@article{146,
  author = {Samuel Nastase and Yun-Fei Liu and Hanna Hillman and Kenneth Norman and Uri Hasson},
  title = {Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space},
  abstract = {<p>Connectivity hyperalignment can be used to estimate a single shared response space across disjoint datasets. We develop a connectivity-based shared response model that factorizes aggregated fMRI datasets into a single reduced-dimension shared connectivity space and subject-specific topographic transformations. These transformations resolve idiosyncratic functional topographies and can be used to project response time series into shared space. We evaluate this algorithm on a large collection of heterogeneous, naturalistic fMRI datasets acquired while subjects listened to spoken stories. Projecting subject data into shared space dramatically improves between-subject story time-segment classification and increases the dimensionality of shared information across subjects. This improvement generalizes to subjects and stories excluded when estimating the shared space. We demonstrate that estimating a simple semantic encoding model in shared space improves between-subject forward encoding and inverted encoding model performance. The shared space estimated across all datasets is distinct from the shared space derived from any particular constituent dataset; the algorithm leverages shared connectivity to yield a consensus shared space conjoining diverse story stimul</p>
},
  year = {2020},
  journal = {NeuroImage},
  volume = {217},
  doi = {10.1016/j.neuroimage.2020.116865},
}