• Email: simon[dot]vandekar(at)gmail[dot]com

I am a third year PhD student in Biostatistics in the Department of Biostatistics and Epidemiology at the University of Pennsylvania under the mentorship of Taki Shinohara. I obtained my undergraduate degree in psychology from Pennsylvania State University some years ago and have been working in neuroimaging since I graduated.

My latest statistical interests are in multiple testing procedures for high-dimensional dependent data. My work is motivated by problems in neuroimaging and psychology, and I am particularly interested in applications in those fields.

Cortical Coupling

Project 01

Cortical Coupling

Cortical Coupling is a measure we created to describe localized relationships between characteristics of the cortex. The work was published in Neuroimage in 2016 with code to estimate coupling from Freesurfer volumes. Several colleagues have extended the work to improve segmentation of MS lesions, and make coupling estimates symmetric between modalities and include more than two modalities.

Cortical Coupling is an extension of previous work where the goal was to investigate the relationship between cortical characteristics as the brain matures through adolescents. In this work, we developed code to perform spatial permutation tests to assess the spatial coherence between two measures on the cortical surface. Spatial permutation tests are appropriate for data that can be projected onto a sphere.

Hypothesis Testing for Neuroimaging

Project 04

Hypothesis Testing for Neuroimaging

We have been working on several approaches to hypothesis testing that aim to control the FWER when performing tests at each voxel or a set of brain regions. The approaches we implement account for the strong dependence structure between the test statistics in neuroimaging. Recent studies (Eklund et al. 2016) have shown cluster-based inference can have hugely inflated type 1 error rates. My future work aims to develop fast and valid cluster based inference. I would also like to investigate finite sample properties of joint multiple testing procedures which rely on asymptotic results.

Recent Publications

For a complete listing of my publications please check out my Google Scholar Page.