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Abstract Background Colonic strictures in Crohn’s disease (CD) present a complex and relatively underexplored clinical issue. Imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and intestinal ultrasound (IUS), have established criteria for evaluating small bowel CD strictures. However, radiologic definitions of benign colonic strictures and their diagnostic accuracy are not well-developed. Endoscopic definitions of colonic strictures are the most well-described. We conducted a systematic review to summarize definitions, diagnosis, and identification of inflammation and fibrosis of colonic strictures on endoscopy, CT, MR, and IUS. Methods A systematic review following PRISMA guidelines, from inception to October 6, 2024, was conducted using MEDLINE, Scopus, CINAHL, and CENTRAL databases to identify literature on diagnostic imaging of patients with colonic CD strictures. Only studies with comparison to endoscopy or imaging to a full-thickness histologic gold standard to differentiate fibrosis from inflammation were included. Two stage screening was completed in duplicate, and risk of bias was assessed using QUADAS-2. Results 46 studies were eligible for inclusion of 4623 abstracts screened. Definitions of colonic strictures on CT, MR, and IUS were highly variable with combinations of bowel wall thickness, luminal narrowing, and pre-stenotic dilation. Endoscopic definitions of colonic strictures were the most consistently reported as lack of passage of a colonoscope through a narrowed lumen. Diagnostic imaging definitions used various combinations of increased bowel wall thickness, luminal narrowing, and pre-stenotic dilation with no clear cutoffs. Few studies directly compared imaging features with histologic or endoscopic gold standards, limiting evaluation of diagnostic accuracy. Meta-analyses were not conducted due to methodological heterogeneity of grading fibrosis across studies. In addition, statistical heterogeneity of sensitivity, specificity, and accuracy of colonic stricture diagnoses was limited by pooling with small bowel strictures in the studies. Conclusion This systematic review highlighted the absence of standardized definitions for diagnosing colonic Crohn’s disease strictures, particularly on diagnostic imaging, and the limited data distinguishing inflammatory from fibrotic components. Bowel wall thickening, luminal narrowing, and pre-stenotic dilation are key features of small bowel strictures, but their relevance to the colon requires further assessment. Establishing standardized definitions for colonic strictures that incorporate both endoscopic and imaging modalities is essential to advancing the field. Conflict of interest: Dr. Lu, Cathy: Advisory board - Abbvie, Johnson and Johnson, Takeda, Ferring, Merck, Celltrion, Lilly, Pfizer, Pendopharm, Agomab. Research Funding - Abbvie, Helmsley Charitable Trust, Alberta Innovates Dhaliwal, Ravdip: No conflict of interest Kellar, Amelia: No conflict of interest Rowan, Catherine: No conflict of interest St-Pierre, Joelle: Consultant for Pfizer, Abbvie, Eli Lilly and Pendopharm, and speaker for Takeda. Ernest-Suárez, Kenneth: Consulting/Advisory Board fees: Abbvie, AstraZeneca, Johnson & Johnson, Pfizer, Ferring, Sandoz, SatisfAI, Takeda O’Brien, Maureen: No conflict of interest Rosentreter, Ryan: No conflict of interest Gulhati, Vishrut: No conflict of interest Baker, Mark: No conflict of interest Bettenworth, Dominik: DB is on the advisory board or consultant for AbbVie, Amgen, Arena, Atheneum, BNG Service GmbH, Bristol Myers Squibb, CED-Service GmbH, Celltrion, Doctorflix, DGVS, Diaplan, Else Kröner-Fresenius Foundation, Falk Foundation, Galapagos, Guidepoint, Impulze, Ferring, Janssen Cilag, Lilly, Medical Tribune, MedTriX, MSD, Mylan, Onkowissen, Pharmacosmos, Pfizer, Roche, Sandoz, Takeda, Tetrameros, Thieme, Tillotts Pharma, UCB Biopiharma, Viatris and Vifor Pharma. Bruining, David: Advisory board fees from Janssen Bari, Dane: Grant from Helmsley Charitable Trust speaker honorarium from Siemens Healthineers support for attending meetings or travel from Siemens Healthineers member of the Olympus advisory board co-chair Society of Abdominal Radiology (SAR) Inflammatory Bowel Disease Disease Focused Panel (IBD DFP) and SAR Photon-Counting Detector CT Emerging Technology Commission and chair of the Society for Advanced Body Imaging Scientific Committee. Dillman, Jonathan: Grants from Philips Healthcare, GE HealthCare, Motilent, Perspectum, Guerbet, and Bracco. El Ouali, Sara: Speaker and/or consulting fees from Takeda, Eli Lilly, Janssen, Abbvie. Fletcher, John: Grant supports from Siemens Healthineers Gordon, Ilyssa: Grants to institution from Morphic Therapeutics, Alimentiv, Pliant Therapeutics, and UCB patent pending with Case Western Reserve University on radiomics signatures Jairath, Vipul: Consulting Fees: Abbvie, Alimentiv, Amgen, Anaptys Bio, Asahi Kasei, Asieris, Astra Zeneca, Attovia, Blackbird Labs, BMS, Boehringer Ingleheim, Biomebank, Caldera, Calluna, Catalytic Health, Celltrion, Ensho, Enthera, Exeliome Biosciences, Ferring, Fresenius Kabi, Gilead, Granite Bio, GSK, Janssen, Lilly, Merck, Mountainfield, MRM Health, Nxera, Organon, OSE Immunotherapeutics, Pendopharm, Pioneering Medicine, Pfizer, Prometheus, Roche/Genentech, Sanofi, SCOPE, Shattuck Labs, Sorriso, Spyre, Synedgen, Takeda, Teva, Tillotts, Union Therapeutics, Ventus, Ventyx, Vividion, Xencor, Zealand Pharma. Feagan, Brian Gordon: Consulting Fees: AbbVie, Abivax, Adaxion, Adiso, AgomAB Therapeutics, Akros, Alira Health, Ally Bridge Group, Apini Therapeutics, Argenx, Attovia Tx, Avoro Capital Advisors, Belmore Law, Biora Therapeutics, Blackbird Laboratories, Boehringer-Ingelheim, BMS, Boxer Capital, Celgene/BMS, Clarivate, Connect Biopharm, EcoR1, Eli Lilly, Ensho Therapeutics, Equillium, Evida, Enveda, Evommune Inc. Faes Farma, First Wave, Forbion, Galapagos, Genentech/Roche, General Atlantic, Genesis Therapeutics, Gerson Lehrman Group, Gilead, Guidepoint, Imhotex, ImiDomics, Immunic Therapeutics, Janssen, Japan Tobacco Inc., LifeMine Therapeutics, Mage Biologics, Merck, Mirador Therapeutics, Mobius Care, Monte Rosa Tx, Morphic Therapeutics, Nimbus Therapeutics, Novartis, Nxera, OncoC3, Palisade Bio, Pendopharm, Pfizer,Q32 Bio, REDX, Roche, Sanofi, Sobi, Sorriso, Spyre Therapeutics, Sun Pharma, Surrozen Inc., Synedgen, Takeda, Tegus Inc., Teva, Tillotts, Trex Bio, TR1X Inc. TVM Lifesciences, Ventyx Biosciences, Versant Ventures, Vida Ventures, Ysios Capital, Zagbio Stock Shareholder: Connect Biopharm, EnGene, Evida, SRT, Imidomics, Enveda Rieder, Florian: Personal Fees: Adiso, Adnovate, Agomab, Allergan, AbbVie, Arena, Astra Zeneca, Boehringer-Ingelheim, Celgene/BMS, Celltrion, CDISC, Celsius, Cowen, Ferring, Galapagos, Galmed, Genentech, Gilead, Gossamer, Granite, Guidepoint, Helmsley, Horizon Therapeutics, Image Analysis Limited, Index Pharma, Landos, Jannsen, Koutif, Mestag, Metacrine, Mopac, Morphic, Organovo, Origo, Palisade, Pfizer, Pliant, Prometheus Biosciences, Receptos, RedX, Roche, Samsung, Sanofi, Surmodics, Surrozen, Takeda, Techlab, Teva, Theravance, Thetis, UCB, Ysios, 89Bio

Abstract Background Older adults with brain tau pathology but no evident amyloid‐beta (A) pathology can be referred to as primary age‐related tauopathy (PART). However, in vivo characterization of PART using biomarkers remains unclear, varying based on the method used to define tau pathology (e.g. different tau‐PET tracers). Here, we conducted a head‐to‐head characterization of PART using two different tau‐PET tracers. Method We studied 433 individuals from the HEAD cohort (244 CU, 138 MCI, and 51 with dementia). Participants underwent clinical assessments, MRI, A‐PET, both MK‐6240 and Flortaucipir scans, and a subset with plasma biomarkers ( n = 332). A‐PET positivity was defined as Centiloid>24. Tau‐positivity(T) was determined by MK‐6240 and Flortaucipir SUVR values exceeding the mean+2SD in at least one Braak region anchored in CU A‐ individuals followed by visual confirmation in A‐/T+ cases. Concordance across biomarkers was assessed among PART (A‐/T+), A‐/T‐ and A+/T+ groups. Plasma GFAP, p ‐tau217, NFL levels, and Centiloids were compared across A/T groups using linear regression. Result We found a total of 25 (5.8 %) cases classified as A‐/T+ defined by at least one tau tracer (74±6.3 years, 52% females, 17 CU, 3 MCI, 5 with dementia). Detection of A‐/T+ cases varied by tracer: MK‐6240( n = 20), Flortaucipir( n = 16) (Figure 1A). Among A‐/T+ individuals, MK‐6240 identified 13 CU, 4 MCI, and 3 dementia cases, while Flortaucipir identified 11 CU, 3 MCI, and 2 dementia cases(Figure 1B). Concordance between MK‐6240 and Flortaucipir in A‐/T+ cases was low ( n = 11, 44%)(Figure 1C). A‐/T+ individuals showed no significant biomarker differences from A‐/T‐ but had lower biomarker levels than A+/T+: Centiloids (MK‐6240: T=15.08, p <0.001; Flortaucipir: T=14.36, p <0.001), GFAP (MK‐6240: T=5.04, p < 0.01; Flortaucipir: T=2.92, p <0.001), p ‐tau217 (MK‐6240: T=7.77, p <0.001; Flortaucipir: T=6.34, p <0.001), except for NFL (MK‐6240: T=1.35, p = 0.18; Flortaucipir: T=1.14, p = 0.25)(Figure 2). Representative A and tau biomarker cases (Figure 3A), along with discordant cases classified as T+ by only one tracer (Figure 3B), are illustrated. Conclusion Our preliminary analysis revealed a higher prevalence of A‐/T+ cases with MK‐6240 than Flortaucipir, low concordance between tracers and consistently lower biomarker levels in A‐/T+ compared to A+/T+. Larger studies and longitudinal analyses are necessary to ascertain the pathological trajectories of A‐/T+ individuals with varying tau biomarker profiles.

Abstract Background Tau phosphorylation and aggregation are hallmark features of Alzheimer's disease pathology(AD). Previous studies have shown that plasma phosphorylated tau( p ‐tau) at threonine 217 can detect AD‐related tau tangle pathology. However, the head‐to‐head association between p ‐tau217 and different tau PET tracers remains unexplored. Here, we conducted a head‐to‐head comparison of the association between plasma p ‐tau217 and four distinct tau PET tracers[MK6240, Flortaucipir(FTP), PI2620, RO948]. Method We studied 338 individuals across the AD continuum from the HEAD study[190 cognitively unimpaired elderly(CU), 109 with mild cognitive impairment(MCI), and 39 with dementia] with available FTP, MK6240, and plasma p ‐tau217 measured by ALZpath assay. A subset of 64 individuals(28 CU, 25 MCI, and 11 dementia) also had PI2620 and RO948, and p ‐tau217 measured by Lumipulse. The strength of the association was determined using t‐value maps from regression models testing the association between each tau PET tracer and plasma p ‐tau217, and the extent was measured by the percentage of significant voxels(t‐value>3.2). R‐square metric was used to determine how much of the variance in tau PET tracer estimates was explained by plasma p ‐tau217 concentrations. Associations between p ‐tau217 with tau PET were evaluated using the Spearman test. Result We found that in CU, both the extent and magnitude of the association with plasma p ‐tau217 were higher for MK6240 compared to FTP (Figure 1A‐D). Plasma p ‐tau217 explained a greater proportion of the variance in MK6240 than in FTP (Figure 1E‐F). In CI, the extent and magnitude of the association were similar for MK6240 and FTP. Additionally, plasma p ‐tau217 explained a similar proportion of the variance in MK6240 and FTP. Using the subset of individuals who had all tau tracers, MK6240 showed a slightly higher extent of association with p ‐tau217, followed by FTP, PI2620, and RO948 (Figure 2A‐B). The magnitude of association was stronger for MK6240, followed by PI2620, RO948, and FTP (Figure 2C‐D). Plasma p ‐tau217 explained a greater proportion of the variance in MK6240, followed by FTP, RO948, and PI2620 (Figure 2E‐F). We compared the associations between different p ‐tau217 assays, and both assays presented similar associations with all tau PET tracers (Figure 3). Conclusion In summary, our results indicate that the four tau PET tracers exhibit robust associations with plasma p ‐tau217 in similar topographical regions.

Abstract Background The overlap index (OI), previously introduced by Lee et al. (2022), has proven to be a reliable method for detecting tau accumulation via tau‐PET imaging with flortaucipir ([ 18 F]AV‐1451). This technique identifies voxel‐wise increases in standardized uptake value ratio (SUVr) across serial scans. However, the relationship between tau‐PET measurements obtained with [ 18 F]MK‐6240 and [ 18 F]AV‐1451 using the OI remains unclear. This study aims to investigate the relationship between [ 18 F]MK‐6240 and [ 18 F]AV‐1451 tau‐PET using the tau‐PET OI. Method The study included 27 participants from the HEAD project, all of whom underwent two serial tau‐PET scans (each [ 18 F]MK‐6240 and [ 18 F]AV‐1451) along with 3T T1‐weighted MRI, acquired within an average interval of 18 months. SUVr maps for each tau‐PET tracer were normalized to the cerebellar crus grey matter, and MR images were co‐registered to the MCALT T1 template. Tau‐PET images were spatially resampled to the template space, and OI was computed within a predefined META‐ROI using an intensity threshold of 1.3 and a cutoff of 0.5. Clusters containing fewer than 20 contiguous voxels were excluded from analysis. The META‐ROI SUVr was calculated as the average SUVr across the selected region's SUVr. Additionally, a visual comparison was performed to assess the spatial overlap of OI‐identified voxels between the two tau‐PET tracers. Result OI values within the META‐ROI exhibited a broad distribution between [ 18 F]MK‐6240 and [ 18 F]AV‐1451 tau‐PET, particularly in cognitively unimpaired (CU) and younger individuals (Figure 1A). Unlike the OI distribution, the relationship between META‐ROI SUVr values of both tracers followed an exponential trend (Figure 1B). Visual inspection revealed that OI‐detected voxels in [ 18 F]MK‐6240 tau‐PET were frequently localized to the meninges, an established site of off‐target binding, which was not observed in [ 18 F]AV‐1451 tau‐PET (Figure 2). Conclusion The association between [ 18 F]MK‐6240 and [ 18 F]AV‐1451 tau‐PET, as assessed using the OI, exhibited considerable variability in CU and younger participants. Additionally, the presence of overlapping voxels in the meninges in [ 18 F]MK‐6240 tau‐PET highlights the need to account for and potentially exclude off‐target binding effects in these populations. The influence of meningeal signal and other off‐target binding on the OI in the temporal META‐ROI needs to be further evaluated.

Abstract Background Colony stimulating factor 1 receptor–related disorder (CSF1R‐RD) is a rare, rapidly progressive neurodegenerative disease with significant clinical heterogeneity. Apolipoprotein E (ApoE) polymorphism, known to modulate microglial function and influence neurodegeneration, may act as a genetic modifier in CSF1R‐RD. Objectives The objectives were to evaluate the distribution of ApoE alleles in CSF1R‐RD and assess their association with clinical and neuropathological features. Methods ApoE genotyping was performed in 55 individuals with CSF1R‐RD. Clinical data from 25 deceased patients were analyzed based on ApoE genotype. Neuropathological evaluation was conducted on brain tissue from 14 patients, with semiquantitative scoring of white matter pathology and microglial burden. Statistical comparisons were made between carriers and noncarriers of the ApoE4 allele. Results ApoE allele frequencies in CSF1R‐RD mirrored those of the general population (ε2: 7.3%, ε3: 79.1%, ε4: 13.6%). However, ApoE4 carriers exhibited significantly earlier symptom onset (median: 37.9 vs. 50.3 years, p = 0.0123) and death (median: 41.1 vs. 55.2 years, p = 0.0072). Neuropathological analysis revealed more severe white matter involvement and reduced microglial preservation in ApoE4 carriers ( p = 0.034). Conclusions Although ApoE allele distribution does not differ from the general population, the presence of the ApoE4 allele may influence the clinical trajectory and white matter pathology in CSF1R‐RD. These findings suggest that ApoE polymorphism is a potential modifier of disease course and should be considered in therapeutic planning and future research. © 2026 International Parkinson and Movement Disorder Society.

Abstract Background Tau accumulation in the brain is postulated to follow a hierarchical pattern, known as Braak staging, originally defined in postmortem brains. PET‐based Braak stages have since been proposed, however, tau PET tracers exhibit distinct binding characteristics that might influence the detectable trajectories of tau accumulation and its relationship with other biomarkers across the AD continuum. In a head‐to‐head study, we investigated the emergence of tau positivity in each Braak region, along with plasma p ‐tau217 and GFAP, as a function of Aβ PET deposition. Method We evaluated 352 individuals from the HEAD study (205 cognitively unimpaired and 147 cognitively impaired) with head‐to‐head Aβ PET, MK6240 and Flortaucipir tau PET, and plasma p ‐tau217 (ALZpath) and GFAP (Quanterix) measures. Tau PET Braak regions, plasma p ‐tau217 and GFAP trajectories were modeled as functions of Aβ burden (Centiloid scale) using the Lowess method. Biomarkers were z‐scored anchored on young individuals ( n = 19, <28 years old). Positivity for plasma biomarkers and for each Braak region was determined when values surpassed 2.5 z‐score from the mean. Result Among the tested markers, plasma GFAP was the earliest to show abnormality as a function of Centiloid, occurring at 11 Centiloids. For MK6240, Braak I was the first regions to became positive (at 22 Centiloids), followed by Braak II (at 32 Centiloids), Braak IV (at 42 Centiloids), Braak III (at 52 Centiloids), Braak V (at 70 Centiloids) and Braak VI (at 123 Centiloid; Figure 1A). For Flortaucipir the first region to became positive was Braak IV (at 50 Centiloids), followed by Braak I and II (both at 56 Centiloids), Braak III (at 85 Centiloids) and Braak V (at 92 Centiloids). Braak VI region did not reach the threshold for positivity in our population (Figure 1B). Plasma p ‐tau217 reached the positivity threshold at 38 Centiloids. Conclusion MK6240 follows a Braak stage‐like pattern of abnormality as a function of Aβ burden, whereas for Flortaucipir, Braak IV is the first region to became abnormal. MK6240 becomes abnormal at lower Aβ burden levels compared to plasma p ‐tau217 and Flortaucipir but after plasma GFAP. Our findings suggest that MK6240 might detect tau pathology in earlier disease stages than Flortaucipir.

Abstract Background 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. Method 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. Result 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). Conclusion 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.

Abstract Background 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. Methods 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). Results 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). Conclusion 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).

Abstract Background 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. Methods 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. Results 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. Conclusion 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.

Abstract Background Precise cross‐tracer harmonization of tau PET imaging is essential for comparing tau burden across different tracers and studies. Here, we evaluated the performance of the universal tau PET scale (Uniτ) in harmonizing Flortaucipir, MK6240, PI2620, and RO948 tau PET images into a universal scale. Method We assessed 485 individuals across aging and AD spectrum scanned head‐to‐head with Flortaucipir and MK6240, and a subset of 90 individuals with additional PI2620 and RO948. We generated tau PET SUVRs using the inferior cerebellar gray matter as reference with a common 8mm FWHM. We estimated Uniτ parameters on a training set ( n = 200) by fitting a smoothed hyperbolic tangent equation to the Meta‐Temporal ROI anchored in the mean SUVR of young participants and the 90th percentile from cognitively impaired individuals. We compared tau positivity on the Uniτ scale with classifications from SUVRs (mean + 3 SD from Youngs). We applied the same equation to all brain voxels to generate Uniτ 3D images. Then, we extracted mean Uniτ values for key ROIs and correlated them with ROI‐based Uniτ values to evaluate voxel‐wise estimates. Result Using tracer‐specific parameters, Uniτ harmonized Flortaucipir, MK6240, PI2620, and RO948 to the same scale, aligning values near the identity line and confirming its applicability across tau PET tracers (Figure 1). For Flortaucipir, Uniτ tau positivity matched original SUVR classifications (ground truth), with only one mismatch for MK6240 (Figure 1). Applying the Uniτ transformation to all brain voxels effectively harmonized 3D images to a common scale, reducing visual variabilities (Figure 2). Furthermore, ROI‐based estimates and those extracted from the 3D Uniτ images were identical (Figure 3). Conclusion Our results indicate that tau PET tracers can be harmonized to a common scale using a large head‐to‐head dataset. The Uniτ scale can harmonize entire 3D tau PET images, suggesting that there is no need to use pre‐established ROIs, which would constrain the analysis to a few brain regions. Uniτ is freely accessible across platforms ( www.unitau.app ) for ROI and 3D tau PET harmonization.