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• W2969402918 abstract "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 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 stimuli. Highlights Connectivity SRM estimates a single shared space across subjects and stimuli Topographic transformations resolve idiosyncrasies across individuals Shared connectivity space enhances spatiotemporal intersubject correlations Semantic model-based encoding and decoding improves across subjects Transformations project into a consensus space conjoining diverse stimuli" @default.
• W2969402918 created "2019-08-29" @default.
• W2969402918 creator A5020024319 @default.
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• W2969402918 creator A5070059815 @default.
• W2969402918 date "2019-08-21" @default.
• W2969402918 modified "2023-10-16" @default.
• W2969402918 title "Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space" @default.
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• W2969402918 doi "https://doi.org/10.1101/741975" @default.
• W2969402918 hasPublicationYear "2019" @default.
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