Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space
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 trans...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-08-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811920303517 |
| _version_ | 1818388944890363904 |
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| author | Samuel A. Nastase Yun-Fei Liu Hanna Hillman Kenneth A. Norman Uri Hasson |
| author_facet | Samuel A. Nastase Yun-Fei Liu Hanna Hillman Kenneth A. Norman Uri Hasson |
| author_sort | Samuel A. Nastase |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T04:33:53Z |
| format | Article |
| id | doaj.art-c31cc88ca1244d108f337881da4171e0 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-14T04:33:53Z |
| publishDate | 2020-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-c31cc88ca1244d108f337881da4171e02022-12-21T23:17:01ZengElsevierNeuroImage1095-95722020-08-01217116865Leveraging shared connectivity to aggregate heterogeneous datasets into a common response spaceSamuel A. Nastase0Yun-Fei Liu1Hanna Hillman2Kenneth A. Norman3Uri Hasson4Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Corresponding author.Department of Psychological & Brain Sciences, Johns Hopkins University, Baltimore, MD, USADepartment of Psychology, Harvard University, Cambridge, MA, USAPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USAPrinceton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USAConnectivity 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.http://www.sciencedirect.com/science/article/pii/S1053811920303517Data harmonizationfMRIFunctional connectivityHyperalignmentNaturalistic stimuli |
| spellingShingle | Samuel A. Nastase Yun-Fei Liu Hanna Hillman Kenneth A. Norman Uri Hasson Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space NeuroImage Data harmonization fMRI Functional connectivity Hyperalignment Naturalistic stimuli |
| title | Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space |
| title_full | Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space |
| title_fullStr | Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space |
| title_full_unstemmed | Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space |
| title_short | Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space |
| title_sort | leveraging shared connectivity to aggregate heterogeneous datasets into a common response space |
| topic | Data harmonization fMRI Functional connectivity Hyperalignment Naturalistic stimuli |
| url | http://www.sciencedirect.com/science/article/pii/S1053811920303517 |
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