Applications of neuroimaging to disease-modification trials in Alzheimer's disease.

Critical to development of new therapies for Alzheimer's disease (AD) is the ability to detect clinical or pathological change over time. Clinical outcome measures typically used in therapeutic trials have unfortunately proven to be relatively variable and somewhat insensitive to change in this slowly progressive disease. For this reason, development of surrogate biomarkers that identify significant disease-associated brain changes are necessary to expedite treatment development in AD. Since AD pathology is present in the brain many years prior to clinical manifestation, ideally we want to develop biomarkers of disease that identify abnormal brain structure or function even prior to cognitive decline. Magnetic resonance imaging, fluorodeoxyglucose positron emission tomography, new amyloid imaging techniques, and spinal fluid markers of AD all have great potential to provide surrogate endpoint measures for AD pathology. The Alzheimer's disease neuroimaging initiative (ADNI) was developed for the distinct purpose of evaluating surrogate biomarkers for drug development in AD. Recent evidence from ADNI demonstrates that imaging may provide more sensitive, and earlier, measures of disease progression than traditional clinical measures for powering clinical drug trials in Alzheimer's disease. This review discusses recently presented data from the ADNI dataset, and the importance of imaging in the future of drug development in AD

Predicting the course of Alzheimer's progression.

Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only

Nets, Spray or Both? The Effectiveness of Insecticide-Treated Nets and Indoor Residual Spraying in Reducing Malaria Morbidity and Child Mortality in sub-Saharan Africa.

Malaria control programmes currently face the challenge of maintaining, as well as accelerating, the progress made against malaria with fewer resources and uncertain funding. There is a critical need to determine what combination of malaria interventions confers the greatest protection against malaria morbidity and child mortality under routine conditions. This study assesses intervention effectiveness experienced by children under the age of five exposed to both insecticide-treated nets (ITNs) and indoor residual spraying (IRS), as compared to each intervention alone, based on nationally representative survey data collected from 17 countries in sub-Saharan Africa. Living in households with both ITNs and IRS was associated with a significant risk reduction against parasitaemia in medium and high transmission areas, 53% (95% CI 37% to 67%) and 31% (95% CI 11% to 47%) respectively. For medium transmission areas, an additional 36% (95% CI 7% to 53%) protection was garnered by having both interventions compared with exposure to only ITNs or only IRS. Having both ITNs and IRS was not significantly more protective against parasitaemia than either intervention alone in low and high malaria transmission areas. In rural and urban areas, exposure to both interventions provided significant protection against parasitaemia, 57% (95% CI 48% to 65%) and 39% (95% CI 10% to 61%) respectively; however, this effect was not significantly greater than having a singular intervention. Statistically, risk for all-cause child mortality was not significantly reduced by having both ITNs and IRS, and no additional protectiveness was detected for having dual intervention coverage over a singular intervention. These findings suggest that greater reductions in malaria morbidity and health gains for children may be achieved with ITNs and IRS combined beyond the protection offered by IRS or ITNs alone

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Motivation: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype

Cost-effectiveness of asthma control: an economic appraisal of the GOAL study

<i>Background</i>: The Gaining Optimal Asthma ControL (GOAL) study has shown the superiority of a combination of salmeterol/fluticasone propionate (SFC) compared with fluticasone propionate alone (FP) in terms of improving guideline defined asthma control. <i>Methods</i>: Clinical and economic data were taken from the GOAL study, supplemented with data on health related quality of life, in order to estimate the cost per quality adjusted life year (QALY) results for each of three strata (previously corticosteroid-free, low- and moderate-dose corticosteroid users). A series of statistical models of trial outcomes was used to construct cost effectiveness estimates across the strata of the multinational GOAL study including adjustment to the UK experience. Uncertainty was handled using the non-parametric bootstrap. Cost-effectiveness was compared with other treatments for chronic conditions. <i>Result</i>: Salmeterol/fluticasone propionate improved the proportion of patients achieving totally and well-controlled weeks resulting in a similar QALY gain across the three strata of GOAL. Additional costs of treatment were greatest in stratum 1 and least in stratum 3, with some of the costs offset by reduced health care resource use. Cost-effectiveness by stratum was £7600 (95% CI: £4800–10 700) per QALY gained for stratum 3; £11 000 (£8600–14 600) per QALY gained for stratum 2; and £13 700 (£11 000–18 300) per QALY gained for stratum 1. <i>Conclusion</i>: The GOAL study previously demonstrated the improvement in total control associated with the use of SFC compared with FP alone. This study suggests that this improvement in control is associated with cost-per-QALY figures that compare favourably with other uses of scarce health care resources

The Relationship Between Anxiety and Incident Agitation in Alzheimer's Disease

Background: Agitation in Alzheimer’s disease (AD) has been hypothesized to be an expression of anxiety, but whether anxiety early in the course of dementia could be a risk factor for developing later agitation is unknown. Objective: We used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to examine the longitudinal relationship between anxiety and incident agitation in individuals with a diagnosis of AD at baseline or during follow-up. Methods: Longitudinal neuropsychiatric symptom data from AD individuals who were agitation-free at study baseline (N = 272) were analyzed using mixed effects regression models to test the longitudinal relationship between baseline and incident anxiety with incident agitation. Results: Anxiety at baseline was not associated with subsequent agitation, but there was a positive linear relationship between incident anxiety and agitation over the study duration. Baseline apathy and delusions were consistently associated with subsequent agitation and greater disease severity and illness duration also appeared to be risk factors for agitation. Conclusion: Our findings support the concept that anxiety and agitation are likely to be distinct rather than equivalent constructs in mild-moderate AD. Future longitudinal cohort studies are needed to replicate these findings and further characterize potential risk factors for agitation, such as apathy and delusions

Scanning electron microscopy image representativeness: morphological data on nanoparticles.

A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca3 (PO4 )2 , and calcium hydroxyphosphate, Ca5 (PO4 )3 (OH), both naturally occurring minerals with a wide range of biomedical applications

Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression

Prediction of subject age from brain anatomical MRI has the potential to provide a sensitive summary of brain changes, indicative of different neurodegenerative diseases. However, existing studies typically neglect the uncertainty of these predictions. In this work we take into account this uncertainty by applying methods of functional data analysis. We propose a penalised func16 tional quantile regression model of age on brain structure with cognitively normal (CN) subjects in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and use it to predict brain age in Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) subjects. Unlike the machine learning approaches available in the literature of brain age prediction, which provide only point predictions, the outcome of our model is a prediction interval for each subject

Selection bias in the reported performances of AD classification pipelines.

The last decade has seen a great proliferation of supervised learning pipelines for individual diagnosis and prognosis in Alzheimer's disease. As more pipelines are developed and evaluated in the search for greater performance, only those results that are relatively impressive will be selected for publication. We present an empirical study to evaluate the potential for optimistic bias in classification performance results as a result of this selection. This is achieved using a novel, resampling-based experiment design that effectively simulates the optimisation of pipeline specifications by individuals or collectives of researchers using cross validation with limited data. Our findings indicate that bias can plausibly account for an appreciable fraction (often greater than half) of the apparent performance improvement associated with the pipeline optimisation, particularly in small samples. We discuss the consistency of our findings with patterns observed in the literature and consider strategies for bias reduction and mitigation