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 < 0.0001, R2 = 0.75). No single region of interest standardized uptake value ratio accurately segregated individuals into the six topographic Braak stages. Sixty-eight per cent of the cognitively unimpaired elderly amyloid-β-positive and 37% of the cognitively unimpaired elderly amyloid-β-negative subjects displayed tau deposition, at least in the transentorhinal cortex (Braak I). Tau deposition solely in the transentorhinal cortex was associated with an elevated prevalence of amyloid-β, neurodegeneration, and cognitive impairment (P < 0.0001). 18F-MK-6240 deposition in regions corresponding to Braak IV–VI was associated with the highest prevalence of neurodegeneration, whereas in Braak V–VI regions with the highest prevalence of cognitive impairment. Our results suggest that the hierarchical six-stage Braak model using 18F-MK-6240 imaging provides an index of early and late tau accumulation as well as disease stage in preclinical and symptomatic individuals. Tau PET Braak staging using high affinity tracers has the potential to be incorporated in the diagnosis of living patients with Alzheimer’s disease in the near future.

IntroductionMild behavioral impairment (MBI) is characterized by the emergence of neuropsychiatric symptoms in elderly persons. Here, we examine the associations between MBI and Alzheimer's disease (AD) biomarkers in asymptomatic elderly individuals.MethodsNinety‐six cognitively normal elderly individuals underwent MRI, [18F]AZD4694 β‐amyloid‐PET, and [18F]MK6240 tau‐PET. MBI was assessed using the MBI Checklist (MBI‐C). Pearson's correlations and voxel‐based regressions were used to evaluate the relationship between MBI‐C score and [18F]AZD4694 retention, [18F]MK6240 retention, and gray matter (GM) volume.ResultsPearson correlations revealed a positive relationship between MBI‐C score and global and striatal [18F]AZD4694 standardized uptake value ratios (SUVRs). Voxel‐based regression analyses revealed a positive correlation between MBI‐C score and [18F]AZD4694 retention. No significant correlations were found between MBI‐C score and [18F]MK6240 retention or GM volume.ConclusionWe demonstrate for the first time a link between MBI and early AD pathology in a cognitively intact elderly population, supporting the use of the MBI‐C as a metric to enhance clinical trial enrolment.

IMPORTANCE Apolipoprotein E ε4 (APOEε4) is the single most important genetic risk factor for Alzheimer disease. While APOEε4 is associated with increased amyloid-β burden, its association with cerebral tau pathology has been controversial. OBJECTIVE To determine whether APOEε4 is associated with medial temporal tau pathology independently of amyloid-β, sex, clinical status, and age. DESIGN, SETTING, AND PARTICIPANTS This is a study of 2 cross-sectional cohorts of volunteers who were cognitively normal, had mild cognitive impairment (MCI), or had Alzheimer disease dementia: the Translational Biomarkers in Aging and Dementia (TRIAD) study (data collected between October 2017 and July 2019) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (collected between November 2015 and June 2019). The first cohort (TRIAD) comprised cognitively normal elderly participants (n = 124), participants with MCI (n = 50), and participants with Alzheimer disease (n = 50) who underwent tau positron emission tomography (PET) with fluorine 18-labeled MK6240 and amyloid-β PET with [ 18 F]AZD4694. The second sample (ADNI) was composed of cognitively normal elderly participants (n = 157), participants with MCI (n = 83), and participants with Alzheimer disease (n = 25) who underwent tau PET with [ 18 F]flortaucipir and amyloid-β PET with [ 18 F]florbetapir. Exclusion criteria were a history of other neurological disorders, stroke, or head trauma. There were 489 eligible participants, selected based on availability of amyloid-PET, tau-PET, magnetic resonance imaging, and genotyping for APOEε4. Forty-five young adults (<30 years) from the TRIAD cohort were not selected for this study. MAIN OUTCOMES AND MEASURES A main association between APOEε4 and tau-PET standardized uptake value ratio, correcting for age, sex, clinical status, and neocortical amyloid-PET standardized uptake value ratio. RESULTS The mean (SD) age of the 489 participants was 70.5 (7.1) years; 171 were APOEε4 carriers (34.9%), and 230 of 489 were men. In both cohorts, APOEε4 was associated in increased tau-PET uptake in the entorhinal cortex and hippocampus independently of amyloid-β, sex, age, and clinical status after multiple comparisons correction (TRIAD: β = 0.33; 95% CI, 0.19-0.49; ADNI: β = 0.13; 95% CI, 0.08-0.19; P < .001). CONCLUSIONS AND RELEVANCE Our results indicate that the elevated risk of developing dementia conferred by APOEε4 genotype involves mechanisms associated with both amyloid-β and tau aggregation. These results contribute to an evolving framework in which APOEε4 has deleterious consequences in Alzheimer disease beyond its link with amyloid-β and suggest APOEε4 as a potential target for future disease-modifying therapeutic trials targeting tau pathology.

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Structural and functional MRI unveil many hidden properties of the human brain. We performed this multi-class classification study on selected subjects from the publically available attention deficit hyperactivity disorder ADHD-200 dataset of patients and healthy children. The dataset has three groups, namely, ADHD inattentive, ADHD combined, and typically developing. We calculated the global averaged functional connectivity maps across the whole cortex to extract anatomical atlas parcellation based features from the resting-state fMRI (rs-fMRI) data and cortical parcellation based features from the structural MRI (sMRI) data. In addition, the preprocessed image volumes from both of these modalities followed an ANOVA analysis separately using all the voxels. This study utilized the average measure from the most significant regions acquired from ANOVA as features for classification in addition to the multi-modal and multi-measure features of structural and functional MRI data. We extracted most discriminative features by hierarchical sparse feature elimination and selection algorithm. These features include cortical thickness, image intensity, volume, cortical thickness standard deviation, surface area, and ANOVA based features respectively. An extreme learning machine performed both the binary and multi-class classifications in comparison with support vector machines. This article reports prediction accuracy of both unimodal and multi-modal features from test data. We achieved 76.190% (p < 0.0001) classification accuracy in multi-class settings as well as 92.857% (p < 0.0001) classification accuracy in binary settings. In addition, we found ANOVA-based significant regions of the brain that also play a vital role in the classification of ADHD. Thus, from a clinical perspective, this multi-modal group analysis approach with multi-measure features may improve the accuracy of the ADHD differential diagnosis.

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Multimodal features of structural and functional magnetic resonance imaging (MRI) of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE) and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001) accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function) support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.