Smoothing dynamic positron emission tomography time courses using functional principal components

A functional smoothing approach to the analysis of PET time course data is presented. By borrowing information across space and accounting for this pooling through the use of a nonparametric covariate adjustment, it is possible to smooth the PET time course data thus reducing the noise. A new model for functional data analysis, the Multiplicative Nonparametric Random Effects Model, is introduced to more accurately account for the variation in the data. A locally adaptive bandwidth choice helps to determine the correct amount of smoothing at each time point. This preprocessing step to smooth the data then allows Subsequent analysis by methods Such as Spectral Analysis to be substantially improved in terms of their mean squared error

Using an illumination discrimination paradigm to investigate the role of illumination priors in colour perception

PhD ThesisPrevious studies suggest human colour constancy is optimised for natural daylight illuminations - a \blue bias" for colour constancy - but it is unclear how such a bias is encoded in the visual system. We use an illumination discrimination task to test two hypothesised mechanisms. Both hypotheses suggest that the human visual system has a prior expectation that illuminations are more likely to vary in a bluer region of chromaticity space. One hypothesis (the nature hypothesis) suggests this has developed in the human visual system through evolution, with selection of colour mechanisms that have reduced sensitivity to global bluer changes across a scene (a species prior). The second hypothesis suggests that the prior is learnt through experience with illuminations (the nurture hypothesis - an individual prior). In Chapter 3 we expand on previous results showing a \blue bias" for colour constancy when the illumination varies from a neutral reference, to show that the \blue bias" prevails in variants of the task where the illuminations are all chromatically biased. This result supports the nature hypothesis. However, depending on the chromatic bias, di erent biases can emerge in the threshold data that are more supportive of the nurture hypothesis. In Chapter 4 we explore individual di erences in illumination discrimination ability, compare illumination discrimination ability with chromatic contrast detection ability, and develop ideal observer models for the task. The results in this Chapter are mostly in support of the nurture hypothesis. In Chapter 5 we show that illumination priors may play a role in the recent visual illusion of a dress photograph that appeared blue and black to some observers but white and gold to others. Finally, in Chapter 6, we search for evidence that observers can learn an illumination prior during a psychophysical task. We conclude that the \blue bias" is likely governed by both a learnt prior over the characteristics of daylight illuminations (the nurture hypothesis) and a generic reduction in sensitivity to bluer changes in an illumination (the nature hypothesis)

A Functional Approach to Deconvolve Dynamic Neuroimaging Data.

Positron emission tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on the assumption that linear first-order kinetics can be used to adequately describe the system under observation. However, there has recently been strong evidence that this is not the case. To provide an analysis of PET data which is free from this compartmental assumption, we propose a nonparametric deconvolution and analysis model for dynamic PET data based on functional principal component analysis. This yields flexibility in the possible deconvolved functions while still performing well when a linear compartmental model setup is the true data generating mechanism. As the deconvolution needs to be performed on only a relative small number of basis functions rather than voxel by voxel in the entire three-dimensional volume, the methodology is both robust to typical brain imaging noise levels while also being computationally efficient. The new methodology is investigated through simulations in both one-dimensional functions and 2D images and also applied to a neuroimaging study whose goal is the quantification of opioid receptor concentration in the brain.The research of Ci-Ren Jiang is supported in part by NSC 101-2118-M-001-013-MY2 (Taiwan); the research of Jane-Ling Wang is supported by NSF grants, DMS-09-06813 and DMS-12-28369. JA is supported by EPSRC grant EP/K021672/2. The authors would like to thank SAMSI and the NDA programme where some of this research was carried out.This is the final version of the article. It first appeared from Taylor & Francis via http://dx.doi.org/10.1080/01621459.2015.106024

Household air pollution, chronic respiratory disease and pneumonia in Malawian adults: A case-control study

Background: Four million people die each year from diseases caused by exposure to household air pollution. There is an association between exposure to household air pollution and pneumonia in children (half a million attributable deaths a year); however, whether this is true in adults is unknown. We conducted a case-control study in urban Malawi to examine the association between exposure to household air pollution and pneumonia in adults. Methods: Hospitalized patients with radiologically confirmed pneumonia (cases) and healthy community controls underwent 48 hours of ambulatory and household particulate matter (µg/m3) and carbon monoxide (ppm) exposure monitoring. Multivariate logistic regression, stratified by HIV status, explored associations between these and other potential risk factors with pneumonia. Results: 145 (117 HIV-positive; 28 HIV-negative) cases and 253 (169 HIV-positive; 84 HIV-negative) controls completed follow up. We found no evidence of association between household air pollution exposure and pneumonia in HIV-positive (e.g. ambulatory particulate matter adjusted odds ratio [aOR] 1.00 [95% CI 1.00–1.01, p=0.141]) or HIV-negative (e.g. ambulatory particulate matter aOR 1.00 [95% CI 0.99–1.01, p=0.872]) participants. Chronic respiratory disease was associated with pneumonia in both HIV-positive (aOR 28.07 [95% CI 9.29–84.83, p<0.001]) and HIV-negative (aOR 104.27 [95% CI 12.86–852.35, p<0.001]) participants. Conclusions: We found no evidence that exposure to household air pollution is associated with pneumonia in Malawian adults. In contrast, chronic respiratory disease was strongly associated with pneumonia