The Dominantly Inherited Alzheimer Network Observational Study (DIAN Obs) is a longitudinal, global cohort study investigating brain aging and autosomal dominant Alzheimer’s disease (ADAD), a rare monogenic form of Alzheimer’s disease (AD). Established in 2008 with support from the National Institute on Aging (NIA), DIAN Obs is designed to collect comprehensive and uniform data with the aim to characterize brain biology and clinical trajectory of individuals at risk for ADAD. Mutations in the amyloid protein precursor (APP), presenilin 1 (PSEN1), or presenilin 2 (PSEN2) genes cause ADAD with virtually full penetrance and a predictable age at symptomatic onset. Participants, both mutation carriers and non-carriers from affected families, undergo longitudinal clinical and cognitive assessments, neurologic and physical examinations, structural and functional neuro-imaging, and amyloid and tau positron emission tomography (PET). Biospecimens include cerebrospinal fluid, plasma, serum, and whole blood for biochemical, genetic and multi-omic analyses, with brain donation upon death. This dataset enables one of the most detailed longitudinal examinations of the human brain across the continuum from presymptomatic to symptomatic AD. The extensive DIAN Obs data and biospecimen repository provides a globally accessible resource to advance understanding of AD pathophysiology, aging, and the development of preventive and therapeutic interventions.

Studies suggest that sex influences the deposition of tau in the human brain and impacts on the relationship of tau with AD-related outcomes. The aim of this study is to evaluate the extent to which sex influences PET-Tau related outcomes and whether this effect is related to specific tau PET tracers. We assessed 456 participants from the HEAD study (mean age = 66.07 ± 13.05). All individuals had available Tau-PET with Flortaucipir and MK6240, Aβ-PET and a subset had plasma p -Tau 217 ( n  = 352). We conducted a linear regression analysis between sex and PET Tau to evaluate the direct influence of sex on PET Tau SUVR. Next, we assessed whether sex affected the relationship between Tau PET (in BRAAK I-II, III-IV, V-VI regions) and Aβ-PET or plasma p -Tau 217 by adding an interaction term in the associations. All analyses were corrected by age, clinical classification, and amyloid burden. No significant differences were observed between male and female SUVR levels in either Flortaucipir and MK6240 tracers (Figure 1). However, when assessing the relationship between Aβ-PET and Tau, a statistically significant interaction with sex was seen between MK6240 in BRAAK III-IV (β = 0.160, p  = 0.020 ; Figure 2) and BRAAK V-VI regions (β = 0.196, p  = 0.008; Figure 2), with women having a stronger association. Furthermore, we found an interaction between sex and p -tau217 on Tau PET SUVR in BRAAK regions I-II using MK6240 or Flortaucipir (β = -0.184, p  =  0.014; β = -0.266, p  =  0.002; Figure 3) indicating that women present a weaker relationship between p -tau217 and Tau PET. Our results did not show any differences between men and women in Tau-PET SUVR uptake across tau tracers. However, we found that sex affected how Aβ-PET and plasma p -tau217 were associated with Tau-PET uptake. Further studies are needed to elucidate the underpinnings of the link between sex and the association between these biomarkers.

We showed that astrocyte reactivity, as measured by plasma GFAP levels, influences Aβ mediated tau PET pathology in cognitively unimpaired (CU) Aβ-positive individuals. However, the link between GFAP and tau PET in individuals without detectable Aβ pathology remains elusive. The aim of the current study is to investigate the association between plasma GFAP and tau PET in CU Aβ PET-negative individuals. We studied 147 CU Aβ PET-negative participants from the HEAD cohort with plasma GFAP and p -tau217, as well as tau PET Flortaucipir and MK6240 data. Aβ positivity was determined by Aβ PET visual reading and Centiloid 12. Voxel-wise linear regression models adjusted for age and sex tested the association of plasma GFAP and p -tau217 with tau PET. Further, the associations of plasma GFAP with peak tau PET SUVR values extracted from voxel-wise association were fitted with a linear regression model adjusted for age and sex. Voxel-wise analysis showed that plasma GFAP levels, but not plasma p -tau217 levels, were associated with tau PET in the medial temporal lobe (e.g., amygdala, entorhinal cortex, hippocampus) predominantly for Flortaucipir tau PET [Figure 1A, B, C, D]. The association between plasma p -tau217 and tau PET was weak in Aβ-negative individuals. These results were similar when Aβ positivity was defined based on Centiloid 12. Furthermore, plasma GFAP and peak tau PET SUVR of Flortaucipir showed stronger association than that of MK6240 [Flortaucipir: β=0.4017, p  <0.0001; MK6240: β=0.194, p  = 0.0424; Figure 2A, B]. We found an association between GFAP levels and tau PET uptake in individuals not expected to exhibit high levels of tau tangle-related tracer uptake. Further analysis will be designed to elucidate the underpinning of this association, which could represent low levels of tau pathology, astrogliosis, or other factors.

Previous studies demonstrated an association between tau pathology and the development of neuropsychiatric symptoms (NPS) in individuals with Alzheimer's disease (AD). However, the extent to which tau influences each specific NPS domain remains unclear. Here, we aim to investigate the association of tau and each NPS domain in the AD continuum. We hypothesized that tau plays a comparatively greater effect on the emergence of hyperactive and psychotic symptoms compared to other NPS domains. We assessed 385 individuals (216 cognitively unimpaired (CU), 128 MCI, and 41 AD dementia) from the HEAD study who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI-Q) and had positron emission tomography (PET) for amyloid-β (Aβ) ([ 18 F]AZD4694 or [ 11 C]PiB), and tau tangles ([ 18 F]MK6240) at the same visit. Tau SUVR values were tailored with a mask from Braak stages I-VI, using the inferior cerebellar gray matter as a reference region. All individuals with dementia had a positive Aß-PET. Voxel-wise and Tobit censored regression tested the association between NPS domains and biomarkers accounting for age, sex, cognitive status, and study site. We used censored regression models to account for skewed data alongside a leave-one-out (loo) approach to identify which NPS domains most contributed to results. CI individuals had significantly higher NPI-Q scores and Tau PET SUVR than CU (Figure 1A, 1B). NPI-Q score was significantly associated with tau-PET predominantly in fronto-parietal regions. Removing irritability from the models strengthened this association (Figures 2A-C). Regression loo models revealed that motor disturbances contributed most to the association between NPS and tau-PET across all Braak stages. Notably, irritability had a negative effect on the association in all Braak stages, suggesting that tau does not play a role in the development of irritability and is highly associated with motor disturbances (Figure 3). Our study suggests that tau contributes to the development of motor disturbances but has no effect on the development of irritability across the AD continuum. These findings provide additional rationale for the development of new therapeutics aiming to mitigate motor disturbances and irritability in AD patients.

BACKGROUND:Tau PET imaging has emerged as a critical biomarker for Alzheimer's disease, informing diagnosis, staging, and therapeutic selection. We investigated whether PET tracer selection alters tau detection. METHODS:We conducted a prospective, multicentre, non-randomised, within-participant comparison of [18F]flortaucipir (Tauvid), currently used in clinical settings in the USA and Europe, and [18F]MK6240, an investigational tau PET tracer. Participants were recruited from eight north American sites and underwent tau PET, amyloid-β (Aβ) PET, and detailed cognitive assessments. Tau PET with both agents was acquired within a 45-day window. Coprimary outcomes were the discriminative accuracy for Alzheimer's disease-related cognitive impairment and the frequency of tau positivity in early medial temporal lobe (MTL) and late neocortical regions. The study is registered with ClinicalTrials.gov, NCT05361382. FINDINGS:Between March 2, 2022, and Aug 27, 2025, 775 individuals were enrolled, with 682 completing all procedures (373 [55%] female, 309 [45%] male; 38 [6%] aged 19-27 years, 214 [31%] aged 50-65 years, and 430 [63%] aged 65-89 years). 32 (5%) participants identified as Hispanic or Latino. 637 (93%) identified as White, 24 (4%) as Black or African American, 16 (2%) as Asian, and five (1%) as other. In addition, 49 (7%) individuals were identified as being from a rural area. [18F]MK6240 showed greater accuracy than [18F]flortaucipir in distinguishing Alzheimer's disease from non-Alzheimer's disease impairment (area under the curve 0·93, 95% CI 0·89-0·95 vs 0·86, 0·75-0·91; p<0·0001). Among the older adults, tau positivity status was concordant in 560 (87%) for MTL and 603 (94%) for neocortical regions. In cognitively unimpaired participants, [18F]MK6240 identified twice as many MTL-positive cases as [18F]flortaucipir (n=54 [15%] vs n=23 [6%]). Prevalence ratio in Aβ-positive was 2·43 (95% CI 1·50-3·94; p=0·0003), identifying 23 additional cases per 100. Among discordant cases, 75 (89%) were [18F]MK6240-positive only and had higher Aβ burden (p<0·0001), APOEε4 frequency (p<0·0001), and cognitive impairment (p=0·0043) than those negative on both tracers. Neocortical tau positivity was more frequent with [18F]MK6240 than with [18F]flortaucipir in cognitively impaired individuals (80 [28%] vs 46 [16%]). Prevalence ratio in Aβ-positive was 1·74 (95% CI 1·32-2·29; p<0·0001), identifying 15 additional mild cognitive impairment and 21 dementia cases per 100. INTERPRETATION:Tau PET tracer selection influences the frequency of detection of tau pathology across the ageing and Alzheimer's disease spectrum. Compared with [18F]flortaucipir, [18F]MK6240 identified more individuals with tau pathology in cognitively unimpaired and cognitively impaired individuals, with direct implications for patient stratification in clinical trials and more precise guidance for therapeutic decision-making. FUNDING:National Institute on Aging.

BACKGROUND:Increasing evidence suggests that accurate prediction of Alzheimer's disease (AD) symptom onset requires more than amyloid- and tau-centric biomarkers such as cerebrospinal fluid (CSF) Aβ42/40, total tau and p-tau181 and plasma p-tau217. Autosomal dominant AD (ADAD), caused by pathogenic PSEN1, PSEN2 and APP mutations with predictable age at symptom onset, presents a unique opportunity to characterize the chronological changes in proteins beyond amyloid and tau and clarify them as early biomarkers of disease onset or as biomarkers related to disease staging and progression monitoring. METHODS:We measured 972 CSF samples corresponding to 484 participants of the Dominantly Inherited Alzheimer Disease Network (DIAN) using the NULISASeq 120 CNS Disease Panel. We first benchmarked the technology against gold-standard measurements followed by the identification of proteins that were differentially abundant in relation to mutation status and symptomatology. Next, we determined the chronological emergence of protein changes in relation to the estimated years to onset (EYO). Finally, we assessed whether specific protein measures improved the prediction of EYO in the ADAD. FINDINGS:NULISA measurements were comparable to those previously published. We demonstrated that known early alterations in CSF amyloid and tau were followed by inflammatory and neurodegenerative responses suggesting that clinical manifestation of AD happens before the inflammatory processes is fully developed. Finally, we found a multi-protein composite approach for predicting EYO that outperformed single biomarker values. INTERPRETATION:Our results suggest that the main CSF proteomic landscape changes in ADAD are due to the presence of a pathogenic mutation and occur prior to symptom onset. Improved performance of multi-protein composite to predict EYO compared to single biomarker values highlights the added value of multiplex proteomic signatures for biomarker panel development.

In vivo Braak staging stratifies patients across the AD spectrum and has the potential to harmonize tau PET tracer staging. This study aims to compare and test harmonization procedures for Braak staging individuals using MK6240 and Flortaucipir tau PET tracers. We assessed 437 participants across the AD spectrum (245 cognitively unimpaired (CU) and 192 cognitively impaired; mean age 68.5 ± 8.6) using head-to-head MK6240 and Flortaucipir scans. We computed SUVRs in Braak regions of interest (ROIs) and assessed four cut-off methods for Braak positivity: (a) mean + 2.5 SD of young controls (age <28 years), (b) mean + 2.5 SD of elderly CU Aβ−, (c) Gaussian mixture modeling (GMM), and (d) the Youden index. Braak stages were assigned using seven (0 to VI) or four (0, I–II, III–IV, V–VI) categories. We evaluated inter- and intra-tracer concordance (intra-tracer, i.e., whether it follows the sequential Braak pattern). The intra-tracer seven-class Braak staging concordance ranged from 63% to 94%. With the highest intra-tracer Braak concordance being achieved when using GMM cutoffs: 94% (MK6240) and 89% (Flortaucipir; Figure 1). Inter-tracer agreement concordance ranged from 56% to 76%. The highest concordance emerged from the CU Elderly Aβ– cutoff optimizing the Braak II region for spill-off (Figure 2). Using the Braak staging simplified version improved intra-tracer concordance in both tracers (MK6240as well as inter-tracer agreement (86.5%). Most inter-tracer discrepancies were observed at Braak stages II–IV. Despite showing staging discordances, the distribution of cognitive status across the Braak stages is similar for both tracers (Figure 3). These preliminary findings reveal some discrepancies in Braak staging when comparing MK6240 and Flortaucipir. Our results also suggest that adjustments in cutoffs and regions of interest can partially mitigate both inter- and intra-tracer divergences. Finally, our analysis suggests robust concordance after adjustment and using 4 classes (0, I-II, III-IV, V-VI).

The Alzheimer's disease (AD) Braak staging is a key framework for classifying tau pathology progression in AD based on histopathological post-mortem brain examinations. However, adapting it to PET imaging can be challenging due to differences in tracer uptake patterns and binding properties, which affect sensitivity, specificity, and regional staging. This study compares Braak staging across four tau PET tracers: Flortaucipir, MK6240, PI2620, and RO948. We assessed 90 participants across the AD spectrum (46 CU, 31 MCI, 13 dementia; mean age 66.1 ± 7.8) using Aβ PET and four tau PET tracers: (Flortaucipir, MK6240, PI2620, and RO948). Braak positivity was defined based on Aβ− CU individuals (mean +2.5 SD, SUVR). To evaluate systematic bias and agreement between tracers, we computed pairwise differences at corresponding Braak stage estimates and applied the Bland-Altman method to assess mean bias and limits of agreement. Additionally, Tau PET Braak region trajectories were modeled as functions of Aβ burden (Centiloid scale) using the Lowess method. Braak stage trajectories as a function of Aβ differ depending on the tracer and the sequential order of abnormality is highly variable. For instance, while for MK6240, RO948 and PI2620, Braak I is the first region to became abnormal, for Flortaucipir the earliest region to became abnormal is Braak IV (Figure 1). This variable pattern of abnormality impacts in the concordance of Braak staging between tracers with the highest Braak staging agreement resulting in concordance levels of approximately 70%. The highest levels of agreement between tracers usually happen at Braak 0 or Braak IV-V, with intermediate stages showing very low concordance (Figure 2). The Bland-Altman analysis identified wide limits of agreement, suggesting high variability and high tracer-specific differences (Figure 3). On the other hand, it also identified that mean differences between tracers were small, indicating minimal systematic bias. These preliminary findings reveal discrepancies in Braak staging when comparing Flortaucipir, MK6240, PI2620 and RO948. These findings suggest that while the tracers provide comparable stages on average, they may not be fully interchangeable in individual cases.

Tau-PET tracers have been used to monitor the progression of Alzheimer's disease (AD). However, different tracers present distinct patterns of binding throughout the brain, challenging the harmonization of their findings. Leveraging the HEAD Study, the largest head-to-head study of tau-PET tracers, we recently developed the Uniτ scale, which cross-sectionally harmonizes Flortaucipir and MK6240 onto a universal tau-PET measurement. Here, we provide a preliminary evaluation of the Uniτ scale's longitudinal performance in HEAD and two independent cohorts. We assessed 422 individuals across the AD spectrum with longitudinal tau-PET from three cohorts: HEAD [13 cognitively unimpaired (CU), 9 cognitively impaired (CI) individuals, scanned head-to-head with Flortaucipir and MK6240], ADNI [74 CU, 208 CI, tracer: Flortaucipir], and TRIAD [72 CU, 46 CI, tracer: MK6240]. Standardized uptake ratios (SUVRs) were harmonized to Uniτ using the Uniτ Ecosystem ( unitau.app ). Braak I-II and the Meta-Temporal regions were used as regions of interest. Annual tau-PET uptake change was calculated, and the effect size was defined as the mean annual change divided by its standard deviation. In HEAD, annual change in tau-PET uptake in Braak I-II across CU and CI was only detectable in MK6240 (Figure 1A). However, both tracers detected significant annual changes in the Meta-Temporal ROI for CI (Figure 1B). In both ADNI (Flortaucipir) and TRIAD (MK6240), changes in tau-PET uptake in Braak I-II regions were not significant (Figures 2-3A). However, across all cohorts, the Meta-Temporal ROI consistently showed detectable annual tau-PET increases—most pronounced among CI—leading to larger effect sizes than in Braak I-II (Figures 1-3B). The Uniτ harmonization did not fundamentally alter the pattern of the findings. However, in ADNI (Flortaucipir), Uniτ yielded a slightly higher effect size than SUVR in CU for both Braak I-II and Meta-Temporal regions. In this large longitudinal sample, our findings confirm that Flortaucipir and MK6240 can detect tau PET changes over time likely associated with the progression of tau tangle pathology. While MK6240 appears to show greater progression in CU, both tracers progress similarly in CI. Our data also suggested that Uniτ harmonized tau PET measurements maintain the longitudinal characteristics of each tau PET tracer for use in clinical trials.

With the advent of disease-modifying treatment for Alzheimer disease (AD), identifying biomarkers for predicting risk for amyloid-related imaging abnormalities (ARIA), hemorrhagic or edema types, is of increased interest. ARIA are thought to be related to disruption of the blood-brain barrier as fibrillary amyloid is cleared from the brain. Molecular and cellular processes related to these events may inform future trials. We investigated proteomics related to abnormal neurovascular imaging phenotypes such as white matter hyperintensities (WMH) in autosomal dominant AD (ADAD), a relatively young population at risk for ARIA. Participants from the Dominantly Inherited Alzheimer Network observational study (n Carriers =290 and n Non-Carriers =183) were assessed for WMH and microhemorrhages using T2-FLAIR and T2*GRE MRI, and for CSF proteomics using the 7k Somalogic ® platform. A subset (n Carriers =92 and n Non-Carriers =51) was evaluated for microhemorrhage incidence. WMH volumes were segmented with Triplanar U-Net ensemble network. Microhemorrhage count and incidence were classified as none, mild, moderate, or severe, based on current FDA recommendations. We performed differential abundance analyses to investigate proteins associated with WMH as a function of mutation status, accounting for age, APOE-e4 status, and sex, and significant proteins were further evaluated in pathway analyses and for associations with microhemorrhages. Eight proteins were differently expressed in carriers with larger WMH volumes (Figure 1). The genes of seven proteins (e.g., neurofilament light-chain (NEFL), neurofilament heavy-chain (NEFH), matrix metalloproteinase 12 (MMP12), fibronectin-1 (FN1), periostin (POSTN)) were overly represented in vascular-related disorders such as subarachnoid hemorrhages, transient ischemic attack, or cerebrovascular diseases (Figure 2). CSF levels of NEFL, NEFH, MMP12, fibronectin1, and periostin differed as a function of CMH severity. Especially, NEFL and MMP12 were higher in carriers with severe CMH compared to those with none or mild CMH (Figure 3A). MMP12 levels were particularly high in participants having severe increase in microhemorrhages (Figure 3B). Carriers with high levels of MMP12 may more likely develop new microhemorrhages. Our findings confirm the contribution of neurofilament light chain in disease processes and suggest a role for matrix metalloproteinase 12 in the development of microhemorrhages and especially severe case in ADAD. Funding : K01AG080123, RF1-AG044546, UF1AG032438

Tau PET measures are inherently continuous and applying dichotomized thresholds introduces conceptual and analytical idiosyncrasies. Understanding the limitations in the transition from tau-negative to tau-positive classifications is crucial for the effective use of these thresholds. This study aims to determine the confidence levels of tau PET thresholds of abnormality for different tau PET tracers by characterizing their “gray zone” using the universal tau PET scale (Uniτ, www.unitau.app ). We evaluated 485 individuals across the aging and AD spectrum from the HEAD study, with head-to-head scans for MK6240 and Flortaucipir. Uniτ estimates were derived from the Meta-Temporal ROI. Tau positivity (T+) was defined as Uniτ values exceeding the mean plus 3 SD of individuals younger than 28 years ( n  = 24). Two physicians independently performed a visual assessment of tau positivity for each tracer, with agreement indicating clear tau pathology (TVR+). Logistic regression was used to estimate the probability of TVR+ across Uniτ values for each tracer ( n  = 189, CU elderly Aβ- and CI Aβ+). Individuals were classified as negative, positive, or within a “gray zone” between the most liberal T+ threshold and varying TVR+ probability thresholds (50%, 75%, 90%, 95%, and 99%). The Uniτ gray zone, defined as a function of TVR+ probabilities, demonstrated consistency between the two tracers, with differences observed only in the decimal range. The most liberal Uniτ threshold for T+ was 11.0 for both MK6240 and Flortaucipir, closely matching the 50% TVR+ probability threshold (11.1 for MK6240 and 11.7 for Flortaucipir). Higher TVR+ probabilities corresponded to increased Uniτ values with MK6240 and Flortaucipir showing near-identical values between the tracers: 14.2 and 14.8 for 75%, 17.3 and 17.9 for 90%, 19.4 and 19.9 for 95%, and 24.1 and 24.6 for 99%, respectively (Figure 1). In total, 26 participants fell within the gray zone for MK6240, compared to 47 participants for Flortaucipir. These findings highlight the potential of Uniτ to provide a standardized approach for assessing tau positivity across tracers. By combining quantitative Uniτ measures with visual assessments, we enhance the understanding of tau positivity certainty, particularly in the transition between negative and positive classifications.

Abstract INTRODUCTION Positron emission tomography (PET) without usable or accompanying magnetic resonance imaging (MRI) is typically excluded in quantitative analyses of Alzheimer's disease, potentially limiting study generalizability. We investigated participant features predicting data exclusion in magnetic resonance (MR)‐dependent analyses and evaluated an existing MR‐free PET pipeline to quantify these missing data. METHODS Imaging, clinical, cognitive, and sociodemographic data were analyzed for 2119 individuals in a multi‐site cohort. Agreement between MR‐dependent and MR‐free Centiloids (CL) assessed using intra‐class correlations and features predicting data exclusion were examined using logistic regressions. RESULTS MR‐free and MR‐dependent CLs generally agreed, but MR‐free CLs underestimated MR‐dependent cross‐sectionally and longitudinally. Approximately 19.5% (n = 405) of our cohort would have been excluded in MR‐dependent analyses. Age and cerebrovascular comorbidities were consistent exclusion features across multiple sites. DISCUSSION Data exclusion in imaging studies is not entirely random. Flexible quantification methods like MR‐free PET could supplement traditional methods to improve generalizability in large, multi‐site studies.

Background Tau PET quantification typically requires structural MRI for reference-region definition, normalisation, and segmentation, which imposes a logistical and operational burden. In this study, we evaluated whether low-dose CT can serve as a reliable MRI alternative for tau PET quantification when MRI is unavailable. Methods In this multicentre, cross-sectional study comparing methods, we developed a PET–CT pipeline using low-dose CT as the sole anatomical reference for tau PET quantification. Participants were recruited from six sites in the multicentre Head-to-head Harmonisation of tau PET tracers (HEAD) study (NCT05361382). The cohort comprised cognitively unimpaired young adults (<28 years), cognitively unimpaired middle-aged adults (50–65 years), cognitively unimpaired older adults (>65 years), and participants with mild cognitive impairment or dementia. Participants underwent [18F]MK6240 and [18F]flortaucipir PET–CT, T1-weighted MRI, and amyloid PET. Standardised uptake value ratios were calculated for three Alzheimer’s disease-relevant composite regions and 34 individual brain regions using a template-based cerebellar reference and compared with PET–MR pipelines using template-based or FreeSurfer subject-space segmentation. Agreement was assessed using linear regression, intraclass correlation coefficients, Bland–Altman analysis, equivalence testing (two one-sided tests), and Cohen’s kappa (κ) for binary positivity concordance. Findings 387 participants were included, with recruitment spanning from Nov 11, 2022, to July 7, 2025 (205 [53%] women, 182 [47%] men; six [2%] aged 19–25 years, 130 [34%] aged 50–65 years, and 251 [65%] aged 66–89 years). CT-derived standardised uptake value ratios showed near-perfect linear correspondence with the template-based PET–MR pipeline for MK6240 (R2=0·959–0·975) and flortaucipir (0·925–0·964), with high absolute agreement (intraclass correlation coefficient 0·976–0·985 for MK6240 and 0·955–0·978 for flortaucipir). Against the FreeSurfer subject-space pipeline, correspondence remained strong for MK6240 (R2=0·952–0·971) and flortaucipir (0·909–0·949). Mean R2 across 34 brain regions was 0·917 (SD 0·049) for MK6240 and 0·874 (0·057) for flortaucipir. Tau-positivity concordance was high (92–97%; κ 0·802–0·895; all p<0·001), with discordant cases near the classification threshold. Associations with amyloid burden and memory were preserved across pipelines (Steiger’s test, all p>0·05). Equivalence was formally confirmed by two one-sided tests (all p<0·001). Interpretation CT-only processing enables robust tau PET quantification, comparable to standard MRI-based pipelines, including computationally intensive FreeSurfer-based approaches. This method was validated across different radiotracers and represents a reliable alternative when MRI is unavailable or contraindicated. Because low-dose CT is routinely acquired during PET–CT sessions, this approach enables quantification of legacy datasets without MRI, broadens participant inclusion in clinical trials, and can reduce imaging costs in settings where MRI is not clinically indicated. Funding National Institute on Aging.

Alzheimer's disease and related dementias (ADRDs) presents significant biological heterogeneity, which influences clinical outcomes and treatment responses. However, current ADRDs research has been predominately conducted in non-Hispanic white cohorts, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), limiting the understanding of ADRDs progression in diverse populations. The Health and Aging Brain Study – Health Disparities (HABS-HD), which includes Black/African American (AA), Hispanic (HIS), and non-Hispanic white (NHW) participants, offers a broader sociodemographic perspective. This study aims to test the generalizability of ADRDs subtypes and progression patterns identified in ADNI to the more diverse HABS-HD cohort. Structural MRI data from HABS-HD (AA n  = 588, HIS n  = 1005, NHW n  = 1009) and ADNI ( n  = 864) cohorts were processed using FreeSurfer to extract brain cortical thickness and hippocampal volume. The Subtype and Stage Inference (SuStaIn) algorithm was then applied to both datasets to identify spatial atrophy subtypes and disease stages. To compare subtypes between cohorts, Positional Variance Diagrams (PVDs) in Figure 1. were used to visualize the variability and uncertainty in disease progression patterns derived by the model. SuStaIn identified three spatial atrophy subtypes and progression stages in HABS-HD and ADNI. HABS-HD's PVD patterns aligned with ADNI's, revealing distinct progression patterns: Subtype 1 was characterized by initial degeneration of the hippocampus, followed by posterior and then anterior spread; Subtype 2 began with occipital atrophy, progressing to parietal regions; Subtype 3 was characterized by initial frontal degeneration, with subsequent spread to posterior regions. The table 1. Shows significant differences in HABS-HD's distinct demographic and clinical features ( p  < 0.05), where Subtype 3 exhibits the most severe cognitive impairment and represents the oldest age group. Similarity of disease progression patterns between HABS-HD and ADNI supports generalizability of AD subtypes to diverse populations, highlighting the subtypes' potential to advance clinical trials and precision medicine for neurodegenerative disorders.

Etalanetug (E2814) is designed to delay the clinical progression of Alzheimer’s disease (AD) by binding to the microtubule binding region (MTBR) of tau implicated in seeding and spreading of tau pathology. Dominantly inherited Alzheimer’s disease (DIAD) is a rare form of the disease (< 1

The link between regional tau load and clinical manifestation of Alzheimer's disease (AD) highlights the importance of characterizing spatial tau distribution. In typical (memory-predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicenters. However, atypical (non-amnestic-predominant) AD variants with heterogeneous tau patterns provide a key opportunity to assess the universality of connectivity as a scaffold for tau progression. We included tau-PET data from 320 subjects with atypical AD, characterized by highly heterogeneous tau patterns ( n  = 139 posterior cortical atrophy/PCA-AD; n  = 103 logopenic variant primary progressive aphasia/lvPPA-AD; n  = 35 behavioural variant AD/bvAD; n  = 43 corticobasal syndrome/CBS-AD) from 14 sites, with a subset of patients ( n  = 78) having longitudinal tau-PET data. As an independent sample, we further included regional post-mortem tau stainings from 93 atypical AD patients from two sites ( n  = 19 PCA-AD, n  = 32 lvPPA-AD, n  = 23 bvAD, n  = 19 CBS-AD). Gaussian mixture modeling was used to harmonize different tau-PET tracers by transforming tau-PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether 1) brain regions with stronger functional connectivity showed greater covariance in cross-sectional and longitudinal tau-PET and post-mortem tau pathology, and 2) functional connectivity of tau-PET epicenters and tau-PET accumulation epicenters was associated with cross-sectional and longitudinal tau patterns. Tau-PET epicenters—defined as the 5% brain regions with the highest tau load—aligned with clinical variants, e.g. a posterior pattern in PCA-AD (“visual AD”) and left-hemispheric temporal predominance in lvPPA-AD (“language AD”) (Figure 1). More strongly functionally connected regions showed correlated concurrent tau-PET levels, which was confirmed with post-mortem data (Figure 2). Moreover, the connectivity profile of tau-PET epicenters and accumulation epicenters corresponded to tau-PET progression patterns (Figure 3). Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicenters, across all AD clinical variants. Since tau proteinopathy is a key driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant-specific endpoints in clinical trials.

Tau-PET tracers are essential for visualizing pathology in Alzheimer's (AD). High detectability to tau is crucial for early detection and monitoring of tau deposition. This study compares the noise to dynamic range ratio (NRR) of [ 18 F]FTP, [ 18 F]MK6240, [ 18 F]PI2620, and [ 18 F]RO948, cross-sectionally and longitudinally. 460 individuals from the HEAD study (23 cognitively unimpaired (CU) young, 249 CU old and 188 cognitively impaired) underwent [ 18 F]FTP and [ 18 F]MK6240 tau-PET scans; 94 additionally received [ 18 F]PI2620 and [ 18 F]RO948. A subset of 28 individuals (15 CU and 13 CI) underwent [ 18 F]FTP and [ 18 F]MK6240 follow-up scans (1.5 ± 0.1 years later). Annual change was measured as [(followup-baseline)/time between scans]. Noise was calculated as the standard deviation (SD) of CU Aβ- participants aged ≤65 (SD CUAβ-≤65 ). The dynamic range was calculated as the SD across all subjects (SD range ). A lower the NRR (=SD CUAβ-≤65 /SD range ) indicates more detectability. Analyses were performed at both region-of-interest and voxel-wise levels. Across the whole cohort, [ 18 F]MK6240 exhibited lower NRR than [ 18 F]FTP in all regions. Voxel-wise, differences were most pronounced in the frontal medial temporal regions (Figure 1). Within the four-tracers subset, [ 18 F]PI2620 and [ 18 F]FTP showed the highest NRR in Braak II, followed by [ 18 F]RO948 and [ 18 F]MK6240. In Braak IV-VI, [ 18 F]MK6240 consistently demonstrated the lowest values, followed by [ 18 F]PI2620, [ 18 F]FTP and [ 18 F]RO948. Similarly, in metatemporal-ROI and Braak III, [ 18 F]MK6240 remained the lowest, however followed by [ 18 F]FTP, [ 18 F]PI2620 and [ 18 F]RO948 (Figure 2). Lower [ 18 F]MK6240 NRR was due to larger SD range , while [ 18 F]FTP's lower values stemmed from smaller SD CUAβ-≤65 . Conversely, high [ 18 F]PI2620 and [ 18 F]RO948 values are driven by larger SD CUAβ-≤65 . Longitudinal analyses further confirmed that [ 18 F]MK6240 exhibited the lowest NRR across the entire brain, including AD-related regions, with voxel-wise differences mainly in the temporal and parietal lobes. Tau-PET tracers exhibit significant variability in detectability. [ 18 F]MK6240 consistently demonstrated lower NRR, implying better detectability across all regions, cross-sectionally and longitudinally. Despite differences in NRR, [ 18 F]FTP, [ 18 F]RO948 and [ 18 F]PI2620 followed similar pattern. These results provide insights into the differential tracer detectability, helping guide their optimal use in detecting and tracking tau pathology. Ongoing follow-up scans will further clarify longitudinal tracer detectability and its implications for tau progression in AD.

Quantifying tau aggregates in the human brain can be achieved using Positron Emission Tomography (PET) techniques, which can potentially be affected by binding competition due to medication use. Patients with dementia often have high rates of comorbidities and polypharmacy. Therefore, this study aims to investigate the potential influence of multiple medications on the uptake of the tau tracers MK6240 (MK) and Flortaucipir (FTP). Five classes of medications were evaluated: Anti-Hypertensives, Statins, Anti-Diabetics, Psychoactive drugs, and NSAIDs (Table 1). We included 292 individuals [170 cognitively unimpaired (CU) Aβ-negative and 122 cognitively impaired (CI) Aβ-positive] from the HEAD study (Table 2). We compared MK and FTP SUVR in the Medial Temporal Lobe (MTL) and Neotemporal Cortex (NTC) in individuals on and off medications. The linear regressions that tested associations were corrected for confounding factors, including age, sex, education, and MoCA score. Correction for multiple comparisons was applied using the Bonferroni method (adjusted p -value at 0.00125). Among CI Aβ-positive individuals, Anti-Diabetics were associated with lower SUVR in the NTC for both FTP and MK. However, these associations did not remain significant after correction for multiple comparisons. (Table 3). Our findings indicate that there are no significant associations between the use of the medications studied and MK or FTP uptake when accounting for covariates and applying multiple comparison corrections.

Predicting progression to mild cognitive impairment (MCI) and dementia in preclinical AD patients is crucial for proper recruitment into anti-amyloid clinical trials. We evaluated the predictive ability of machine learning (ML) classifiers for distinguishing MCI-progressors from non-progressors using baseline amyloid positron emission tomography (PET) and magnetic resonance imaging (MRI) features as predictors. We selected cognitively-normal, amyloid-positive participants from ADNI ( N  = 86, 36 progressors), OASIS ( N  = 56, 12 progressors), and A4 ( N  = 210, 79 progressors). Subjects were classified as stable (remain CDR=0 at least 3 years from baseline) or progressor (convert to CDR>0 within 3 years of baseline). Each subject's first amyloid-positive [ 18 F]-florbetapir PET scan and matching T1-weighted MRI underwent standard PET-MRI processing to obtain 86 regions-of-interest, from which regional standardized uptake value ratios and volume were computed. Principal component analysis was applied to reduce the number of amyloid and volume features. Age, sex, and APOE-e4 carriership were also included as predictors. Three ML classifiers – logistic regression, support vector machine (SVM), and random forest – were trained to predict binary stable/progressor class. Data from two sites were used to train models and optimize hyperparameters using 5-fold cross-validation, while the third site was held out for testing. Performance was quantified using receiver operating characteristics area-under-the-curve (AUC). To assess the importance of each predictor type (non-imaging, amyloid, volume), nested models were trained by omitting one predictor type, and its AUC was compared to the model trained on all predictor types. Logistic regression with the full set of features performed the best with A4 (AUC=0.7391) and ADNI (AUC=0.8367) as the testing set, while SVM with either full features or with non-imaging features omitted performed the best with OASIS as the testing set (AUC=0.8826) (Figure 1, Table 1). Omission of volumetric features generally resulted in the largest dip in AUC for A4 and OASIS testing sets, whereas omission of amyloid features resulted in the largest dip in AUC for ADNI (Table 1). ML classifiers utilizing multimodal imaging are predictive of progression to MCI in preclinical AD individuals and are robust to external testing sites.

Biological staging models are a key tool for assessing the severity of Alzheimer's disease (AD), supporting personalized medicine and playing a critical role in clinical trial design. Recently, researchers have leveraged positron emission tomography (PET) to inform data-driven staging models of brain pathology related to AD. However, most approaches have focused on staging either amyloid or tau progressions separately, while both pathologies constitute defining factors of AD. Here, we aimed to derive a data-driven staging model which encompasses the spatial spread of both amyloid and tau. We assembled a large sample (n=3,293) of individuals with both amyloid and tau PET imaging stemming from 8 neuroimaging studies of AD and aging. We applied unsupervised machine learning to estimate brain areas which showed coordinated pathological accumulation across our sample, and we used these regions to inform a data-driven model for staging amyloid and tau. The resulting six stage model showed two stages of amyloid progression followed by four stages of tau spread, which were associated with cross-sectional and longitudinal assessments of cognitive decline. Comparison of our biological staging model with clinical disease stages recommended by the Alzheimer's Association showed evidence of heterogenous symptom profiles. Replication of results in holdout data demonstrated the generalizability and prognostic value of our staging model. Together, these findings establish a comprehensive and rigorously validated biological staging model that jointly characterizes amyloid and tau progression, advances beyond global or anatomically predefined summaries, and provides a scalable framework for studying disease heterogeneity and progression in AD.