Shared latent structures between imaging features and biomarkers in early stages of Alzheimer's disease: a predictive study

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e. the capacity of predicting biomarker values) is assessed in a cross-validation framework.Peer ReviewedPostprint (author's final draft

A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed locally in any brain region. The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The generative model is first estimated on a control population, then, for each subject, the markers are computed for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolution. In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are more located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quiet high. In this context, the model can be used to generate plausible morphological trajectories associated with the disease. Our method gives two interpretable scalar imaging biomarkers assessing the effects of aging and disease on brain morphology at the individual and population level. These markers confirm an acceleration of apparent aging for Alzheimer's subjects and can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres

Neuroinflammation in Preclinical Alzheimer's Disease: A Review of Current Evidence

The pathology of sporadic Alzheimer’s disease (AD) may be present at mid-life and precede the prodromal and clinical dementia syndromes associated with the disorder by decades. Few successful therapeutic treatments exist and, as a result, attention is turning to the preclinical stages of the disease for the development of future intervention strategies. The success of such strategies will rely on well-defined biomarkers of preclinical disease to identify and monitor changes earlier in the disease course. Here, we consider whether immune function changes are potentially useful markers of preclinical disease. We have selected studies spanning epidemiological, animal, clinical and imaging research pertaining to the earliest stages of AD pathogenesis, as well as studies of non-demented adults at high AD risk. We examine changes in inflammatory markers, alongside changes in established biomarkers, to highlight their suitability as disease indicators across preclinical and prodromal stages. We conclude that further work surrounding this topic is required, calling for larger prospective epidemiological studies of preclinical disease that incorporate serial assessment designs with a wider range of inflammatory mediators. We anticipate that future benefits of work in this area include improved disease detection and modification, as well as diagnostic accuracy of trial participants, leading to more cost-effective observation and intervention studies

Spectroscopic detection of pathological severity in Alzheimer's disease

Alzheimer’s disease (AD) has emerged as one of the most widespread and devastating forms of dementia. Over the past few decades, AD has consistently increased in prevalence worldwide due to the rising proportion of elderly individuals and lack of effective screening and treatment modalities. To date, few economically viable and widely applicable tools exist to make definitive, early diagnoses of the disease. Therefore, there is a clear need for interventions that facilitate accurate diagnoses, monitoring, and therapeutic treatment of AD. In the course of AD, cognitive impairment is preceded by physiological changes to the central nervous system (CNS). This includes neuronal atrophy, synaptic dysfunction, and the abnormal post-translational modification of the proteins tau and beta-amyloid (A), which contributes to the deposition of intracellular neurofibrillary tangles (NFTs) and extracellular neuritic plaques (NPs). The pathological cellular changes in AD occur long before the clinical course of the disease, and biomarkers for these changes can be detected prior to measurable cognitive decline. Because the biochemical changes associated with AD are irreversible, effective tools for diagnosis must detect the presence and severity of molecular pathology during the preliminary stages of the disease’s insidious onset. Biomarkers of AD can be detected by neuroimaging technologies, including magnetic resonance imaging (MRI), positron emission tomography (PET), and blood or cerebrospinal fluid (CSF) analyses. However, these methods are not currently suited to diagnose and monitor the unique pathogenesis of AD prior to cognitive decline. An ideal instrument for widespread AD screening, diagnosis, and monitoring must be noninvasive, inexpensive, portable, and accommodating to the cognitive sensitivities of patients on a spectrum from mild cognitive impairment (MCI) to full-blown dementia. Recently, several spectroscopic methods of assessing AD pathology have met these criteria and may be better suited for widespread clinical application. The objective of this thesis is to evaluate the use of near-infrared optical spectroscopy (NIRS) to detect pathological severity in human AD. Near-infrared (NIR) light is poorly absorbed by biological tissue, and can safely penetrate bone, skin, vasculature, and neuronal tissue. NIRS has traditionally been used in biomedical contexts to evaluate cerebral oxygenation changes, however the dense protein aggregates NFTs and NPs in AD tissue have recently been shown to characteristically affect several optical parameters of a NIR signal, including fluorescence and particle path (scattering). To date, applications of NIRS have been used to differentiate AD brains from non-AD controls in vitro, and further identify MCI patients in vivo, suggesting the NIR signal can identify molecular changes in AD. Severe AD cases are characterized by increased involvement of NFTs and NPs in the cerebral cortex, which would be expected to further affect the extent of NIR scatter. The current study aims to quantify AD-related pathology for investigation into whether the extent of optical scattering is correlated with the severity of amyloid plaque load and NFT density in the temporal cortex. Quantification of these lesions was accomplished using immunohistochemistry (IHC) and stereological analyses. Preliminary results show that the severity of AD pathology detected via IHC can be correlated with measured parameters of an in vitro near-infrared signal. Future studies aim to further characterize the relationship between scattering intensity and pathological severity, as well as evaluate the in vivo potential of this technology in predicting the clinical outcome and cognitive status of individuals in different stages of AD

Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials

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 desig

Longitudinal trajectories in cortical thickness and volume atrophy: Superior cognitive performance does not protect against brain atrophy in older adults

Background: Previous research has identified a small subgroup of older adults that maintain a high level of cognitive functioning well into advanced age. Investigation of those with superior cognitive performance (SCP) for their age is important, as age-related decline has previously been thought to be inevitable. Objective: Preservation of cortical thickness and volume was evaluated in 76 older adults with SCP and 100 typical older adults (TOAs) assessed up to five times over six years. Methods: Regions of interest (ROIs) found to have been associated with super-aging status (a construct similar to SCP status) in previous literature were investigated, followed by a discovery phase analyses of additional regions. SCPs were aged 70 + at baseline, scoring at/above normative memory (CVLT-II) levels for demographically similar individuals aged 30–44 years old, and in the unimpaired range for all other cognitive domains over the course of the study. Results: In linear mixed models, following adjustment for multiple comparisons, there were no significant differences between rates of thinning or volume atrophy between SCPs and TOAs in previously identified ROIs, or the discovery phase analyses. With only amyloid-β negative individuals in the analyses, again there were no significant differences between SCPs and TOAs. Conclusion: The increased methodological rigor in classifying groups, together with the influence of cognitive reserve, are discussed as potential factors accounting for our findings as compared to the extant literature on those with superior cognitive performance for their age

Neural correlates of cognitive intervention in persons at risk of developing Alzheimer's disease.

Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer's disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training