Petersen, Ronald C.

AbstractBackgroundAmyloidosis‐driven tau deposition has been a significant focus of imaging research in Alzheimer’s disease (AD). However, the 3R/4R tau seen in AD can be observed in the transentorhinal/entorhinal regions (Braak I/II) in the absence of amyloidosis. The underlying drivers of this phenomenon are not well understood. Our goal was to examine amyloid‐independent pathways of longitudinal Tau‐PET SUVR progression in the entorhinal cortex (ERC) using a broad range of risk and protective factors.MethodWe identified 449 participants from the population‐based sample of Mayo Clinic Study of Aging >50 years old with baseline amyloid PET and at least two serial tau PET scans (mean number of scans=2.3; mean follow‐up time=3.2 years). Using an amyloid SUVR cut‐point of 1.48, we categorized participants into A+ (n=167; mean age=77.5 years; 56% male; baseline ERC tau=1.20; 83% cognitively unimpaired) and A‐ (n=282; mean age=67.5; 56% male; baseline ERC tau=1.07; 92% cognitively unimpaired). We used an ERC tau SUVR cut‐point of 1.21 to determine T+. We fit mixed effects and growth curve models with age, sex, baseline amyloid, risk factors (midlife activities, sleep, stress, education/occupation, cardiovascular metabolic conditions) as predictors and longitudinal ERC tau as the outcome. We fit separate models in A‐, A+, and all participants.ResultsFor models of all participants and A+ participants, the only predictors of higher rates of ERC tau deposition were increasing age and amyloid SUVR (p<0.05). In the A‐ models (figure below), amyloid SUVR and age were predictors of baseline, but not longitudinal ERC tau. However, a greater number of cardiovascular metabolic conditions predicted a higher rate of ERC tau accumulation (0.007 (0.002), p<0.05). Only 13% of the 23 A‐T+ participants were APOE4 carriers (compared to 31% in the overall sample), suggesting the role of non‐APOE4 pathways to tau deposition.ConclusionOur results support previous evidence that significant amyloidosis is causal to higher rates of tau deposition. However, in those without significant amyloidosis, cardiovascular metabolic conditions predicted higher rates of ERC tau accumulation. Further work is warranted to investigate the mechanisms through which genetics, comorbidities, and lifestyle may play a role in early tau deposition.

AbstractBackgroundβ‐amyloid plaques, a pathological hallmark of Alzheimer’s disease, are common in dementia with Lewy bodies (DLB). However, little is known about when β‐amyloid levels increase throughout the Lewy body disease (LBD) continuum, from an early prodromal stage of isolated REM sleep behavior disorder (iRBD), to a stage of mild cognitive impairment‐Lewy bodies (MCI‐LB), and finally DLB. Our objective was to investigate β‐amyloid deposition throughout the LBD continuum, as well as the influence of sex and APOE ε4 status, as potential factors contributing to Alzheimer’s disease pathological changes.MethodPatients with iRBD (n=24), MCI‐LB (n=62), and DLB (n=82) from the Mayo Clinic Alzheimer’s Disease Research Center and Center for Sleep Medicine were included. β‐amyloid levels were measured by Pittsburgh compound B (PiB) PET and global cortical standard uptake value ratio (SUVR) was calculated. Global cortical PiB SUVR values from each clinical group were compared to each other and to cognitively unimpaired individuals (CU; n=100) balanced on age and sex from the Mayo Clinic Study of Aging (MCSA). Clinical groups were compared using ANCOVA after adjusting by age.ResultMCI‐LB and DLB patients had significantly higher global cortical PiB SUVR than CU participants, while PiB SUVR in iRBD patients was comparable to that of CU participants (Figure 1). DLB patients also had significantly higher global cortical PiB SUVR compared to iRBD patients (p=0.004). In MCI‐LB and DLB, global cortical PiB SUVR was higher in APOE ε4 carriers compared to APOE ε4 non‐carriers (p<0.001), and women had higher global cortical PiB SUVR compared to men (p=0.024).ConclusionIn this cross‐sectional study, β‐amyloid pathology gradually increased throughout the clinically defined LBD continuum. Whereas β‐amyloid levels are comparable to cognitively unimpaired individuals in iRBD, a significant elevation in β‐amyloid levels coincides with cognitive impairment observed in MCI‐LB and DLB. Specifically, women and APOE ε4 carriers tend to have higher β‐amyloid levels than men and APOE ε4 non‐carriers. These findings have important implications for targeting patients within the LBD continuum, who may benefit from amyloid modifying therapies.

AbstractBackgroundStudies of aging and dementia often acquire brain MRI and PET images, then de‐identify and share these widely with other researchers. It is known that research participants can potentially be re‐identified from brain MRI and CT using face recognition, but this has not been tested with PET imaging.MethodWe generated face reconstruction images of 182 volunteers using their existing amyloid (PiB), tau (FTP), and FDG PET scans from clinical GE PET/CT scanners, and we measured how often commercial automatic face recognition (Microsoft Azure) correctly matched these reconstructions with photographs of their faces. We additionally compared these match rates with face reconstructions from CT (from PET/CT) and MRI. When testing face recognition from PET scans, CT was used only during PET reconstruction, not for face recognition. We also validated our updated mri_reface software, originally developed to de‐identify (“de‐face”) faces in MRI, with new support for PET images. We measured face recognition rates before and after applying mri_reface to PET, CT, and MRI. In addition, we measured effects of de‐facing on biomarker measurements (global and regional SUVR) using 244 ADNI Florbetapir (FBP) amyloid PET scans with two automated pipelines: FreeSurfer 6.0 (PETSurfer) and an in‐house SPM12‐based pipeline.ResultRates of successful face recognition with PET ranged from 32‐41% (Table 1) across tracers. These were lower than MRI (98%) and CT (78%) but still concerning for participant privacy. Using mri_reface reduced face recognition to 0‐3.5% for PET, 5% for CT, and 8% for MRI. mri_reface had little effect on global amyloid SUVR measurements from ADNI PET images (Figure 1). ICC values between original and de‐faced images were 1.00 with biases and median absolute differences < 0.5%, across both pipelines. Effects on individual regions all had ICC values > 0.98 with biases and median absolute differences < 2%.ConclusionFace reconstructions from PET images have sufficient quality for potential re‐identification using publicly available face recognition technology. Research studies should consider using face de‐identification (de‐facing) software on PET images in addition to CT and structural MRI. Our free, automatic mri_reface software greatly reduced the potential for face recognition without substantially affecting SUVR measurements.

AbstractBackgroundQuantitative susceptibility mapping (QSM) can detect iron distribution in the brain by estimating the local tissue magnetic susceptibility of every voxel. Dysregulation of neuronal iron has been correlated with typical Alzheimer’s disease (tAD), but little is known about iron distribution in atypical clinical presentations of AD such as logopenic progressive aphasia (LPA) and posterior cortical atrophy (PCA).MethodEleven PCA patients and eight LPA patients were recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, and underwent MRI that included a five‐echo gradient echo sequence for calculation of QSM. Mean QSM signal was extracted from gray and white matter for regions‐of‐interest across the brain using the Mayo Clinic Adult Lifespan Template. Hierarchical models were fit per‐region and per‐hemisphere to compare PCA and LPA to each other, to 20 healthy controls, and to 20 patients with tAD recruited by the Alzheimer’s Disease Research Center.ResultStrong evidence (posterior probability>0.99) was observed for greater susceptibility in the left middle occipital gyrus and amygdala in both LPA and PCA, and in the right precuneus and inferior temporal gyrus in PCA compared to controls. Moderate evidence for greater susceptibility (posterior probability>0.90) was also observed in the left insula, inferior occipital gyrus, entorhinal cortex, and right inferior parietal gyrus and amygdala in both LPA and PCA, along with right angular gyrus and middle occipital gyrus in PCA and right insula and caudate in LPA, when compared to controls. Comparing between phenotypes, LPA showed moderate evidence for greater susceptibility in the caudate, hippocampus, substantia nigra, left supramarginal gyrus and posterior cingulate compared to PCA, while PCA showed greater susceptibility in the right precuneus compared to LPA. Both LPA and PCA showed moderate‐strong evidence for greater susceptibility than tAD, particularly in medial and lateral parietal regions.ConclusionUnique iron profiles were identified in LPA and PCA within cortical and subcortical structures. These changes are in line with disease‐related neurodegenerative changes in the clinical phenotypes, suggesting that QSM could be an informative marker to study iron deposition in these patients.

AbstractBackgroundThe dynamics of white matter (WM) changes are understudied in Alzheimer’s disease (AD). Leveraging tau deposition measured from Tau‐PET, our goal was to study the association between tau and white matter health using NODDI from multi‐shell diffusion MRI (dMRI). Specifically, we focused on the Neurite Density Index (NDI) from NODDI, which in WM captures the signal from axons.MethodWe identified 251 participants (126 cognitively normal, 58 MCI, 67 dementia) from the Mayo ADRC and Mayo Clinic Study of Aging with Aβ‐PET, Tau‐PET, and multi‐shell dMRI who were Aβ‐positive (mean age of 77 years and 55% male). We limited to Aβ‐positive participants because Aβ deposition accelerates tau deposition. For Tau‐PET, we estimated SUVRs from three regions corresponding to Braak I/II, III/IV, and V/VI (Figure 1.a). To understand the associations between Tau‐PET SUVRs at three Braak regions and NDIs at 30 candidate WM tracts, we implemented three analyses methods ‐ Pearson correlation, linear regression, and multilayer perceptron machine learning regression.ResultHigher tau burden in all three Braak regions was associated with lower neurite density in 10 significant WM tracts (Table 1). Interestingly, uncinate fasciculus (UNC) was found significantly associated with all three Braak regions. All three analysis methods consistently showed differential involvement of WM pathways for Braak staging such as ‐ Cingulum adjoining Hippocampus (CGH) for Braak I/II regions, Medial Temporal WM (MTWM) for Braak III/IV regions, and Inferior Occipital WM (IOWM) for Braak V/VI regions (Figure 1.b). The linear regression and machine learning regression models also suggested that WM measurements reflected 18‐35% of variance due to tau depositionConclusionWe found that CGH and UNC WM were the most impacted ROIs associated with Braak I/II regions as expected. UNC, directly connecting anterior temporal lobes to the orbitofrontal cortex, was a key WM tract identified as important for all Braak I‐VI regions and warrants further investigation in the study of AD progression. This study highlights the significant impact tau deposition may have on WM. This work also demonstrates the feasibility of using NODDI as an informative supplementary imaging modality for monitoring AD progression.

AbstractBackgroundBlood‐based biomarkers of amyloid and neurodegeneration are increasingly used in clinical trials and entering clinical use. Differentially mapping the plasma markers to post‐mortem neuropathology in a community‐based sample is critical for interpretation.MethodWe identified 64 Mayo Clinic Study of Aging (MCSA) participants with antemortem plasma markers (Aβ42, Aβ40, NfL, T‐tau, Aβ42/40 ratio) measured on the Quanterix Simoa HD‐1 analyzer and with postmortem neuropathologic assessments of Alzheimer’s pathology (Braak tangle stage, Thal phase, neuritic plaque burden) and cerebrovascular pathology (Strozyk scale focused on macrovascular brain changes and Kalaria scale focused on detailed micro‐ and macro‐ vascular brain changes). Weighted linear regression models with adjustment for time between plasma markers and death were conducted with exact permutation tests to evaluate the relationships between antemortem plasma and postmortem neuropathology markers.ResultHigher plasma NfL levels and Aβ42/40 ratio were associated with both cerebrovascular neuropathologic scales (p<0.05) but were not associated with Braak neurofibrillary tangle stage, neuritic plaque score, or Thal stage (p>0.5). Plasma T‐tau was not associated with any pathological assessment. Plasma Aβ42/40 and NfL explained up to 18% of the variability in cerebrovascular neuropathologic scales.ConclusionIn this community sample of participants predominantly without dementia, plasma markers for amyloid (Aβ42/40) and neurodegeneration (NfL) were associated with cerebrovascular neuropathology at autopsy. NfL is a non‐specific marker of brain injury, therefore, its association with cerebrovascular neuropathology was in the expected direction. The association between greater cerebrovascular neuropathology and elevated Aβ42/40 requires further investigation. These findings provide evidence that cerebrovascular disease may significantly influence levels of plasma biomarkers proposed for Alzheimer’s disease. This information will be important to consider for accurate clinical interpretation of the proposed biomarkers.

AbstractBackgroundReduced striatal uptake on ioflupane SPECT is considered an indicative biomarker for Dementia with Lewy Bodies (DLB), and has been proposed as an indicative biomarker for Mild Cognitive Impairment with Lewy Bodies (MCI‐LB). Little data exists on the profile of uptake on key striatal regions of interest (ROIs) in DLB or MCI‐LB.MethodParticipants with DLB or MCI‐LB at baseline based on the presence of ≥2 core clinical DLB features, and ≥2 ioflupane SPECT scans performed approximately annually, were identified from the Mayo ADRC database. Minimum brain laterality z‐score values for the putamen (whole, anterior and posterior), caudate and striatum were determined using DaTQUANT 2 software. Baseline clinical and scan data was used for all analyses, and the annualized minimum putamen z‐change was determined on serial scans. The scan was considered abnormal if the minimum putamen z‐score was <‐0.98 based on clinicopathologic data.Result27 participants were included (21 DLB and 6 MCI‐LB, 96% male). Compared to MCI‐LB, DLB had a lower mean MoCA score and a trend toward a higher UPDRS score compared to MCI‐LB; there were no statistically significant differences in age, sex, onset age or duration of cognitive decline, or presence of other core DLB features. The majority (89%) had an abnormal scan at baseline. All ROI z‐scores showed the expected general trend of DLB<MCI‐LB, and the annualized z‐change was more negative in DLB, but none of the findings reached statistical significance.ConclusionThese findings suggest in MCI‐LB and DLB that 1) an abnormal ioflupane SPECT scan is frequently present at diagnosis, 2) z‐scores become more negative over time, and 3) the annualized z‐change is similar. Similar findings in larger cohorts would support the use of ioflupane SPECT as a biomarker in both MCI‐LB and DLB clinical trials.

AbstractBackgroundFluorodeoxyglucose positron emission tomography (FDG‐PET) is an established modality for differential diagnosis of dementia. Deriving phenotypic signatures from FDG‐PET via machine learning is challenging due to the high dimensional nature of brain images relative to the generally small number of labeled examples available for training, the class imbalance among those labels, and the cooccurrence of multiple pathologies. In this study, we developed a multi‐class, multi‐label framework to address these challenges.MethodA database of clinically acquired PET/CT images from 3,000 unique patients was used to develop a latent space model using matrix decomposition. This model was then applied to images from a separate cohort of Mayo Clinic Alzheimer’s Disease Research Center participants (n=1,745) labeled as cognitively unimpaired (CU) (n=1,436) or with the following potentially co‐occurring phenotypes: Alzheimer’s disease (AD) (n=165), Lewy body dementia (DLB) (n=92), behavioral variant frontotemporal dementia (bvFTD) (n=43), semantic (svPPA) (n=10) and logopenic (lvPPA) (n=13) variant PPA, and posterior cortical atrophy (PCA) (n=17). A k‐nearest neighbors classifier that is robust to these imbalanced and overlapping labels was then trained on these examples. The resulting classifier was evaluated by area under receiver‐operator characteristic curve (ROC‐AUC) via leave one out cross validation, using clinical diagnosis as the gold standard.ResultROC curves and AUC scores for each phenotype are illustrated in Fig. 1a. Because the classifier is based on a k‐nearest neighbors connectivity matrix, it has a convenient graphical representation, where images are nodes and edges are drawn between an image and its set of nearest neighbors in latent space. A self‐organizing force directed graph constructed in this way is illustrated in Fig 1b, highlighting the strong separation of CU and degenerative images, as well as the segregation of each phenotype within the neurodegenerative region of the graph.ConclusionIn this study, we developed a machine learning framework for classification of neurodegenerative disease based on k‐nearest neighbor analysis in a low dimensional latent space projection of FDG‐PET images. By leveraging low‐dimensional representations and k‐nearest neighbors analysis, this framework is robust in multi‐class, multi‐label tasks with strong class imbalance and provides a highly interpretable graphical representation.

AbstractBackgroundIn 2018 we presented the Computational DESH (CDESH) metric (DOI:10.1016/j.nicl.2018.11.015), a machine learning measure of abnormal CSF space patterns commonly observed on MRI in Disproportionately Enlarged Subarachnoid‐space Hydrocephalus (DESH‐type) NPH. We have shown that CDESH positive individuals often have discordant CSF and PET amyloid measures (DOI:10.1016/j.jalz.2019.06.4921). Positive CDESH scores are predictive of cognitive decline (DOI:10.1016/j.neuroimage.2021.117899, DOI: 10.1212/WNL.0000000000008616). The score is calculated from regional CSF volumes in a sulcal atlas and within the ventricles using a support vector machine (SVM). The Mayo Clinic Study of Aging (MCSA) changed scanner platforms (GE to Siemens) and T1‐weighted image resolution (1.2mm x 1.05mm x 1.05mm to 0.8mm isotropic) in 2017. We refer to these as “isotropic” or “anisotropic” scans. Our objective was to assess the dependence of a machine learning method (specifically CDESH) to image input variation and, if necessary, determine what needed corrections.MethodAll data considered is from the MCSA or Mayo Clinic ADRC. The SVM was trained on anisotropic images with laboriously labelled ground truth. We resampled the isotropic data from a crossover study (N=112, typically scanned same‐day with both platforms/resolutions) to match the anisotropic resolution. Two approaches to correction were evaluated (Figure 1). In Figure 2 we compare of the score with and without correction cross‐sectionally in 998 MCSA participants. In Figure 3 we present longitudinal series from 23 participants with multiple scans using the isotropic resolution protocol and at least one prior anisotropic image.ResultsComparisons of data from the crossover study are presented in Figure 1. Matching voxel resolution to the data used to train the SVM is critical and a subsequent mild linear score adjustment may be warranted. Cross‐sectional comparisons (Figure 2) confirm improvement matching voxel resolution with a possible mild linear score adjustment. After matching voxel resolution CDESH score trajectories (Figure 3) are markedly more stable across this scanner/protocol change.ConclusionThe SVM‐based CDESH score is sensitive to voxel resolution. Corrections are needed in image processing and possibly in the scalar output score to achieve stability across protocol and scanner changes. The resulting correction supports coherent long timeseries data and longitudinal extension of previous work.

AbstractBackgroundNeurofilament light chain (NfL) is an emerging blood‐based biomarker useful for assessing neuro‐axonal injury in several neurological conditions. Plasma NfL concentrations reflect the extent of axonal damage, making it a general marker of neurodegenerative disease progression. This study evaluates the analytical performance characteristics of the Quanterix NfL Advantage method in EDTA‐plasma.MethodThe assay was performed per manufacturer’s instructions on the Quanterix Simoa HD‐X Analyzer. Analytical validation included analyte stability, imprecision, lower limit of quantification (LLOQ), analytical measurement range (AMR), accuracy, and interferences. One‐sided reference intervals were established using quantile regression methods among 1100 cognitive normal individuals, age 20 to 95 years without known chronic kidney disease, stroke, or myocardial infarction.ResultNfL was stable in plasma three days ambient, seven days refrigerate, 90 days frozen (‐20C), and over 3 freeze/thaw cycles. Intra and inter‐assay imprecision, expressed as % coefficient of variation (%CV), ranged from 3.4 – 6.0% and 5.9 – 11.6% respectively. The LLOQ was evaluated using patient plasma pools at concentrations of 1.7 and 3.6 pg/mL. %CVs were 16.8% and 9.7% respectively, and the LLOQ was set at 3.0 pg/mL. Accuracy was assessed by spiking 5 plasma samples with NfL calibratoran 87% average recovery (range 81‐94%) was observed. The AMR of 3.0 – 1500 pg/mL was verified with a regression slope of 0.93 and r2 > 0.99. Additionally, plasma admixtures covering the AMR showed acceptable linear response (slope of 1.02 and r² >0.99). The assay was not affected by icterus to 100 mg/dL, lipemia to 1000 mg/dL, hemolysis to 1000 mg/dL at NfL concentrations >30 pg/mL or to 200 mg/dL at NfL concentrations ≤30 pg/mL. NfL increased with age but did not differ by sex. The data supported age‐based reference intervals broken down into 10‐year intervals. The 97.5th percentile limits were: 20s: ≤8.4 pg/mL, 30s: ≤11.4 pg/mL, 40s: ≤15.4 pg/mL, 50s: ≤20.8 pg/mL, 60s: ≤28.0 pg/mL, 70s: ≤37.9 pg/mL, 80+: ≤51.2 pg/mL.ConclusionThe analytical performance characteristics of the plasma Quanterix NfL advantage assay were deemed acceptable for clinical laboratory implementation. The establishment of NfL reference intervals will aid in the clinical interpretation of patient plasma NfL concentrations.