GC-LDA: Generalized Correspondence Latent Dirichlet Allocation

The gclda package can be used to perform functional decoding and encoding of neuroimaging results.

Citations

If you use GC-LDA, please cite:

Rubin, T., Koyejo, O. O., Jones, M. N., & Yarkoni, T. (2016). Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain. In Advances in Neural Information Processing Systems (pp. 1118-1126).

Additionally, if you use GC-LDA’s decoding/encoding methods, please cite:

Rubin, T. N., Koyejo, O., Gorgolewski, K. J., Jones, M. N., Poldrack, R. A., & Yarkoni, T. (2017). Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition. PLOS Computational Biology, 13(10), e1005649.

The GC-LDA datasets and models available with the package are derived from Neurosynth, so if you use those data, please cite:

Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature methods, 8(8), 665-670.

Finally, the gclda code may use a number of tools not listed above, so it is recommended to use duecredit to output references associated with any code you may run.

Contents

• Introduction
  • Notation
• API Reference
  • gclda.dataset: Importing and formatting data
  • gclda.model: Model training and evaluation
  • gclda.decode: Functional decoding and encoding
  • gclda.utils: Miscellaneous utilities
• Examples
  • Examples from Rubin et al. (2017)
  • General Examples

Indices and tables

• Index
• Module Index
• Search Page