Associations between tau, Aβ, and cortical thickness with cognition in Alzheimer disease.
TL;DR: The findings suggest that tau PET is more sensitive than Aβ PET and measures of cortical thickness for detecting early cognitive changes in preclinical AD and both [18F]flortaucipir PET and cortical thickness show strong cognitive correlates at the clinical stages of AD.
Abstract: Objective To examine the cross-sectional associations between regional tau, β-amyloid (Aβ), and cortical thickness and neuropsychological function across the preclinical and clinical spectrum of Alzheimer disease (AD). Methods We included 106 participants from the Swedish Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably (BioFINDER) study, of whom 33 had preclinical AD (Aβ-positive cognitively normal individuals), 25 had prodromal AD (Aβ-positive mild cognitive impairment), and 48 had probable AD dementia. All underwent [18F]flortaucipir (tau) and structural MRI (cortical thickness), and 88 of 106 underwent [18F]flutemetamol (Aβ) PET. Linear regression models adjusted for age, sex, and education were performed to examine associations between 7 regions of interest and 7 neuropsychological tests for all 3 imaging modalities. Results In preclinical AD, [18F]flortaucipir, but not [18F]flutemetamol or cortical thickness, was associated with decreased global cognition, memory, and processing speed (range standardized β = 0.35–0.52, p Conclusion Our findings suggest that tau PET is more sensitive than Aβ PET and measures of cortical thickness for detecting early cognitive changes in preclinical AD. Furthermore, both [18F]flortaucipir PET and cortical thickness show strong cognitive correlates at the clinical stages of AD.
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TL;DR: Plasma P-tau18 level increased with progression of Alzheimer’s disease (AD) and differentiated AD dementia from other neurodegenerative diseases, supporting its further development as a blood-based biomarker for AD.
Abstract: Plasma phosphorylated tau181 (P-tau181) might be increased in Alzheimer’s disease (AD), but its usefulness for differential diagnosis and prognosis is unclear. We studied plasma P-tau181 in three cohorts, with a total of 589 individuals, including cognitively unimpaired participants and patients with mild cognitive impairment (MCI), AD dementia and non-AD neurodegenerative diseases. Plasma P-tau181 was increased in preclinical AD and further increased at the MCI and dementia stages. It correlated with CSF P-tau181 and predicted positive Tau positron emission tomography (PET) scans (area under the curve (AUC) = 0.87–0.91 for different brain regions). Plasma P-tau181 differentiated AD dementia from non-AD neurodegenerative diseases with an accuracy similar to that of Tau PET and CSF P-tau181 (AUC = 0.94–0.98), and detected AD neuropathology in an autopsy-confirmed cohort. High plasma P-tau181 was associated with subsequent development of AD dementia in cognitively unimpaired and MCI subjects. In conclusion, plasma P-tau181 is a noninvasive diagnostic and prognostic biomarker of AD, which may be useful in clinical practice and trials. Plasma P-tau18 level increased with progression of Alzheimer’s disease (AD) and differentiated AD dementia from other neurodegenerative diseases, supporting its further development as a blood-based biomarker for AD.
565 citations
TL;DR: Novel insights from preclinical research are outlined that implicate apolipoprotein E, the endocytic system, cholesterol metabolism and microglial activation as Aβ-independent regulators of tau pathology in Alzheimer disease.
Abstract: The global epidemic of Alzheimer disease (AD) is worsening, and no approved treatment can revert or arrest progression of this disease. AD pathology is characterized by the accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles in the brain. Genetic data, as well as autopsy and neuroimaging studies in patients with AD, indicate that Aβ plaque deposition precedes cortical tau pathology. Because Aβ accumulation has been considered the initial insult that drives both the accumulation of tau pathology and tau-mediated neurodegeneration in AD, the development of AD therapeutics has focused mostly on removing Aβ from the brain. However, striking preclinical evidence from AD mouse models and patient-derived human induced pluripotent stem cell models indicates that tau pathology can progress independently of Aβ accumulation and arises downstream of genetic risk factors for AD and aberrant metabolic pathways. This Review outlines novel insights from preclinical research that implicate apolipoprotein E, the endocytic system, cholesterol metabolism and microglial activation as Aβ-independent regulators of tau pathology. These factors are discussed in the context of emerging findings from clinical pathology, functional neuroimaging and other approaches in humans. Finally, we discuss the implications of these new insights for current Aβ-targeted strategies and highlight the emergence of novel therapeutic strategies that target processes upstream of both Aβ and tau.
291 citations
TL;DR: An overview of the latest advances in the field of biomarkers for neurodegenerative diseases is presented in this paper, where future directions are discussed regarding implementation of novel biomarkers in clinical practice and trials.
Abstract: Biomarkers for neurodegenerative diseases are needed to improve the diagnostic workup in the clinic but also to facilitate the development and monitoring of effective disease-modifying therapies. Positron emission tomography methods detecting amyloid-β and tau pathology in Alzheimer's disease have been increasingly used to improve the design of clinical trials and observational studies. In recent years, easily accessible and cost-effective blood-based biomarkers detecting the same Alzheimer's disease pathologies have been developed, which might revolutionize the diagnostic workup of Alzheimer's disease globally. Relevant biomarkers for α-synuclein pathology in Parkinson's disease are also emerging, as well as blood-based markers of general neurodegeneration and glial activation. This review presents an overview of the latest advances in the field of biomarkers for neurodegenerative diseases. Future directions are discussed regarding implementation of novel biomarkers in clinical practice and trials.
248 citations
TL;DR: P-tau217 correlates better with CSF and PET measures of neocortical amyloid-β burden and more accurately distinguishes AD dementia from non-AD neurodegenerative disorders and suggest that p-tAU217 might be more useful than p- tau181 in the diagnostic work up of AD.
Abstract: Cerebrospinal fluid (CSF) p-tau181 (tau phosphorylated at threonine 181) is an established biomarker of Alzheimer’s disease (AD), reflecting abnormal tau metabolism in the brain. Here we investigate the performance of CSF p-tau217 as a biomarker of AD in comparison to p-tau181. In the Swedish BioFINDER cohort (n = 194), p-tau217 shows stronger correlations with the tau positron emission tomography (PET) tracer [18F]flortaucipir, and more accurately identifies individuals with abnormally increased [18F]flortaucipir retention. Furthermore, longitudinal increases in p-tau217 are higher compared to p-tau181 and better correlate with [18F]flortaucipir uptake. P-tau217 correlates better than p-tau181 with CSF and PET measures of neocortical amyloid-β burden and more accurately distinguishes AD dementia from non-AD neurodegenerative disorders. Higher correlations between p-tau217 and [18F]flortaucipir are corroborated in an independent EXPEDITION3 trial cohort (n = 32). The main results are validated using a different p-tau217 immunoassay. These findings suggest that p-tau217 might be more useful than p-tau181 in the diagnostic work up of AD. Cerebrospinal fluid (CSF) p-tau181 (tau phosphorylated at threonine 181) is an established biomarker of Alzheimer’s disease (AD) reflecting abnormal tau metabolism in the AD brain. Here the authors demonstrate that CSF p-tau217 shows better performance as an AD biomarker than p-tau181.
232 citations
TL;DR: A novel deep learning strategy is presented to generate high-resolution visualizations of Alzheimer’s disease risk in humans that are highly interpretable and can accurately predict Alzheimer's disease status.
Abstract: Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.
191 citations
References
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Johns Hopkins University School of Medicine1, Johns Hopkins University2, Mayo Clinic3, McGill University4, Harvard University5, University of California, Irvine6, University of Pittsburgh7, Columbia University Medical Center8, Eli Lilly and Company9, Washington University in St. Louis10, UCL Institute of Neurology11, VU University Medical Center12, Alzheimer's Association13, Northwestern University14, National Institutes of Health15
TL;DR: The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available.
Abstract: The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.
13,710 citations
"Associations between tau, Aβ, and c..." refers methods in this paper
...We included 33 Aβ-positive cognitively normal individuals (called preclinical AD(3)), 25 Aβ-positive patients with MCI (called prodromal AD(14,15)), and 48 Aβ-positive patients with probable AD dementia.(16) The preclinical AD groups consisted of both cognitively normal research volunteers (n = 17) and patients with subjective cognitive decline(17) referred to the Memory Clinic of Skåne University Hospital in Sweden (n = 8)....
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TL;DR: Past observations are synthesized to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment, and for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.
Abstract: Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cognition Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations These two subsystems converge on important nodes of integration including the posterior cingulate cortex The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer’s disease
8,448 citations
TL;DR: In this paper, a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set is presented.
7,120 citations
Brigham and Women's Hospital1, University of California, San Diego2, University of California, Davis3, Rush University Medical Center4, University of Washington5, Washington University in St. Louis6, University of Tokyo7, Mayo Clinic8, Oregon Health & Science University9, University of Texas at Dallas10, University of Melbourne11, Eli Lilly and Company12, Columbia University13, University of California, San Francisco14, Alzheimer's Association15, National Institutes of Health16
TL;DR: A conceptual framework and operational research criteria are proposed, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies and it is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD.
Abstract: The pathophysiological process of Alzheimer's disease (AD) is thought to begin many years before the diagnosis of AD dementia. This long "preclinical" phase of AD would provide a critical opportunity for therapeutic intervention; however, we need to further elucidate the link between the pathological cascade of AD and the emergence of clinical symptoms. The National Institute on Aging and the Alzheimer's Association convened an international workgroup to review the biomarker, epidemiological, and neuropsychological evidence, and to develop recommendations to determine the factors which best predict the risk of progression from "normal" cognition to mild cognitive impairment and AD dementia. We propose a conceptual framework and operational research criteria, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies. These recommendations are solely intended for research purposes and do not have any clinical implications at this time. It is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD, and ultimately, aid the field in moving toward earlier intervention at a stage of AD when some disease-modifying therapies may be most efficacious.
5,671 citations
TL;DR: An automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy is presented.
Abstract: Accurate and automated methods for measuring the thickness of human cerebral cortex could provide powerful tools for diagnosing and studying a variety of neurodegenerative and psychiatric disorders. Manual methods for estimating cortical thickness from neuroimaging data are labor intensive, requiring several days of effort by a trained anatomist. Furthermore, the highly folded nature of the cortex is problematic for manual techniques, frequently resulting in measurement errors in regions in which the cortical surface is not perpendicular to any of the cardinal axes. As a consequence, it has been impractical to obtain accurate thickness estimates for the entire cortex in individual subjects, or group statistics for patient or control populations. Here, we present an automated method for accurately measuring the thickness of the cerebral cortex across the entire brain and for generating cross-subject statistics in a coordinate system based on cortical anatomy. The intersubject standard deviation of the thickness measures is shown to be less than 0.5 mm, implying the ability to detect focal atrophy in small populations or even individual subjects. The reliability and accuracy of this new method are assessed by within-subject test-retest studies, as well as by comparison of cross-subject regional thickness measures with published values.
5,171 citations
"Associations between tau, Aβ, and c..." refers methods in this paper
...Cortical thickness was measured as the distance from the gray matter/white matter boundary to the corresponding pial surface.(24) Reconstructed datasets were visually inspected for accuracy, and segmentation errors were corrected....
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