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. Author manuscript; available in PMC: 2019 May 1.
Published in final edited form as: Epilepsia. 2018 Apr 10;59(5):982–992. doi: 10.1111/epi.14064

Figure 4.

Figure 4

Example patient showing measures generated by the surface-based morphometry approach in the parietal lobe. Note the multifocal appearance of each individual feature map. Information from each individual feature map was integrated by machine learning to generate a final cluster showing excellent concordance with expert labeling. Gray-white matter intensity measures blurring at the gray-white boundary; local cortical deformation (LCD) measures folding complexity; doughnut thickness measures local thickness variability; doughnut intensity measures local intensity variability at the gray-white boundary; interhemispheric gray-white matter intensity asymmetry measures the difference of bihemispheric blurring at the gray-white boundary; interhemispheric LCD asymmetry measures the difference of bihemispheric folding complexity; interhemispheric doughnut thickness measures the difference of bihemispheric local thickness variability; interhemispheric doughnut intensity measures the difference of bihemispheric local intensity variability at the gray-white boundary.