Under Review

Conditional Correlation Models with Association Size (CoCoA)

Under review at Biostatistics (bioRxiv preprint).

There is an inherent trade-off between speed and accuracy: you can either take the time to do things well, or rush and increase your chances of making errors. Speed and accuracy are therefore coupled outcomes, where the coupling strength is thought to change under different conditions (e.g., depending on task incentives, motivation); in certain cases, the two can become decoupled. We ask, what is the effect of attention on speed-accuracy coupling?

To quantify this effect, we conceptualize speed-accuracy coupling in terms of conditional correlations, where the attention and other confounders or variables of interest (i.e., age) are the conditioning variables. We identify and gauge the performance of three parametric and estimating equations-based estimators of conditional correlation, and then propose a novel measure of effect size, which we term the association size. We apply our framework (CoCoA: a Conditional Correlation Model with Association Size) to speed/accuracy data obtained from adolescents participating in a neurocognitive task.

Image Source: Wikimedia Commons

Dynamic Prediction of Cognitive Performance in Space

Accepted; to appear in Nature Scientific Reports (NASA preprint).

Astronauts are exposed to a unique set of stressors in spaceflight:

  • microgravity,

  • isolation and confinement,

  • environmental hazards like space radiation, and

  • occupational stressors, like spacewalks.

All of these can negatively impact sleep and alertness, which are important to mission success.

In this paper, we identify predictors of neurobehavioral alertness over the course of a 6-month spaceflight mission, using self-reported, cognitive, and environmental data collected from 24 astronauts on the International Space Station.

Using time-varying and discordantly-measured environmental, operational, and psychological covariates, we propose an ensemble prediction model to accurately and dynamically predict neurobehavioral alertness at the individual level. Our method is broadly applicable to environmental studies where the main goal is accurate, individualized prediction of human behavior involving a mixture of person-level traits and irregularly measured time series. [Code on Github]

Automated Analysis of Low-Field Brain MRI in Cerebral Malaria

Accepted; to appear in Biometrics (bioRxiv preprint).

Cerebral malaria is a serious complication of malaria infection, with a fatality rate of 15-20% in children, despite optimal treatment. Brain imaging with magnetic resonance imaging (MRI) has been useful in showing us how cerebral malaria works, as well as identifying children who have severe brain swelling and are therefore at greater mortality risk. However, advanced MRI technology is not uniformly available across the globe. In low resource settings, low-field MRI scanners (which can produce lower-resolution images) are more common, and there is also a shortage of available radiologists to manually interpret MRI scans. These challenges motivate the development of fully automated methods that can assess patients' brain images, augmenting or replacing manual interpretation, while also accommodating reduced image quality.

In response, we develop and validate a biologically and statistically principled method for the statistical image analysis of low resolution, noisy brain MRI. We leverage existing and publicly available, high-quality brain imaging data to identify brain tissue in the images in our sample. We extract volume-, intensity-, and curvature-based image features, which we hypothesize are related to severe brain swelling, by adapting currently available image processing techniques. To ensure the accessibility and reproducibility of our pipeline, the proposed method relies solely on open-source software and publicly available resources, requires only the raw MRI scans as input, and is available on Github.