Limits...
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.

Show MeSH

Related in: MedlinePlus

The influence of the parameter α on the smoothness of the distance D. Values of α of 1, 2, 3 and 5 are illustrated in this graph. Solid line represents the value of α used for the rest of the paper.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2724061&req=5

fig3: The influence of the parameter α on the smoothness of the distance D. Values of α of 1, 2, 3 and 5 are illustrated in this graph. Solid line represents the value of α used for the rest of the paper.

Mentions: To illustrate the effect of α, we considered a voxel i with a given mean effect X¯i and a standard deviation equal to one. Fig. 3 illustrates the influence of parameter α on D. Increasing α leads to “smooth” D values, suggesting that α can be considered as a “smoothness” parameter. Critically, in order to keep the method independent of the scaling of X, we set α equal to 3 · α, where α is the standard deviation of the group (i.e., standard deviation over all voxels and all subjects). Voxels that are driven by high positive activation (i.e., large effects) are identified with a positive α value (3 · α) and voxels that are driven by low or negative effects (e.g., deactivation) are identified with a negative α value (− 3 · α).


Detecting subject-specific activations using fuzzy clustering.

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

The influence of the parameter α on the smoothness of the distance D. Values of α of 1, 2, 3 and 5 are illustrated in this graph. Solid line represents the value of α used for the rest of the paper.
© Copyright Policy
Related In: Results  -  Collection

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

fig3: The influence of the parameter α on the smoothness of the distance D. Values of α of 1, 2, 3 and 5 are illustrated in this graph. Solid line represents the value of α used for the rest of the paper.
Mentions: To illustrate the effect of α, we considered a voxel i with a given mean effect X¯i and a standard deviation equal to one. Fig. 3 illustrates the influence of parameter α on D. Increasing α leads to “smooth” D values, suggesting that α can be considered as a “smoothness” parameter. Critically, in order to keep the method independent of the scaling of X, we set α equal to 3 · α, where α is the standard deviation of the group (i.e., standard deviation over all voxels and all subjects). Voxels that are driven by high positive activation (i.e., large effects) are identified with a positive α value (3 · α) and voxels that are driven by low or negative effects (e.g., deactivation) are identified with a negative α value (− 3 · α).

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