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I am very excited to soon start an industry position after the end of my post-doc period. In this post I want to share my experience and give tips to people in a similar situation thinking as well on changing to industry.
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In this post I will explain the nits and grits on how to create a bullseye parcellation of the cerebral white matter using (part of) the FreeSurfer outputs and commands. The software package recreating the parcellations according to this post is freely-available in this github repository. So, let’s get started!
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This post summarizes the talk that I gave to the study technicians of the Rhineland Study explaining the challenges in predicting cognitive abilities from high-dimensional brain imaging data using conventional (unpenalized) regression, but the ideas here explained apply generally to the modeling of relationships between any input and output data in high dimensions. Conventional regression techniques are not useful in high-dimensional settings. I show how machine learning provides an explanation for this issue and proposes a solution.
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Pysurfer is a Python package to display brain cortical surfaces with color overlays. In its most common configuration, it needs a working graphics card and physical display to generate the graphics via OpenGL. Therefore, you need to do some tweaking if you want to use in a remote (headless) server. In this post, I explain how to set up offscreen rendering with Pysurfer. A docker file reproducing all the steps in detail is available here.
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In this post I explain how to prepare the data to do voxel-based morphometry (VBM) using FreeSurfer segmentations.