Adaptive thresholding for reliable topological inference in single subject fMRI analysis
Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumour resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were dev...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-08-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2012.00245/full |
| _version_ | 1818494280286601216 |
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| author | Krzysztof eGorgolewski Krzysztof eGorgolewski Krzysztof eGorgolewski Amos J Storkey Mark E Bastin Mark E Bastin Cyril R Pernet |
| author_facet | Krzysztof eGorgolewski Krzysztof eGorgolewski Krzysztof eGorgolewski Amos J Storkey Mark E Bastin Mark E Bastin Cyril R Pernet |
| author_sort | Krzysztof eGorgolewski |
| collection | DOAJ |
| description | Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumour resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyses. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modelling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of the trade-off between false negative and positive cluster error rates as well as in terms of over and underestimation of the true activation border. We also show through simulations and a motor test-retest study on ten volunteer subjects that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined. |
| first_indexed | 2024-12-10T18:04:30Z |
| format | Article |
| id | doaj.art-e3ab2945bd5948639025fa7427951b3d |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-10T18:04:30Z |
| publishDate | 2012-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-e3ab2945bd5948639025fa7427951b3d2022-12-22T01:38:40ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612012-08-01610.3389/fnhum.2012.0024525363Adaptive thresholding for reliable topological inference in single subject fMRI analysisKrzysztof eGorgolewski0Krzysztof eGorgolewski1Krzysztof eGorgolewski2Amos J Storkey3Mark E Bastin4Mark E Bastin5Cyril R Pernet6University of EdinburghUniversity of EdinburghUniversity of EdinburghUniversity of EdinburghUniversity of EdinburghUniversity of EdinburghUniversity of EdinburghSingle subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumour resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyses. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modelling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of the trade-off between false negative and positive cluster error rates as well as in terms of over and underestimation of the true activation border. We also show through simulations and a motor test-retest study on ten volunteer subjects that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined.http://journal.frontiersin.org/Journal/10.3389/fnhum.2012.00245/fullMixture ModelsReliabilityfalse negative errorsrandom field theoryspatial accuracy |
| spellingShingle | Krzysztof eGorgolewski Krzysztof eGorgolewski Krzysztof eGorgolewski Amos J Storkey Mark E Bastin Mark E Bastin Cyril R Pernet Adaptive thresholding for reliable topological inference in single subject fMRI analysis Frontiers in Human Neuroscience Mixture Models Reliability false negative errors random field theory spatial accuracy |
| title | Adaptive thresholding for reliable topological inference in single subject fMRI analysis |
| title_full | Adaptive thresholding for reliable topological inference in single subject fMRI analysis |
| title_fullStr | Adaptive thresholding for reliable topological inference in single subject fMRI analysis |
| title_full_unstemmed | Adaptive thresholding for reliable topological inference in single subject fMRI analysis |
| title_short | Adaptive thresholding for reliable topological inference in single subject fMRI analysis |
| title_sort | adaptive thresholding for reliable topological inference in single subject fmri analysis |
| topic | Mixture Models Reliability false negative errors random field theory spatial accuracy |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2012.00245/full |
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