Abstract

Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. Here, we present an initial support of cortical surfaces in Python within the neuroimaging data processing toolbox Nilearn. We provide loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. Limitations of the current implementation and potential next steps are discussed.

Highlights

  • The human cerebral cortex is highly convoluted

  • Surface representations of neuroimaging data are essential to study cortical topography and to expose areas buried in sulcal depths

  • While the development of versatile Python tools for neuroimaging has recently gained momentum, most of these tools focus on volumetric data

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Summary

Introduction

The human cerebral cortex is highly convoluted. Surface representations of neuroimaging data are essential to study cortical topography and to expose areas buried in sulcal depths. A notable exception is PySurfer (https:// pysurfer.github.io/), a Python package for rendering neuroimaging data on the cortical surface. We present a project that departs from this landscape in two ways: it strives 1) to provide plotting for cortical surface data in Python under minimal dependencies, and 2) to integrate surface data with multivariate processing in the Nilearn toolbox (Abraham et al 2014).

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Conclusion

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