Focal epilepsy is increasingly recognized as a disorder of distributed brain systems, yet characterizations of patient-specific alterations in network organization that are clinically meaningful remain limited. Here, we used resting-state functional MRI to introduce an overlap-permitting individualized network-estimation framework anchored to normative references to derive two complementary, system-level axes of topology: (1) network correspondence, indexing the expression of canonical networks and the fidelity of intrinsic system boundaries, and (2) k-hubness, indexing cross-system integration and multi-functionality through multi-network participation. We used a large presurgical focal epilepsy cohort (305 patients, including temporal and extratemporal epilepsy; 224 healthy participants) and an independent multi-syndrome validation cohort spanning focal and common generalized and childhood epilepsy syndromes (903 patients; 666 healthy participants), to show: (1) correspondence disruption of both canonical and non-normative networks constitutes a broadly shared system-level endpoint that displays heterogeneity across individuals and differential impacts on cognition, (2) k-hubness reconfigurations reflect lateralized and syndrome-dependent redistributions of cross-system integration, demonstrating epilepsy's impact on the functional multiplicity inherent to specific brain areas. These complementary axes shared variance yet showed dissociable clinical associations, with correspondence preferentially relating to neurocognitive deficits and k-hubness aligning with epilepsy clinical features. Accordingly, these axes demonstrate the presence of distinct topological phenotypes, providing clinically relevant, reproducible patient-level signatures that explain both the syndrome-specific and common effects of seizures on the organization and integration of canonical and individualized brain systems.
Keywords: Focal epilepsies; Individualized brain networks; Macroscale brain organization; Network phenotypes; Network topology; Sparse dictionary learning.