Schwarz, Christopher G.

Abstract BACKGROUND We compared the ability of several plasma biomarkers versus amyloid positron emission tomography (PET) to predict rates of memory decline among cognitively unimpaired individuals. METHODS We studied 645 Mayo Clinic Study of Aging participants. Predictor variables were age, sex, education, apolipoprotein E ( APOE ) ε 4 genotype, amyloid PET, and plasma amyloid beta (Aβ)42/40, phosphorylated tau (p‐tau)181, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and p‐tau217. The outcome was a change in a memory composite measure. RESULTS All plasma biomarkers, except NfL, were associated with mean memory decline in models with individual biomarkers. However, amyloid PET and plasma p‐tau217, along with age, were key variables independently associated with mean memory decline in models combining all predictors. Confidence intervals were narrow for estimates of population mean prediction, but person‐level prediction intervals were wide. DISCUSSION Plasma p‐tau217 and amyloid PET provide useful information about predicting rates of future cognitive decline in cognitively unimpaired individuals at the population mean level, but not at the individual person level.

Abstract Progressive apraxia of speech (PAOS) is a neurodegenerative motor-speech disorder that most commonly arises from a four-repeat tauopathy. Recent studies have established that progressive apraxia of speech is not a homogenous disease but rather there are distinct subtypes: the phonetic subtype is characterized by distorted sound substitutions, the prosodic subtype by slow and segmented speech and the mixed subtype by a combination of both but lack of predominance of either. There is some evidence that cross-sectional patterns of neurodegeneration differ across subtypes, although it is unknown whether longitudinal patterns of neurodegeneration differ. We examined longitudinal patterns of atrophy on MRI, hypometabolism on 18F-fluorodeoxyglucose-PET and tau uptake on flortaucipir-PET in a large cohort of subjects with PAOS that had been followed for many years. Ninety-one subjects with PAOS (51 phonetic, 40 prosodic) were recruited by the Neurodegenerative Research Group. Of these, 54 (27 phonetic, 27 prosodic) returned for annual follow-up, with up to seven longitudinal visits (total visits analysed = 217). Volumes, metabolism and flortaucipir uptake were measured for subcortical and cortical regions, for all scans. Bayesian hierarchical models were used to model longitudinal change across imaging modalities with PAOS subtypes being compared at baseline, 4 years from baseline, and in terms of rates of change. The phonetic group showed smaller volumes and worse metabolism in Broca’s area and the striatum at baseline and after 4 years, and faster rates of change in these regions, compared with the prosodic group. There was also evidence of faster spread of hypometabolism and flortaucipir uptake into the temporal and parietal lobes in the phonetic group. In contrast, the prosodic group showed smaller cerebellar dentate, midbrain, substantia nigra and thalamus volumes at baseline and after 4 years, as well as faster rates of atrophy, than the phonetic group. Greater hypometabolism and flortaucipir uptake were also observed in the cerebellar dentate and substantia nigra in the prosodic group. Mixed findings were observed in the supplementary motor area and precentral cortex, with no clear differences observed across phonetic and prosodic groups. These findings support different patterns of disease spread in PAOS subtypes, with corticostriatal patterns in the phonetic subtype and brainstem and thalamic patterns in the prosodic subtype, providing insight into the pathophysiology and heterogeneity of PAOS.

Abstract Disruption of the default mode network is a hallmark of Alzheimer’s disease, which has not been extensively examined in atypical phenotypes. We investigated cross-sectional and 1-year longitudinal changes in default mode network sub-systems in the visual and language variants of Alzheimer’s disease, in relation to age and tau. Sixty-one amyloid-positive Alzheimer’s disease participants diagnosed with posterior cortical atrophy (n = 33) or logopenic progressive aphasia (n = 28) underwent structural MRI, resting-state functional MRI and [18F]flortaucipir PET. One-hundred and twenty-two amyloid-negative cognitively unimpaired individuals and 60 amyloid-positive individuals diagnosed with amnestic Alzheimer’s disease were included as controls and as a comparison group, respectively, and had structural and resting-state functional MRI. Forty-one atypical Alzheimer’s disease participants, 26 amnestic Alzheimer’s disease participants and 40 cognitively unimpaired individuals had one follow-up functional MRI ∼1–2 years after the baseline scan. Default mode network connectivity was calculated using the dual regression method for posterior, ventral, anterior ventral and anterior dorsal sub-systems derived from independent component analysis. A global measure of default mode network connectivity, the network failure quotient, was also calculated. Linear mixed-effects models and voxel-based analyses were computed for each connectivity measure. Both atypical and amnestic Alzheimer’s disease participants had lower cross-sectional posterior and ventral and higher anterior dorsal connectivity and network failure quotient relative to cognitively unimpaired individuals. Age had opposite effects on connectivity in Alzheimer’s disease participants and cognitively unimpaired individuals. While connectivity declined with age in cognitively unimpaired individuals, younger Alzheimer’s disease participants had lower connectivity than the older ones, particularly in the ventral default mode network. Greater baseline tau-PET uptake was associated with lower ventral and anterior ventral default mode network connectivity in atypical Alzheimer’s disease. Connectivity in the ventral default mode network declined over time in atypical Alzheimer’s disease, particularly in older participants, with lower tau burden. Voxel-based analyses validated the findings of higher anterior dorsal default mode network connectivity, lower posterior and ventral default mode network connectivity and decline in ventral default mode network connectivity over time in atypical Alzheimer’s disease. Visuospatial symptoms were associated with default mode network connectivity disruption. In summary, default mode connectivity disruption was similar between atypical and amnestic Alzheimer’s disease variants, and discriminated Alzheimer’s disease from cognitively unimpaired individuals, with decreased posterior and increased anterior connectivity and with disruption more pronounced in younger participants. The ventral default mode network declined over time in atypical Alzheimer’s disease, suggesting a shift in default mode network connectivity likely related to tau pathology.

AbstractBackgroundDecreased image self‐similarity of repeated FDG‐PET scans has been suggested to indicate increased risk for disease progression. Here, we compared several self‐similarity measures based on conventionally used voxel‐ and ROI‐based approaches and those based on patterns of cortical metabolism.MethodFrom the MCSA/ADRC, we included 376 cognitively unimpaired (CU) individuals and those with a clinical diagnosis of MCI (n = 55) or AD (n = 20) who had two FDG‐PET images within 2‐3 years and clinical follow‐up available. We compared image self‐similarity for three approaches: voxel, ROI and “eigenbrain” (Fig.1). For the voxel approach, FDG‐PET images were normalized to template space, grey matter masked, and intensity normalized to the pons to create standard uptake value ratios (SUVR) for each voxel. From these images, mean SUVR were extracted for 122 ROIs (MCALT‐ADIR122) for the ROI approach. For the “eigenbrain” approach, the SUVR images were transformed to a latent space embedding composed of 253 components (“eigenbrains”) and reflecting metabolism patterns, that was generated in an independent sample of 7008 FDG‐PET images. For each approach, image self‐similarity between baseline and follow‐up values (voxel, ROI or eigenbrain) was determined using three metrics (Pearson correlation, cosine, Euclidean distance). We computed AUC to determine which approach and metric would best discriminate between groups. We used Cox regression analyses to assess whether the metrics could predict disease progression.ResultComparing each distance metric across approaches, AUC was highest in differentiating clinical diagnoses using self‐similarity measures based on eigenbrains, then voxels, then ROIs (e.g., Euclidean distance CU vs AD AUC eigenbrain: 0.86, voxel: 0.75, ROI: 0.71; Fig.1). Across all approaches, Euclidean distance always discriminated best between classes. Eigenbrain‐based Euclidean distance was associated with risk for progression to MCI in CU (HR [95%CI]: 1.25 [1.05, 1.49]), and eigenbrain‐based cosine and Euclidean distances were associated with risk for progression to AD in MCI (1.61 [1.1, 2.34] and 1.53 [1.01, 2.31], respectively), while we found no associations for voxel‐ or ROI‐based distance measures.ConclusionEigenbrain‐based measures outperformed other, more conventional approaches in discriminating clinical diagnoses, and were associated with risk for disease progression. Our results suggest that decreased self‐similarity in metabolism patterns informs on disease stage and prognosis.

AbstractBackgroundBoth 11C‐Pittsburgh compound‐B (PiB) and 18F‐Flutemetamol (FMT) have off‐target white matter (WM) signal that increases with age, but WM uptake is stronger with FMT than PiB. Although their WM patterns are comparably associated with age, the difference between them may impact visual and quantitative assessments of cortical amyloid‐β uptake. Our objective was to determine whether PiB PET and FMT PET visual assessments agree with quantitative cortical amyloid‐β evaluations.MethodA cohort of cognitively unimpaired (CU) younger adults with median (range) of 39 (30, 48) years (N = 30), CU older adults with age of 67 (61, 83) years (N = 30) and older adults with AD dementia with age of 67 (54‐84) years (N = 23) underwent MRI, PiB PET and FMT PET. PiB‐PET and FMT PET scans were evaluated visually by two nuclear medicine specialists blinded to participant information. Disagreements were reconciled with a second, joint round of visual reads. Readers applied standard visual assessment criteria using the absence of regional gray matter (GM) and WM contrast as an indication of a positive scan. The global cortical PiB standard uptake value ratio (SUVr) was obtained referencing to cerebellar crus uptake and converted to centiloid values. Participants were classified as PiB positive/negative using a centiloid cut‐off of 22.ResultPiB and FMT visual positivity agreed with quantitative positivity on PiB‐based centiloid values in 69/83 for PiB and 78/83 for FMT. There were 10 disagreements with PiB, 1 disagreement with FMT and 4 disagreements with both. Few disagreements occurred near the centiloid threshold. Interestingly, all disagreements between visual reads (amyloid‐β positive) and centiloid (amyloid‐β negative) in the CU younger were with PiB. FMT disagreements were always in the CU older and AD groups.ConclusionDisagreements between amyloid‐β positivity on visual reads and quantitative measurements (centiloid) may depend on age and tracer. PiB WM uptake is lower than FMT WM uptake, which may lead to reduced contrast between GM and WM (part of the standard criteria for visual reads), especially in younger ages. In contrast, WM uptake increases at older ages, and increased WM signal may bleed more into cortical regions, hiding subtle, near‐threshold cortical uptake, especially with FMT.

AbstractBackgroundHigher blood pressure (BP) is associated with greater white matter hyperintensity (WMH) volume, but research is limited in recently menopausal and postmenopausal women. Postmenopausal women have a greater burden of WMH than men and premenopausal women, but whether hormone therapy (HT) modifies the association between systolic and diastolic BP (SBP, DBP) and WMH is unknown. We investigated the association of SBP and DBP in recently postmenopausal women who participated in an HT trial with WMH volumes measured 14 years later and explored whether HT modified the results.MethodWomen with good cardiovascular (CV) health, who were previously enrolled in a randomized placebo‐controlled HT trial, the Kronos Early Estrogen Prevention Study (KEEPS), participated in the current observational KEEPS continuation (n = 212; median age = 67; range 58‐72). SBP and DBP were measured at KEEPS baseline (median age = 54 years; range 44‐59) and continuation. Antihypertensive users were excluded (baseline, n = 34; continuation, n = 61). Approximately 10 years after the end of KEEPS HT versus placebo 4‐year interventions, automated WMH volumes were measured from 3D‐FLAIR MRI. Associations of KEEPS baseline and continuation SBP and DBP with log WMH volume at KEEPS continuation were tested using linear regression analyses adjusting for covariates in model 1 (age, total intracranial volume, study site) and model 2 (model 1 plus cardiovascular risk factors (CVRF)).ResultHigher KEEPS baseline SBP and DBP were associated with greater WMH volume measured 14 years later, and after adjusting for CVRF (p≤0.01). Similarly, in KEEPS continuation, higher SBP and DBP were associated with greater WMH volume measured concurrently and after adjusting for CVRF (p<0.001). We did not find statistically significant evidence that HT versus placebo modified these associations. Topographically, higher SBP and DBP were associated with greater periventricular WMH in the frontal and parietal lobes after adjusting for age (Figure).ConclusionHigher SBP and DBP in recently menopausal women with good CV health were associated with greater WMH volumes more than a decade later after adjusting for CVRF, independent of randomization to HT. Our results show the importance of blood pressure control in recently menopausal women, that may reduce the white matter injury later in life.

AbstractBackgroundPatients with a yes‐no reversal verbally and non‐verbally respond “yes” when they mean “no” and vice versa. They are typically aware of the error and able to correct it given additional time. This symptom commonly, but not exclusively, occurs in patients with primary progressive apraxia of speech (PPAOS). PPAOS is a form of frontotemporal dementia associated with hypometabolism in the supplementary motor area (SMA; e.g., Utianski et al., 2018); as the disease progresses, a wider network of frontal and subcortical regions become implicated. Behaviorally, this symptom seems to reflect poor response inhibition, but the specific anatomical underpinning is not yet understood. Given the brain regions known to be involved in PPAOS, we hypothesized that yes‐no reversals emerge due to degeneration of regions near the SMA and/or frontostriatal regions.MethodWe conducted an exploratory retrospective study of 28 patients who presented with PPAOS and had an FDG‐PET scan; 14 (50%) endorsed yes‐no reversals (see Table 1 for group characteristics). Clinically, AOS severity was measured using clinician ratings (0 = absent, 4 = severe) and frontal dysfunction was captured by the Frontal Assessment Battery (FAB) and the Frontal Behavioral Inventory (FBI). We also measured median standardized uptake value ratios (SUVRs) in the SMA and pre‐SMA, as well as the head and body of the caudate with automated, in‐house software. The caudate ROI was selected based on visual comparison of the FDG‐PET scans between groups and the association between this region and inhibition during response interference (e.g., Schmidt et al., 2020).ResultNeither AOS severity nor hypometabolism in the pre‐SMA/SMA regions differed significantly between groups. In contrast, patients with a yes‐no reversal demonstrated significantly lower scores on the FAB and greater hypometabolism in the caudate body relative to patients without (see Figure 1).ConclusionResults suggest that yes‐no reversals relate to frontostriatal dysfunction rather than AOS severity or SMA activity in these patients. Further research is needed to address why a disproportionate number of patients with PPAOS develop this symptom.

AbstractBackgroundThe language and visual variants are two common clinical phenotypes of atypical Alzheimer’s disease (AD). There are reports of overlap between these clinical phenotypes, however, the extent and frequency of overlap, and its neuroanatomical underpinnings remain unclear.MethodEighty‐two patients with biomarker‐confirmed AD who presented with either predominant language (n = 34) or visuospatial/perceptual (n = 48) deficits were recruited by the Neurodegenerative Research Group and underwent neurological and neuropsychological testing, MRI and [18F]flortaucipir‐PET. The clinical examination included testing for simultanagnosia, Gerstmann’s syndrome, ideomotor apraxia, visuospatial‐visuoperceptual function, confrontational naming, sentence repetition and letter and animal fluency. Cut‐points for abnormality were defined for each test and used to classify the following groups: language‐pure (no visual‐function deficit), language‐plus (impaired on one visual‐function), language‐mixed (impaired on two visual‐functions), visual‐pure (no language‐function deficit), visual‐plus (impaired on one language‐function) and visual‐mixed (impaired on two language‐functions). Patterns of relative volume loss and flortaucipir uptake were assessed using SPM12.ResultOf the 82 patients, 11 (13%) were language‐pure, 16 (20%) language‐plus, 7 (8%) language‐mixed, 10 (12%) visual‐pure, 17 (21%) visual‐plus and 21 (26%) visual‐mixed, with no difference in disease duration across groups. Visual deficits in the language‐plus group consisted of mild simultanagnosia in 29%, Gerstmann’s syndrome in 24%, and mild visuospatial deficits. In the language‐mixed group 86% had simultanagnosia and 43% had Gerstmann’s syndrome, with visuospatial‐visuoperceptual deficits comparable to the visual‐pure group. Language deficits in the visual‐plus group consisted of mild anomia and reduced letter and animal fluency. The visual‐mixed group reported naming and repetition deficits, and reduced fluency, comparable to the language‐predominant groups. All three language groups showed left temporal volume loss and flortaucipir uptake, with greater involvement of the parieto‐occipital and frontal lobes in language‐plus and language‐mixed groups. All three visual groups showed occipital volume loss and flortaucipir uptake, with greater involvement of the parieto‐temporal and frontal lobes in visual‐plus and visual‐mixed groups.ConclusionOnly 25% of our cohort showed a pure language or visual presentation, highlighting the high frequency of syndromic overlap in atypical AD and the diagnostic challenge of categorical phenotyping. Syndromic overlap was associated with neuroanatomical overlap and more widespread patterns of atrophy and tau deposition.

AbstractBackgroundPatients with dementia with Lewy bodies (DLB) may have Alzheimer´s disease (AD) pathology. Plasma biomarkers of beta‐amyloid (Aβ), phosphorylated tau (pTau), and neurodegeneration are sensitive to AD neuropathologic changes in AD dementia. While these plasma biomarkers are well tested in the AD continuum, their performance for concomitant AD pathology in the DLB continuum is still unclear.MethodWe included patients with isolated REM sleep behavior disorder (iRBD; n = 15), with mild cognitive impairment with Lewy bodies (MCI‐LB; n = 37), and with DLB (n = 70) from the Mayo Clinic Alzheimer’s Disease Research Center. Cognitively unimpaired individuals (CU; n = 100) included from the Mayo Clinic Study of Aging were balanced on age and sex. A panel of plasma biomarkers of Aβ40, Aβ42, p‐tau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) measures was obtained. We calculated the ratio of plasma Aβ40/42. Lower Aβ40/42 indicates increased Aβ pathology. Plasma Aβ40, Aβ42, GFAP, and NfL were measured using the Simoa® Neurology 4‐Plex E Advantage kit and pTau‐181 was measured with the Simoa® pTau‐181 Advantage V2 kit. Both were run on a Quanterix HD‐X analyzer. AD imaging biomarkers were assessed on PET using Pittsburgh compound B (PiB) for Aβ and 18F‐Flortaucipir for tau.ResultHigher levels of plasma GFAP were observed in the MCI‐LB (p = 0.014) and DLB (p<0.001) groups compared to CU (Figure). DLB patients also showed higher levels of plasma pTau181 (p<0.001) and NfL (p<0.001). Higher amyloid and tau PET standardized uptake value ratio (SUVr) in the DLB continuum were associated with higher levels of plasma p‐tau181, NfL, and GFAP (p<0.001). After accounting for age, plasma Aβ40/42 did not correlate with amyloid or tau PET SUVr.ConclusionIn the DLB continuum, abnormal levels of GFAP in plasma can be detected as early as the prodromal stages of the disease. Plasma pTau181 reached abnormal levels in the DLB stage. Higher plasma GFAP, NfL, and p‐tau181 are associated with higher amyloid and tau PET SUVr in the DLB continuum. Plasma biomarkers have the potential to contribute to screening and early diagnosis in the DLB continuum, which has implications in designing new treatments.

AbstractBackgroundWe have previously shown that 3D T1‐weighted and 3D T2‐FLAIR brain MRI can potentially be re‐identified using face recognition, but this has not been tested for other MRI sequences commonly used in imaging studies of aging and dementia. Here we compare match rates of automated face recognition to re‐identify 3D T1‐weighted, 3D T2‐FLAIR, 3D T2‐weighted, 2D Axial T2‐FLAIR, multi‐echo GRE (T2*), diffusion MRI, functional MRI, ASL, and T2* GRE images.MethodWe used a dataset of 182 Mayo Clinic participants each with MRI and face photographs. MRIs used Siemens and GE vendor‐product sequences similar to ADNI3 and ADNI4 protocols. We created face reconstructions (“basic”, with minimal preprocessing, and “advanced” that automatically filled missing face regions using a template) from each MRI sequence, and we trained a Microsoft Azure PersonGroup face recognition classifier to recognize these MRI‐based faces. We then queried the classifier with five face photographs for each participant, and we recorded how often the classifier chose the correct MRI as the top‐ranked match. For image types with match rates > = 10%, we then repeated the experiment after de‐facing each MRI with mri_reface, our leading software that replaces faces in each image (“re‐faces”) with an average face.ResultResults are presented in Table 1. 3D T2‐FLAIR, 3D T1‐weighted, and 3D T2‐weighted images were matched at 96‐98%, and mri_reface reduced all to 8%. Axial T2‐FLAIR and ME‐GRE were matched at 32‐45%, and mri_reface reduced both to 1%. T2*‐GRE matched at 10%, and mri_reface reduced this to 1%. ASL matched at 8%, while dMRI and fMRI matched at only 0‐2%.ConclusionAmong current‐generation vendor‐product sequences, we recommend that de‐facing be applied before research data sharing of T1‐weighted, T2‐weighted, T2‐FLAIR, T2*, and ME‐GRE images. Minimal match rates for current‐generation product fMRI, ASL, and fMRI images suggest that they present low risk of re‐identification and can be shared without de‐facing, but this conclusion should be re‐evaluated if they are acquired without fat‐suppression, with a larger field of view, or if newer developments reduce the current levels of artifacts and distortion around the face.