Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping
Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with uni...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-04-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00066/full |
| _version_ | 1818858584267554816 |
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| author | Johannes eStelzer Johannes eStelzer Tilo eBuschmann Tilo eBuschmann Gabriele eLohmann Gabriele eLohmann Daniel S Margulies Robert eTrampel Robert eTurner |
| author_facet | Johannes eStelzer Johannes eStelzer Tilo eBuschmann Tilo eBuschmann Gabriele eLohmann Gabriele eLohmann Daniel S Margulies Robert eTrampel Robert eTurner |
| author_sort | Johannes eStelzer |
| collection | DOAJ |
| description | Although ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with univariate methods, then becomes especially welcome. Information mapping approaches are commonly used, such as the searchlight technique, which take into account the spatially distributed patterns of activation in order to predict stimulus conditions. However, the popular searchlight decoding technique, in particular, has been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. We propose the combination of a nonparametric and permutation-based statistical framework with linear classifiers. We term this new combined method Feature Weight Mapping (FWM). The main goal of the proposed method is to map the specific contribution of each voxel to the classification decision while including a correction for the multiple comparisons problem. Next, we compare this new method to the searchlight approach using a simulation and ultra-high-field 7T experimental data. We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, FWM was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, global multivariate methods provide a substantial improvement for characterizing structure-function relationships. |
| first_indexed | 2024-12-19T08:58:36Z |
| format | Article |
| id | doaj.art-2862847131244f20a56c997a1a0a16a5 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-19T08:58:36Z |
| publishDate | 2014-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-2862847131244f20a56c997a1a0a16a52022-12-21T20:28:32ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2014-04-01810.3389/fnins.2014.0006678248Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mappingJohannes eStelzer0Johannes eStelzer1Tilo eBuschmann2Tilo eBuschmann3Gabriele eLohmann4Gabriele eLohmann5Daniel S Margulies6Robert eTrampel7Robert eTurner8Max-Planck-Institute for Human Cognitive and Brain SciencesCopenhagen University Clinic HvidovreMax-Planck-Institute for Human Cognitive and Brain SciencesFraunhofer Institute for Cell Therapy and ImmunologyMax-Planck-Institute for Human Cognitive and Brain SciencesMax Planck Institute for biological CyberneticsMax Planck Institute for Human Cognitive and Brain SciencesMax-Planck-Institute for Human Cognitive and Brain SciencesMax-Planck-Institute for Human Cognitive and Brain SciencesAlthough ultra-high-field fMRI at field strengths of 7T or above provides substantial gains in BOLD contrast-to-noise ratio, when very high-resolution fMRI is required such gains are inevitably reduced. The improvement in sensitivity provided by multivariate analysis techniques, as compared with univariate methods, then becomes especially welcome. Information mapping approaches are commonly used, such as the searchlight technique, which take into account the spatially distributed patterns of activation in order to predict stimulus conditions. However, the popular searchlight decoding technique, in particular, has been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. We propose the combination of a nonparametric and permutation-based statistical framework with linear classifiers. We term this new combined method Feature Weight Mapping (FWM). The main goal of the proposed method is to map the specific contribution of each voxel to the classification decision while including a correction for the multiple comparisons problem. Next, we compare this new method to the searchlight approach using a simulation and ultra-high-field 7T experimental data. We found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, FWM was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, global multivariate methods provide a substantial improvement for characterizing structure-function relationships.http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00066/fullfMRIDecodingMVPAnonparametric statisticssearchlight |
| spellingShingle | Johannes eStelzer Johannes eStelzer Tilo eBuschmann Tilo eBuschmann Gabriele eLohmann Gabriele eLohmann Daniel S Margulies Robert eTrampel Robert eTurner Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping Frontiers in Neuroscience fMRI Decoding MVPA nonparametric statistics searchlight |
| title | Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping |
| title_full | Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping |
| title_fullStr | Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping |
| title_full_unstemmed | Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping |
| title_short | Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping |
| title_sort | prioritizing spatial accuracy in high resolution fmri data using multivariate feature weight mapping |
| topic | fMRI Decoding MVPA nonparametric statistics searchlight |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2014.00066/full |
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