Jack, Clifford R.

Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. Methods We used standard searches to find publications using ADNI data. Results (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β‐amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white‐matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion‐like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by “classic” AD pathology but also by normal biomarkers, accelerated decline, and suspected non‐Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. Discussion Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.

In the history of medicine, one means to progress is when we make the decision that our assumptions and definitions of disease are no longer consistent with the scientific evidence, and no longer serve our health care needs. The arc of scientific progress is now requiring a change in how we diagnose Alzheimer's disease. Both the National Institute on Aging—Alzheimer's Association (NIA‐AA) 2011 workgroup and the International Work Group (IWG) have proposed guidelines that use detectable measures of biological changes in the brain, commonly known as biological markers, or biomarkers, as part of the diagnosis. This Special Report examines how the development and validation of Alzheimer's disease biomarkers—including those detectable in the blood or cerebral spinal fluid, or through neuroimaging—is a top research priority. This has the potential to markedly change how we diagnose Alzheimer's disease and, as a result, how we count the number of people with this disease. As research advances a biomarker‐based method for diagnosis and treatment at the earliest stages of Alzheimer's disease, we envision a future in which Alzheimer's disease is placed in the same category as other chronic diseases, such as cardiovascular disease or diabetes, which can be readily identified with biomarkers and treated before irrevocable disability occurs.

Abstract Introduction Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) have previously been reported to be associated with increased risk of Alzheimer's disease (AD). We are using biomarkers to study Vietnam Veterans with/without mild cognitive impairment with a history of at least one TBI and/or ongoing PTSD to determine whether these contribute to the development of AD. Methods Potential subjects identified by Veterans Administration records underwent an initial telephone screen. Consented subjects underwent clinical evaluation, lumbar puncture, structural magnetic resonance imaging, and amyloid positron emission tomography (PET) scans. Results We observed worse cognitive functioning in PTSD and TBI + PTSD groups, worse global cognitive functioning in the PTSD group, lower superior parietal volume in the TBI + PTSD group, and lower amyloid positivity in the PTSD group, but not the TBI group compared to controls without TBI/PTSD. Medial temporal lobe atrophy was not increased in the PTSD and/or TBI groups. Discussion Preliminary results do not indicate that TBI or PTSD increase the risk for AD measured by amyloid PET. Additional recruitment, longitudinal follow‐up, and tau‐PET scans will provide more information in the future.

PurposeIn diffusion MRI (dMRI), fractional anisotropy derived from the single‐tensor model (FADTI) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors.MethodsWe compared the ability of standard FADTIand TDF‐derived FA (FATDF), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume.ResultsAcross angular resolutions and statistical tests, FATDFshowed larger effect sizes than FADTI, particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies.ConclusionThe TDF “corrected” form of FA may be a more sensitive and accurate alternative to the commonly used FADTI, even in clinical quality dMRI data. Magn Reson Med 78:2322–2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

AbstractIntroductionBiomarkers for Alzheimer's disease (AD) pathophysiology have been developed that focus on various levels of brain organization. However, no robust biomarker of large‐scale network failure has been developed. Using the recently introduced cascading network failure model of AD, we developed the network failure quotient (NFQ) as a biomarker of this process.MethodsWe developed and optimized the NFQ using our recently published analyses of task‐free functional magnetic resonance imaging data in clinically normal (n = 43) and AD dementia participants (n = 28) from the Alzheimer's Disease Neuroimaging Initiative. The optimized NFQ (oNFQ) was then validated in a cohort spanning the AD spectrum from the Mayo Clinic (n = 218).ResultsThe oNFQ (d = 1.25, 95% confidence interval [1.25, 1.26]) had the highest effect size for differentiating persons with AD dementia from clinically normal participants. The oNFQ measure performed similarly well on the validation Mayo Clinic sample (d = 1.44, 95% confidence interval [1.43, 1.44]). The oNFQ was also associated with other available key biomarkers in the Mayo cohort.DiscussionThis study demonstrates a measure of functional connectivity, based on a cascading network failure model of AD, and was highly successful in identifying AD dementia. A robust biomarker of the large‐scale effects of AD pathophysiology will allow for richer descriptions of the disease process and its modifiers, but is not currently suitable for discriminating clinical diagnostic categories. The large‐scale network level may be one of the earliest manifestations of AD, making this an attractive target for continued biomarker development to be used in prevention trials.

AbstractIntroductionTau‐positron emission tomography (PET) imaging with AV1451 is sensitive to Alzheimer disease (AD)–related tau deposition in the brain. We (1) examined regional variation of average tau‐PET standardized uptake value ratios (SUVRs) in a young normal population (30–49 years) and corrected for the regional variability and (2) tested if the standardized values (z‐scores) scaled appropriately to capture regional Alzheimer‐specific (i.e., amyloid sensitive) tau‐PET changes in individuals aged 50+ years.MethodsWe identified 490 individuals (70 between 30–49 years as a reference group and 420 cognitively normal between 50–95 years of age) with tau‐PET and amyloid PET scans from the Mayo Clinic Study of Aging.ResultsThere was intrinsic regional variability in average tau‐PET SUVR with uptakes higher in some regions than others, even in the younger individuals who would have minimal or no neurofibrillary tangles. We corrected for this using region of interest–specific z‐scores based on the reference group. Amyloid and tau‐PET uptake were associated throughout the brain after adjusting for age, with the highest correlations in the medial temporal regions.DiscussionRegions with high‐average SUVR are not necessarily those with the greatest tau pathology. Standardization is therefore recommended. Standardization of the data “realigns” the data such that the regional tau z‐scores are informative of the disease process, that is, regions with high z‐scores now coincide with regions correlated with amyloid deposition. Medial temporal structures, specifically entorhinal cortex–tau, may be useful as an AD‐specific tau‐PET signature due to its sensitivity to amyloid.

AbstractCerebrovascular pathologies (CVPs) are common pathologies associated with age‐related cognitive decline along with Alzheimer disease pathologies. The impact of CVP on the prevalence of dementia is increasingly being recognized. The goal of this review is to improve our understanding of the pathophysiological underpinnings and the multimodal magnetic resonance imaging and positron emission tomography imaging changes that are associated with the hallmarks of CVP. This knowledge will facilitate the development of early detection, intervention, and prevention strategies that may contribute to lowering the risk of dementia. In this review, we will first discuss currently known risk factors of CVPs including cardiovascular, lifestyle, genetic, sex differences, and head injury. Next, we will focus on the pathophysiology of CVPs and their impact on neurodegeneration and downstream cognitive impairment. Specifically, we will discuss three of the most common cerebrovascular lesions seen on MRI: white‐matter hyperintensity, microbleeds, and infarcts. Finally, we will discuss the unanswered open questions in this field.

Abstract Introduction The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to validate biomarkers for Alzheimer's disease (AD) clinical trials. ADNI‐3, which began on August 1, 2016, is a 5‐year renewal of the current ADNI‐2 study. Methods ADNI‐3 will follow current and additional subjects with normal cognition, mild cognitive impairment, and AD using innovative technologies such as tau imaging, magnetic resonance imaging sequences for connectivity analyses, and a highly automated immunoassay platform and mass spectroscopy approach for cerebrospinal fluid biomarker analysis. A Systems Biology/pathway approach will be used to identify genetic factors for subject selection/enrichment. Amyloid positron emission tomography scanning will be standardized using the Centiloid method. The Brain Health Registry will help recruit subjects and monitor subject cognition. Results Multimodal analyses will provide insight into AD pathophysiology and disease progression. Discussion ADNI‐3 will aim to inform AD treatment trials and facilitate development of AD disease‐modifying treatments.

AbstractAlzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.