Jack, Clifford R.

Abstract Background Amyloid related imaging abnormality edema (ARIA‐E) occurs in about 19% of individuals with autosomal dominant Alzheimer disease (ADAD) treated with an anti‐amyloid‐b monoclonal antibody (Salloway et al., 2021). In a previously reported case, ARIA‐E appeared to colocalize with decreases in PiB‐PET uptake (Joseph‐Mathurin, Llibre‐Guerra, et al, 2022), suggesting an association between amyloid‐b (Aβ) removal and ARIA‐E. Here, we compare longitudinal neuroimaging and corresponding neuropathology in an ADAD individual treated with gantenerumab, who experienced multiple ARIA‐E episodes as sulcal effusions that resolved by the end of the four‐year trial. Methods PiB‐PET and MRI were collected pre‐ and post‐randomization (2 and 4 years). The participant consented to brain donation, which was received a year after trial completion. Four tissue samples were taken from the location of the ARIA‐E in the parieto‐occipital left hemibrain as identified by MRI (one block contained the sulcus affected by ARIA‐E; the rest of the coronal section was captured in the remaining three blocks). Area fractions (AFs) of Aβ (10D5 immunohistochemistry (IHC)), tauopathy (PHF1), microglia (Iba1), and astrocytes (GFAP) in one sulcal region of interest (ROI) from each block and PiB‐PET SUVRs of corresponding ROIs were extracted ( n = 4, Figure 1). Additionally, we included PiB‐PET SUVRs corresponding to ARIA‐E findings observed in the right hemisphere at baseline, two‐year, and four‐year visits ( n = 6). Results Overall PiB‐PET uptake increased during the first two years (before ARIA‐E) and decreased during the last two years (including ARIA‐E episodes) (0.03±0.05 vs. ‐0.04±0.04 SUVR/year, p ‐value=0.01, Figure 2). The decrease seemed more pronounced in ARIA‐E ROIs versus normal‐appearing sulcus ROIs (‐0.05±0.04 vs. ‐0.02±0.03 SUVR/year). Lower PiB uptake at last visit appeared associated with lower Aβ AF (estimates=0.03±0.01, p ‐value=0.07). The ARIA‐E ROI had an Aβ AF of 0.009 while normal‐appearing sulcus ROIs had a mean of 0.02+0.003 (Figure 3). AFs for tauopathy, microglia, and astrocytes were within the range of those of normal‐appearing sulcus ROIs, suggesting an Aβ‐specific effect, although unexamined markers may play a role. Conclusions Our preliminary findings indicated that ARIA‐E is associated with longitudinal PiB‐PET decrease and Aβ AF, supporting the link between ARIA‐E, changes in PiB‐PET, and local Aβ removal observed at autopsy. Funding : K01AG080123; U01AG042791; R01AG046179; R01AG053267

Abstract Background The IWG and AA diagnostic criteria are major references in the Alzheimer's domain. Although they share analogies, some key differences should be recognized. Method The original IWG 2024 JAMA Neurology and AA 2024 Alzheimers Dement papers will be used as the major data references. Result I will outline the similarities and differences between the IWG and the AA criteria for Alzheimer's disease from the point of view of the former. I will discuss similarities regarding: Aim of clinical research on AD and other cognitive disorders, Role of co‐pathology, Role of brain reserve, Biomarker use, Indication for anti‐β‐amyloid, Contra‐indication for anti‐β‐amyloid, and Health care delivery model. I will discuss differences regarding: Core question, Scientific discourse, Knowledge source, Definition of AD, Diagnosis of AD, and Interventions for AD. Conclusion The differences between the two sets of criteria are not “just semantics”. A unified definition of AD is essential to advance the field and improve patient care.

Abstract Background Plasma neurofilament light chains (NfL) and glial fibrillary acidic protein (GFAP) are blood‐based biomarkers of axonal injury and astrocytic activation, respectively, that are elevated in those with Alzheimer's disease (AD) and mild cognitive impairment (MCI) relative to cognitively unimpaired (CU) individuals. Neurite orientation dispersion and density imaging (NODDI) is a multishell diffusion MRI method capable of evaluating tissue microstructure at the axonal and dendritic level. We evaluated associations between temporal cortex orientation dispersion index (ODI), neurite density index (NDI) and isotropic volume fraction (ISOVF) and plasma NfL and GFAP concentrations. Method We included Mayo Clinic Study of Aging (MCSA) participants (ages 60‐95 years) with diagnoses or CU ( n = 356) or MCI ( n = 60), and Alzheimer's Dementia Research Center (ADRC) participants with dementia ( n = 51). NODDI imaging was obtained on a 3T Siemens Prisma scanner with 2 mm isotropic resolution. NfL and GFAP were measured using the Simoa® Neurology 4‐Plex E Advantage kit. We fit separate linear regression models within each MCALT_ADIR122 cortical ROI with regional NODDI as the primary predictor and log‐transformed plasma concentration as the outcome. Models were adjusted for age, sex, BMI, cortical thickness (or hippocampal & amygdalar volume) and total intracranial volume. Result The Table displays demographic data. Figures 1 and 2 show the relationship of NDI and ISOVF measures to plasma NfL concentrations across participants in individual ROIs (similar findings were noted with GFAP). A significant inverse relationship between NfL concentrations and NDI was observed in hippocampus, amygdala, entorhinal cortex, parahippocampal gyrus as well as all temporal neocortical regions except the superior temporal gyrus ( p <0.001) (Figure 1), whereas higher NfL concentrations were associated with higher ISOVF in all regions ( p <0.001) (Figure 2). Fewer significant associations were also seen with ODI. After adjusting for age, cortical thickness/volume and other confounds, significant associations with NDI remain, particularly in hippocampal and amygdalar regions. Conclusion NODDI‐derived measures (NDI and ISOVF) within the temporal lobe cortical regions demonstrate significant associations with plasma biomarkers of axonal and astrocytic dysfunction that are elevated during cognitive decline. This association provides preliminary validation of the utility of NODDI in assessing cortical microstructural changes associated with cognitive decline.

Abstract Background Neuroinflammation plays a crucial role in neuronal damage, synaptic dysfunction, and impaired neurogenesis, significantly contributing to Alzheimer's Disease (AD) progression. To address this, we assessed ER176's effectiveness in detecting neuroinflammation in cognitively unimpaired (CU) A–, CU A+, mild cognitive impairment (MCI), and AD participants, with a particular focus on their amyloid and tau status. This research aims to help identify suitable candidates for specialized treatment when adversely impacted by neuroinflammation. Method Participants from the Mayo Clinic Study of Aging were recruited for this study. The study included four participant groups: CU A– ( n = 25), CU A+ ( n = 25), MCI A+ ( n = 25), and AD ( n = 15). We explored the variations in neuroinflammation PET results among different groups. Result When compared to CU A–, both CU A+ and MCI A+ groups (Figure 1) showed greater regional neuroinflammation. AD exhibited more widely affected regions with neuroinflammation (temporal, occipital, and parietal lobes) than CU A‐ over the PVC spectrum (Figure 1). Minimally more neuroinflammation was seen (temporal lobes) in MCI A+ than CU A+ (Figure 1). AD had more widespread regional neuroinflammation vs CU A+ (Figure 1) than in MCI A+ vs CU+. The least difference in neuroinflammation is seen in MCI A+ vs AD (Figure 1). For A+ vs A‐, A+ showed more widespread neuroinflammation (Figure 2). T+ (tau>=1.29) vs T‐ (tau<1.29) showed more neuro inflammation with T+ (Figure 2). Comparisons varied modestly in significance level with PVC method (Figures 1 and 2). Conclusion The findings demonstrate that neuroinflammatory changes are associated with AD pathology. Even at early stages, neuroinflammation is associated with CU+. As the condition progresses, neuroinflammation extends to more brain regions and is associated with increased A+ and T+ pathology in MCI A+ and AD. These results suggest an early and persistent role of neuroinflammation in the progression AD pathology, indicating that therapy options could include early anti‐inflammatory intervention, even prior to clinical symptoms, along with treatment during clinical manifestations. PVC methods used may provide somewhat variable results.

Abstract Background Neuroimaging data typically arrives as Digital Imaging and Communications in Medicine (DICOM) files, batched by exam. We use exam or study to denote a single imaging session of a participant, containing up to ∼40 series (the generic term for scans and derived products). Different series have different properties and applications, making their labeling complex. Filenames often change in transit, and Series Description tags (0008, 103E) are arbitrary, operator‐entered, and unreliably coupled to the series type. Furthermore, label preferences vary from site to site, making the application of a common classification system mandatory for multisite studies. Here we describe the system that we have developed and applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) and SCAN. Method Although Series Description is unreliable, DICOM includes other tags that parameterize MR pulse sequences and suggest their purpose. The mapping from these features to types is difficult to explicitly specify, especially since different manufacturers and protocols use different conventions. Thus we developed a machine learning classifier using 4060 semiautomatically labeled head and spinal series from 1.5 to 7T MR scanners, including both site‐specific and standard protocols designed to study a wide range of neurological conditions, including dementia, hydrocephalus, and multiple sclerosis. An 80%/20% split, stratified by type, was used for the training/test sets. After initial training, a few cases in the training set with high errors due to rarity were augmented and the model retrained. Performance was evaluated on the clean test set. Result The taxonomy is presented in Table 1. The classifier correctly identified 807 out of 812 (99.4%) series, with the error cases coming from outside the ADNI or SCAN protocols (Table 2). The confusion matrix is shown in Figure 1. Conclusion We implemented an automatic series type classifier with labels chosen to be generally useful for the brain MRI community. Some errors were noted, but the test set was deliberately challenging and the errors appear to be ones that could easily be made by a human. The classifier is expected to have good tolerance for as yet unseen head MR protocols but is likely unsatisfactory for body MRI.

Abstract Background Decreased kidney function, indicated by a lower estimated glomerular filtration rate (eGFR), may affect interpretation of Alzheimer's disease (AD) blood biomarkers, including phosphorylated tau ( p ‐tau) and amyloid‐beta (Aβ) peptides, due to altered clearance. However, there is limited data available to determine the influence of eGFR on these biomarkers and whether it differs by biomarker or assay type, especially in racially diverse studies of community‐based older adults. Methods We used data from 557 participants in the Mayo Clinic Study of Aging (Rochester, MN n = 182; age 79±8 years; 36.3% Female, White participants) and the UMMC MIND Center‐Mayo Clinic Study of Aging (Jackson, MS n = 375; age 69±8 years; 73% Female 27% Black participants). eGFR was determined using the 2021 CKD‐EPI Creatinine equation. Plasma biomarkers were measured using the Lumipulse immunoassays for p ‐tau217, Aβ42, and Aβ40 (Fujirebio Diagnostics; n = 420); or the mass spectrometry‐based PrecivityAD2 assay (C2N Diagnostics; n = 253) for p ‐tau217, np‐tau217, Aβ42, and Aβ40. Common ratio values were calculated. Aβ42/40 measures were inverted so higher values are worse for all biomarkers. Log base‐2 transformed regression analysis related eGFR to each single analyte and ratio; coefficients (95% CI) reflect estimated doubling in biomarker per doubling in eGFR. Group specific [MN White ( n = 182), MS White ( n = 224), MS Black ( n = 151) participants] estimates are reported, as the distribution of social determinants of health can vary across populations and locations. Results Lower eGFR was associated with higher (worse) AD‐plasma biomarkers, and group stratified estimates were mostly consistent (Figure 1). Ratios of biomarkers diminished but did not entirely alleviate associations with eGFR. For example, MN White participants’ C2N p ‐tau217 association of ‐1.47 (‐1.94, ‐1.00), was attenuated to ‐0.67 (‐1.11, ‐0.22), when using p ‐tau217/np‐tau217. Conclusions Kidney function may be associated with AD‐plasma biomarker values, even with common ratio corrections. There was not strong evidence for highly differential associations of eGFR by race/site categories, though sample sizes are currently limited. The implications for diagnosis and risk classification need to be explored further to support health equity. Optimal consideration of comorbidities on blood biomarker concentrations must continue to be informed by investigation in large, racially and ethnically diverse, community‐based studies.

Abstract Background Plasma biomarkers show promise as a noninvasive method for measuring Alzheimer's disease pathology and neurodegeneration throughout the lifecourse. However, the relationship between longitudinal changes in these biomarkers and late‐life brain morphology in community‐dwelling populations requires further investigation. Method Between 2011 and 2013, 1,977 participants from the Atherosclerosis Risk in Communities Study received an adjudicated cognitive diagnosis and underwent 3 Tesla magnetic resonance imaging scans. Whole‐brain cortical thickness was quantified by Freesurfer. Stored plasma samples collected in midlife (1993‐95, mean age 59.0 years) and late‐life (2011‐13, mean age 76.8 years) from a subsample of 1,515 participants were assayed in 2022 using Quanterix SiMoA. The assay quantified amyloid‐β (Aβ)42/40, phosphorylated tau at threonine 181 ( p ‐Tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Linear regression models estimated the association of plasma biomarkers from midlife, late‐life, and the change from midlife to late‐life with cortical thickness in late‐life. Interactions and effect modification by age were examined in exploratory analyses. Result The sample (Table 1) included 920 (60.7%) women, 387 (25.5%) Black participants, 513 (33.9%) participants with mild cognitive impairment, and 85 (5.6%) participants with dementia. The average age in late‐life was 76.8 (5.3 SD) years and the average cortical thickness was 2.27 (0.11 SD) millimeters. Cortical thickness was lower in individuals with adjudicated mild cognitive impairment or dementia compared to individuals without cognitive impairment (2.25 mm versus 2.29 mm, p <0.0001). In models adjusted for demographics, lifestyle factors, cardiovascular factors, and the presence of APOE ε4 alleles, higher midlife measures of p ‐Tau181 and increases from midlife to late‐life in NfL and GFAP were associated with lower cortical thickness in late‐life (Table 2). Concurrent measures of plasma biomarkers in late‐life were associated with lower cortical thickness (Table 2) and exhibited a stronger association among participants ≥75 years compared to <75 years (Table 3). Conclusion Plasma biomarkers of Alzheimer's disease pathology and neurodegeneration obtained in midlife and late‐life were associated with late‐life differences in cortical thickness. Additional longitudinal research is needed to determine whether plasma biomarkers predict cortical thinning over time and may serve as an early indicator of accelerated brain atrophy.

Abstract Background Sex, education and race/ethnicity are all associated with risk of Alzheimer's disease dementia. Here, we assess the effects of self‐reported sex, educational attainment and race/ethnicity on amyloid‐positivity, and tau‐PET‐positivity in 12,048 (7,394 cognitively unimpaired [CU], 2,177 MCI, and 2,477 dementia) individuals from 42 cohorts worldwide. Method Logistic generalized estimating equations were used to estimate frequency of amyloid‐positivity (using cohort‐specific thresholds for amyloid‐PET [84%] or CSF) and tau‐PET‐positivity (cohort‐specific thresholds of 2SD above mean temporal uptake in amyloid‐negative controls). We assessed: i) sex and APOEε4 ( N = 10,098) associations, to complement earlier findings of a higher frequency of tau‐positivity in females, ii) effects of lower/higher education ( N = 10,970; cohort‐specific median‐split), and iii) effects of race/ethnicity (non‐Hispanic White [hereafter: White], N = 4880; Asian, N = 116; Black or African‐American [hereafter: Black], N = 353; Hispanic, N = 356, only from Northern‐American cohorts). Outcomes were frequency of amyloid‐positivity in CU individuals only, and tau‐PET‐positivity in both amyloid‐positive (AB+) CU and cognitively impaired (CI, i.e. MCI and dementia) individuals. Interaction effects on the relationship between age and amyloid/tau‐positivity were assessed and only retained in the models when significant. Result Female sex was associated with an APOEε4 ‐independent increased frequency of amyloid‐positivity (β=0.51[0.22], p = 0.02) in CU and increase of tau‐positivity in both AB+CU (β=0.27[0.08]) and AB+CI (β=0.37[0.08], both p <0.01). Remarkably, tau‐positivity frequencies of female APOEε4 non‐carriers were equivalent to male APOEε4 carriers in AB+CI (Figure 1). No significant sex* APOE interactions were observed. In CU, higher education was associated with lower amyloid‐positivity frequency (β=‐0.12[0.05], p = 0.02). In contrast, among AB+CU, there was an age*education interaction effect that indicated more pronounced age effects on tau‐positivity in individuals with higher education (age*education:β interaction =0.03[0.01], p <0.01). There were no education effects in AB+CI (Figure 2). In CU, an age*race/ethnicity interaction effect was observed across all non‐White groups compared to White (Hispanic:β interaction =‐0.05[0.01], p <0.01; Black:‐0.04[0.01], p <0.01; Asian:‐0.02[0.01], p = 0.04). This suggests that the impact of age on amyloid‐positivity was less pronounced in non‐White groups. Furthermore, in AB+CI, Hispanic ethnicity was related to higher tau‐positivity frequency than White (β=0.51[0.22], p = 0.02; Figure 3). Conclusion In this multi‐center initiative comprised of clinical and community‐based cohorts, we observed that self‐reported sex, educational attainment and race/ethnicity were related to positivity‐frequencies of Alzheimer's disease pathology.

Abstract Background The Overlap Index (OI) as previously published (Lee et al., 2022) is a reliable method for detecting tau accumulation using tau‐PET imaging (flortaucipir; AV1451). It identifies voxel‐wise standardized uptake value ratio (SUVr) elevations across serial scans. Although OI showed more sensitivity to tau signal elevation and longitudinal change than standard ROI measures, to our knowledge, the relationship of the tau PET overlap index to neuropathologic findings is still unclear. This study aims to investigate the potential of OI to detect NFTs of the neuropathologic Braak tangle stage, especially the early Braak tangle stage (I–IV). Method This study included 57 participants from the Mayo Clinic Study of Aging or Mayo Alzheimer's Disease Research Center, all of whom underwent autopsy and at least two serial AV1451 tau‐PET scans and 3T T1‐weighted MRI within an average of 16 months. Tau‐PET SUVr images were normalized to cerebellar crus grey matter, and MR images were co‐registered to a single‐subject MRI mean. Tau‐PET scans were resampled into MRI space, and OI was calculated by applying intensity thresholds 1.3. Clusters with fewer than 20 contiguous voxels were excluded. An OI cutoff of 0.5 was used and an SUVr cutoff of 1.29. Postmortem immunohistochemistry (IHC) using phospho‐tau antibodies (AT8, PHF‐1) was performed on the amygdala, temporal cortex (middle and superior), and anterior/posterior hippocampus. Neuropathological Braak staging was determined based on AT8‐stained sections. We conducted visual comparison of tau‐PET overlap signal in these regions with IHC‐confirmed tau pathology across Braak tangle stages. Result OI‐detected voxels corresponded to NFT distribution across Braak stages. In early Braak stages, standard SUVr‐based META‐ROI values remained negative, whereas OI showed positive signals, indicating greater sensitivity to early tau pathology (Figure 1). The spatial distribution of OI‐derived overlap regions aligned with high NFT burden, suggesting that OI captures early‐stage tau pathology more effectively than conventional SUVr methods. Conclusion OI in serial tau‐PET imaging demonstrated better detection of early‐stage tau pathology than standard ROI‐based SUVr measures. The alignment between OI‐detected voxels and neuropathological tau burden supports its potential as an in vivo biomarker for detecting early neurofibrillary tangles.

Abstract Background The Joint Propagation Model (JPM)‐based CenTauR scale was recently introduced to harmonize tau‐PET quantification across different radiotracers. This study examined how CenTauR harmonization enhances the comparability of tau‐PET quantification across matched cohorts using different tracers. The evaluation focused on three key aspects: (1) comparing tau‐PET positivity rates as defined by CenTauR, (2) evaluating its diagnostic accuracy for symptomatic AD, and (3) analyzing longitudinal changes in tau‐PET rates over time. Method The JPM, developed by the CPAD‐led Tau PET Harmonization Working Group (Leuzy et al., Alzheimers Dement. 2024; Figure 1A), models relationships between anchor point subjects and head‐to‐head tau‐PET SUVR data onto the CenTauR scale, providing conversion equations for multiple tracers. JPM equations were applied to [ 18 F]flortaucipir SUVR (Meta‐temporal ROI) data from 561 cognitively impaired participants in the ADNI and OASIS‐3 studies. Using ROC analysis, we determined the CenTauR cut‐off for positivity (T+) that maximized the Youden index for discriminating between FDA‐approved positive/negative visual reads. Three separate cohorts, scanned using [ 18 F]flortaucipir (ADNI), [ 18 F]MK‐6240 (CPAS), and [ 18 F]RO‐948 (BioFINDER‐2), were matched 1:1 by age, MMSE, amyloid‐β, and clinical diagnosis. CenTauR‐based metrics were compared across these cohorts. Result A cut‐off of 18 CenTauRs was found to optimally classify [ 18 F]flortaucipir PET visual reads (Figure 1B). The matching procedure identified 1089 participants (363 per cohort). The frequency of T+ using the 18 CenTauRs cut‐off for the Meta‐Temporal ROI was highly comparable across tracers across groups, except for the Aβ‐positive cognitively unimpaired group, where variability was more pronounced due to a smaller sample size (Figure 2A). Similarly, tau‐PET discriminative accuracy for symptomatic AD vs controls remained similar across [ 18 F]flortaucipir and [ 18 F]MK‐6240 (Figure 2B). In a separate sample of 212 matched participants from ADNI ([ 18 F]flortaucipir) and AIBL ([ 18 F]MK‐6240) with baseline and 1‐year follow‐up tau‐PET scans, 1‐year change in CenTauRs was comparable (Figure 2C). An exploratory voxelwise transformation, utilizing the Meta‐Temporal conversion equation in a representative case scanned with both [ 18 F]flortaucipir and [ 18 F]MK‐6240, demonstrates the potential for voxelwise CenTauR harmonization (Figure 3). Conclusion These analyses suggest that CenTauR harmonization increases the comparability of tau‐PET data acquired with different tracers. Additional validation analyses with larger cohorts including other radiotracers ([ 18 F]PI‐2620) are underway.