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Hi there 👋

I'm a postdoctoral scholar at the University of Southern California working with Dr. Megan Herting in the Environmental Health Sciences Division of the Department of Population and Public Health Sciences in the Keck School of Medicine. Currently, my research focuses on understanding both within- and between-individual variability in development, and developing interpretable machine learning approaches to better understand the developing brain.

Open science is important to me, in most senses of the term. I am passionate about developing open neuroimaging software and resources. But beyond developing and sharing tools, I find it important to provide resources and instruction to increase their utility, encourage others to engage in open science practices, and further democratize access to scientific tools, ideas, and knowledge. Futhermore, I am passionate about visualizing and communicating research findings, to this end.

I am currently developing an open pipeline for analyzing individual differences in brain connectivity, incorporating principles from neuroimaging, network science, and machine learning.

Katie Bottenhorn, PhD's Projects

abcd-selfreg-factors icon abcd-selfreg-factors

code, data, and figures used to create 2019 SANS poster entitled "Uncovering a latent factor structure underlying pre-adolescent self-regulation and its neural substrates"

bloom-defunct icon bloom-defunct

Use your online presence to dynamically populate publications, code, etc. on your personal or lab website(s)

brainconn icon brainconn

A Python implementation of the brain connectivity toolbox.

brainspell-neo icon brainspell-neo

Working on a new version of Brainspell (an open-source platform for neuroimaging literature) to make a public JSON API that collaborators can contribute to, and to switch out the stack for better scalability.

docopt icon docopt

Pythonic command line arguments parser, that will make you smile

ds000114 icon ds000114

Demo derived dataset based on OpenfMRI ds000114

ds_prep icon ds_prep

All the scripts to prepare the Courtois-Neuromod dataset

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