE genotype. Entorhinal GWC correlated positively only with the posterior hippocampal volume in BRAAK III‐IV.ConclusionGWC is highly modulated by age, but not by classical AD neuropathology, APOE genotype or dementia severity in this cohort. Entorhinal GWC correlates with entorhinal and posterior hippocampal volume, suggesting it might reflect an early pathophysiological process distinct from classical AD neuropathology.
tau‐neurodegeneration mismatch from inter‐modality image translation using deep learning
Authors: Lyu Xueying , Khandelwal Pulkit , Duong Michael Tran , Wang Jiancong , Xie Long , Wisse Laura EM , Yushkevich Paul A. , Das Sandhitsu R. , Wolk David A. 
Published: December 2023

AbstractBackgroundThe relationship between tau neurofibrillary tangles (T) and neurodegeneration (N) may offer clues to potential presence of comorbidities, as well as relative resilience and vulnerability to Alzheimer’s disease (AD) pathology. We have previously developed measures of tau PET (T) and cortical thickness (N) mismatch based on linear model residuals. However, the underlying relationships between T and N are likely complex and non‐linear. Moreover, predicting cortical thickness based on local tau level may miss non‐local contributions of tauopathy. Here we investigate T‐N mismatch using a deep‐learning 3D image translation neural network to estimate synthetic maps of cortical thickness based on tau PET images. Deviation between the synthetic and actual cortical thickness serves as a metric of T‐N mismatch.MethodWe derived 3D cortical thickness from T1‐MRI and SUVR from 18F‐flortaucipir tracer uptake to represent N and T maps respectively. We predicted cortical thickness maps from tau SUVR by training a 3D U‐Net (Ronneberger et al. 2015) to learn the relationship between paired cortical tau SUVR and thickness maps from 70 symptomatic patients from ADNI. Approximately 70% of the training set was A+. We used regional standardized mean absolute error between predicted and actual thickness across 104 bilateral gray matter regions of interest as T‐N mismatch for clustering in a separate independent sample of 194 A+ symptomatic patients.ResultThe voxel‐wise mean absolute error of thickness translation on 194 tested patients was 0.54 mm. We obtained six T‐N data‐driven clusters (Figure 2). The group with lowest reconstruction error was defined as canonical – meaning that the degree of neurodegeneration was accurately predicted by tau PET. There were three groups with lower thickness than predicted, which were denoted as temporal‐limbic (TL) vulnerable, posterior vulnerable and diffuse vulnerable based on their spatial patterns. Additionally, two groups with greater thickness than predicted were defined as anterior resilient and diffuse resilient. These groups differed in subsequent longitudinal cognitive decline measured by CDR‐SB (Figure 3). The vulnerable groups displayed greater cognitive decline than the canonical group, which may be attributable to associations with comorbidities.ConclusionOur findings support a deep‐learning T‐N mismatch approach for disentangling AD heterogeneity.
Association between brain [18F]FDG‐PET signal and genetic cell markers in the brain tissue
Authors: Machado Luiza Santos , De Bastiani Marco Antônio , da Rocha Andreia Silva , Bellaver Bruna , Soares Carolina , Povala Guilherme , Rahmouni Nesrine , de Souza Débora Guerini , Pascoal Tharick A. , Souza Diogo O. , Rosa‐Neto Pedro , Benedet Andrea Lessa , Blennow Kaj , Zetterberg Henrik , Ashton Nicholas J. , Ferreira Pamela C.L. , Zimmer Eduardo R. 
Published: December 2023

AbstractBackgroundBrain glucose hypometabolism, indexed by [18F]FDG‐PET, is considered a biomarker of neurodegeneration in Alzheimer’s disease (AD). However, other brain cells, such as astrocytes and microglia, also consume considerable amounts of glucose and may contribute to the [18F]FDG‐PET signal in the brain. Thus, the cellular source of the [18F]FDG‐PET signal remains controversial. Here, we aimed to evaluate whether canonical markers of different brain cell types (neuron, oligodendrocyte, astrocyte, and microglia) associate with brain [18F]FDG‐PET signal in humans and a rat model of human amyloidosis.Method[18F]FDG‐PET images were acquired in ten‐month‐old APP/PS1 (TgF344‐AD, n = 8) and wild‐type (WT, n = 6) rats. Next, we evaluated the gene expression of NeuN, Olig2, GFAP, S100B, and IBA1 in the frontal and temporoparietal cortices and cerebellum. Association maps integrating mRNA with brain [18F]FDG‐PET signal were conducted at the voxel level using RMINC. [18F]FDG‐PET and brain tissue mRNA (GFAP, ACSA2, S100B, GLAST, IBA1, CSF1R, TREM2, Olig2, Cldn11, NeuN) of cognitively unimpaired patients were obtained from ADNI and Allen Brain Atlas database, respectively. In humans, mean regional [18F]FDG‐PET SUVr (reference region: pons) and mRNA expression were used to evaluate their association using a gaussian model. Differences were considered statistically significant at p<0.05 (t>2).ResultGFAP and S100B mRNA levels in the temporoparietal and frontal cortices positively correlated with brain [18F]FDG‐PET signal (local maxima, GFAPt(13) = 9.4, S100Bt(13) = 6.6; Fig1B‐C). No associations were found with TREM2, Olig2 and NeuN (t(13)<2; Fig1D‐F). We expanded our analysis by comparing gene astrocyte markers and astrocyte gene cell markers in humans. At the regional level, we found that FDG‐PET SUVr and astrocyte mRNA were positively associated: S100B (R‐squared = 0.34), GFAP (R‐squared = 0.33), GLAST (R‐squared = 0.31) and ACSA‐2 (R‐squared = 0.31; Fig1G).ConclusionOur findings suggest astrocyte mRNA markers are more closely associated with brain [18F]FDG‐PET signal than neuronal, oligodendrocyte, and microglial mRNA markers.
Deciphering the role of p‐tau, iron and MAOB in neurobiological basis of F18‐flortaucipir signal with voxel‐to‐voxel histology to PET analysis in different tauopathies
Authors: Chen Yuheng , La Joie Renaud , Li Song Hua , Tosun Duygu , Ushizima Daniela , Seeley William W. , Spina Salvatore , Heinsen Helmut , Rabinovici Gil D. , Grinberg Lea T. 
Published: December 2023

AbstractBackgroundFlortaucipir F18 was the first FDA approved PET tracer to detect tau pathology. Previous studies show that Flortaucipir is more sensitive to Alzheimer’s disease (AD) tau than four‐repeat tauopathies. Furthermore, the neurobiological basis of low Flortaucipir signal remain unclear, especially in basal ganglia. In this study, we aimed to identify the contributions of p‐tau (Ser 202), iron and MAOB to Flortaucipir signal in different tauopathies by performing voxel‐to‐voxel correlations between histological inclusion density maps and ante‐mortem flortaucipir PET uptake.MethodCoronal slides of ∼1cm thickness from four tauopathy cases (AD, progressive supranuclear palsy, corticobasal degeneration, and FTLD due to MAPT mutation P305S) and one FTLD‐TDP type‐A case were processed following a computational/histopathology pipeline (Ushizima, Chen, et al. 2021), allowing for whole 3D reconstruction of the histological maps at microscopical level, point‐by‐point aligned to their corresponding MRI T1 images (Figure 1). Serial 120 µm‐thick histological slides underwent immunohistochemistry to p‐tau (Ser 202, CP‐13) and MAOB, and Perl’s iron staining. Density of pathological changes per voxel (1.22×1.22×120um³) was measured using convolution neural network algorithm. Flortaucipir images were thresholded at 1.2 SUVR. Spearman’s bivariate and multiple linear regressions were used to compare Flortaucipir PET uptake to histological CP13/iron/MAOB counterparts. We examined both areas with high Flortaucipir PET uptake and high histological CP13/iron/MAOB burden.ResultTable 1 show patient features. Figure 2 depicts voxel‐to‐voxel correlations result in scatter plot and bivariate/multiple linear regression analysis.ConclusionThe correlation of CP13 (p‐Tau) to Flortaucipir was higher in AD than other tauopathies. Iron deposits contributed to Flortaucipir uptake in regions both with or without histopathologically confirmed p‐tau, including basal ganglia, whereas MAOB contribution was almost null in gray matter. The time gap between PET and autopsy and lower PET resolution compared to histology may explain the moderate regression coefficients, but other unexplored contributors cannot be discarded. Voxel‐to‐voxel histology to neuroimaging correlations allow for investigating multiple components of the neurological basis of PET. We are finalizing the regression analysis based on areas of high histological burden.
Effect of Age Range on Power for a Clinical Trial Investigating the Effect of Intensive Systolic Blood Pressure Control on White Matter Hyperintensity Volume Accumulation
Authors: Pruzin Jeremy J. , Chen Yinghua , Chen Kewei , Koijs Daniel , Newby Danielle , Winchester Laura M , Thomas Ronald G. , McGrath Emer R , Su Yi , Reboussin David M. , Pajewski Nicholas M. , Beiser Alexa S. , Seshadri Sudha , Tariot Pierre N. 
Published: December 2023

AbstractBackgroundEpidemiological evidence has repeatedly demonstrated elevated systolic blood pressure (SBP) in midlife as a modifiable risk factor for Alzheimer’s Disease and Related Dementia (ADRD) later in life. Despite SBP being readily treatable with safe and inexpensive medications, no clinical intervention implementing a lower SBP goal (<120 mm Hg) in midlife to lower future ADRD risk has ever been attempted. Clinical outcomes are not practical in the age range of interest (45‐65 or 45‐70) during an initial trial period of 5 years however robust evidence demonstrates white matter hyperintensity volume (WMHV) as an MRI finding associated with and predictive of incident ADRD.MethodWe used four data sets (SPRINT, Wisconsin Registry for Alzheimer’s Prevention, Framingham, and UK Biobank) with longitudinal MRI data to examine the effect of age range on power for a clinical trial proposal investigating the effect of intensive SBP control on annualized rates of WMHV accumulation in persons starting in middle age. Analyses were conducted separately in each data set. We calculated mean, standard deviations, and annualized rates of WMHV accumulation (derived from T2 FLAIR MRI), using the linear rate of change for accumulation rate, first stratifying by SBP ( = 132 mm Hg vs <132mm Hg except in SPRINT where groups were stratified by goal SBP group) and then by various age ranges. Next, we examined the effect of age range on necessary sample size with 80% power and two‐tailed alpha of 0.05 for a prospective clinical trial.ResultAs expected, annualized WMHV accumulation rate is lower with younger age in all data sets. However, variability decreased (all four data sets) and the percent reduction achieved at lower SBPs is higher at younger ages (3/4 data sets), thus increasing power when excluding older individuals (e.g., >65 years old, Table 1).ConclusionRestricting age to individuals to = 65 years‐old may unexpectedly increase power in a clinical trial designed to investigate the effect of goal SBP<120 mm Hg on annualized WMHV accumulation rate. 45‐65 years‐old is a practical age range for the first ever clinical trial intended to investigate the effects of a lower SBP goal in middle age to reduce future ADRD risk.
Multi‐channel segmentation allows simultaneous estimation of classic tissue compartments, WMH and enlarged perivascular spaces
Authors: Gunter Jeffrey L. , Przybelski Scott A. , Sparrman Kohl Johnson , Cogswell Petrice M , Radford Jonathan Graff , Knopman David S. , Petersen Ronald C. , Vemuri Prashanthi , Jack Clifford R. , Persons Benjamin 
Published: December 2023

AbstractBackgroundLarge neuroimaging studies like ADNI‐4 now include T1‐weighted, T2‐weighted and FLAIR images at isotropic1mm resolution for investigation of both neurodegenerative and cerebrovascular disease. These three input types allow segmentation of GM, WM, DeepGM, WMH, and CSF compartments. Additionally, this supports automated assessment of enlarged perivascular spaces (PVS). Our objective was to propose a unified framework for segmentation of intracranial classes and PVS while reducing human effort.MethodFLAIR and T2‐weighted images are aligned to the T1‐weighted image.. Standard SPM12 unified segmentation using all three input images establishes deformations between subject and template space and bias field corrections. Extended tissue priors including deep GM are deformed into subject space. Adding mean intensities from high probability deep GM voxels as seeds and using the extended prior set, segmentation is re‐started, refining model parameters, deformations and bias‐field estimates. The procedure repeats, adding a WMH channel, resulting in a segmentation including GM, WM, CSF, deep GM, WMH and nuisance classes. Frangi filters are applied to the T1‐ and T2‐ weighted images. Filter response images are smoothed, averaged, masked and inserted in place of the CSF prior. Probability maps of vessel‐like shapes with CSF‐like contrast are estimated to identify PVS. Visual grading was also performed for comparison.ResultThe method was applied to 300 participants. Visual quality control found two instances of poor WMH over‐segmentation. Perivascular space maps were all visually reasonable. Standard segmentation classes were visibly superior to single T1‐based segmentation. Figures 1,2 present examples. Pearson correlations of PVS fractional volume with multiple human raters’ PVS counts in CSO/CR was over 0.7 in a subset of 125 participants (Figure 3).ConclusionModern dementia protocols include high‐resolution T1‐weighted, T2‐weighted, and FLAIR images that can be used together as inputs to extend probabilistic segmentation adding deep grey and WMH classes with automated PVS assessment.
An automated pipeline for Centiloid quantification of amyloid‐β using multiple 11C‐PiB‐PET and 18F‐PET tracers
Authors: Gordon Elizabeth , Borrot Mathilde , Gueddou Ayoub , Jubault Thomas , Guizard Nicolas 
Published: December 2023

AbstractBackgroundQuantitative measures of amyloid‐ß (Aß) pathology using positron emission tomography (PET) imaging are sensitive to identify pathological changes early in Alzheimer’s disease (AD). The Centiloid scale aims to standardize these in vivo amyloid quantifications to a 100‐point scale, where an average value of zero signifies high certainty of amyloid negativity and 100 identifies average typical AD Aß‐pathology load (Klunk et al., 2015). The current study developed and validated a single and fully automated Centiloid quantification pipeline for multiple amyloid PET compounds.MethodQyScore’s® fully automated pipeline was validated on 11C‐PiB‐PET and 18F‐PET images from the Centiloid project (https://www.gaain.org/centiloid‐project): 34 young controls [age = 31.5 ± 6.3 years] and 47 AD patients [age = 67.5 ± 10.5 years; CDR = 0.5–1]. 18F tracers included Florbetapir (FBP, N = 46), Forbetaben (FBB, N = 35), Flutemetamol (FTM, N = 74) and NAV4694 (NAV, N = 55). PET/MR image pairs were both co‐registered and normalized in the MNI template space. The fully automated segmentation from QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device, parcellated the masks of the grey matter tissue (target) and of the cerebellum (reference) region (Figure 1). The standardized uptake value ratio (SUVr) was computed as the ratio of the mean signal in both regions. Correlations of 11C‐PiB‐PET and 18F‐PET SUVr values with published SUVr data were computed. Further, correlations between 18F‐PET SUVr and paired 11C‐PiB‐PET SUVr were computed. Correlation coefficients (R2) > 0.7 were required to consider the Centiloid calibration valid.ResultQyScore’s® fully automated quantitative pipeline produced SUVr values well within the bounds defined by the Centiloid method (SUVr_AD‐100 = 2.08 +/‐ 0.2; SUVr_YC‐0 = 1.01+/‐ 0.05; R2 = 0.99; slope = 1.00; intercept = ‐0.44). 11C‐PiB SUVr correlation coefficients with published values were above 0.99 (Figure 2). Correlation coefficients of 18F‐PET SUVr with 11C‐PiB‐PET SUVr were respectively 0.91, 0.95, 0.96, 0.99 (Figure 3). Equations for converting to Centiloid were respectively: • Centiloid = 177.79 FBP_SUVr ‐ 183.56 • Centiloid = 153.08 FBB_SUVr – 152.93 • Centiloid = 122.39 FTM_SUVr – 120.97 • Centiloid = 90.20 NAV_SUVr – 91.61ConclusionWe demonstrate the feasibility and reliability of a fully automated amyloid PET pipeline for multiple amyloid‐PET compounds (11C‐PiB and 18F) suitable for implementation in clinical trials.
Cohort heterogeneity and AD biomarkers in older adults without significant cognitive impairment: A comparison of U.S. POINTER and ADNI
Authors: Landau Susan M. , Harrison Theresa M. , Ward Tyler J. , Baker Suzanne L. , Koeppe Robert A. , Vemuri Prashanthi , Lockhart Samuel N. , Jung Youngkyoo , Harvey Danielle J. , Lovato Laura , Toga Arthur W. , Masdeu Joseph C. , Oh Hwamee , Gitelman Darren R. , Aggarwal Neelum T. , Farias Sarah Tomaszewski , Papp Kathryn V. , Snyder Heather M , Baker Laura D , Decarli Charles , Jagust William J. 
Published: December 2023

AbstractBackgroundA key focus of the U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is recruitment of unimpaired older individuals at risk for cognitive decline with heterogeneous characteristics (under‐represented racial and ethnicity groups (URG), family history of cognitive decline, hypertension, geographic diversity), and a subset of individuals undergo PET and MRI.MethodTo examine effects of heterogeneity on relationships between risk factors, AD biomarkers (Aß and entorhinal tau PET, hippocampal volume (HV)) and cognition (global, executive function, and memory) were examined from U.S. POINTER neuroimaging participants (N = 602, about 65% of the anticipated total sample) and a subset of Alzheimer’s Disease Neuroimaging Initiative participants (N = 503). Neuroimaging data were processed and analyzed using harmonized methods across studies. Some cohort‐specific measurements were standardized using a within‐cohort reference group (Aß‐negative individuals <70yrs and with global CDR = 0).ResultU.S. POINTER imaging participants are younger than those in ADNI (68.6+/‐5.0yrs vs 70.7+/‐4.4yrs), more likely to be female (65% vs 58%) and from URG (27% vs 15%), and had lower education (14.7+/‐3.3yrs vs 16.7+/‐2.3yrs) and higher Framingham Cardiovascular Risk scores (9.9+/‐8.3 vs 7.7+/‐5.3). The groups differed on several AD biomarkers and cognitive measures (Table). In regression models, AD biomarkers were stronger predictors of cognitive performance in ADNI compared to POINTER. Specifically, Aß+ individuals with elevated tau had disproportionately poorer cognitive performance in ADNI, and this relationship remained consistent across cognitive measures (Figure). Conversely, demographic/risk variables were stronger predictors in POINTER. Demographic/risk predictors (age, sex, education, race, and FRS) together explained twice as much variance in global cognition in POINTER compared to ADNI (Adjusted R2 = 29% vs 14%). The addition of AD biomarkers to the model further increased the amount of variance in global cognition explained from 14% to 34% in ADNI, but addition of AD biomarkers did not increase variance explained in the POINTER model.ConclusionHeterogeneity among study participants appears to influence baseline biomarker‐cognition relationships such that AD biomarkers explain more variability in cognitive performance in ADNI than POINTER. These findings further suggest that cohort characteristics may affect the potential influence of amyloid‐ and tau‐modifying treatments on cognition.