Connectome-Based Pattern Learning Predicts Histology and Surgical Outcome of Epileptogenic Malformations of Cortical Development
Focal cortical dysplasia (FCD) type II, a surgically amenable epileptogenic malformation, is characterized by intracortical dyslamination and dysmorphic neurons, either in isolation (IIA) or together with balloon cells (IIB). While evidence suggests diverging local function between these two histological grades, patterns of connectivity to the rest of the brain remain unknown. We present a novel MRI framework that subdivides a given FCD lesion into a set of smaller cortical patches using hierarchical clustering of resting-state functional connectivity profiles. We assessed the yield of this connectome-based subtyping to predict histological grade and response to surgery in individual patients. As the human functional connectome consists of multiple large-scale communities (e.g., the default mode and fronto-parietal networks), we dichotomized connectivity profiles of lesional patches into connectivity to the cortices belonging to the same functional community (intra-community) and to other communities (inter-community). Clustering these community-based patch profiles in 27 patients with histologically-proven FCD objectively identified three distinct lesional classes with (1) decreased intra- and inter-community connectivity, (2) decreased intra-community but normal inter-community connectivity, and (3) increased intra- as well as inter-community connectivity, relative to 34 healthy controls. Ensemble classifiers informed by these classes predicted histological grading (i.e., IIA vs. IIB) and post-surgical outcome (i.e., seizure-free vs. non-free) with high accuracy (≥84%, above-chance significance based on permutation tests, p < 0.01), suggesting benefits of MRI-based connectome stratification for individualized presurgical prognostics.
KeywordsMRI Functional connectivity Epilepsy Disease prediction
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