Spatial parcellations, spectral filtering and connectivity measures in fMRI optimizing for discrimination
The dataset includes 15 subjects x 5 steady state conditions. Connectivity data has been computed using FSLNETS with the following options: - Timeseries from a set of 33 ROIs derived from functional data (Study-ROIs). - Correlation as dependency measures. - No bandpass filtering. The connectivity_...
| Main Authors: | , |
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| פורמט: | Dataset |
| יצא לאור: |
University of Oxford
2018
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| _version_ | 1826297853106978816 |
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| author | Duff, E Sala, R |
| author2 | Sala, R |
| author_facet | Sala, R Duff, E Sala, R |
| author_sort | Duff, E |
| collection | OXFORD |
| description | The dataset includes 15 subjects x 5 steady state conditions.
Connectivity data has been computed using FSLNETS with the following options:
- Timeseries from a set of 33 ROIs derived from functional data (Study-ROIs).
- Correlation as dependency measures.
- No bandpass filtering.
The connectivity_data.mat file includes:
netmatZ: connectivity data
Roi_names: names of the nodes
Roi_RSNs: classification of the 33 nodes into Motor, Visual and DMN networks.
Tval/Pval: results of the node-wise t-test comparison between rest and each of the active tasks.
The plot_connectivity.m file can be used to plot the average connectivity matrices for each task and the differences.
Connectivity results are grouped by RSN.
Published in:
Sala-Llonch R, Smith SM, Woolrich M, Duff EP. Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination. Hum Brain Mapp. 2018 |
| first_indexed | 2024-03-07T04:37:57Z |
| format | Dataset |
| id | oxford-uuid:d0a08fb9-11f8-44e4-8908-6f85d3a8c66d |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:37:57Z |
| publishDate | 2018 |
| publisher | University of Oxford |
| record_format | dspace |
| spelling | oxford-uuid:d0a08fb9-11f8-44e4-8908-6f85d3a8c66d2022-03-27T07:51:15ZSpatial parcellations, spectral filtering and connectivity measures in fMRI optimizing for discriminationDatasethttp://purl.org/coar/resource_type/c_ddb1uuid:d0a08fb9-11f8-44e4-8908-6f85d3a8c66dORA DepositUniversity of Oxford2018Duff, ESala, RSala, RSala, RThe dataset includes 15 subjects x 5 steady state conditions. Connectivity data has been computed using FSLNETS with the following options: - Timeseries from a set of 33 ROIs derived from functional data (Study-ROIs). - Correlation as dependency measures. - No bandpass filtering. The connectivity_data.mat file includes: netmatZ: connectivity data Roi_names: names of the nodes Roi_RSNs: classification of the 33 nodes into Motor, Visual and DMN networks. Tval/Pval: results of the node-wise t-test comparison between rest and each of the active tasks. The plot_connectivity.m file can be used to plot the average connectivity matrices for each task and the differences. Connectivity results are grouped by RSN. Published in: Sala-Llonch R, Smith SM, Woolrich M, Duff EP. Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination. Hum Brain Mapp. 2018 |
| spellingShingle | Duff, E Sala, R Spatial parcellations, spectral filtering and connectivity measures in fMRI optimizing for discrimination |
| title | Spatial parcellations, spectral filtering and connectivity measures in fMRI optimizing for discrimination |
| title_full | Spatial parcellations, spectral filtering and connectivity measures in fMRI optimizing for discrimination |
| title_fullStr | Spatial parcellations, spectral filtering and connectivity measures in fMRI optimizing for discrimination |
| title_full_unstemmed | Spatial parcellations, spectral filtering and connectivity measures in fMRI optimizing for discrimination |
| title_short | Spatial parcellations, spectral filtering and connectivity measures in fMRI optimizing for discrimination |
| title_sort | spatial parcellations spectral filtering and connectivity measures in fmri optimizing for discrimination |
| work_keys_str_mv | AT duffe spatialparcellationsspectralfilteringandconnectivitymeasuresinfmrioptimizingfordiscrimination AT salar spatialparcellationsspectralfilteringandconnectivitymeasuresinfmrioptimizingfordiscrimination |