Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke
AbstractBackgroundThe ability to model long-term functional outcomes after acute ischemic stroke (AIS) 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 (RC) organization, a high capacity backbone system of brain function.MethodsIn a hospital-based cohort of 41 AIS patients, we investigated the effect of acute infarcts on the brain’s pre-stroke RC backbone and post-stroke functional connectomes with respect to post-stroke outcome. Functional connectomes were created utilizing three anatomical atlases and characteristic path-length (L) was calculated for each connectome. The number of RC regions (NRC) affected were manually determined using each patient’s diffusion weighted image (DWI). We investigated differences inLwith respect to outcome (modified Rankin Scale score (mRS); 90-days; poor: mRS>2) 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 NRCandLin ‘outcome’ models, using linear regression and assessing the explained variance (R2).ResultsOf 41 patients (mean age (range): 70 (45-89) years), 61% were male. There were differences inLbetween patients with good and poor outcome (mRS). Including NRC in the backward selection models of outcome, R2increased between 1.3- and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and mRS.ConclusionIn this proof-of-concept study, we showed that information on network topology can be leveraged to improve modeling of post-stroke functional outcome. Future studies are warranted to validate this approach in larger prospective studies of outcome prediction in stroke.