Jagust, William J.

Abstract The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to optimize and validate biomarkers for clinical trials while sharing all data and biofluid samples with the global scientific community. ADNI has been instrumental in standardizing and validating amyloid beta (Aβ) and tau positron emission tomography (PET) imaging. ADNI data were used for the US Food and Drug Administration (FDA) approval of the Fujirebio and Roche Elecsys cerebrospinal fluid diagnostic tests. Additionally, ADNI provided data for the trials of the FDA‐approved treatments aducanumab, lecanemab, and donanemab. More than 6000 scientific papers have been published using ADNI data, reflecting ADNI's promotion of open science and data sharing. Despite its enormous success, ADNI has some limitations, particularly in generalizing its data and findings to the entire US/Canadian population. This introduction provides a historical overview of ADNI and highlights its significant accomplishments and future vision to pioneer “the clinical trial of the future” focusing on demographic inclusivity. Highlights The Alzheimer's Disease Neuroimaging Initiative (ADNI) introduced a novel model for public‐private partnerships and data sharing. It successfully validated amyloid and Tau PET imaging, as well as CSF and plasma biomarkers, for diagnosing Alzheimer's disease. ADNI generated and disseminated vital data for designing AD clinical trials.

Abstract INTRODUCTION A key goal of the Alzheimer's Disease NeuroImaging Initiative (ADNI) positron emission tomography (PET) Core is to harmonize quantification of β‐amyloid (Aβ) and tau PET image data across multiple scanners and tracers. METHODS We developed an analysis pipeline (Berkeley PET Imaging Pipeline, B‐PIP) for ADNI Aβ and tau PET images and applied it to PET data from other multisite studies. Steps include image pre‐processing, refacing, magnetic resonance imaging (MRI)/PET co‐registration, visual quality control (QC), quantification of tracer uptake, and standardization of Aβ and tau standardized uptake value ratios (SUVrs) across tracers. RESULTS Measurements from 10,105 cross‐sectional and longitudinal Aβ and tau PET scans acquired in several studies between 2010 and 2024 can be processed, harmonized, and directly merged across tracers and cohorts. DISCUSSION The B‐PIP developed in ADNI is a scalable image harmonization approach used in several observational studies and clinical trials that facilitates rigorous Aβ and tau PET quantification and data sharing. Highlights Quantitative results from ADNI Aβ and tau PET data are generated using a rigorous, scalable image processing pipeline This pipeline has been applied to PET data from several other large, multisite studies and trials Quantitative outcomes are harmonizable across studies and are shared with the scientific community

Abstract INTRODUCTION Tau‐positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS Using head‐to‐head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed “CenTauR” [CTR]). JPM is a statistical model that simultaneously models the relationships between head‐to‐head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION Preliminary findings using the JPM support the development and adoption of a universal scale for tau‐PET quantification. Highlights Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed “CenTauRs”. Tested a Centiloid‐like linear regression approach. Using five cohorts with head‐to‐head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.

Abstract The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step‐by‐step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. Highlights We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early‐changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later‐changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.

AbstractBackgroundA key focus of the U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is recruitment of unimpaired older individuals at risk for cognitive decline with heterogeneous characteristics (under‐represented racial and ethnicity groups (URG), family history of cognitive decline, hypertension, geographic diversity), and a subset of individuals undergo PET and MRI.MethodTo examine effects of heterogeneity on relationships between risk factors, AD biomarkers (Aβ and entorhinal tau PET, hippocampal volume (HV)) and cognition (global, executive function, and memory) were examined from U.S. POINTER neuroimaging participants (N = 602, about 65% of the anticipated total sample) and a subset of Alzheimer’s Disease Neuroimaging Initiative participants (N = 503). Neuroimaging data were processed and analyzed using harmonized methods across studies. Some cohort‐specific measurements were standardized using a within‐cohort reference group (Aβ‐negative individuals <70yrs and with global CDR = 0).ResultU.S. POINTER imaging participants are younger than those in ADNI (68.6+/‐5.0yrs vs 70.7+/‐4.4yrs), more likely to be female (65% vs 58%) and from URG (27% vs 15%), and had lower education (14.7+/‐3.3yrs vs 16.7+/‐2.3yrs) and higher Framingham Cardiovascular Risk scores (9.9+/‐8.3 vs 7.7+/‐5.3). The groups differed on several AD biomarkers and cognitive measures (Table). In regression models, AD biomarkers were stronger predictors of cognitive performance in ADNI compared to POINTER. Specifically, Aβ+ individuals with elevated tau had disproportionately poorer cognitive performance in ADNI, and this relationship remained consistent across cognitive measures (Figure). Conversely, demographic/risk variables were stronger predictors in POINTER. Demographic/risk predictors (age, sex, education, race, and FRS) together explained twice as much variance in global cognition in POINTER compared to ADNI (Adjusted R2 = 29% vs 14%). The addition of AD biomarkers to the model further increased the amount of variance in global cognition explained from 14% to 34% in ADNI, but addition of AD biomarkers did not increase variance explained in the POINTER model.ConclusionHeterogeneity among study participants appears to influence baseline biomarker‐cognition relationships such that AD biomarkers explain more variability in cognitive performance in ADNI than POINTER. These findings further suggest that cohort characteristics may affect the potential influence of amyloid‐ and tau‐modifying treatments on cognition.

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aβ)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aβ‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aβ‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aβ‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aβ‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aβ‐PET data across 4 tracers in CLs, there were expected differences in Aβ burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.

AbstractBackgroundThe U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is a 2‐year randomized controlled trial to evaluate the effect of lifestyle interventions in cognitively unimpaired older adults (60‐79 years) at risk for decline/dementia. Here, using data from the POINTER Imaging ancillary substudy, we explore how Alzheimer’s disease pathology and cardiovascular risk influence brain structure, cognition and their relationship.MethodPOINTER Imaging participants underwent cross‐sectional MRI, [18F]Florbetaben (FBB) Aβ‐PET and [18F]MK6240 tau‐PET. FBB and MK6240 data were processed using previously validated methods. MRIs were processed with voxel‐based morphometry. To quantify cardiovascular risk, the Framingham risk score (FRS) was calculated for each participant. We used linear regression models in the whole sample as well as in Aβ status subgroups to evaluate voxel‐wise associations of brain structure, measured using voxel‐based morphometry, with age, FRS, AD pathology (cortical summary Aβ and entorhinal cortex (ERC) tau burden), and a global cognition composite score.ResultAcross all 624 participants (Table 1), cognition (age‐adjusted) was positively related to brain structure across temporal, frontal and medial parietal cortices (Fig. 1A). ERC tau‐PET was negatively associated with brain structure in bilateral medial temporal lobes (MTL) and, notably, in bilateral basal forebrain (Fig. 1B). FRS showed significant negative associations in superior temporal gyrus and occipital cortex (Fig. 1C), but tau‐PET associations remained similar when adjusting for FRS. In Aβ+ participants, cognition‐ and FRS‐structure relationships were weaker but tau‐PET‐structure associations were stronger and extended outside the MTL. In Aβ‐ participants, cognition‐ and FRS‐structure relationships were similar to those observed in the whole cohort and tau‐PET associations were no longer significant.ConclusionIn this heterogeneous cohort of older adults, tau was associated with brain structure in strikingly specific patterns that were related to Aβ but not to cardiovascular risk as measured by the FRS. Tau‐structure associations were restricted to temporal lobes and basal forebrain and were independent of more widespread cognition‐structure associations, indicating that factors other than tau may play a critical role. Our next steps include comparing the current cohort to others with different at‐risk characteristics (e.g., ADNI, Berkeley Aging Cohort Study).

AbstractBackgroundA key focus of the U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (U.S. POINTER) is recruitment of unimpaired older individuals at risk for cognitive decline with heterogeneous characteristics (under‐represented racial and ethnicity groups (URG), family history of cognitive decline, hypertension, geographic diversity), and a subset of individuals undergo PET and MRI.MethodTo examine effects of heterogeneity on relationships between risk factors, AD biomarkers (Aß and entorhinal tau PET, hippocampal volume (HV)) and cognition (global, executive function, and memory) were examined from U.S. POINTER neuroimaging participants (N = 602, about 65% of the anticipated total sample) and a subset of Alzheimer’s Disease Neuroimaging Initiative participants (N = 503). Neuroimaging data were processed and analyzed using harmonized methods across studies. Some cohort‐specific measurements were standardized using a within‐cohort reference group (Aß‐negative individuals <70yrs and with global CDR = 0).ResultU.S. POINTER imaging participants are younger than those in ADNI (68.6+/‐5.0yrs vs 70.7+/‐4.4yrs), more likely to be female (65% vs 58%) and from URG (27% vs 15%), and had lower education (14.7+/‐3.3yrs vs 16.7+/‐2.3yrs) and higher Framingham Cardiovascular Risk scores (9.9+/‐8.3 vs 7.7+/‐5.3). The groups differed on several AD biomarkers and cognitive measures (Table). In regression models, AD biomarkers were stronger predictors of cognitive performance in ADNI compared to POINTER. Specifically, Aß+ individuals with elevated tau had disproportionately poorer cognitive performance in ADNI, and this relationship remained consistent across cognitive measures (Figure). Conversely, demographic/risk variables were stronger predictors in POINTER. Demographic/risk predictors (age, sex, education, race, and FRS) together explained twice as much variance in global cognition in POINTER compared to ADNI (Adjusted R2 = 29% vs 14%). The addition of AD biomarkers to the model further increased the amount of variance in global cognition explained from 14% to 34% in ADNI, but addition of AD biomarkers did not increase variance explained in the POINTER model.ConclusionHeterogeneity among study participants appears to influence baseline biomarker‐cognition relationships such that AD biomarkers explain more variability in cognitive performance in ADNI than POINTER. These findings further suggest that cohort characteristics may affect the potential influence of amyloid‐ and tau‐modifying treatments on cognition.

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aß)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aß‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aß‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aß‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aß‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aß‐PET data across 4 tracers in CLs, there were expected differences in Aß burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.