Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming.
This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the...
| Main Authors: | , , , , , , |
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| Format: | Conference item |
| Published: |
2006
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| _version_ | 1797093893867569152 |
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| author | Beckmann, C Jenkinson, M Woolrich, M Behrens, T Flitney, D Devlin, J Smith, S |
| author_facet | Beckmann, C Jenkinson, M Woolrich, M Behrens, T Flitney, D Devlin, J Smith, S |
| author_sort | Beckmann, C |
| collection | OXFORD |
| description | This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing. |
| first_indexed | 2024-03-07T04:06:38Z |
| format | Conference item |
| id | oxford-uuid:c6643a1d-127e-4e6d-919a-faeebf6f2c3c |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:06:38Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:c6643a1d-127e-4e6d-919a-faeebf6f2c3c2022-03-27T06:37:44ZApplying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming.Conference itemhttp://purl.org/coar/resource_type/c_5794uuid:c6643a1d-127e-4e6d-919a-faeebf6f2c3cSymplectic Elements at Oxford2006Beckmann, CJenkinson, MWoolrich, MBehrens, TFlitney, DDevlin, JSmith, SThis article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model-based General Linear Model (GLM) tool (FEAT) and from the model-free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing. |
| spellingShingle | Beckmann, C Jenkinson, M Woolrich, M Behrens, T Flitney, D Devlin, J Smith, S Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming. |
| title | Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming. |
| title_full | Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming. |
| title_fullStr | Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming. |
| title_full_unstemmed | Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming. |
| title_short | Applying FSL to the FIAC data: model-based and model-free analysis of voice and sentence repetition priming. |
| title_sort | applying fsl to the fiac data model based and model free analysis of voice and sentence repetition priming |
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