Citations

AbstractBackgroundThe NIA Health Disparities Research (NIA‐HD) Framework organizes factors in four domains (i.e., environmental, sociocultural, behavioral, and biological), which work together over the lifetime to influence health and health disparities.MethodIn previous work, we employed a 2‐stage approach using factor and principal component (PC) analyses to identify patterns of factors in the domains of the NIA‐HD Framework. We found three PCs that identified related groupings from more than one NIA‐HD domain. PC1: retirement status, residence type, multivitamin use, heart disease, Charlson comorbidity index, walking/balance difficulties, and Beck Depression Inventory (BDI)‐II questions – mainly from the somatic dimension; PC2: physical activity/exercise, social and group activities, education, occupation, cardiometabolic risk factors, and self‐rated health; PC3: national and state area deprivation index ranking and BDI‐II questions – mainly in the cognitive dimension. Age and sex‐adjusted linear regression models were used to assess the cross‐sectional association of each PC with AD‐plasma biomarkers measured using the Quanterix Simoa platform in the Mayo Clinic Study of Aging (MCSA) participants. AD‐plasma biomarkers included Aβ 42/40, phosphorylated‐tau 181 (p‐tau181), plasma glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). All analyses were considered statistically significant at a p <0.05.ResultThere were 1300 cognitively unimpaired participants, 50 years old or older, with available plasma biomarkers (mean age (SD) 68.6 (9.5) years; 49.1% were female) at the current study baseline. PC1 and PC3 were associated with higher NfL and p‐tau 181 values (p<0.05). PC1 and PC2 were associated with higher GFAP values (p<0.05). No PCs were associated with the Aβ 42/40 ratio. Findings were mostly similar after additionally adjusting for global cognitive performance and chronic kidney disease.ConclusionWe found that patterns in a wide range of environmental, sociocultural, behavioral, and biological factors were associated with AD plasma biomarkers. Longitudinal studies are warranted to validate findings and support multi‐faceted approaches to address health disparities and achieve disease delay and prevention.

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total β‐amyloid Aβ42 relative to Aβ40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.

AbstractBackgroundAs new anti‐amyloid immunotherapies emerge for Alzheimer’s disease (AD), it is clear that early diagnosis of AD pathology is crucial for treatment success. This can be challenging in atypical presentations of AD and, together with our reliance on CSF or PET scans, can, at times, lead to delayed diagnosis. Here, we further explore the possible role of plasma tau phosphorylated at threonine 217 (P‐tau217) for the detection of primary AD or AD co‐pathology when frontotemporal dementia spectrum disorders are the main clinical presentation.MethodParticipants were recruited through ALLFTD, a North American multisite research consortium for the longitudinal assessment of frontotemporal lobar degeneration. After excluding cases with known FTD‐causing mutations, 573 participants with sporadic disease, mild cognitive/behavioral impairment, or healthy familial controls evaluated between 2014 and 2023 were included in this analysis (45.8% female, median age 66 years). 39 of those cases underwent neuropathological evaluation. Plasma P‐tau217 was measured with the Eli Lilly electrochemiluminescence‐based assay. P‐tau217 concentrations were compared by phenotype, disease severity, genotype, and, when available, neuropathological diagnosis with non‐parametric tests. Its diagnostic performance was tested with ROC curves. Associations between plasma P‐tau217 concentrations and clinical scales of global cognition, memory, executive function, motor function, and social cognition with linear regressions and non‐parametric tests.ResultPlasma P‐tau217 concentrations were higher in logopenic variant primary progressive aphasia (lvPPA) and amnestic dementia (AmDem), compared to participants with normal cognition or other FTD phenotypes. P‐tau217 concentrations were higher in APOE e4 carriers, compared to non‐carriers, regardless of phenotype, but were not affected by disease severity. P‐tau217 concentrations showed associations with clinical measures of global cognition, memory, and executive function, but had no associations with measures of motor function or social cognition. Plasma P‐tau217 concentrations were elevated in high and intermediate ADNC scoring, or more advanced Braak stages, even when AD was not the primary pathology.ConclusionEven when FTD is suspected, high plasma p‐tau 217 concentrations are strongly associated with underlying AD as primary pathology or contributing co‐pathology. Plasma P‐tau217 could be a tool to support the development of disease‐modifying therapies for atypical Alzheimer’s disease presentations or for FTD cases with Alzheimer’s disease co‐pathology.

AbstractBackgroundThere is increasing need for noninvasive biomarkers of Alzheimer’s Disease (AD) neuropathologic change for early detection and intervention through risk‐factor modification and disease‐modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease. However, the association between EKG‐based age and underlying neuropathology has not been studied.MethodWe used a cohort of 336 participants from the Mayo Clinic Study of Aging, a population‐based prospective study of residents in Olmsted County, Minnesota, with antemortem EKG and post‐mortem brain autopsy (Table 1 & 2). Age was predicted from EKG using a validated convolutional neural network algorithm. We used ordinal logistic regression to identify associations between lower neuropathologic scores of AD (Thal phase, neuritic plaque score, Braak stage, and overall AD neuropathologic change) and the Age‐Gap, defined as EKG‐predicted age minus chronological age. Results were controlled for true age, sex, and APOE e4 carrier status. Neuropathologic scales were grouped into none, low, intermediate, and high categories. Observations were weighted by the inverse of years between last EKG and time of death to account for the time difference between acquisition of EKG and autopsy.ResultWe found that an increasing Age‐Gap (i.e. older predicted age) was inversely associated with lower overall AD neuropathologic change (OR 0.93 [0.89, 0.97], p = 0.002). Investigation of the components of AD neuropathologic change showed that an increasing Age‐Gap was also inversely associated with lower amyloid metrics such as Thal phase (OR 0.85 [0.80, 0.89], p < 0.001) and neuritic plaque scores (OR 0.91 [0.87, 0.96], p < 0.001), but not tau metrics like Braak staging (OR 1.05 [1.00, 1.10], p = 0.05).ConclusionIn this work, we show that older predicted age on EKG, a noninvasive biomarker associated with vascular and biological aging, is associated with higher amyloid burden and neuropathologic change of AD.

Abstract Background Diffusion tensor imaging along perivascular spaces index (DTI‐ALPS), which measures diffusivity increases in the perivascular spaces along the medullary veins, is being increasingly utilized as a surrogate marker of glymphatic clearance (Taoka et. al. Jpn J Radio 2017). Lower DTI‐ALPS index means higher measured diffusivity from the perivascular space, and so it is expected to decrease with age. However, correlation with aging and dementia markers have not been systematically evaluated. The goal of this work was to automatically estimate DTI‐ALPS in a large sample and evaluate the variance explained by various demographic and imaging markers. Methods We identified 2739 participants aged 30+ years (mean 69.6, 48% female, 88% CU) in the population‐based Mayo Clinic Study of Aging with diffusion MRI. We estimated DTI‐ALPS through automated placement of regions as published previously (Liu et al. AAIC 2023) and shown in Figure 1. We examined the associations of DTI‐ALPS with demographics (age, sex), vascular risk (cardiovascular and metabolic condition score), clinical data (diagnosis, global cognition) and imaging markers (white matter hyperintensity (WMH), global amyloid load from PIB‐PET, and the temporal meta‐ROI Tau‐PET SUVR) using partial Pearson correlations and ANCOVA with adjustments for age and sex. Results DTI‐ALPS was negatively correlated with age, higher in females, declined with vascular risk, and lower in MCI/dementia groups (Figure 2). Higher WMH and higher amyloid were associated with lower DTI‐ALPS whereas Tau‐PET SUVR was not associated with DTI‐ALPS. Conclusions DTI‐ALPS can be automatically computed in large samples. Vascular risk and WMH had stronger associations with the computed DTI‐ALPS compared to AD biomarkers (amyloid and tau) suggesting that the surrogate was more likely linked to vascular injury.

AbstractBackgroundThere is a strong link between tau and progression of Alzheimer’s disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity‐based tau spreading model in AD variants with distinct tau deposition patterns is crucial.MethodWe included 269 amyloid‐β‐positive (PET/CSF) individuals with clinically diagnosed atypical AD (113 posterior cortical atrophy, PCA‐AD; 83 logopenic variant primary progressive aphasia, lvPPA‐AD; 33 behavioural variant AD, bvAD; 40 corticobasal syndrome, CBS‐AD) and 68 with typical AD from 12 international cohorts, who underwent tau‐PET (54% [18F]AV1451/[18F]flortaucipir/Tauvid, 27% [18F]MK6240, 19% [18F]PI2620). Using Gaussian mixture modeling including amyloid‐β‐negative controls, cross‐sectional tau‐PET standardized uptake value ratios within Schaefer‐200 atlas regions were transformed to tau positivity probabilities. Tau epicenters were defined as the 5% regions with highest tau positivity probabilities. For each variant, the association between functional connectivity‐based distance (using the 30% strongest positive region‐to‐region connections of a group‐average connectivity matrix from ADNI elderly controls) and tau‐PET covariance (group‐average correlation per region pair) was assessed through linear regression, adjusting for age, sex, site, and Euclidean distance. Regions were categorized based on functional proximity to the epicenter (quartiles 1‐4) and tau positivity probabilities were assessed accordingly.ResultTau positivity probabilities matched clinical variants, with a posterior pattern in PCA‐AD, left‐hemispheric dominant pattern in lvPPA‐AD, widespread pattern in bvAD, sensorimotor cortex involvement in CBS‐AD, and temporo‐parietal predominance in typical AD (Figure 1). In line with this, tau epicenters were highly heterogeneous across variants (Figure 1). In all variants, greater tau‐PET covariance was associated with shorter functional connectivity‐based distance (Figure 2). We observed that regions in closer functional proximity to the epicenter exhibited higher tau positivity probabilities than regions functionally further away (p<0.05, Figure 3).ConclusionThis multi‐center study shows that the brain’s functional architecture serves as a universal predictor of tau spreading in AD. Since tau is a key driver of neurodegeneration and cognitive decline in AD, this finding holds potential for personalized medicine and defining participant‐specific endpoints in clinical trials.

AbstractBackgroundMany proposed clinical decision support systems (CDSS) require multiple disparate data elements as input, which makes implementation difficult, and furthermore have a black‐box nature leading to low interpretability. Fluorodeoxyglucose Positron Emission Tomography (FDG‐PET) is an established modality for the diagnosis of dementia, and a CDSS that uses only an FDG‐PET image to produce a reliable and understandable result would ease both of these challenges to clinical application.MethodA deep variational autoencoder (VAE) was used to extract a latent representation of each image through prior training from FDG‐PET brain images (n=2000). This unsupervised VAE has a novel graph convolutional architecture that makes it applicable to masked template space images. A logistic regression model was used to classify each image. A parametric study of the latent space revealed the imaging features that were used in the logistic regression model to differentiate each class. A separate dataset of participants labeled with their clinical diagnosis (n=1239) was used to assess the logistic regression model’s diagnostic accuracy of cognitively unimpaired (CU) (n=679) and between common dementia subtypes: Alzheimer’s disease (AD) (n=310), Lewy body dementia (DLB) (n=151), and behavioral variant frontotemporal dementia (bvFTD) (n=99). The logistic regression classifier was evaluated using receiver‐operator characteristic (ROC‐AUC) curves.ResultROC‐AUC curves for CU and each dementia subtype are illustrated in Figure 1a. A k nearest neighbors model was used to develop a graphical representation of the latent space where nodes are images and edges are drawn between nearest neighbors, illustrated in Figure 1b. The graph of the latent space demonstrates a comparison between dementia subtypes and CU images.ConclusionIn this study, we developed a proof‐of‐concept VAE that can achieve differential diagnosis with 84.8% balanced accuracy. The model architecture provides visual interpretability, and shows that the hippocampi, parietal lobes, and cerebellum, were useful for distinguishing AD, the frontal lobe, posterior cingulate cortex, and hippocampi were useful for distinguishing DLB, and prefrontal pole, subcortical structures, and occipital lobe were useful for distinguishing bvFTD. These regions of metabolism align with clinical pathology and metabolic patterns.

AbstractBackgroundCortical structural changes in the temporal lobes including global and hippocampal volume loss and decrease in cortical thickness are well‐associated findings linked to cognitive decline in MCI and dementia patients. Neurite orientation dispersion and density imaging (NODDI) is a multishell diffusion MRI method enabling assessment of tissue microstructural integrity at the axonal and dendritic level. We wished to determine whether changes in temporal cortex orientation dispersion index (ODI) & neurite density index (NDI) correlate with decreases in memory function.MethodThe MCALT_ADIR122 cortical atlas was used for ROI designation. NODDI imaging was obtained on a 3T Siemens Prisma scanner with 2 mm isotropic resolution. Participants (ages 60‐95 years) were cognitively unimpaired (CU) or had mild cognitive impairment (MCI). CU & MCI subjects were enrolled in the Mayo Clinic Study of Aging (MCSA) and underwent neuropsychological testing including 3 memory tests from which a composite memory z score was calculated. A generalized additive regression model was used to estimate z scores as a smooth function of ODI, NDI and cortical thickness or volume with adjustment for age, gender, education, MCSA cycle number, WMH and intracranial volume.ResultThe Table displays demographic data. Figures 1 and 2 show the relationship of NDI and ODI measures to memory z scores across participants in individual ROIs both before and after adjustment for cortical thickness (“structural MRI”) as well as for confounds listed above. Both higher cortical thickness and higher NDI values in the hippocampus and entorhinal cortex were associated with significantly higher memory z‐scores (p<0.001) (Figure 1). Higher ODI values in addition to greater cortical thickness in temporal lobe regions, including within the hippocampus, parahippocampal gyrus, entorhinal cortex and inferior temporal gyrus, were associated with higher memory z‐scores (Figure 2). These significant associations (p <=0.01) were maintained after adjusting for cortical thickness in all these regions except entorhinal cortex for ODI.ConclusionNODDI‐derived measures of microstructural integrity (NDI & ODI) within the temporal cortex may provide information about memory decline, beyond what cortical thickness analysis provides, and may be potentially useful for monitoring therapeutic effects of AD immunotherapy in the future.

AbstractBackgroundParkinson’s disease and dementia with Lewy bodies are characterized by abnormal iron deposition in the substantia nigra, which can be measured with quantitative susceptibility mapping (QSM) on MRI. However, neuropathologic validation of the increased QSM in the substantia nigra associated with Lewy body disease (LBD) is lacking. We compared substantia nigra QSM on antemortem MRI between patients with and without Lewy‐related pathology which was confirmed by autopsy in all cases.MethodWe studied cases with antemortem QSM who underwent autopsy from the Mayo Clinic Alzheimer’s Disease Research Center and Mayo Clinic Study of Aging (n=54). Immunohistochemistry was performed with rabbit NACP antibody against the amino acids 98‐115 of the α‐synuclein protein to detect Lewy‐related pathology. After neuropathologic evaluation, cases were classified as LBD‐present (n=13) if they had Lewy‐related pathology and LBD‐absent (n=42) if they did not have Lewy‐related pathology. Alzheimer’s disease co‐pathology was present in 9 of the LBD‐present cohort (70%). QSM was calculated for the whole substantia nigra and the two sub‐regions: pars compacta and pars reticulata. Non‐parametric Wilcoxon rank‐sum test was used to compare substantia nigra QSM values between LBD‐present and LBD‐absent cases. Area under the curve (AUC) analyses tested the accuracy of QSM to distinguish the two groups.ResultsLBD‐present group had higher QSM values in the substantia nigra than LBD‐absent group. Furthermore, sub‐regional analyses showed that the LBD‐present group had higher QSM in the substantia nigra pars compacta (p=0.008) but not in the pars reticulata than the LBD‐absent group (Figure 1). QSM of the substantia nigra pars compacta distinguished LBD‐present and LBD‐absent cases with good accuracy (AUC=0.74; p=0.006; Figure 2).ConclusionThis study provides neuropathologic insight into the relevance of QSM of substantia nigra pars compacta as an in‐vivo biomarker of autopsy‐confirmed LBD. These findings indicate that imaging evidence of abnormal iron deposition in the substantia nigra may serve as a biomarker of Lewy‐related pathology in patients with primary LBD (i.e., Parkinson disease, dementia with Lewy bodies) and patients with potential LBD co‐pathology (e.g., patients with Alzheimer disease).

AbstractBackgroundResearch subjects can potentially be re‐identified from de‐identified MRI, CT, and PET brain scans with up to 98% accuracy using Microsoft Azure’s cloud‐based commercial facial recognition software. This showed the need to “de‐face” publicly shared research brain scans. Subsequently, Microsoft has begun restricting its face recognition services, intending to prevent misuse. This study tests a variety of popular open‐source computer vision and facial recognition software packages to measure the re‐identification risk with only unrestricted free software.MethodThis study used brain MRI from Siemens 3T scanners and face photographs from 182 participants (ages 30‐90+) in the Mayo Clinic Study of Aging. Each participant had photos taken with an iPad, which were cropped loosely around the head and converted to black and white. A total of 81 renders (2D reconstruction images; Figure 1) were made from each 3D T2‐FLAIR MRI, simulating various camera and lighting positions. These face renders approximate black and white photos, allowing use with software packages developed for matching faces/features between photographs.Open‐source Python packages (OpenCV, DeepFace, face_recognition, scikit‐learn) and their included pre‐trained models (designed for photographs) were used to match participant photographs to the labelled library of MRI‐based face renders. A correct match was recorded when the top‐scoring render or set of renders automatically chosen by the algorithm was from the MRI of the correct participant.ResultFeature matching with ORB and SWIFT (both from OpenCV) produced no correct matches, despite a prior publication reporting moderate success. The best match rates (Figure 2) were 58‐59% accuracy using face_recognition or DeepFace packages with support vector machines (SVMs). Without SVMs, these packages performed modestly worse (51%‐55%).ConclusionWhile the highest match rates obtained by widely available open‐source packages (roughly 60%) are much lower than those of a limited‐access commercial product (Microsoft Azure, with 98%), they show that actors with only access to no‐cost readily available software could still re‐identify a research subject from a brain scan most of the time. This further highlights the need for image refacing software, such as mri_reface, to protect the privacy and identifies of research volunteers.