3,467 research outputs found
Measuring the Health and Development of School-age Zimbabwean Children
Health, growth and development during mid-childhood (from 5 to 14 years) are poorly characterised, and this period has been termed the ‘missing middle’. This thesis describes the piloting and application of the School-Age Health, Activity, Resilience, Anthropometry and Neurocognitive (SAHARAN) toolbox to measure growth, cognitive and physical function amongst the SHINE cohort in rural Zimbabwe. The SHINE cluster-randomised trial tested the effects of a household WASH intervention and/or infant and young child feeding (IYCF) on child stunting and anaemia at age 18 months in rural Zimbabwe. SHINE showed that IYCF modestly increased linear growth and reduced stunting by age 18 months, while WASH had no effects. The SAHARAN toolbox was used to measure 1000 HIV-unexposed children (250 in each intervention arm), and 275 HIV-exposed children within the SHINE cohort to evaluate long-term outcomes. Children were re-enrolled at age seven years to evaluate growth, body composition, cognitive and physical function. Four main findings are presented from the SAHARAN toolbox measurements of this cohort. Firstly, child sex, growth and contemporary environmental conditions are associated with school-age physical and cognitive function at seven years. Secondly, early-life growth and baseline environmental conditions suggest the impact of early-life trajectories on multiple aspects of school-age growth, physical and cognitive function. Thirdly, the long-term impact of HIV-exposure in pregnancy is explored, which indicate reduced cognitive function, cardiovascular fitness and head circumference by age 7 years. Finally, associations with the SHINE trial early life interventions are explored, demonstrating that the SHINE early-life nutrition intervention has minimal impact by 7 years of age, except marginally stronger handgrip strength. The public health implications advocate that child interventions need to be earlier (including antenatal), broader (incorporating nurturing care), deeper (providing transformational WASH) and longer (supporting throughout childhood), as well as targeting particularly vulnerable groups such as children born HIV-free
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging
No abstract available
The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts
Randomized clinical trials, while often viewed as the highest evidentiary bar
by which to judge the quality of a medical intervention, are far from perfect.
In silico imaging trials are computational studies that seek to ascertain the
performance of a medical device by collecting this information entirely via
computer simulations. The benefits of in silico trials for evaluating new
technology include significant resource and time savings, minimization of
subject risk, the ability to study devices that are not achievable in the
physical world, allow for the rapid and effective investigation of new
technologies and ensure representation from all relevant subgroups. To conduct
in silico trials, digital representations of humans are needed. We review the
latest developments in methods and tools for obtaining digital humans for in
silico imaging studies. First, we introduce terminology and a classification of
digital human models. Second, we survey available methodologies for generating
digital humans with healthy and diseased status and examine briefly the role of
augmentation methods. Finally, we discuss the trade-offs of four approaches for
sampling digital cohorts and the associated potential for study bias with
selecting specific patient distributions
Statistical models of complex brain networks: a maximum entropy approach
The brain is a highly complex system. Most of such complexity stems from the
intermingled connections between its parts, which give rise to rich dynamics
and to the emergence of high-level cognitive functions. Disentangling the
underlying network structure is crucial to understand the brain functioning
under both healthy and pathological conditions. Yet, analyzing brain networks
is challenging, in part because their structure represents only one possible
realization of a generative stochastic process which is in general unknown.
Having a formal way to cope with such intrinsic variability is therefore
central for the characterization of brain network properties. Addressing this
issue entails the development of appropriate tools mostly adapted from network
science and statistics. Here, we focus on a particular class of maximum entropy
models for networks, i.e. exponential random graph models (ERGMs), as a
parsimonious approach to identify the local connection mechanisms behind
observed global network structure. Efforts are reviewed on the quest for basic
organizational properties of human brain networks, as well as on the
identification of predictive biomarkers of neurological diseases such as
stroke. We conclude with a discussion on how emerging results and tools from
statistical graph modeling, associated with forthcoming improvements in
experimental data acquisition, could lead to a finer probabilistic description
of complex systems in network neuroscience.Comment: 34 pages, 8 figure
EMERGING APPLICATIONS IN THE MEASUREMENT OF BODY COMPOSITION AND THEIR RELATIONSHIPS TO DISEASE RISK
Ph.D
Riemannian statistical techniques with applications in fMRI
Over the past 30 years functional magnetic resonance imaging (fMRI) has become a fundamental
tool in cognitive neuroimaging studies. In particular, the emergence of restingstate
fMRI has gained popularity in determining biomarkers of mental health disorders
(Woodward & Cascio, 2015). Resting-state fMRI can be analysed using the functional
connectivity matrix, an object that encodes the temporal correlation of blood activity
within the brain. Functional connectivity matrices are symmetric positive definite (SPD)
matrices, but common analysis methods either reduce the functional connectivity matrices
to summary statistics or fail to account for the positive definite criteria. However,
through the lens of Riemannian geometry functional connectivity matrices have an intrinsic
non-linear shape that respects the positive definite criteria (the affine-invariant
geometry (Pennec, Fillard, & Ayache, 2006)). With methods from Riemannian geometric
statistics, we can begin to explore the shape of the functional brain to understand this
non-linear structure and reduce data-loss in our analyses.
This thesis o↵ers two novel methodological developments to the field of Riemannian geometric
statistics inspired by methods used in fMRI research. First we propose geometric-
MDMR, a generalisation of multivariate distance matrix regression (MDMR) (McArdle &
Anderson, 2001) to Riemannian manifolds. Our second development is Riemannian partial
least squares (R-PLS), the generalisation of the predictive modelling technique partial least squares (PLS) (H. Wold, 1975) to Riemannian manifolds. R-PLS extends geodesic
regression (Fletcher, 2013) to manifold-valued response and predictor variables, similar to
how PLS extends multiple linear regression. We also generalise the NIPALS algorithm to
Riemannian manifolds and suggest a tangent space approximation as a proposed method
to fit R-PLS.
In addition to our methodological developments, this thesis o↵ers three more contributions
to the literature. Firstly, we develop a novel simulation procedure to simulate
realistic functional connectivity matrices through a combination of bootstrapping and the
Wishart distribution. Second, we propose the R2S
statistic for measuring subspace similarity
using the theory of principal angles between subspaces. Finally, we propose an
extension of the VIP statistic from PLS (S. Wold, Johansson, & Cocchi, 1993) to describe
the relationship between individual predictors and response variables when predicting a
multivariate response with PLS.
All methods in this thesis are applied to two fMRI datasets: the COBRE dataset
relating to schizophrenia, and the ABIDE dataset relating to Autism Spectrum Disorder
(ASD). We show that geometric-MDMR can detect group-based di↵erences between ASD
and neurotypical controls (NTC), unlike its Euclidean counterparts. We also demonstrate
the efficacy of R-PLS through the detection of functional connections related to
schizophrenia and ASD. These results are encouraging for the role of Riemannian geometric
statistics in the future of neuroscientific research.Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 202
Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions
The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field
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