GC-LDA: Generalized Correspondence Latent Dirichlet Allocation

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

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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.

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