Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space.

ABSTRACT: 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 gene

SUBMITTER: Nastase SA 

PROVIDER: S-EPMC7958465 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature