Detecting subject-specific activations using fuzzy clustering
Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect chan...
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| Format: | Online |
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Academic Press
2007
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724061/ |
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pubmed-2724061 |
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pubmed-27240612009-08-18 Detecting subject-specific activations using fuzzy clustering Seghier, Mohamed L. Friston, Karl J. Price, Cathy J. Technical Note Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm (FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner. Academic Press 2007-07-01 /pmc/articles/PMC2724061/ /pubmed/17478103 http://dx.doi.org/10.1016/j.neuroimage.2007.03.021 Text en © 2007 Elsevier Inc. https://creativecommons.org/licenses/by/3.0/ Open Access under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/) license |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Seghier, Mohamed L. Friston, Karl J. Price, Cathy J. |
| spellingShingle |
Seghier, Mohamed L. Friston, Karl J. Price, Cathy J. Detecting subject-specific activations using fuzzy clustering |
| author_facet |
Seghier, Mohamed L. Friston, Karl J. Price, Cathy J. |
| author_sort |
Seghier, Mohamed L. |
| title |
Detecting subject-specific activations using fuzzy clustering |
| title_short |
Detecting subject-specific activations using fuzzy clustering |
| title_full |
Detecting subject-specific activations using fuzzy clustering |
| title_fullStr |
Detecting subject-specific activations using fuzzy clustering |
| title_full_unstemmed |
Detecting subject-specific activations using fuzzy clustering |
| title_sort |
detecting subject-specific activations using fuzzy clustering |
| description |
Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm (FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner. |
| publisher |
Academic Press |
| publishDate |
2007 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2724061/ |
| _version_ |
1611441267316621312 |