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Background: A definitive diagnosis of progressive supranuclear palsy (PSP) can only be established through neuropathological evaluations where four cardinal tau lesions are identified. Relationships between regional tau burden and disease duration/age at death is unclear., Objective: To investigate relationships between tau burden in different brain regions and disease duration and age at death in PSP and determine whether association are influenced by PSP subtype (subcortical/cortical) or co-pathologies., Methods: We identified 45 patients with definite PSP who were evaluated at Mayo Clinic between 2009 and 2023, died and underwent histopathological evaluation. We performed semi-quantitative lesion count for each of four cardinal lesions (pretangles/globose neurofibrillary tangles, threads, tufted astrocytes, and coiled bodies) across 10 brain regions. We fit Bayesian linear hierarchical regression models to estimate the relationship between total pathological burden, and disease duration and age at death by region and the influence of subtype and co-pathologies., Results: Of the 45 patients, 18 (40 %) were female. Median age at death was 75 (56-87) years and median disease duration was 8 (3,15) years. Younger age at death was associated with greater total tau burden in the pallidum, red nucleus, striatum, and subthalamic nucleus (all p ≤ 0.01). Shorter disease duration was associated with greater total tau burden in the red nucleus (p = 0.05). There was no evidence for a difference in association between lesion types. PSP subtype and co-pathologies did not influence associations., Conclusions: The findings from this study suggest that age and disease duration influence burden of tau pathology in subcortical regions in PSP., Competing Interests: Declaration of competing interest As the corresponding author I confirm that this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere. We have no declaration of competing interest pertinent to this study. I confirm that I take full responsibility for the data, the analyses and interpretation, and the conduct of the research. I further confirm that the manuscript has been read and approved for submission by all named authors., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: To evaluate the performance of Alzheimer disease (AD) cerebrospinal fluid (CSF) biomarkers in a tertiary neurology clinic setting with high frequency of non-AD cases, including normal pressure hydrocephalus (NPH)., Methods: There were 534 patients who underwent AD CSF biomarkers (Roche Elecsys Aβ42, p-Tau181, total-Tau) from April 1, 2020, through April 23, 2021. A behavioral neurologist blinded to CSF results assigned a clinical diagnosis retrospectively on the basis of consensus criteria, and a neuroradiologist blinded to the diagnosis and CSF studies graded brain magnetic resonance images for indicators of CSF dynamics disorders. Associations between biomarkers, diagnoses, and imaging were assessed by χ 2 , analysis of covariance, and linear regression methods., Results: Median age at time of testing was 67 years (range, 19 to 96 years), median symptom duration was 2 years (range, 0.4 to 28 years), and median Short Test of Mental Status score was 30 (range, 0 to 38). Clinical diagnoses significantly correlated with different CSF biomarker values (χ 2 =208.3; P=10e-4). p-Tau181/Aβ42 ratios above 0.023 positively correlated with Alzheimer dementia (more than individual measures). This ratio also had the best performance for differentiating Alzheimer dementia from NPH (area under the curve, 0.869). Imaging markers supportive of CSF dynamics disorders correlated with low Aβ42, p-Tau181, and total-Tau., Conclusion: In a heterogeneous clinical population, abnormal p-Tau181/Aβ42 ratios (>0.023) have the strongest association with Alzheimer dementia and probably represent a comorbid AD pathologic component in persons clearly matching non-AD neurodegenerative syndromes. Altered CSF dynamics were associated with lower concentrations of AD CSF biomarkers regardless of clinical diagnosis, but the ratio compensates for these changes. In the appropriate clinical setting, an isolated abnormal Aβ42 should prompt consideration of NPH., (Copyright © 2024 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

We assessed white matter (WM) integrity in MAPT mutation carriers (16 asymptomatic, 5 symptomatic) compared to 31 non-carrier family controls using diffusion tensor imaging (DTI) (fractional anisotropy; FA, mean diffusivity; MD) and neurite orientation dispersion and density imaging (NODDI) (neurite density index; NDI, orientation and dispersion index; ODI). Linear mixed-effects models accounting for age and family relatedness revealed alterations across DTI and NODDI metrics in all mutation carriers and in symptomatic carriers, with the most significant differences involving fronto-temporal WM tracts. Asymptomatic carriers showed higher entorhinal MD and lower cingulum FA and patterns of higher ODI mostly involving temporal areas and long association and projections fibers. Regression models between estimated time to or time from disease and DTI and NODDI metrics in key regions (amygdala, cingulum, entorhinal, inferior temporal, uncinate fasciculus) in all carriers showed increasing abnormalities with estimated time to or time from disease onset, with FA and NDI showing the strongest relationships. Neurite-based metrics, particularly ODI, appear to be particularly sensitive to early WM involvement in asymptomatic carriers., Competing Interests: Declaration of Competing Interest CRJ receives no personal compensation from any commercial entity. He receives research support from National Institute of Health (NIH), the GHR Foundation, and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Clinic. RCP consults for Roche, Inc., Merck, Inc., Biogen, Inc., Genentech, Inc., Eisai, Inc., and Nestle, Inc. but does not receive significant fees due to NIH limitations from the U24 AG057437 Co-PI role. BB receives honoraria for SAB activities for the Tau Consortium, and has received research grant support but no personal compensation for clinical trials from Alector, Biogen, Transposon, Cognition Therapeutics, GE Healthcare. VKR receives research funding from the NIH and the Mangurian Foundation for Lewy Body disease research, has provided educational content for Medscape, is co-PI for a clinical trial supported by the Alzheimer’s Association, and is a site clinician for clinical trials supported by Eisai, the Alzheimer's Treatment and Research Institute at USC, and Transposon Therapeutics, Inc. EMR has no disclosure and receives funding from the NIH. ZKW is partially supported by the NIH/NIA and NIH/NINDS (1U19AG063911, FAIN: U19AG063911), Mayo Clinic Center for Regenerative Medicine, the gifts from the Donald G. and Jodi P. Heeringa Family, the Haworth Family Professorship in Neurodegenerative Diseases fund, and The Albertson Parkinson's Research Foundation. He serves as PI or Co-PI on Biohaven Pharmaceuticals, Inc. (BHV4157–206) and Vigil Neuroscience, Inc. (VGL101–01.002, VGL101–01.201, PET tracer development protocol, Csf1r biomarker and repository project, and ultra-high field MRI in the diagnosis and management of CSF1R-related adult-onset leukoencephalopathy with axonal spheroids and pigmented glia) projects/grants. He serves as Co-PI of the Mayo Clinic APDA Center for Advanced Research and as an external advisory board member for the Vigil Neuroscience, Inc., and as a consultant on neurodegenerative medical research for Eli Lilli & Company. KK consults Biogen, Inc.; receives research support from Avid Radiopharmaceuticals and Eli Lilly and receives funding from NIH and Alzheimer’s Drug Discovery Foundation., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Using positron emission tomography (PET)-derived amyloid and tau measurements from 1,495 participants, we explore the evolution of these values over time via an accelerated failure time (AFT) model. The AFT model assumes a shared pattern of progression, but one which is shifted earlier or later in time for each individual; an individual's time shift for amyloid and for tau are assumed to be linked. The resulting pattern for each outcome consists of an earlier indolent phase followed by sharp progression of the accumulation rate. APOE ε4 shifts the amyloid curve leftward (earlier) by 6.1 years, and the tau curve leftward by 2.6 years. Female sex shifts the amyloid curve leftward by 2.4 years and the tau curve leftward by 2.6 years. Per-person shifts (i.e., the individual's deviation from the population mean) for the onset of amyloid accumulation ranged from 13 years earlier to 13 years later (10th to 90th percentile) than average and 11 years earlier to 14 years later for tau, with an estimated correlation of 0.49. The average delay between amyloid increase and tau increase was 13.3 years., (Copyright © 2021. Published by Elsevier Inc.)

Since tau PET tracers were introduced, investigators have quantified them using a wide variety of automated methods. As longitudinal cohort studies acquire second and third time points of serial within-person tau PET data, determining the best pipeline to measure change has become crucial. We compared a total of 415 different quantification methods (each a combination of multiple options) according to their effects on a) differences in annual SUVR change between clinical groups, and b) longitudinal measurement repeatability as measured by the error term from a linear mixed-effects model. Our comparisons used MRI and Flortaucipir scans of 97 Mayo Clinic study participants who clinically either: a) were cognitively unimpaired, or b) had cognitive impairments that were consistent with Alzheimer's disease pathology. Tested methods included cross-sectional and longitudinal variants of two overarching pipelines (FreeSurfer 6.0, and an in-house pipeline based on SPM12), three choices of target region (entorhinal, inferior temporal, and a temporal lobe meta-ROI), five types of partial volume correction (PVC) (none, two-compartment, three-compartment, geometric transfer matrix (GTM), and a tau-specific GTM variant), seven choices of reference region (cerebellar crus, cerebellar gray matter, whole cerebellum, pons, supratentorial white matter, eroded supratentorial WM, and a composite of eroded supratentorial WM, pons, and whole cerebellum), two choices of region masking (GM or GM and WM), and two choices of statistic (voxel-wise mean vs. median). Our strongest findings were: 1) larger temporal-lobe target regions greatly outperformed entorhinal cortex (median sample size estimates based on a hypothetical clinical trial were 520-526 vs. 1740); 2) longitudinal processing pipelines outperformed cross-sectional pipelines (median sample size estimates were 483 vs. 572); and 3) reference regions including supratentorial WM outperformed traditional cerebellar and pontine options (median sample size estimates were 370 vs. 559). Altogether, our results favored longitudinally SUVR methods and a temporal-lobe meta-ROI that includes adjacent (juxtacortical) WM, a composite reference region (eroded supratentorial WM + pons + whole cerebellum), 2-class voxel-based PVC, and median statistics., Competing Interests: Declaration of Competing Interest Christopher Schwarz receives research support from the NIH. Terry Therneau reports no disclosures. Stephen Weigand reports no disclosures. Jeffrey Gunter reports no disclosures. Val Lowe consults for Bayer Schering Pharma, Piramal Life Sciences, Eisai, Inc., and Merck Research and receives research support from GE Healthcare, Siemens Molecular Imaging, AVID Radiopharmaceuticals and the NIH (NIA, NCI). Scott Przybelski reports no disclosures. Prashanthi Vemuri is funded by the NIH. Matthew Senjem has owned stock in medical related companies, unrelated to the current work within the past 12 months: Align Technology, Inc., Inovio Pharmaceuticals, Inc., LHC Group, Inc., Mesa Laboratories, Inc., Natus Medical Inc., and Varex Imaging Corporation. He has also owned stock in these medical related companies within the past three years, unrelated to the current work: CRISPR Therapeutics, Gilead Sciences, Inc., Ionis Pharmaceuticals, Johnson & Johnson, Medtronic, Inc. Hugo Botha is funded by the NIH. Kejal Kantarci serves on the data safety monitoring board for Takeda Global Research and Development Center, Inc., receives research support from Avid Radioparmaceuticals and Eli Lilly, and receives funding from NIH and Alzheimer's Drug Discovery Foundation. Bradley Boeve has served as an investigator for clinical trials sponsored by Axovant and Biogen. He receives royalties from the publication of a book entitled Behavioral Neurology Of Dementia (Cambridge Medicine, 2009, 2017). He serves on the Scientific Advisory Board of the Tau Consortium. He receives research support from the NIH, the Mayo Clinic Dorothy and Harry T. Mangurian Jr. Lewy Body Dementia Program and the Little Family Foundation. Jennifer Whitwell is funded by the NIH. Keith Josephs is funded by the NIH. Ronald Petersen is a consultant for Roche, Inc., Biogen, Inc., and Eisai, Inc., served on a DSMB for Genentech, Inc.; receives royalties from publishing Mild Cognitive Impairment (Oxford University Press, 2003) and UpToDate; and receives research support from the NIH (P30 AG062677 (PI) and U01-AG006786 (PI), R01-AG011378 (Co-I), and U01–024904 (Co-I)). David Knopman serves on a Data Safety Monitoring Board for the DIAN study; is an investigator in clinical trials sponsored by Biogen and Lilly Pharmaceuticals; and receives research support from the NIH. Clifford Jack serves on an independent data monitoring board for Roche, has served as a speaker for Eisai, and consulted for Biogen, but he receives no personal compensation from any commercial entity. He receives research support from NIH and the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Clinic., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

The mechanisms through which tau and amyloid-beta (Aβ) accumulate in the brain of Alzheimer's disease patients may differ but both are related to neuronal networks. We examined such mechanisms on neuroimaging in 58 participants with atypical Alzheimer's disease (posterior cortical atrophy or logopenic progressive aphasia). Participants underwent Aβ-PET, longitudinal tau-PET, structural MRI and resting-state functional MRI, which was analyzed with graph theory. Regions with high levels of Aβ were more likely to be functional hubs, with a high number of functional connections important for resilience to cascading network failures. Regions with high levels of tau were more likely to have low clustering coefficients and degrees, suggesting a lack of trophic support or vulnerability to local network failures. Regions strongly functionally connected to the disease epicenters were more likely to have higher levels of tau and, less strongly, of Aβ. The regional rate of tau accumulation was associated with tau levels in functionally connected regions, in support of tau accumulation in a functional network. This study elucidates the relations of tau and Aβ to functional connectivity metrics in atypical Alzheimer's disease, strengthening the hypothesis that the spread of the 2 proteins is driven by different biological mechanisms related to functional networks., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Objective: To examine associations between tau and amyloid β (Aβ) molecular positron emission tomography (PET) and both Alzheimer-related pathology and 4-repeat tau pathology in autopsy-confirmed frontotemporal lobar degeneration (FTLD)., Methods: Twenty-four patients had [ 18 F]-flortaucipir-PET and died with FTLD (progressive supranuclear palsy [PSP], n = 10; corticobasal degeneration [CBD], n = 10; FTLD-TDP, n = 3; and Pick disease, n = 1). All but 1 had Pittsburgh compound B (PiB)-PET. Braak staging, Aβ plaque and neurofibrillary tangle counts, and semiquantitative tau lesion scores were performed. Flortaucipir standard uptake value ratios (SUVRs) were calculated in a temporal meta region of interest (meta-ROI), entorhinal cortex and cortical/subcortical regions selected to match the tau lesion analysis. Global PiB SUVR was calculated. Autoradiography was performed in 1 PSP patient, with digital pathology used to quantify tau burden., Results: Nine cases (37.5%) had Aβ plaques. Global PiB SUVR correlated with Aβ plaque count, with 100% specificity and 50% sensitivity for diffuse plaques. Twenty-one (87.5%) had Braak stages I to IV. Flortaucipir correlated with neurofibrillary tangle counts in entorhinal cortex, but entorhinal and meta-ROI SUVRs were not elevated in Braak IV or primary age-related tauopathy. Flortaucipir uptake patterns differed across FTLD pathologies and could separate PSP and CBD. Flortaucipir correlated with tau lesion score in red nucleus and midbrain tegmentum across patients, but not in cortical or basal ganglia regions. Autoradiography demonstrated minimal uptake of flortaucipir, although flortaucipir correlated with quantitative tau burden across regions., Interpretation: Molecular PET shows expected correlations with Alzheimer-related pathology but lacks sensitivity to detect mild Alzheimer pathology in FTLD. Regional flortaucipir uptake was able to separate CBD and PSP. ANN NEUROL 2020;88:1009-1022., (© 2020 American Neurological Association.)

Clinical trials with anti-tau drugs will need to target individuals at risk of accumulating tau. Our objective was to identify variables available in a research setting that predict future rates of tau PET accumulation separately among individuals who were either cognitively unimpaired or cognitively impaired. All 337 participants had: a baseline study visit with MRI, amyloid PET, and tau PET exams, at least one follow-up tau PET exam; and met clinical criteria for membership in one of two clinical diagnostic groups: cognitively unimpaired (n = 203); or cognitively impaired (n = 134, a combined group of participants with either mild cognitive impairment or dementia with Alzheimer's clinical syndrome). Our primary analyses were in these two clinical groups; however, we also evaluated subgroups dividing the unimpaired group by normal/abnormal amyloid PET and the impaired group by clinical phenotype (mild cognitive impairment, amnestic dementia, and non-amnestic dementia). Linear mixed effects models were used to estimate associations between age, sex, education, APOE genotype, amyloid and tau PET standardized uptake value ratio (SUVR), cognitive performance, cortical thickness, and white matter hyperintensity volume at baseline, and the rate of subsequent tau PET accumulation. Log-transformed tau PET SUVR was used as the response and rates were summarized as annual per cent change. A temporal lobe tau PET meta-region of interest was used. In the cognitively unimpaired group, only higher baseline amyloid PET was a significant independent predictor of higher tau accumulation rates (P < 0.001). Higher rates of tau accumulation were associated with faster rates of cognitive decline in the cognitively unimpaired subgroup with abnormal amyloid PET (P = 0.03), but among the subgroup with normal amyloid PET. In the cognitively impaired group, younger age (P = 0.02), higher baseline amyloid PET (P = 0.05), APOE ε4 (P = 0.05), and better cognitive performance (P = 0.05) were significant independent predictors of higher tau accumulation rates. Among impaired individuals, faster cognitive decline was associated with faster rates of tau accumulation (P = 0.01). While we examined many possible predictor variables, our results indicate that screening of unimpaired individuals for potential inclusion in anti-tau trials may be straightforward because the only independent predictor of high tau rates was amyloidosis. In cognitively impaired individuals, imaging and clinical variables consistent with early onset Alzheimer's disease phenotype were associated with higher rates of tau PET accumulation suggesting this may be a highly advantageous group in which to conduct proof-of-concept clinical trials that target tau-related mechanisms. The nature of the dementia phenotype (amnestic versus non-amnestic) did not affect this conclusion., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Large phenotypically diverse research cohorts with both amyloid and tau PET have only recently come into existence. Our objective was to determine relationships between the bivariate distribution of amyloid-β and tau on PET and established clinical syndromes that are relevant to cognitive ageing and dementia. All individuals in this study were enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study of cognitive ageing, or the Mayo Alzheimer Disease Research Center, a longitudinal study of individuals recruited from clinical practice. We studied 1343 participants who had amyloid PET and tau PET from 2 April 2015 to 3 May 2019, and met criteria for membership in one of five clinical diagnostic groups: cognitively unimpaired, mild cognitive impairment, frontotemporal dementia, probable dementia with Lewy bodies, and Alzheimer clinical syndrome. We examined these clinical groups in relation to the bivariate distribution of amyloid and tau PET values. Individuals were grouped into amyloid (A)/tau (T) quadrants based on previously established abnormality cut points of standardized uptake value ratio 1.48 (A) and 1.33 (T). Individual participants largely fell into one of three amyloid/tau quadrants: low amyloid and low tau (A-T-), high amyloid and low tau (A+T-), or high amyloid and high tau (A+T+). Seventy per cent of cognitively unimpaired and 74% of FTD participants fell into the A-T- quadrant. Participants with mild cognitive impairment spanned the A-T- (42%), A+T- (28%), and A+T+ (27%) quadrants. Probable dementia with Lewy body participants spanned the A-T- (38%) and A+T- (44%) quadrants. Most (89%) participants with Alzheimer clinical syndrome fell into the A+T+ quadrant. These data support several conclusions. First, among 1343 participants, abnormal tau PET rarely occurred in the absence of abnormal amyloid PET, but the reverse was common. Thus, with rare exceptions, amyloidosis appears to be required for high levels of 3R/4R tau deposition. Second, abnormal amyloid PET is compatible with normal cognition but highly abnormal tau PET is not. These two conclusions support a dynamic biomarker model in which Alzheimer's disease is characterized first by the appearance of amyloidosis and later by tauopathy, with tauopathy being the proteinopathy associated with clinical symptoms. Third, bivariate amyloid and tau PET relationships differed across clinical groups and thus have a role for clarifying the aetiologies underlying neurocognitive clinical syndromes., (© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain.)

See Gordon (doi:10.1093/brain/awy052) for a scientific commentary on this article.Predicting underlying pathology based on clinical presentation has historically proven difficult, especially in older cohorts. Age-related hippocampal sclerosis may account for a significant proportion of elderly participants with amnestic dementia. Advances in molecular neuroimaging have allowed for detailed biomarker-based phenotyping, but in the absence of antemortem markers of hippocampal sclerosis, cases of mixed pathology remain problematic. We evaluated the utility of 18F-FDG-PET to differentiate flortaucipir tau PET negative from flortaucipir positive amnestic mild cognitive impairment and dementia and used an autopsy confirmed cohort to test the hypothesis that hippocampal sclerosis might account for the observed pattern. We identified impaired participants (Clinical Dementia Rating > 0) with amnestic presentations ≥ 75 years who had MRI and PET imaging with 18F-FDG (glucose metabolism), Pittsburgh compound B (amyloid) and flortaucipir (tau) performed within a year of cognitive assessment. These were stratified into amyloid positive/negative and tau positive/negative according to the A/T/N classification scheme. Our sample included 15 amyloid and tau-positive participants, and nine tau-negative participants (five of whom were amyloid-positive). For the autopsy cohort, sequential cases with antemortem 18F-FDG-PET were screened and those with TDP-43-negative Alzheimer's disease (10 cases) and TDP-43-positive hippocampal sclerosis (eight cases) were included. We compared each group to controls and to each other in a voxel-based analysis, and supplemented this with a region of interest-based analysis comparing medial to inferior temporal metabolism. Tau-positive and negative cases did not differ on neuropsychological testing or structural magnetic resonance biomarkers. Tau-negative cases had focal medial temporal and posterior cingulate/retrosplenial hypometabolism regardless of amyloid status, whereas tau-positive cases had additional lateral parietal and inferior temporal involvement. The inferior/medial temporal metabolism ratio was significantly different between the groups with the tau-negative group having a higher ratio. In the autopsy series, hippocampal sclerosis cases had greater medial temporal hypometabolism than Alzheimer's disease cases, who had more parietal and lateral/inferior temporal hypometabolism. Again, the ratio between temporal regions of interest differed significantly between groups. Two of the tau-negative patients, both of whom had an elevated inferior/medial temporal ratio, came to autopsy during the study and were found to have hippocampal sclerosis. Our finding that tau-negative amnestic mild cognitive impairment and dementia is associated with focal medial temporal and posterior cingulate hypometabolism extends prior reports in amyloid-negative cases. The inferior/medial temporal metabolism ratio can help identify tau-negative cases of amnestic dementia and may serve as a biomarker for hippocampal sclerosis.

Functionally related brain regions are selectively vulnerable to Alzheimer's disease pathophysiology. However, molecular markers of this pathophysiology (i.e., beta-amyloid and tau aggregates) have discrepant spatial and temporal patterns of progression within these selectively vulnerable brain regions. Existing reductionist pathophysiologic models cannot account for these large-scale spatiotemporal inconsistencies. Within the framework of the recently proposed cascading network failure model of Alzheimer's disease, however, these large-scale patterns are to be expected. This model postulates the following: 1) a tau-associated, circumscribed network disruption occurs in brain regions specific to a given phenotype in clinically normal individuals; 2) this disruption can trigger phenotype independent, stereotypic, and amyloid-associated compensatory brain network changes indexed by changes in the default mode network; 3) amyloid deposition marks a saturation of functional compensation and portends an acceleration of the inciting phenotype specific, and tau-associated, network failure. With the advent of in vivo molecular imaging of tau pathology, combined with amyloid and functional network imaging, it is now possible to investigate the relationship between functional brain networks, tau, and amyloid across the disease spectrum within these selectively vulnerable brain regions. In a large cohort (n = 218) spanning the Alzheimer's disease spectrum from young, amyloid negative, cognitively normal subjects to Alzheimer's disease dementia, we found several distinct spatial patterns of tau deposition, including 'Braak-like' and 'non-Braak-like', across functionally related brain regions. Rather than arising focally and spreading sequentially, elevated tau signal seems to occur system-wide based on inferences made from multiple cross-sectional analyses we conducted looking at regional patterns of tau signal. Younger age-of-disease-onset was associated with 'non-Braak-like' patterns of tau, suggesting an association with atypical clinical phenotypes. As predicted by the cascading network failure model of Alzheimer's disease, we found that amyloid is a partial mediator of the relationship between functional network failure and tau deposition in functionally connected brain regions. This study implicates large-scale brain networks in the pathophysiology of tau deposition and offers support to models incorporating large-scale network physiology into disease models linking tau and amyloid, such as the cascading network failure model of Alzheimer's disease., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background and purpose: The non‐fluent/agrammatic variant of primary progressive aphasia (agPPA) is a heterogeneous diagnosis wherein some individuals have apraxia of speech (AOS). When agPPA includes AOS, a tauopathy is the likely underlying pathology. Recently, [18F]AV‐1451 was developed for the in‐vivo assessment of tau. In this study, we compared patterns of tau tracer uptake in patients with agPPA with and without AOS. Methods: Nine patients with agPPA (four without AOS) underwent tau positron emission tomography imaging with [18F]AV‐1451. Uptake of [18F]AV‐1451 was assessed as cortical to cerebellar crus ratio (standard uptake value ratio) in cortical regions of interest measured using the MCALT atlas and compared voxel‐wise in SPM12. Each patient was age‐ and sex‐matched to three controls. Results: The agPPA without AOS showed uptake in the left frontal and temporal lobes, whereas agPPA with AOS showed uptake in the bilateral supplementary motor areas, frontal lobes, precuneus and precentral gyrus relative to controls. The left precentral gyrus had uptake in agPPA with AOS relative to those without AOS. Conclusions: This cross‐sectional study suggests that [18F]AV‐1451 uptake in the precentral gyrus is implicated in AOS in agPPA. [ABSTRACT FROM AUTHOR]