SubCortexMesh: A Python toolbox for surface-based analysis of subcortical brain regions
Neuroimaging research focusing on subcortical brain regions showed that they play a role in a wide range of cognitive functions and associated their structural alterations to various psychiatric disorders. Methods to analyse such structures were developed to observe changes, not simply in terms of volumes, but in terms of surfaces, which are able to detect subtle and local changes in shape and…
SubCortexMesh, a Python toolbox, has been developed for surface-based analysis of subcortical brain regions. Research has shown that subcortical areas play a significant role in various cognitive functions and are associated with structural alterations in psychiatric disorders. Advanced analyses of these structures have typically been limited to command line environments, with restricted accessibility for Python users and researchers with limited technical expertise.
SubCortexMesh addresses this gap by offering a user-friendly solution. It can automatically estimate surfaces from popular subcortical volume segmentations, such as those from FreeSurfer and the FSL library. The toolbox computes vertex-wise metrics, like thickness, surface area, and curvature, for entire cohorts. It also includes statistical analyses within Python, automating the process for all subjects within a preprocessed directory.
The toolbox aims to minimize the number of steps and manual coding required from users. SubCortexMesh features statistical tools that enable conventional random field theory-based cluster analyses on native subject metrics and standardizes these within common surface templates. This comprehensive workflow allows for up-to-date shape-wise subcortical analyses to be conducted efficiently within a single package.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
