kzhoulab / connectivity-srm

Connectivity-based shared response model

Connectivity-based shared response model

This repo accompanies a manuscript by Samuel A. Nastase, Yun-Fei Liu, Hanna Hillman, Kenneth A. Norman, and Uri Hasson published in NeuroImage (Nastase et al., 2020). Following the logic of connectivity hyperalignment (Guntupalli et al., 2018), we leverage intersubject functional correlations (ISFCs; Simony et al., 2016) to estimate a single connectivity-based shared response model (SRM; Chen et al., 2015) across disjoint datasets. We evaluate this model on 10 publicly-available story-listening fMRI datasets from the Narratives collection (Nastase et al., 2019b).

References

• Chen, P. H. C., Chen, J., Yeshurun, Y., Hasson, U., Haxby, J., & Ramadge, P. J. (2015). A reduced-dimension fMRI shared response model. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett, R. (Eds.), Advances in Neural Information Processing Systems 28. Paper presented at Neural Information Processing Systems 2015, Montreal (pp. 460-468). Red Hook, NY: Curran Associates, Inc. http://papers.nips.cc/paper/5855-a-reduced-dimension-fmri-shared-response-model

• Guntupalli, J. S., Feilong, M., & Haxby, J. V. (2018). A computational model of shared fine-scale structure in the human connectome. PLOS Computational Biology, 14(4), e1006120. https://doi.org/10.1371/journal.pcbi.1006120

• Nastase, S. A., Liu, Y.-F., Hillman, H., Norman, K. A., & Hasson, U. (2020). Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space. NeuroImage, 116865. https://doi.org/10.1016/j.neuroimage.2020.116865

• Nastase, S. A., Liu, Y.-F., Hillman, H., Zadbood, A., Hasenfratz, L., Keshavarzian, N., Chen, J., Honey, C. J., Yeshurun, Y., Regev, M., Nguyen, M., Chang, C. H. C., Baldassano, C. B., Lositsky, O., Simony, E., Chow, M. A., Leong, Y. C., Brooks, P. P., Micciche, E., Choe, G., Goldstein, A., Halchenko, Y. O., Norman, K. A., & Hasson, U. (2019). Narratives: fMRI data for evaluating models of naturalistic language comprehension. OpenNeuro, ds002345. https://doi.org/10.18112/openneuro.ds002345.v1.0.1

• Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., & Hasson, U. (2016). Dynamic reconfiguration of the default mode network during narrative comprehension. Nature Communications, 7, 12141. https://doi.org/10.1038/ncomms12141