Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke

Background and Purpose—The ability to model long-term functional outcomes after acute ischemic stroke represents a major clinical challenge. One approach to potentially improve prediction modeling involves the analysis of connectomics. The field of connectomics represents the brain’s connectivity as a graph, whose topological properties have helped uncover underlying mechanisms of brain function in health and disease. Specifically, we assessed the impact of stroke lesions on rich club organization, a high capacity backbone system of brain function. Methods—In a hospital-based cohort of 41 acute ischemic stroke patients, we investigated the effect of acute infarcts on the brain’s prestroke rich club backbone and poststroke functional connectomes with respect to poststroke outcome. Functional connectomes were created using 3 anatomic atlases, and characteristic path-length (L) was calculated for each connectome. The number of rich club regions affected were manually determined using each patient’s diffusion weighted image. We investigated differences in L with respect to outcome (modified Rankin Scale score; 90 days) and the National Institutes of Health Stroke Scale (NIHSS; early: 2–5 days; late: 90-day follow-up). Furthermore, we assessed the effect of including number of rich club regions and L in outcome models, using linear regression and assessing the explained variance (R²). Results—Of 41 patients (mean age [range]: 70 [45–89] years), 61% were male. Lower L was generally associated with better outcome. Including number of rich club regions in the backward selection models of outcome, R² increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and modified Rankin Scale score. Conclusions—In this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of poststroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.