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Detecting subject-specific activations using fuzzy clustering.

Seghier ML, Friston KJ, Price CJ - Neuroimage (2007)

Bottom Line: The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects).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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK. m.seghier@fil.ion.ucl.ac.uk

ABSTRACT
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.

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Related in: MedlinePlus

(Top) Activation level in all subjects at a given voxel. The activation level in subject 1 is artificially modified to perturb the mean and the standard deviation (SD) across subjects (e.g., dashed line). (Bottom) The influence of activation level in subject 1 (x-axis) on the mean (dash-dot line), SD (dashed line) and t values (solid line) is illustrated. Significant effect across subjects (t values at p < 0.001) is indicated by a horizontal gray bar.
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fig1: (Top) Activation level in all subjects at a given voxel. The activation level in subject 1 is artificially modified to perturb the mean and the standard deviation (SD) across subjects (e.g., dashed line). (Bottom) The influence of activation level in subject 1 (x-axis) on the mean (dash-dot line), SD (dashed line) and t values (solid line) is illustrated. Significant effect across subjects (t values at p < 0.001) is indicated by a horizontal gray bar.

Mentions: The effect of an outlier on the group activation at a given voxel is illustrated in Fig. 1 using simulated data. When the effect size of one subject (subject 1 in Fig. 1) is increased or decreased, it perturbs the group mean and dispersion across subjects. In this example, the heightened response from one subject increases the variance and reduces the significance of the group effect (i.e., lowers t values). True group effects (i.e., activations that are consistent across subjects) can therefore be lost if one subject has an atypical response.


Detecting subject-specific activations using fuzzy clustering.

Seghier ML, Friston KJ, Price CJ - Neuroimage (2007)

(Top) Activation level in all subjects at a given voxel. The activation level in subject 1 is artificially modified to perturb the mean and the standard deviation (SD) across subjects (e.g., dashed line). (Bottom) The influence of activation level in subject 1 (x-axis) on the mean (dash-dot line), SD (dashed line) and t values (solid line) is illustrated. Significant effect across subjects (t values at p < 0.001) is indicated by a horizontal gray bar.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2724061&req=5

fig1: (Top) Activation level in all subjects at a given voxel. The activation level in subject 1 is artificially modified to perturb the mean and the standard deviation (SD) across subjects (e.g., dashed line). (Bottom) The influence of activation level in subject 1 (x-axis) on the mean (dash-dot line), SD (dashed line) and t values (solid line) is illustrated. Significant effect across subjects (t values at p < 0.001) is indicated by a horizontal gray bar.
Mentions: The effect of an outlier on the group activation at a given voxel is illustrated in Fig. 1 using simulated data. When the effect size of one subject (subject 1 in Fig. 1) is increased or decreased, it perturbs the group mean and dispersion across subjects. In this example, the heightened response from one subject increases the variance and reduces the significance of the group effect (i.e., lowers t values). True group effects (i.e., activations that are consistent across subjects) can therefore be lost if one subject has an atypical response.

Bottom Line: The output identifies subjects who drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects).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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, 12 Queen Square, London WC1N 3BG, UK. m.seghier@fil.ion.ucl.ac.uk

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus