Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning.

Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

Predicting amyloid PET and tau PET stages with plasma biomarkers.

Staging the severity of Alzheimer's disease pathology using biomarkers is useful for therapeutic trials and clinical prognosis. Disease staging with amyloid and tau PET has face validity; however, this would be more practical with plasma biomarkers. Our objectives were, first, to examine approaches for staging amyloid and tau PET and, second, to examine prediction of amyloid and tau PET stages using plasma biomarkers. Participants (n = 1136) were enrolled in either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center; had a concurrent amyloid PET, tau PET and blood draw; and met clinical criteria for cognitively unimpaired (n = 864), mild cognitive impairment (n = 148) or Alzheimer's clinical syndrome with dementia (n = 124). The latter two groups were combined into a cognitively impaired group (n = 272). We used multinomial regression models to estimate discrimination [concordance (C) statistics] among three amyloid PET stages (low, intermediate, high), four tau PET stages (Braak 0, 1-2, 3-4, 5-6) and a combined amyloid and tau PET stage (none/low versus intermediate/high severity) using plasma biomarkers as predictors separately within unimpaired and impaired individuals. Plasma analytes, p-tau181, Aß1-42 and Aß1-40 (analysed as the Aß42/Aß40 ratio), glial fibrillary acidic protein and neurofilament light chain were measured on the HD-X Simoa Quanterix platform. Plasma p-tau217 was also measured in a subset (n = 355) of cognitively unimpaired participants using the Lilly Meso Scale Discovery assay. Models with all Quanterix plasma analytes along with risk factors (age, sex and APOE) most often provided the best discrimination among amyloid PET stages (C = 0.78-0.82). Models with p-tau181 provided similar discrimination of tau PET stages to models with all four plasma analytes (C = 0.72-0.85 versus C = 0.73-0.86). Discriminating a PET proxy of intermediate/high from none/low Alzheimer's disease neuropathological change with all four Quanterix plasma analytes was excellent but not better than p-tau181 only (C = 0.88 versus 0.87 for unimpaired and C = 0.91 versus 0.90 for impaired). Lilly p-tau217 outperformed the Quanterix p-tau181 assay for discriminating high versus intermediate amyloid (C = 0.85 versus 0.74) but did not improve over a model with all Quanterix plasma analytes and risk factors (C = 0.85 versus 0.83). Plasma analytes along with risk factors can discriminate between amyloid and tau PET stages and between a PET surrogate for intermediate/high versus none/low neuropathological change with accuracy in the acceptable to excellent range. Combinations of plasma analytes are better than single analytes for many staging predictions with the exception that Quanterix p-tau181 alone usually performed equivalently to combinations of Quanterix analytes for tau PET discrimination.

Modeling the temporal evolution of plasma p-tau in relation to amyloid beta and tau PET.

INTRODUCTION: The timing of plasma biomarker changes is not well understood. The goal of this study was to evaluate the temporal co-evolution of plasma and positron emission tomography (PET) Alzheimer's disease (AD) biomarkers. METHODS: We included 1408 Mayo Clinic Study of Aging and Alzheimer's Disease Research Center participants. An accelerated failure time (AFT) model was fit with amyloid beta (Aß) PET, tau PET, plasma p-tau217, p-tau181, and glial fibrillary acidic protein (GFAP) as endpoints. RESULTS: Individual timing of plasma p-tau progression was strongly associated with Aß PET and GFAP progression. In the population, GFAP became abnormal first, then Aß PET, plasma p-tau, and tau PET temporal meta-regions of interest when applying cut points based on young, cognitively unimpaired participants. DISCUSSION: Plasma p-tau is a stronger indicator of a temporally linked response to elevated brain Aß than of tau pathology. While Aß deposition and a rise in GFAP are upstream events associated with tau phosphorylation, the temporal link between p-tau and Aß PET was the strongest. HIGHLIGHTS: Plasma p-tau progression was more strongly associated with Aß than tau PET. Progression on plasma p-tau was associated with Aß PET and GFAP progression. P-tau181 and p-tau217 become abnormal after Aß PET and before tau PET. GFAP became abnormal first, before plasma p-tau and Aß PET.

Comparison of plasma biomarkers and amyloid PET for predicting memory decline in cognitively unimpaired individuals.

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) ε4 genotype, amyloid PET, and plasma amyloid beta (Aß)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.

NIA-AA Alzheimer's Disease Framework: Clinical Characterization of Stages.

BACKGROUND: To operationalize the National Institute on Aging - Alzheimer's Association (NIA-AA) Research Framework for Alzheimer's Disease 6-stage continuum of clinical progression for persons with abnormal amyloid. METHODS: The Mayo Clinic Study of Aging is a population-based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We evaluated persons without dementia having 3 consecutive clinical visits. Measures for cross-sectional categories included objective cognitive impairment (OBJ) and function (FXN). Measures for change included subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (ΔNBS). We calculated frequencies of the stages using different cutoff points and assessed stability of the stages over 15 months. RESULTS: Among 243 abnormal amyloid participants, the frequencies of the stages varied with age: 66 to 90% were classified as stage 1 at age 50 but at age 80, 24 to 36% were stage 1, 32 to 47% were stage 2, 18 to 27% were stage 3, 1 to 3% were stage 4 to 6, and 3 to 9% were indeterminate. Most stage 2 participants were classified as stage 2 because of abnormal ΔOBJ only (44-59%), whereas 11 to 21% had SCD only, and 9 to 13% had ΔNBS only. Short-term stability varied by stage and OBJ cutoff points but the most notable changes were seen in stage 2 with 38 to 63% remaining stable, 4 to 13% worsening, and 24 to 41% improving (moving to stage 1). INTERPRETATION: The frequency of the stages varied by age and the precise membership fluctuated by the parameters used to define the stages. The staging framework may require revisions before it can be adopted for clinical trials. ANN NEUROL 2021;89:1145-1156.

Left-Right Intensity Asymmetries Vary Depending on Scanner Model for FLAIR and T1 Weighted MRI Images.

BACKGROUND: Localized regions of left-right image intensity asymmetry (LRIA) were incidentally observed on T2 -weighted (T2 -w) and T1 -weighted (T1 -w) diagnostic magnetic resonance imaging (MRI) images. Suspicion of herpes encephalitis resulted in unnecessary follow-up imaging. A nonbiological imaging artifact that can lead to diagnostic uncertainty was identified. PURPOSE: To investigate whether systematic LRIA exist for a range of scanner models and to determine if LRIA can introduce diagnostic uncertainty. STUDY TYPE: A retrospective study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data base. SUBJECTS: One thousand seven hundred fifty-three (median age: 72, males/females: 878/875) unique participants with longitudinal data were included. FIELD STRENGTH: 3T. SEQUENCES: T1 -w three-dimensional inversion-recovery spoiled gradient-echo (IR-SPGR) or magnetization-prepared rapid gradient-echo (MP-RAGE) and T2 -w fluid-attenuated inversion recovery (FLAIR) long tau fast spin echo inversion recovery (LT-FSE-IR). Only General Electric, Philips, and Siemens' product sequences were used. ASSESSMENT: LRIA was calculated as the left-right percent difference with respect to the mean intensity from automated anatomical atlas segmented regions. Three neuroradiologists with 37 (**), 32 (**), and 3 (**) years of experience rated the clinical impact of 30 T2 -w three-dimensional FLAIR exams with LRIA to determine the diagnostic uncertainty. Statistical comparisons between retrospective intensity normalized T1 m and original T1 -w images were made. STATISTICAL TESTS: For each image type, a linear mixed effects model was fit using LRIA scores from all scanners, regions, and participants as the outcome and age and sex as predictors. Statistical significance was defined as having a P-value <0.05. RESULTS: LRIA scores were significantly different from zero on most scanners. All clinicians were uncertain or recommended definite diagnostic follow-up in 62.5% of cases with LRIA >10%. Individuals with acute brain pathology or focal neurologic deficits are not enrolled in ADNI; therefore, focal signal abnormalities were considered false positives. DATA CONCLUSION: LRIA is system specific, systematic, creates diagnostic uncertainty, and impacts IR-SPGR, MP-RAGE, and LT-FSE-IR product sequences. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 3.

Detection of Alzheimer's disease amyloid beta 1-42, p-tau, and t-tau assays.

INTRODUCTION: We aimed to provide cut points for the automated Elecsys Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers. METHODS: Cut points for Elecsys amyloid beta 42 (Aß42), total tau (t-tau), hyperphosphorylated tau (p-tau), and t-tau/Aß42 and p-tau/Aß42 ratios were evaluated in Mayo Clinic Study of Aging (n = 804) and Mayo Clinic Alzheimer's Disease Research Center (n = 70) participants. RESULTS: The t-tau/Aß42 and p-tau/Aß42 ratios had a higher percent agreement with normal/abnormal amyloid positron emission tomography (PET) than the individual CSF markers. Reciever Operating Characteristic (ROC)-based cut points were 0.26 (0.24-0.27) for t-tau/Aß42 and 0.023 (0.020-0.025) for p-tau/Aß42. Ratio cut points derived from other cohorts performed as well in our cohort as our own did. Individual biomarkers had worse diagnostic properties and more variable results in terms of positive and negative percent agreement (PPA and NPA). CONCLUSION: CSF t-tau/Aß42 and p-tau/Aß42 ratios are very robust indicators of AD. For individual biomarkers, the intended use should determine which cut point is chosen.

CSF dynamics as a predictor of cognitive progression.

Disproportionately enlarged subarachnoid-space hydrocephalus (DESH), characterized by tight high convexity CSF spaces, ventriculomegaly, and enlarged Sylvian fissures, is thought to be an indirect marker of a CSF dynamics disorder. The clinical significance of DESH with regard to cognitive decline in a community setting is not yet well defined. The goal of this work is to determine if DESH is associated with cognitive decline. Participants in the population-based Mayo Clinic Study of Aging (MCSA) who met the following criteria were included: age ≥ 65 years, 3T MRI, and diagnosis of cognitively unimpaired or mild cognitive impairment at enrollment as well as at least one follow-up visit with cognitive testing. A support vector machine based method to detect the DESH imaging features on T1-weighted MRI was used to calculate a "DESH score", with positive scores indicating a more DESH-like imaging pattern. For the participants who were cognitively unimpaired at enrollment, a Cox proportional hazards model was fit with time defined as years from enrollment to first diagnosis of mild cognitive impairment or dementia, or as years to last known cognitively unimpaired diagnosis for those who did not progress. Linear mixed effects models were fit among all participants to estimate annual change in cognitive z scores for each domain (memory, attention, language, and visuospatial) and a global z score. For all models, covariates included age, sex, education, APOE genotype, cortical thickness, white matter hyperintensity volume, and total intracranial volume. The hazard of progression to cognitive impairment was an estimated 12% greater for a DESH score of +1 versus -1 (HR 1.12, 95% CI 0.97-1.31, p = 0.11). Global and attention cognition declined 0.015 (95% CI 0.005-0.025) and 0.016 (95% CI 0.005-0.028) z/year more, respectively, for a DESH score of +1 vs -1 (p = 0.01 and p = 0.02), with similar, though not statistically significant DESH effects in the other cognitive domains. Imaging features of disordered CSF dynamics are an independent predictor of subsequent cognitive decline in the MCSA, among other well-known factors including age, cortical thickness, and APOE status. Therefore, since DESH contributes to cognitive decline and is present in the general population, identifying individuals with DESH features may be important clinically as well as for selection in clinical trials.

Changing the face of neuroimaging research: Comparing a new MRI de-facing technique with popular alternatives.

Recent advances in automated face recognition algorithms have increased the risk that de-identified research MRI scans may be re-identifiable by matching them to identified photographs using face recognition. A variety of software exist to de-face (remove faces from) MRI, but their ability to prevent face recognition has never been measured and their image modifications can alter automated brain measurements. In this study, we compared three popular de-facing techniques and introduce our mri_reface technique designed to minimize effects on brain measurements by replacing the face with a population average, rather than removing it. For each technique, we measured 1) how well it prevented automated face recognition (i.e. effects on exceptionally-motivated individuals) and 2) how it altered brain measurements from SPM12, FreeSurfer, and FSL (i.e. effects on the average user of de-identified data). Before de-facing, 97% of scans from a sample of 157 volunteers were correctly matched to photographs using automated face recognition. After de-facing with popular software, 28-38% of scans still retained enough data for successful automated face matching. Our proposed mri_reface had similar performance with the best existing method (fsl_deface) at preventing face recognition (28-30%) and it had the smallest effects on brain measurements in more pipelines than any other, but these differences were modest.

Associations of quantitative susceptibility mapping with Alzheimer's disease clinical and imaging markers.

Altered iron metabolism has been hypothesized to be associated with Alzheimer's disease pathology, and prior work has shown associations between iron load and beta amyloid plaques. Quantitative susceptibility mapping (QSM) is a recently popularized MR technique to infer local tissue susceptibility secondary to the presence of iron as well as other minerals. Greater QSM values imply greater iron concentration in tissue. QSM has been used to study relationships between cerebral iron load and established markers of Alzheimer's disease, however relationships remain unclear. In this work we study QSM signal characteristics and associations between susceptibility measured on QSM and established clinical and imaging markers of Alzheimer's disease. The study included 421 participants (234 male, median age 70 years, range 34-97 years) from the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center; 296 (70%) had a diagnosis of cognitively unimpaired, 69 (16%) mild cognitive impairment, and 56 (13%) amnestic dementia. All participants had multi-echo gradient recalled echo imaging, PiB amyloid PET, and Tauvid tau PET. Variance components analysis showed that variation in cortical susceptibility across participants was low. Linear regression models were fit to assess associations with regional susceptibility. Expected increases in susceptibility were found with older age and cognitive impairment in the deep and inferior gray nuclei (pallidum, putamen, substantia nigra, subthalamic nucleus) (betas: 0.0017 to 0.0053 ppm for a 10 year increase in age, p = 0.03 to <0.001; betas: 0.0021 to 0.0058 ppm for a 5 point decrease in Short Test of Mental Status, p = 0.003 to p<0.001). Effect sizes in cortical regions were smaller, and the age associations were generally negative. Higher susceptibility was significantly associated with higher amyloid PET SUVR in the pallidum and putamen (betas: 0.0029 and 0.0012 ppm for a 20% increase in amyloid PET, p = 0.05 and 0.02, respectively), higher tau PET in the basal ganglia with the largest effect size in the pallidum (0.0082 ppm for a 20% increase in tau PET, p<0.001), and with lower cortical gray matter volume in the medial temporal lobe (0.0006 ppm for a 20% decrease in volume, p = 0.03). Overall, these findings suggest that susceptibility in the deep and inferior gray nuclei, particularly the pallidum and putamen, may be a marker of cognitive decline, amyloid deposition, and off-target binding of the tau ligand. Although iron has been demonstrated in amyloid plaques and in association with neurodegeneration, it is of insufficient quantity to be reliably detected in the cortex using this implementation of QSM.

Predicting future rates of tau accumulation on PET.

Clinical trials with anti-tau drugs will need to target individuals at risk of accumulating tau. Our objective was to identify variables available in a research setting that predict future rates of tau PET accumulation separately among individuals who were either cognitively unimpaired or cognitively impaired. All 337 participants had: a baseline study visit with MRI, amyloid PET, and tau PET exams, at least one follow-up tau PET exam; and met clinical criteria for membership in one of two clinical diagnostic groups: cognitively unimpaired (n = 203); or cognitively impaired (n = 134, a combined group of participants with either mild cognitive impairment or dementia with Alzheimer's clinical syndrome). Our primary analyses were in these two clinical groups; however, we also evaluated subgroups dividing the unimpaired group by normal/abnormal amyloid PET and the impaired group by clinical phenotype (mild cognitive impairment, amnestic dementia, and non-amnestic dementia). Linear mixed effects models were used to estimate associations between age, sex, education, APOE genotype, amyloid and tau PET standardized uptake value ratio (SUVR), cognitive performance, cortical thickness, and white matter hyperintensity volume at baseline, and the rate of subsequent tau PET accumulation. Log-transformed tau PET SUVR was used as the response and rates were summarized as annual per cent change. A temporal lobe tau PET meta-region of interest was used. In the cognitively unimpaired group, only higher baseline amyloid PET was a significant independent predictor of higher tau accumulation rates (P < 0.001). Higher rates of tau accumulation were associated with faster rates of cognitive decline in the cognitively unimpaired subgroup with abnormal amyloid PET (P = 0.03), but among the subgroup with normal amyloid PET. In the cognitively impaired group, younger age (P = 0.02), higher baseline amyloid PET (P = 0.05), APOE ε4 (P = 0.05), and better cognitive performance (P = 0.05) were significant independent predictors of higher tau accumulation rates. Among impaired individuals, faster cognitive decline was associated with faster rates of tau accumulation (P = 0.01). While we examined many possible predictor variables, our results indicate that screening of unimpaired individuals for potential inclusion in anti-tau trials may be straightforward because the only independent predictor of high tau rates was amyloidosis. In cognitively impaired individuals, imaging and clinical variables consistent with early onset Alzheimer's disease phenotype were associated with higher rates of tau PET accumulation suggesting this may be a highly advantageous group in which to conduct proof-of-concept clinical trials that target tau-related mechanisms. The nature of the dementia phenotype (amnestic versus non-amnestic) did not affect this conclusion.

The bivariate distribution of amyloid-ß and tau: relationship with established neurocognitive clinical syndromes.

Large phenotypically diverse research cohorts with both amyloid and tau PET have only recently come into existence. Our objective was to determine relationships between the bivariate distribution of amyloid-ß and tau on PET and established clinical syndromes that are relevant to cognitive ageing and dementia. All individuals in this study were enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study of cognitive ageing, or the Mayo Alzheimer Disease Research Center, a longitudinal study of individuals recruited from clinical practice. We studied 1343 participants who had amyloid PET and tau PET from 2 April 2015 to 3 May 2019, and met criteria for membership in one of five clinical diagnostic groups: cognitively unimpaired, mild cognitive impairment, frontotemporal dementia, probable dementia with Lewy bodies, and Alzheimer clinical syndrome. We examined these clinical groups in relation to the bivariate distribution of amyloid and tau PET values. Individuals were grouped into amyloid (A)/tau (T) quadrants based on previously established abnormality cut points of standardized uptake value ratio 1.48 (A) and 1.33 (T). Individual participants largely fell into one of three amyloid/tau quadrants: low amyloid and low tau (A-T-), high amyloid and low tau (A+T-), or high amyloid and high tau (A+T+). Seventy per cent of cognitively unimpaired and 74% of FTD participants fell into the A-T- quadrant. Participants with mild cognitive impairment spanned the A-T- (42%), A+T- (28%), and A+T+ (27%) quadrants. Probable dementia with Lewy body participants spanned the A-T- (38%) and A+T- (44%) quadrants. Most (89%) participants with Alzheimer clinical syndrome fell into the A+T+ quadrant. These data support several conclusions. First, among 1343 participants, abnormal tau PET rarely occurred in the absence of abnormal amyloid PET, but the reverse was common. Thus, with rare exceptions, amyloidosis appears to be required for high levels of 3R/4R tau deposition. Second, abnormal amyloid PET is compatible with normal cognition but highly abnormal tau PET is not. These two conclusions support a dynamic biomarker model in which Alzheimer's disease is characterized first by the appearance of amyloidosis and later by tauopathy, with tauopathy being the proteinopathy associated with clinical symptoms. Third, bivariate amyloid and tau PET relationships differed across clinical groups and thus have a role for clarifying the aetiologies underlying neurocognitive clinical syndromes.

Widespread brain tau and its association with ageing, Braak stage and Alzheimer's dementia.

See Herholz (doi:10.1093/brain/awx340) for a scientific commentary on this article.Autopsy data have proposed that a topographical pattern of tauopathy occurs in the brain with the development of dementia due to Alzheimer's disease. We evaluated the findings of tau-PET to better understand neurofibrillary tangle development as it is seen in cognitively unimpaired and impaired individuals. The evolution of Alzheimer's disease tauopathy in cognitively unimpaired individuals needs to be examined to better understand disease pathogenesis. Tau-PET was performed in 86 cognitively impaired individuals who all had abnormal amyloid levels and 601 cognitively unimpaired individuals. Tau-PET findings were assessed for relationships with clinical diagnosis, age, and regional uptake patterns relative to Braak stage. Regional and voxel-wise analyses were performed. Topographical findings from tau-PET were characterized using hierarchical clustering and clinical characteristic-based subcategorization. In older cognitively unimpaired individuals (≥50 years), widespread, age-related elevated tau signal was seen among those with normal or abnormal amyloid status as compared to younger cognitively unimpaired individuals (30-49 years). More frequent regional tau signal elevation throughout the brain was seen in cognitively unimpaired individuals with abnormal versus normal amyloid. Elevated tau signal was seen in regions that are considered high Braak Stage in cognitively unimpaired and cognitively impaired individuals. Hierarchical clustering and clinical characteristic-based categorizations both showed different patterns of tau signal between groups such as greater tau signal in frontal regions in younger onset Alzheimer's disease dementia participants (most of whom had a dysexecutive clinical presentation). Tau-PET signal increases modestly with age throughout the brain in cognitively unimpaired individuals and elevated tau is seen more often when amyloid brain accumulation is present. Tau signal patterns in cognitively unimpaired correspond to early Braak stage but also suggest tangle involvement in extra-medial temporal and extra-temporal regions that are considered more advanced in the Braak scheme even when amyloid negative. Our findings also suggest the possibility of widespread development of early tangle pathology rather than a pattern defined exclusively by adjacent, region-to-region spread, prior to onset of clinical symptoms. Distinct patterns of neurofibrillary tangle deposition in younger-onset Alzheimer's disease dementia versus older-onset Alzheimer's disease dementia provide evidence for variability in regional tangle deposition patterns and demonstrate that different disease phenotypes have different patterns of tauopathy. Pathological correlation with imaging is needed to assess the implications of these observations.

Longitudinal tau PET in ageing and Alzheimer's disease.

See Hansson and Mormino (doi:10.1093/brain/awy065) for a scientific commentary on this article.Our objective was to compare different whole-brain and region-specific measurements of within-person change on serial tau PET and evaluate its utility for clinical trials. We studied 126 individuals: 59 cognitively unimpaired with normal amyloid, 37 cognitively unimpaired with abnormal amyloid, and 30 cognitively impaired with an amnestic phenotype and abnormal amyloid. All had baseline amyloid PET and two tau PET, MRI, and clinical assessments. We compared the topography across all cortical regions of interest of tau PET accumulation rates and the rates of four different whole-brain or region-specific meta-regions of interest among the three clinical groups. We computed sample size estimates for change in tau PET, cortical volume, and memory/mental status indices for use as outcome measures in clinical trials. The cognitively unimpaired normal amyloid group had no observable tau accumulation throughout the brain. Tau accumulation rates in cognitively unimpaired abnormal amyloid were low [0.006 standardized uptake value ratio (SUVR), 0.5%, per year] but greater than rates in the cognitively unimpaired normal amyloid group in the basal and mid-temporal, retrosplenial, posterior cingulate, and entorhinal regions of interest. Thus, the earliest elevation in accumulation rates was widespread and not confined to the entorhinal cortex. Tau accumulation rates in the cognitively impaired abnormal amyloid group were 0.053 SUVR (3%) per year and greater than rates in cognitively unimpaired abnormal amyloid in all cortical areas except medial temporal. Rates of accumulation in the four meta-regions of interest differed but only slightly from one another. Among all tau PET meta-regions of interest, sample size estimates were smallest for a temporal lobe composite within cognitively unimpaired abnormal amyloid and for the late Alzheimer's disease meta-region of interest within cognitively impaired abnormal amyloid. The ordering of the sample size estimates by outcome measure was MRI < tau PET < cognitive measures. At a group-wise level, observable rates of short-term serial tau accumulation were only seen in the presence of abnormal amyloid. As disease progressed to clinically symptomatic stages (cognitively impaired abnormal amyloid), observable rates of tau accumulation were seen uniformly throughout the brain providing evidence that tau does not accumulate in one area at a time or in start-stop, stepwise sequence. The information captured by rate measures in different meta-regions of interest, even those with little topographic overlap, was similar. The implication is that rate measurements from simple meta-regions of interest, without the need for Braak-like staging, may be sufficient to capture progressive within-person accumulation of pathologic tau. Tau PET SUVR measures should be an efficient outcome measure in disease-modifying clinical trials.

FDG-PET in tau-negative amnestic dementia resembles that of autopsy-proven hippocampal sclerosis.

See Gordon (doi:10.1093/brain/awy052) for a scientific commentary on this article.Predicting underlying pathology based on clinical presentation has historically proven difficult, especially in older cohorts. Age-related hippocampal sclerosis may account for a significant proportion of elderly participants with amnestic dementia. Advances in molecular neuroimaging have allowed for detailed biomarker-based phenotyping, but in the absence of antemortem markers of hippocampal sclerosis, cases of mixed pathology remain problematic. We evaluated the utility of 18F-FDG-PET to differentiate flortaucipir tau PET negative from flortaucipir positive amnestic mild cognitive impairment and dementia and used an autopsy confirmed cohort to test the hypothesis that hippocampal sclerosis might account for the observed pattern. We identified impaired participants (Clinical Dementia Rating > 0) with amnestic presentations ≥ 75 years who had MRI and PET imaging with 18F-FDG (glucose metabolism), Pittsburgh compound B (amyloid) and flortaucipir (tau) performed within a year of cognitive assessment. These were stratified into amyloid positive/negative and tau positive/negative according to the A/T/N classification scheme. Our sample included 15 amyloid and tau-positive participants, and nine tau-negative participants (five of whom were amyloid-positive). For the autopsy cohort, sequential cases with antemortem 18F-FDG-PET were screened and those with TDP-43-negative Alzheimer's disease (10 cases) and TDP-43-positive hippocampal sclerosis (eight cases) were included. We compared each group to controls and to each other in a voxel-based analysis, and supplemented this with a region of interest-based analysis comparing medial to inferior temporal metabolism. Tau-positive and negative cases did not differ on neuropsychological testing or structural magnetic resonance biomarkers. Tau-negative cases had focal medial temporal and posterior cingulate/retrosplenial hypometabolism regardless of amyloid status, whereas tau-positive cases had additional lateral parietal and inferior temporal involvement. The inferior/medial temporal metabolism ratio was significantly different between the groups with the tau-negative group having a higher ratio. In the autopsy series, hippocampal sclerosis cases had greater medial temporal hypometabolism than Alzheimer's disease cases, who had more parietal and lateral/inferior temporal hypometabolism. Again, the ratio between temporal regions of interest differed significantly between groups. Two of the tau-negative patients, both of whom had an elevated inferior/medial temporal ratio, came to autopsy during the study and were found to have hippocampal sclerosis. Our finding that tau-negative amnestic mild cognitive impairment and dementia is associated with focal medial temporal and posterior cingulate hypometabolism extends prior reports in amyloid-negative cases. The inferior/medial temporal metabolism ratio can help identify tau-negative cases of amnestic dementia and may serve as a biomarker for hippocampal sclerosis.

Associations of Amyloid, Tau, and Neurodegeneration Biomarker Profiles With Rates of Memory Decline Among Individuals Without Dementia.

Importance: A National Institute on Aging and Alzheimer's Association workgroup proposed a research framework for Alzheimer disease in which biomarker classification of research participants is labeled AT(N) for amyloid, tau, and neurodegeneration biomarkers. Objective: To determine the associations between AT(N) biomarker profiles and memory decline in a population-based cohort of individuals without dementia age 60 years or older, and to determine whether biomarkers provide incremental prognostic value beyond more readily available clinical and genetic information. Design, Setting, and Participants: Population-based cohort study of cognitive aging in Olmsted County, Minnesota, that included 480 nondemented Mayo Clinic Study of Aging participants who had a clinical evaluation and amyloid positron emission tomography (PET) (A), tau PET (T), and magnetic resonance imaging (MRI) cortical thickness (N) measures between April 16, 2015, and November 1, 2017, and at least 1 clinical evaluation follow-up by November 12, 2018. Exposures: Age, sex, education, cardiovascular and metabolic conditions score, APOE genotype, and AT(N) biomarker profiles. Each of A, T, or (N) can be abnormal (+) or normal (-), resulting in 8 AT(N) profiles. Main Outcomes and Measures: Primary outcome was a composite memory score measured longitudinally at 15-month intervals. Analyses measured the associations between predictor variables and the memory score, and whether AT(N) biomarker profiles significantly improved prediction of memory z score rates of change beyond a model with clinical and genetic variables only. Results: Participants were followed up for a median of 4.8 years (interquartile range [IQR], 3.8-5.1) and 44% were women (211/480). Median (IQR) ages ranged from 67 years (65-73) in the A-T-(N)- group to 83 years (76-87) in the A+T+(N)+ group. Of the participants, 92% (441/480) were cognitively unimpaired but the A+T+(N)+ group had the largest proportion of mild cognitive impairment (30%). AT(N) biomarkers improved the prediction of memory performance over a clinical model from an R2 of 0.26 to 0.31 (P < .001). Memory declined fastest in the A+T+(N)+, A+T+(N)-, and A+T-(N)+ groups compared with the other 5 AT(N) groups (P = .002). Estimated rates of decline in the 3 fastest declining groups were -0.13 (95% CI, -0.17 to -0.09), -0.10 (95% CI, -0.16 to -0.05), and -0.10 (95% CI, -0.13 to -0.06) z score units per year, respectively, for an 85-year-old APOE ε4 noncarrier. Conclusions and Relevance: Among older persons without baseline dementia followed for a median of 4.8 years, a prediction model that included amyloid PET, tau PET, and MRI cortical thickness resulted in a small but statistically significant improvement in predicting memory decline over a model with more readily available clinical and genetic variables. The clinical importance of this difference is uncertain.

Defining imaging biomarker cut points for brain aging and Alzheimer's disease.

INTRODUCTION: Our goal was to develop cut points for amyloid positron emission tomography (PET), tau PET, flouro-deoxyglucose (FDG) PET, and MRI cortical thickness. METHODS: We examined five methods for determining cut points. RESULTS: The reliable worsening method produced a cut point only for amyloid PET. The specificity, sensitivity, and accuracy of cognitively impaired versus young clinically normal (CN) methods labeled the most people abnormal and all gave similar cut points for tau PET, FDG PET, and cortical thickness. Cut points defined using the accuracy of cognitively impaired versus age-matched CN method labeled fewer people abnormal. DISCUSSION: In the future, we will use a single cut point for amyloid PET (standardized uptake value ratio, 1.42; centiloid, 19) based on the reliable worsening cut point method. We will base lenient cut points for tau PET, FDG PET, and cortical thickness on the accuracy of cognitively impaired versus young CN method and base conservative cut points on the accuracy of cognitively impaired versus age-matched CN method.

Different definitions of neurodegeneration produce similar amyloid/neurodegeneration biomarker group findings.

We recently demonstrated that the frequencies of biomarker groups defined by the presence or absence of both amyloidosis (A+) and neurodegeneration (N+) changed dramatically by age in cognitively non-impaired subjects. Our present objectives were to assess the consequences of defining neurodegeneration in five different ways on the frequency of subjects classified as N+, on the demographic associations with N+, and on amyloidosis and neurodegeneration (A/N) biomarker group frequencies by age. This was a largely cross-sectional observational study of 1331 cognitively non-impaired subjects aged 50-89 drawn from a population-based study of cognitive ageing. We assessed demographic associations with N+, and A/N biomarker group frequencies by age where A+ was defined by amyloid PET and N+ was defined in five different ways: (i) abnormal adjusted hippocampal volume alone; (ii) abnormal Alzheimer's disease signature cortical thickness alone; (iii) abnormal fluorodeoxyglucose positron emission tomography alone; (iv) abnormal adjusted hippocampal volume or abnormal fluorodeoxyglucose positron emission tomography; and (v) abnormal Alzheimer's disease signature cortical thickness or abnormal fluorodeoxyglucose positron emission tomography. For each N+ definition, participants were assigned to one of four biomarker groups; A-N-, A+N-, A-N+, or A+N+. The three continuous individual neurodegeneration measures were moderately correlated (rs = 0.42 to 0.54) but when classified as normal or abnormal had only weak agreement (κ = 0.20 to 0.29). The adjusted hippocampal volume alone definition classified the fewest subjects as N+ while the Alzheimer's disease signature cortical thickness or abnormal fluorodeoxyglucose positron emission tomography definition classified the most as N+. Across all N+ definitions, N+ subjects tended to be older, more often male and APOE4 carriers, and performed less well on functional status and learning and memory than N- subjects. For all definitions of neurodegeneration, (i) the frequency of A-N- was 100% at age 50 and declined monotonically thereafter; (ii) the frequency of A+N- increased from age 50 to a maximum in the mid-70s and declined thereafter; and3 (iii) the frequency of A-N+ (suspected non-Alzheimer's pathophysiology) and of A+N+ increased monotonically beginning in the mid-50s and mid-60s, respectively. Overall, different neurodegeneration measures provide similar but not completely redundant information. Despite quantitative differences, the overall qualitative pattern of the A-N-, A+N-, A-N+, and A+N+ biomarker group frequency curves by age were similar across the five different definitions of neurodegeneration. We conclude that grouping subjects by amyloidosis and neurodegeneration status (normal/abnormal) is robust to different imaging definitions of neurodegeneration and thus is a useful way for investigators throughout the field to communicate in a common classification framework.

Clinical Outcomes of Cardiac Resynchronization with Epicardial Left Ventricular Lead.

BACKGROUND: Left ventricular (LV) pacing in cardiac resynchronization therapy (CRT) can be achieved via a transvenous or epicardial route. A surgically implanted epicardial LV (eLV) lead is used after a standard transvenous LV (tLV) lead implantation has failed. However, studies of clinical outcomes in patients with eLV leads and comparisons of outcome between tLV and eLV-CRT are sparse. Therefore, the purpose of this study is to compare clinical response between tLV-CRT and eLV-CRT, as well as to understand the differences within the eLV-CRT population. METHODS: Forty-four patients received eLV-CRT following unsuccessful attempts of tLV-CRT implantation between 2002 and 2013 at the University of California, San Diego (UCSD) and Mayo Clinics. These patients were matched for age, gender, and etiology of cardiomyopathy in a 1:2 ratio with a cohort of patients who received tLV-CRT during the same time period. RESULTS: During a mean follow-up of 57 months, similar clinical outcomes and survival rate were noted between tLV and eLV-CRT patients (all P > 0.05). Within the eLV-CRT group, dilated cardiomyopathy patients had significant improvement in New York Heart Association class and ejection fraction (both P < 0.05), while ischemic cardiomyopathy patients did not (both P > 0.05). eLV-CRT patients with nonanterior lead location had significantly improved survival (P < 0.001). There was also a trend for improved survival in those with nonapical lead location (P = 0.09). CONCLUSION: In this case-matched two-centered study, comparable improvements were noted in patients with tLV-CRT and eLV-CRT. Operators should target nonanterior and nonapical locations during eLV-CRT implantation. Use of eLV-CRT should be considered a viable alternative for CRT candidates.