Automated FCD Detection
Our automated FCD detection method is based on the post-normalization FCD similarity maps, in which the mean feature vector for every cortical patch is projected onto the FCD average unit vector (Figure 4B). The top 1% of patches were identified; clusters with five or more adjacent remaining vertices were retained. The final detection threshold was selected based on the optimal trade-off between sensitivity and specificity. We initially evaluated the 11 MRI+ patients (five initially MRI-) included in the leave-one-out analysis, resulting in an AUC of 0.91. At the optimal threshold determined from this analysis, our classifier correctly identified the lesions in 11/11 MRI+ patients (100% sensitivity) and 12/15 patients overall (80% sensitivity). Of the three patients without detected lesions, one had a true positive (TP) lesion that did not reach the threshold; the others had no detected clusters that overlapped with their resection masks. Of 13 patients with TP clusters, the known FCD had the highest mean cluster weight in 11, with 1-3 clusters detected in each patient. One to three presumably false positive clusters were identified in 9/30 HVs (70% specificity). Table 2 summarizes the results individually for each patient; data for three exemplar patients are shown in Figure 5.