Validating supervised learning approaches to the prediction of disease status in neuroimaging

Alzheimer’s disease (AD) is a serious global health problem with growing human and monetary costs. Neuroimaging data offers a rich source of information about pathological changes in the brain related to AD, but its high dimensionality makes it difficult to fully exploit using conventional methods. Automated neuroimage assessment (ANA) uses supervised learning to model the relationships between imaging signatures and measures of disease. ANA methods are assessed on the basis of their predictive performance, which is measured using cross validation (CV). Despite its ubiquity, CV is not always well understood, and there is a lack of guidance as to best practice. This thesis is concerned with the practice of validation in ANA. It introduces several key challenges and considers potential solutions, including several novel contributions. Part I of this thesis reviews the field and introduces key theoretical concepts related to CV. Part II is concerned with bias due to selective reporting of performance results. It describes an empirical investigation to assess the likely level of this bias in the ANA literature and relative importance of several contributory factors. Mitigation strategies are then discussed. Part III is concerned with the optimal selection of CV strategy with respect to bias, variance and computational cost. Part IV is concerned with the statistical analysis of CV performance results. It discusses the failure of conventional statistical procedures, reviews previous alternative approaches, and demonstrates a new heuristic solution that fares well in preliminary investigations. Though the focus of this thesis is AD ANA, the issues it addresses are of great importance to all applied machine learning fields where samples are limited and predictive performance is critical

Stability, Structure and Scale: Improvements in Multi-modal Vessel Extraction for SEEG Trajectory Planning

Purpose Brain vessels are among the most critical landmarks that need to be assessed for mitigating surgical risks in stereo-electroencephalography (SEEG) implantation. Intracranial haemorrhage is the most common complication associated with implantation, carrying signi cant associated morbidity. SEEG planning is done pre-operatively to identify avascular trajectories for the electrodes. In current practice, neurosurgeons have no assistance in the planning of electrode trajectories. There is great interest in developing computer assisted planning systems that can optimise the safety pro le of electrode trajectories, maximising the distance to critical structures. This paper presents a method that integrates the concepts of scale, neighbourhood structure and feature stability with the aim of improving robustness and accuracy of vessel extraction within a SEEG planning system. Methods The developed method accounts for scale and vicinity of a voxel by formulating the problem within a multi-scale tensor voting framework. Feature stability is achieved through a similarity measure that evaluates the multi-modal consistency in vesselness responses. The proposed measurement allows the combination of multiple images modalities into a single image that is used within the planning system to visualise critical vessels. Results Twelve paired datasets from two image modalities available within the planning system were used for evaluation. The mean Dice similarity coe cient was 0.89 ± 0.04, representing a statistically signi cantly improvement when compared to a semi-automated single human rater, single-modality segmentation protocol used in clinical practice (0.80 ±0.03). Conclusions Multi-modal vessel extraction is superior to semi-automated single-modality segmentation, indicating the possibility of safer SEEG planning, with reduced patient morbidity

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

A meta-analytic review of stand-alone interventions to improve body image

Objective Numerous stand-alone interventions to improve body image have been developed. The present review used meta-analysis to estimate the effectiveness of such interventions, and to identify the specific change techniques that lead to improvement in body image. Methods The inclusion criteria were that (a) the intervention was stand-alone (i.e., solely focused on improving body image), (b) a control group was used, (c) participants were randomly assigned to conditions, and (d) at least one pretest and one posttest measure of body image was taken. Effect sizes were meta-analysed and moderator analyses were conducted. A taxonomy of 48 change techniques used in interventions targeted at body image was developed; all interventions were coded using this taxonomy. Results The literature search identified 62 tests of interventions (N = 3,846). Interventions produced a small-to-medium improvement in body image (d+ = 0.38), a small-to-medium reduction in beauty ideal internalisation (d+ = -0.37), and a large reduction in social comparison tendencies (d+ = -0.72). However, the effect size for body image was inflated by bias both within and across studies, and was reliable but of small magnitude once corrections for bias were applied. Effect sizes for the other outcomes were no longer reliable once corrections for bias were applied. Several features of the sample, intervention, and methodology moderated intervention effects. Twelve change techniques were associated with improvements in body image, and three techniques were contra-indicated. Conclusions The findings show that interventions engender only small improvements in body image, and underline the need for large-scale, high-quality trials in this area. The review identifies effective techniques that could be deployed in future interventions

Positron Emission Tomography Imaging of CD105 Expression with a 64Cu-Labeled Monoclonal Antibody: NOTA Is Superior to DOTA

Optimizing the in vivo stability of positron emission tomography (PET) tracers is of critical importance to cancer diagnosis. In the case of 64Cu-labeled monoclonal antibodies (mAb), in vivo behavior and biodistribution is critically dependent on the performance of the bifunctional chelator used to conjugate the mAb to the radiolabel. This study compared the in vivo characteristics of 64Cu-labeled TRC105 (a chimeric mAb that binds to both human and murine CD105), through two commonly used chelators: 1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA) and 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA). Flow cytometry analysis confirmed that chelator conjugation of TRC105 did not affect its CD105 binding affinity or specificity. PET imaging and biodistribution studies in 4T1 murine breast tumor-bearing mice revealed that 64Cu-NOTA-TRC105 exhibited better stability than 64Cu-DOTA-TRC105 in vivo, which resulted in significantly lower liver uptake without compromising the tumor targeting efficiency. In conclusion, this study confirmed that NOTA is a superior chelator to DOTA for PET imaging with 64Cu-labeled TRC105

Patterns of Reproductive Isolation in Toads

Understanding the general features of speciation is an important goal in evolutionary biology, and despite significant progress, several unresolved questions remain. We analyzed an extensive comparative dataset consisting of more than 1900 crosses between 92 species of toads to infer patterns of reproductive isolation. This unique dataset provides an opportunity to examine the strength of reproductive isolation, the development and sex ratios of hybrid offspring, patterns of fertility and infertility, and polyploidization in hybrids all in the context of genetic divergence between parental species. We found that the strength of intrinsic postzygotic isolation increases with genetic divergence, but relatively high levels of divergence are necessary before reproductive isolation is complete in toads. Fertilization rates were not correlated to genetic divergence, but hatching success, the number of larvae produced, and the percentage of tadpoles reaching metamorphosis were all inversely related with genetic divergence. Hybrids between species with lower levels of divergence developed to metamorphosis, while hybrids with higher levels of divergence stopped developing in gastrula and larval stages. Sex ratios of hybrid offspring were biased towards males in 70% of crosses and biased towards females in 30% of crosses. Hybrid females from crosses between closely related species were completely fertile, while approximately half (53%) of hybrid males were sterile, with sterility predicted by genetic divergence. The degree of abnormal ploidy in hybrids was positively related to genetic divergence between parental species, but surprisingly, polyploidization had no effect on patterns of asymmetrical inviability. We discuss explanations for these patterns, including the role of Haldane's rule in toads and anurans in general, and suggest mechanisms generating patterns of reproductive isolation in anurans

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

Spontaneous Abortion and Preterm Labor and Delivery in Nonhuman Primates: Evidence from a Captive Colony of Chimpanzees (Pan troglodytes)

Preterm birth is a leading cause of perinatal mortality, yet the evolutionary history of this obstetrical syndrome is largely unknown in nonhuman primate species.We examined the length of gestation during pregnancies that occurred in a captive chimpanzee colony by inspecting veterinary and behavioral records spanning a total of thirty years. Upon examination of these records we were able to confidently estimate gestation length for 93 of the 97 (96%) pregnancies recorded at the colony. In total, 78 singleton gestations resulted in live birth, and from these pregnancies we estimated the mean gestation length of normal chimpanzee pregnancies to be 228 days, a finding consistent with other published reports. We also calculated that the range of gestation in normal chimpanzee pregnancies is approximately forty days. Of the remaining fifteen pregnancies, only one of the offspring survived, suggesting viability for chimpanzees requires a gestation of approximately 200 days. These fifteen pregnancies constitute spontaneous abortions and preterm deliveries, for which the upper gestational age limit was defined as 2 SD from the mean length of gestation (208 days).The present study documents that preterm birth occurred within our study population of captive chimpanzees. As in humans, pregnancy loss is not uncommon in chimpanzees, In addition, our findings indicate that both humans and chimpanzees show a similar range of normal variation in gestation length, suggesting this was the case at the time of their last common ancestor (LCA). Nevertheless, our data suggest that whereas chimpanzees' normal gestation length is ∼20-30 days after reaching viability, humans' normal gestation length is approximately 50 days beyond the estimated date of viability without medical intervention. Future research using a comparative evolutionary framework should help to clarify the extent to which mechanisms at work in normal and preterm parturition are shared in these species

Epigenetic Signatures of Cigarette Smoking

BACKGROUND: DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders. METHODS AND RESULTS: To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15 907 blood-derived DNA samples from participants in 16 cohorts (including 2433 current, 6518 former, and 6956 never smokers). Comparing current versus never smokers, 2623 cytosine-phosphate-guanine sites (CpGs), annotated to 1405 genes, were statistically significantly differentially methylated at Bonferroni threshold of P<1×10$^{-7}$ (18 760 CpGs at false discovery rate <0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases, and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant P<1×10$^{-7}$ (2623 CpGs at false discovery rate <0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs. CONCLUSIONS: Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biological effects of smoking and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.Biotechnology and Biological Sciences Research Council, British Heart Foundation, Cancer Research UK, Medical Research Council, National Institutes of Health, Royal Society, Wellcome Trus