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Tau is one of several proteins associated with frontotemporal dementia. While knowing which protein is causing a patient’s disease is crucial, no biomarker currently exists for identifying tau in vivo in frontotemporal dementia. The objective of this study was to investigate the potential for the promising 18F-MK-6240 PET tracer to bind to tau in vivo in genetic frontotemporal dementia. We enrolled subjects with genetic frontotemporal dementia, who constitute an ideal population for testing because their pathology is already known based on their mutation. Ten participants (three with symptomatic P301L and R406W MAPT mutations expected to show tau binding, three with presymptomatic MAPT mutations and four with non-tau mutations who acted as disease controls) underwent clinical characterization, tau-PET scanning with 18F-MK-6240, amyloid-PET imaging with 18F-NAV-4694 to rule out confounding Alzheimer’s pathology, and high-resolution structural MRI. Tau-PET scans of all three symptomatic MAPT carriers demonstrated at least mild 18F-MK-6240 binding in expected regions, with particularly strong binding in a subject with an R406W MAPT mutation (known to be associated with Alzheimer’s like neurofibrillary tangles). Two asymptomatic MAPT carriers estimated to be 5 years from disease onset both showed modest 18F-MK-6240 binding, while one ∼30 years from disease onset did not exhibit any binding. Additionally, four individuals with symptomatic frontotemporal dementia caused by a non-tau mutation were scanned (two C9orf72; one GRN; one VCP): 18F-MK-6240 scans were negative for three subjects, while one advanced C9orf72 case showed minimal regionally non-specific binding. All 10 amyloid-PET scans were negative. Furthermore, a general linear model contrasting genetic frontotemporal dementia subjects to a set of 83 age-matched controls showed significant binding only in the MAPT carriers in selected frontal, temporal and subcortical regions. In summary, our findings demonstrate mild but significant binding of MK-6240 in amyloid-negative P301L and R406W MAPT mutation subjects, with higher standardized uptake value ratio in the R406W mutation associated with the presence of NFTs, and little non-specific binding. These results highlight that a positive 18F-MK-6240 tau-PET does not necessarily imply a diagnosis of Alzheimer’s disease and point towards a potential use for 18F-MK-6240 as a biomarker in certain tauopathies beyond Alzheimer’s, although further patient recruitment and autopsy studies will be necessary to determine clinical applicability.

Neurofilament light (NfL) is a marker of neuroaxonal injury, a prominent feature of Alzheimer’s disease. It remains uncertain, however, how it relates to amyloid and tau pathology or neurodegeneration across the Alzheimer’s disease continuum. The aim of this study was to investigate how plasma NfL relates to amyloid and tau PET and MRI measures of brain atrophy in participants with and without cognitive impairment. We retrospectively examined the association between plasma NfL and MRI measures of grey/white matter volumes in the Alzheimer’s Disease Neuroimaging Initiative [ADNI: n = 1149; 382 cognitively unimpaired control subjects and 767 cognitively impaired participants (mild cognitive impairment n = 420, Alzheimer’s disease dementia n = 347)]. Longitudinal plasma NfL was measured using single molecule array (Simoa) technology. Cross-sectional associations between plasma NfL and PET amyloid and tau measures were independently assessed in two cohorts: ADNI [n = 198; 110 cognitively unimpaired, 88 cognitively impaired (MCI n = 67, Alzheimer’s disease dementia n = 21), data accessed October 2018]; and Translational Biomarkers in Aging and Dementia [TRIAD, n = 116; 74 cognitively unimpaired, 42 cognitively impaired (MCI n = 16, Alzheimer’s disease dementia n = 26), data obtained November 2017 to January 2019]. Associations between plasma NfL and imaging-derived measures were examined voxel-wise using linear regression (cross-sectional) and linear mixed effect models (longitudinal). Cross-sectional analyses in both cohorts showed that plasma NfL was associated with PET findings in brain regions typically affected by Alzheimer’s disease; associations were specific to amyloid PET in cognitively unimpaired and tau PET in cognitively impaired (P

Braak stages of tau neurofibrillary tangle accumulation have been incorporated in the criteria for the neuropathological diagnosis of Alzheimer’s disease. It is expected that Braak staging using brain imaging can stratify living individuals according to their individual patterns of tau deposition, which may prove crucial for clinical trials and practice. However, previous studies using the first-generation tau PET agents have shown a low sensitivity to detect tau pathology in areas corresponding to early Braak histopathological stages (∼20% of cognitively unimpaired elderly with tau deposition in regions corresponding to Braak I–II), in contrast to ∼80–90% reported in post-mortem cohorts. Here, we tested whether the novel high affinity tau tangles tracer 18F-MK-6240 can better identify individuals in the early stages of tau accumulation. To this end, we studied 301 individuals (30 cognitively unimpaired young, 138 cognitively unimpaired elderly, 67 with mild cognitive impairment, 54 with Alzheimer’s disease dementia, and 12 with frontotemporal dementia) with amyloid-β 18F-NAV4694, tau 18F-MK-6240, MRI, and clinical assessments. 18F-MK-6240 standardized uptake value ratio images were acquired at 90–110 min after the tracer injection. 18F-MK-6240 discriminated Alzheimer’s disease dementia from mild cognitive impairment and frontotemporal dementia with high accuracy (∼85–100%). 18F-MK-6240 recapitulated topographical patterns consistent with the six hierarchical stages proposed by Braak in 98% of our population. Cognition and amyloid-β status explained most of the Braak stages variance (P

Estimated years to symptom onset (EYO) is a measurement developed to track disease progression in patients with genetic Alzheimer’s disease. Vogel et al. apply this concept to individuals with a parental history of sporadic Alzheimer’s disease. A neuroimaging-based model successfully predicts EYO and years to clinical conversion in unseen data. See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article., See Tijms and Visser (doi:10.1093/brain/awy113) for a scientific commentary on this article. Alzheimer’s disease is preceded by a lengthy ‘preclinical’ stage spanning many years, during which subtle brain changes occur in the absence of overt cognitive symptoms. Predicting when the onset of disease symptoms will occur is an unsolved challenge in individuals with sporadic Alzheimer’s disease. In individuals with autosomal dominant genetic Alzheimer’s disease, the age of symptom onset is similar across generations, allowing the prediction of individual onset times with some accuracy. We extend this concept to persons with a parental history of sporadic Alzheimer’s disease to test whether an individual’s symptom onset age can be informed by the onset age of their affected parent, and whether this estimated onset age can be predicted using only MRI. Structural and functional MRIs were acquired from 255 ageing cognitively healthy subjects with a parental history of sporadic Alzheimer’s disease from the PREVENT-AD cohort. Years to estimated symptom onset was calculated as participant age minus age of parental symptom onset. Grey matter volume was extracted from T1-weighted images and whole-brain resting state functional connectivity was evaluated using degree count. Both modalities were summarized using a 444-region cortical-subcortical atlas. The entire sample was divided into training (n = 138) and testing (n = 68) sets. Within the training set, individuals closer to or beyond their parent’s symptom onset demonstrated reduced grey matter volume and altered functional connectivity, specifically in regions known to be vulnerable in Alzheimer’s disease. Machine learning was used to identify a weighted set of imaging features trained to predict years to estimated symptom onset. This feature set alone significantly predicted years to estimated symptom onset in the unseen testing data. This model, using only neuroimaging features, significantly outperformed a similar model instead trained with cognitive, genetic, imaging and demographic features used in a traditional clinical setting. We next tested if these brain properties could be generalized to predict time to clinical progression in a subgroup of 26 individuals from the Alzheimer’s Disease Neuroimaging Initiative, who eventually converted either to mild cognitive impairment or to Alzheimer’s dementia. The feature set trained on years to estimated symptom onset in the PREVENT-AD predicted variance in time to clinical conversion in this separate longitudinal dataset. Adjusting for participant age did not impact any of the results. These findings demonstrate that years to estimated symptom onset or similar measures can be predicted from brain features and may help estimate presymptomatic disease progression in at-risk individuals.