Results: 4

1.
Figure 1

Figure 1. Voxel-based morphometry results. From: Neuroanatomical Spatial Patterns in Turner Syndrome.

Axial slice representations of significant GMV clusters from whole brain VBM (p<0.05, corrected for FWE and non-stationary smoothness) overlaid on custom GM templates. Significant regions displaying TS < TD are shown at 1.5T (upper left quadrant) and 3.0T (upper right quadrant). Significant regions displaying TS > TD are also shown at 1.5T (lower left quadrant) and 3.0T (lower right quadrant).

Matthew J. Marzelli, et al. Neuroimage. ;55(2):439-447.
2.
Figure 2

Figure 2. Voxel-based morphometry spatial overlap between cohorts. From: Neuroanatomical Spatial Patterns in Turner Syndrome.

3-dimensional renderings of significant clusters exhibiting reduced GMV from univariate VBM analyses (p<0.05, corrected for FWE and non-stationary smoothness). Spatially overlapping voxels between a young pediatric cohort (3.0T, red) and an older cohort (1.5T, yellow) are displayed as orange. Negative weight clusters are not included due to a low extent of overlap.

Matthew J. Marzelli, et al. Neuroimage. ;55(2):439-447.
3.
Figure 3

Figure 3. Support vector machine pattern classification results. From: Neuroanatomical Spatial Patterns in Turner Syndrome.

Leave-one-out (LOO) SVM classifier training and pattern classification results based on common GM voxels (using GMV as a regressor) between the 1.5T and 3.0T cohorts to discriminate between TS and TD subjects. PCA and RFE (30% increments) were applied for dimensionality reduction and parameter optimization. Left: Linear SVM classifier training results based on the 1.5T cohort (p<0.001, 2000 permutations) and resulting classifier accuracies when applied to the 3.0T cohort. Right: Linear SVM classifier training results based on the 3.0T cohort (p<0.001, 2000 permutations) and resulting classifier accuracies when applied to the 1.5 cohort.

Matthew J. Marzelli, et al. Neuroimage. ;55(2):439-447.
4.
Figure 4

Figure 4. Whole-brain representation of pattern classifiers. From: Neuroanatomical Spatial Patterns in Turner Syndrome.

Visual representation of whole brain pattern classifiers discriminating TS from TD using only GMV voxels common to both cohorts. Axial slice representations of the weight vectors (representing the degree to which each voxel contributes to the discrimination between TS and TD based on GMV measurements) are displayed from a leave-one-out linear SVM (employing PCA and RFE) using training data from the1.5T cohort (left) and using training data from the 3.0T cohort (right). Notable regions exhibiting relatively stronger classification weights (negative or positive) are identified for each classifier.

Matthew J. Marzelli, et al. Neuroimage. ;55(2):439-447.

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