AT(N) biomarker profiles and Alzheimer's disease symptomology in Down syndrome.

INTRODUCTION: Down syndrome (DS) is a genetic cause of early-onset Alzheimer's disease (AD). The National Institute on Aging-Alzheimer's Association AT(N) Research Framework is a staging model for AD biomarkers but has not been assessed in DS. METHOD: Data are from the Alzheimer's Biomarker Consortium-Down Syndrome. Positron emission tomography (PET) amyloid beta (Aß; 15 mCi of [11 C]Pittsburgh compound B) and tau (10 mCi of [18 F]AV-1451) were used to classify amyloid (A) -/+ and tau (T) +/-. Hippocampal volume classified neurodegeneration (N) -/+. The modified Cued Recall Test assessed episodic memory. RESULTS: Analyses included 162 adults with DS (aged M = 38.84 years, standard deviation = 8.41). Overall, 69.8% of participants were classified as A-/T-/(N)-, 11.1% were A+/T-/(N)-, 5.6% were A+/T+/(N)-, and 9.3% were A+/T+/(N)+. Participants deemed cognitively stable were most likely to be A-T-(N)- and A+T-(N)-. Tau PET (T+) most closely aligning with memory impairment and AD clinical status. DISCUSSION: Findings add to understanding of AT(N) biomarker profiles in DS. HIGHLIGHTS: Overall, 69.8% of adults with Down syndrome (DS) aged 25 to 61 years were classified as amyloid (A)-/tau (T)-/neurodegeneration (N)-, 11.1% were A+/T-/(N)-, 5.6% were A+/T+/(N)-, and 9.3% were A+/T+/(N)+. The AT(N) profiles were associated with clinical Alzheimer's disease (AD) status and with memory performance, with the presence of T+ aligned with AD clinical symptomology. Findings inform models for predicting the transition to the prodromal stage of AD in DS.

Characterizing the emergence of amyloid and tau burden in Down syndrome.

INTRODUCTION: Almost all individuals with Down syndrome (DS) will develop neuropathological features of Alzheimer's disease (AD). Understanding AD biomarker trajectories is necessary for DS-specific clinical interventions and interpretation of drug-related changes in the disease trajectory. METHODS: A total of 177 adults with DS from the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) underwent positron emission tomography (PET) and MR imaging. Amyloid-beta (Aß) trajectories were modeled to provide individual-level estimates of Aß-positive (A+) chronicity, which were compared against longitudinal tau change. RESULTS: Elevated tau was observed in all NFT regions following A+ and longitudinal tau increased with respect to A+ chronicity. Tau increases in NFT regions I-III was observed 0-2.5 years following A+. Nearly all A+ individuals had tau increases in the medial temporal lobe. DISCUSSION: These findings highlight the rapid accumulation of amyloid and early onset of tau relative to amyloid in DS and provide a strategy for temporally characterizing AD neuropathology progression that is specific to the DS population and independent of chronological age. HIGHLIGHTS: Longitudinal amyloid trajectories reveal rapid Aß accumulation in Down syndrome NFT stage tau was strongly associated with A+ chronicity Early longitudinal tau increases were observed 2.5-5 years after reaching A.

A multi-scanner neuroimaging data harmonization using RAVEL and ComBat.

Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI's) pertinent to Alzheimer's disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.

The Relationship of Current Cognitive Activity to Brain Amyloid Burden and Glucose Metabolism.

Several studies have investigated how lifetime cognitive engagement affects levels of amyloid-beta (Aß) deposition in the brain. However, there has been some disagreement, leaving the relationship of cognitive activity (CA) to Aß a largely open question. The present study investigated the relationship between CA, Aß deposition, and glucose metabolism. One hundred nine cognitively normal participants underwent Pittsburgh Compound-B (PiB) and [18F]fluorodeoxyglucose-positron emission tomography and completed a questionnaire designed to measure current CA. Statistical analyses revealed significant differences in PiB retention between those in the high and low CA groups. Linear regression models revealed a significant negative relationship between PiB retention and CA and a significant positive relationship between glucose metabolism and CA. These data suggest that CA may have a direct beneficial effect on the pathophysiology of AD or reflect another underlying process that results in both higher CA and lower AD pathophysiology.

The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET.

Although amyloid imaging with PiB-PET ([C-11]Pittsburgh Compound-B positron emission tomography), and now with F-18-labeled tracers, has produced remarkably consistent qualitative findings across a large number of centers, there has been considerable variability in the exact numbers reported as quantitative outcome measures of tracer retention. In some cases this is as trivial as the choice of units, in some cases it is scanner dependent, and of course, different tracers yield different numbers. Our working group was formed to standardize quantitative amyloid imaging measures by scaling the outcome of each particular analysis method or tracer to a 0 to 100 scale, anchored by young controls (≤ 45 years) and typical Alzheimer's disease patients. The units of this scale have been named "Centiloids." Basically, we describe a "standard" method of analyzing PiB PET data and then a method for scaling any "nonstandard" method of PiB PET analysis (or any other tracer) to the Centiloid scale.

Characterization of pulsations in the brain and cerebrospinal fluid using ultra-high field magnetic resonance imaging.

The development of innovative non-invasive neuroimaging methods and biomarkers is critical for studying brain disease. Imaging of cerebrospinal fluid (CSF) pulsatility may inform the brain fluid dynamics involved in clearance of cerebral metabolic waste. In this work, we developed a methodology to characterize the frequency and spatial localization of whole brain CSF pulsations in humans. Using 7 Tesla (T) human magnetic resonance imaging (MRI) and ultrafast echo-planar imaging (EPI), in-vivo images were obtained to capture pulsations of the CSF signal. Physiological data were simultaneously collected and compared with the 7 T MR data. The primary components of signal pulsations were identified using spectral analysis, with the most evident frequency bands identified around 0.3, 1.2, and 2.4 Hz. These pulsations were mapped spatially and temporally onto the MR image domain and temporally onto the physiological measures of electrocardiogram and respiration. We identified peaks in CSF pulsations that were distinct from peaks in grey matter and white matter regions. This methodology may provide novel in vivo biomarkers of disrupted brain fluid dynamics.

Implementation and Assessment of Tau Thresholds in Non-Demented Individuals as Predictors of Cognitive Decline in Tau Imaging Studies.

Background: Tau accumulation in Alzheimer's disease is associated with short term clinical progression and faster rates of cognitive decline in individuals with high amyloid-ß deposition. Defining an optimal threshold of tau accumulation predictive of cognitive decline remains a challenge. Objective: We tested the ability of regional tau PET sensitivity and specificity thresholds to predict longitudinal cognitive decline. We also tested the predictive performance of thresholds in the proposed new NIA-AA biological staging for Alzheimer's disease where multiple levels of tau positivity are used to stage participants. Methods: 18F-flortaucipir scans from 301 non-demented participants were processed and sampled. Four cognitive measures were assessed longitudinally. Regional standardized uptake value ratios were split into infra- and suprathreshold groups at baseline using previously derived thresholds. Survival analysis, log rank testing, and Generalized Estimation Equations assessed the relationship between the application of regional sensitivity/specificity thresholds and change in cognitive measures as well as tau threshold performance in predicting cognitive decline within the new NIA-AA biological staging. Results: The meta temporal region was best for predicting risk of short-term cognitive decline in suprathreshold, as compared to infrathreshold participants. When applying multiple levels of tau positivity, each subsequent level of tau identified cognitive decline at earlier timepoints. Conclusions: When using 18F-flortaucipir, meta temporal suprathreshold classification was associated with increased risk of cognitive decline, suggesting that abnormal tau deposition in the cortex predicts decline. Likewise, the application of multiple levels of tau clearly predicts the distinctive cognitive trajectories in the new NIA-AA biological staging framework.

Characterization of oscillations in the brain and cerebrospinal fluid using ultra-high field magnetic resonance imaging.

Development of innovative non-invasive neuroimaging methods and biomarkers are critical for studying brain disease. In this work, we have developed a methodology to characterize the frequency responses and spatial localization of oscillations and movements of cerebrospinal fluid (CSF) flow in the human brain. Using 7 Tesla human MRI and ultrafast echo-planar imaging (EPI), in-vivo images were obtained to capture CSF oscillations and movements. Physiological data was simultaneously collected and correlated with the 7T MR data. The primary components of CSF oscillations were identified using spectral analysis (with frequency bands identified around 0.3Hz, 1.2Hz and 2.4Hz) and were mapped spatially and temporally onto the MR image domain and temporally onto the physiological domain. The developed methodology shows a good consistency and repeatability (standard deviation of 0.052 and 0.078 for 0.3Hz and 1.2Hz bands respectively) in-vivo for potential brain dynamics and CSF flow and clearance studies.

MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.

Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream analyses on it. Such a challenge calls for data normalization and/or harmonization frameworks, in addition to comprehensive criteria to estimate the scanner-related variability and evaluate the harmonization frameworks. In this study, we propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a supervised multi-scanner harmonization method that is naturally extendable to more than two scanners. We also designed a set of criteria to investigate the scanner-related technical variability and evaluate the harmonization techniques. As an essential requirement of our criteria, we introduced a multi-scanner matched dataset of 3T T1 images across four scanners, which, to the best of our knowledge is one of the few datasets of this kind. We also investigated our evaluations using two popular segmentation frameworks: FSL and segmentation in statistical parametric mapping (SPM). Lastly, we compared MISPEL to popular methods of normalization and harmonization, namely White Stripe, RAVEL, and CALAMITI. MISPEL outperformed these methods and is promising for many other neuroimaging modalities.

Biostatistical Estimation of Tau Threshold Hallmarks (BETTH) Algorithm for Human Tau PET Imaging Studies.

A methodology for determining tau PET thresholds is needed to confidently detect early tau deposition. We compared multiple threshold-determining methods in participants who underwent either 18F-flortaucipir or 18F-MK-6240 PET scans. Methods: 18F-flortaucipir (n = 798) and 18F-MK-6240 (n = 216) scans were processed and sampled to obtain regional SUV ratios. Subsamples of the cohorts were based on participant diagnosis, age, amyloid-ß status (positive or negative), and neurodegeneration status (positive or negative), creating older-adult (age ≥ 55 y) cognitively unimpaired (amyloid-ß-negative, neurodegeneration-negative) and cognitively impaired (mild cognitive impairment/Alzheimer disease, amyloid-ß-positive, neurodegeneration-positive) groups, and then were further subsampled via matching to reduce significant differences in diagnostic prevalence, age, and Mini-Mental State Examination score. We used the biostatistical estimation of tau threshold hallmarks (BETTH) algorithm to determine sensitivity and specificity in 6 composite regions. Results: Parametric double receiver operating characteristic analysis yielded the greatest joint sensitivity in 5 of the 6 regions, whereas hierarchic clustering, gaussian mixture modeling, and k-means clustering all yielded perfect joint specificity (2.00) in all regions. Conclusion: When 18F-flortaucipir and 18F-MK-6240 are used, Alzheimer disease-related tau status is best assessed using 2 thresholds, a sensitivity one based on parametric double receiver operating characteristic analysis and a specificity one based on gaussian mixture modeling, delimiting an uncertainty zone indicating participants who may require further evaluation.

CenTauR: Toward a universal scale and masks for standardizing tau imaging studies.

INTRODUCTION: Recently, an increasing number of tau tracers have become available. There is a need to standardize quantitative tau measures across tracers, supporting a universal scale. We developed several cortical tau masks and applied them to generate a tau imaging universal scale. METHOD: One thousand forty-five participants underwent tau scans with either 18F-flortaucipir, 18F-MK6240, 18F-PI2620, 18F-PM-PBB3, 18F-GTP1, or 18F-RO948. The universal mask was generated from cognitively unimpaired amyloid beta (Aß)- subjects and Alzheimer's disease (AD) patients with Aß+. Four additional regional cortical masks were defined within the constraints of the universal mask. A universal scale, the CenTauRz, was constructed. RESULTS: None of the regions known to display off-target signal were included in the masks. The CenTauRz allows robust discrimination between low and high levels of tau deposits. DISCUSSION: We constructed several tau-specific cortical masks for the AD continuum and a universal standard scale designed to capture the location and degree of abnormality that can be applied across tracers and across centers. The masks are freely available at https://www.gaain.org/centaur-project.

Timing of Alzheimer's Disease by Intellectual Disability Level in Down Syndrome.

BACKGROUND: Trisomy 21 causes Down syndrome (DS) and is a recognized cause of early-onset Alzheimer's disease (AD). OBJECTIVE: The current study sought to determine if premorbid intellectual disability level (ID) was associated with variability in age-trajectories of AD biomarkers and cognitive impairments. General linear mixed models compared the age-trajectory of the AD biomarkers PET Aß and tau and cognitive decline across premorbid ID levels (mild, moderate, and severe/profound), in models controlling trisomy type, APOE status, biological sex, and site. METHODS: Analyses involved adults with DS from the Alzheimer's Biomarkers Consortium-Down Syndrome. Participants completed measures of memory, mental status, and visuospatial ability. Premorbid ID level was based on IQ or mental age scores prior to dementia concerns. PET was acquired using [11C] PiB for Aß, and [18F] AV-1451 for tau. RESULTS: Cognitive data was available for 361 participants with a mean age of 45.22 (SD = 9.92) and PET biomarker data was available for 154 participants. There was not a significant effect of premorbid ID level by age on cognitive outcomes. There was not a significant effect of premorbid ID by age on PET Aß or on tau PET. There was not a significant difference in age at time of study visit of those with mild cognitive impairment-DS or dementia by premorbid ID level. CONCLUSION: Findings provide robust evidence of a similar time course in AD trajectory across premorbid ID levels, laying the groundwork for the inclusion of individuals with DS with a variety of IQ levels in clinical AD trials.

Direct Comparison of the Tau PET Tracers 18F-Flortaucipir and 18F-MK-6240 in Human Subjects.

Tau PET tracers exhibit varying levels of specific signal and distinct off-target binding patterns that are more diverse than amyloid PET tracers. This study compared 2 frequently used tau PET tracers, 18F-flortaucipir and 18F-MK-6240, in the same subjects. Methods:18F-flortaucipir and 18F-MK-6240 scans were collected within 2 mo in 15 elderly subjects varying in clinical diagnosis and cognition. FreeSurfer, version 5.3, was applied to 3-T MR images to segment Braak pathologic regions (I-VI) for PET analyses. Off-target binding was assessed in the choroid plexus, meninges, and striatum. SUV ratio (SUVR) outcomes were determined over 80-100 min (18F-flortaucipir) or 70-90 min (18F-MK-6240) normalized to cerebellar gray matter. Masked visual interpretation of images was performed by 5 raters for both the medial temporal lobe and the neocortex, and an overall (majority) rating was determined. Results: Overall visual ratings showed complete concordance between radiotracers for both the medial temporal lobe and the neocortex. SUVR outcomes were highly correlated (r2 > 0.92; P ≪ 0.001) for all Braak regions except Braak II. The dynamic range of SUVRs in target regions was approximately 2-fold higher for 18F-MK-6240 than for 18F-flortaucipir. Cerebellar SUVs were similar for 18F-MK-6240 and 18F-flortaucipir, suggesting that differences in SUVRs are driven by specific signals. Apparent off-target binding was observed often in the striatum and choroid plexus with 18F-flortaucipir and most often in the meninges with 18F-MK-6240. Conclusion: Both 18F-MK-6240 and 18F-flortaucipir are capable of quantifying signal in a common set of brain regions that develop tau pathology in Alzheimer disease; these tracers perform equally well in visual interpretations. Each also shows distinct patterns of apparent off-target binding. 18F-MK-6240 showed a greater dynamic range in SUVR estimates, which may be an advantage in detecting early tau pathology or in performing longitudinal studies to detect small interval changes.

Multi-scanner Harmonization of Paired Neuroimaging Data via Structure Preserving Embedding Learning.

Combining datasets from multiple sites/scanners has been becoming increasingly more prevalent in modern neuroimaging studies. Despite numerous benefits from the growth in sample size, substantial technical variability associated with site/scanner-related effects exists which may inadvertently bias subsequent downstream analyses. Such a challenge calls for a data harmonization procedure which reduces the scanner effects and allows the scans to be combined for pooled analyses. In this work, we present MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a multi-scanner harmonization framework. Unlike existing techniques, MISPEL does not assume a perfect coregistration across the scans, and the framework is naturally extendable to more than two scanners. Importantly, we incorporate our multi-scanner dataset where each subject is scanned on four different scanners. This unique paired dataset allows us to define and aim for an ideal harmonization (e.g., each subject with identical brain tissue volumes on all scanners). We extensively view scanner effects under varying metrics and demonstrate how MISPEL significantly improves them.

Acute Regression in Down Syndrome.

BACKGROUND: Acute regression has been reported in some individuals with Down syndrome (DS), typically occurring between the teenage years and mid to late 20s. Characterized by sudden, and often unexplained, reductions in language skills, functional living skills and reduced psychomotor activity, some individuals have been incorrectly diagnosed with Alzheimer's disease (AD). METHODS: This paper compares five individuals with DS who previously experienced acute regression with a matched group of 15 unaffected individuals with DS using a set of AD biomarkers. RESULTS: While the sample was too small to conduct statistical analyses, findings suggest there are possible meaningful differences between the groups on proteomics biomarkers (e.g., NfL, total tau). Hippocampal, caudate and putamen volumes were slightly larger in the regression group, the opposite of what was hypothesized. A slightly lower amyloid load was found on the PET scans for the regression group, but no differences were noted on tau PET. CONCLUSIONS: Some proteomics biomarker findings suggest that individuals with DS who experience acute regression may be at increased risk for AD at an earlier age in comparison to unaffected adults with DS. However, due to the age of the group (mean 38 years), it may be too early to observe meaningful group differences on image-based biomarkers.

Characterization of point-spread function specification error on Geometric Transfer Matrix partial volume correction in [11C]PiB amyloid imaging.

PURPOSE: Partial-volume correction (PVC) using the Geometric Transfer Matrix (GTM) method is used in positron emission tomography (PET) to compensate for the effects of spatial resolution on quantitation. We evaluate the effect of misspecification of scanner point-spread function (PSF) on GTM results in amyloid imaging, including the effect on amyloid status classification (positive or negative). METHODS: Twenty-nine subjects with Pittsburgh Compound B ([11C]PiB) PET and structural T1 MR imaging were analyzed. FreeSurfer 5.3 (FS) was used to parcellate MR images into regions-of-interest (ROIs) that were used to extract radioactivity concentration values from the PET images. GTM PVC was performed using our "standard" PSF parameterization [3D Gaussian, full-width at half-maximum (w) of approximately 5 mm]. Additional GTM PVC was performed with "incorrect" parameterizations, taken around the correct value. The result is a set of regional activity values for each of the GTM applications. For each case, activity values from various ROIs were combined and normalized to produce standardized uptake value ratios (SUVRs) for nine standard [11C]PiB quantitation ROIs and a global region. GTM operating-point characteristics were determined from the slope of apparent SUVR versus w curves. RESULTS: Errors in specification of w on the order of 1 mm (3D) mainly produce only modest errors of up to a few percent. An exception was the anterior ventral striatum in which fractional errors of up to 0.29 per millimeter (3D) of error in w were observed. CONCLUSION: While this study does not address all the issues regarding the quantitative strengths and weakness of GTM PVC, we find that with reasonable caution, the unavoidable inaccuracies associated with PSF specification do not preclude its use in amyloid quantitation.

MULTI-DOMAIN LEARNING BY META-LEARNING: TAKING OPTIMAL STEPS IN MULTI-DOMAIN LOSS LANDSCAPES BY INNER-LOOP LEARNING.

We consider a model-agnostic solution to the problem of Multi-Domain Learning (MDL) for multi-modal applications. Many existing MDL techniques are model-dependent solutions which explicitly require nontrivial architectural changes to construct domain-specific modules. Thus, properly applying these MDL techniques for new problems with well-established models, e.g. U-Net for semantic segmentation, may demand various low-level implementation efforts. In this paper, given emerging multi-modal data (e.g., various structural neuroimaging modalities), we aim to enable MDL purely algorithmically so that widely used neural networks can trivially achieve MDL in a model-independent manner. To this end, we consider a weighted loss function and extend it to an effective procedure by employing techniques from the recently active area of learning-to-learn (meta-learning). Specifically, we take inner-loop gradient steps to dynamically estimate posterior distributions over the hyperparameters of our loss function. Thus, our method is model-agnostic, requiring no additional model parameters and no network architecture changes; instead, only a few efficient algorithmic modifications are needed to improve performance in MDL. We demonstrate our solution to a fitting problem in medical imaging, specifically, in the automatic segmentation of white matter hyperintensity (WMH). We look at two neuroimaging modalities (T1-MR and FLAIR) with complementary information fitting for our problem.

ROBUST WHITE MATTER HYPERINTENSITY SEGMENTATION ON UNSEEN DOMAIN.

Typical machine learning frameworks heavily rely on an underlying assumption that training and test data follow the same distribution. In medical imaging which increasingly begun acquiring datasets from multiple sites or scanners, this identical distribution assumption often fails to hold due to systematic variability induced by site or scanner dependent factors. Therefore, we cannot simply expect a model trained on a given dataset to consistently work well, or generalize, on a dataset from another distribution. In this work, we address this problem, investigating the application of machine learning models to unseen medical imaging data. Specifically, we consider the challenging case of Domain Generalization (DG) where we train a model without any knowledge about the testing distribution. That is, we train on samples from a set of distributions (sources) and test on samples from a new, unseen distribution (target). We focus on the task of white matter hyperintensity (WMH) prediction using the multi-site WMH Segmentation Challenge dataset and our local in-house dataset. We identify how two mechanically distinct DG approaches, namely domain adversarial learning and mix-up, have theoretical synergy. Then, we show drastic improvements of WMH prediction on an unseen target domain.

Imaging beta-amyloid (Aß) burden in the brains of middle-aged individuals with alcohol-use disorders: a [11C]PIB PET study.

No in vivo human studies have examined the prevalence of Alzheimer's disease (AD) neuropathology in individuals with alcohol-use disorder (AUD), although recent research suggests that a relationship between the two exists. Therefore, this study used Pittsburgh Compound-B ([11C]PiB) PET imaging to test the hypothesis that AUD is associated with greater brain amyloid (Aß) burden in middle-aged adults compared to healthy controls. Twenty healthy participants (14M and 6F) and 19 individuals with AUD (15M and 4F), all aged 40-65 years, underwent clinical assessment, MRI, neurocognitive testing, and positron emission tomography (PET) imaging. Global [11C]PiB standard uptake value ratios (SUVRs), cortical thickness, gray matter volumes (GMVs), and neurocognitive function in subjects with AUD were compared to healthy controls. These measures were selected because they are considered markers of risk for future AD and other types of neurocognitive dysfunction. The results of this study showed no significant differences in % global Aß positivity or subthreshold Aß loads between AUD and controls. However, relative to controls, we observed a significant 6.1% lower cortical thickness in both AD-signature regions and in regions not typically associated with AD, lower GMV in the hippocampus, and lower performance on tests of attention as well as immediate and delayed memory in individuals with AUD. This suggest that Aß accumulation is not greater in middle-aged individuals with AUD. However, other markers of neurodegeneration, such as impaired memory, cortical thinning, and reduced hippocampal GMV, are present. Further studies are needed to elucidate the patterns and temporal staging of AUD-related pathophysiology and cognitive impairment. Imaging ß-amyloid in middle age alcoholics as a mechanism that increases their risk for Alzheimer's disease; Registration Number: NCT03746366 .

Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach.

BACKGROUND: Inconsistent positivity thresholds, image analysis pipelines, and quantitative outcomes are key challenges of multisite studies using more than one ß-amyloid (Aß) radiotracer in positron emission tomography (PET). Variability related to these factors contributes to disagreement and lack of replicability in research and clinical trials. To address these problems and promote Aß PET harmonization, we used [18F]florbetaben (FBB) and [18F]florbetapir (FBP) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to derive (1) standardized Centiloid (CL) transformations and (2) internally consistent positivity thresholds based on separate young control samples. METHODS: We analyzed Aß PET data using a native-space, automated image processing pipeline that is used for PET quantification in many large, multisite AD studies and trials and made available to the research community. With this pipeline, we derived SUVR-to-CL transformations using the Global Alzheimer's Association Interactive Network data; we used reference regions for cross-sectional (whole cerebellum) and longitudinal (subcortical white matter, brain stem, whole cerebellum) analyses. Finally, we developed a FBB positivity threshold using an independent young control sample (N=62) with methods parallel to our existing FBP positivity threshold and validated the FBB threshold using a data-driven approach in ADNI participants (N=295). RESULTS: The FBB threshold based on the young sample (1.08; 18 CL) was consistent with that of the data-driven approach (1.10; 21 CL), and the existing FBP threshold converted to CL with the derived transformation (1.11; 20 CL). The following equations can be used to convert whole cerebellum- (cross-sectional) and composite- (longitudinal) normalized FBB and FBP data quantified with the native-space pipeline to CL units: [18F]FBB: CLwhole cerebellum = 157.15 × SUVRFBB - 151.87; threshold=1.08, 18 CL [18F]FBP: CLwhole cerebellum = 188.22 × SUVRFBP - 189.16; threshold=1.11, 20 CL [18F]FBB: CLcomposite = 244.20 × SUVRFBB - 170.80 [18F]FBP: CLcomposite = 300.66 × SUVRFBP - 208.84 CONCLUSIONS: FBB and FBP positivity thresholds derived from independent young control samples and quantified using an automated, native-space approach result in similar CL values. These findings are applicable to thousands of available and anticipated outcomes analyzed using this pipeline and shared with the scientific community. This work demonstrates the feasibility of harmonized PET acquisition and analysis in multisite PET studies and internal consistency of positivity thresholds in standardized units.