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

Abstract

Background : The 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. Methods : In 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 (N RC ) affected were manually determined using each patient's diffusion weighted image (DWI). We investigated differences in L with 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 N RC and L in 'outcome' models, using linear regression and assessing the explained variance (R 2 ). Results : Of 41 patients (mean age (range): 70 (45-89) years), 61% were male. There were differences in L between patients with good and poor outcome (mRS). Including NRC in the backward selection models of outcome, R 2 increased between 1.3-and 2.6-fold beyond that of traditional markers (age and acute lesion volume) for NIHSS and mRS. Conclusion : In 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.

Functional Connectivity Decreases with Metabolic Stress in Sickle Cell Disease

“…Our finding that this cortical association network has decreased connectivity in children with SCD compared with controls suggests a plausible mechanism for the cognitive impairments prevalent in SCD. Functional connectivity has been shown to predict clinical outcomes in patients with neurological disease, including stroke 48 and Alzheimer's disease 49 . A sensitive preclinical biomarker for the cognitive complications of SCD could improve risk stratification of patients with SCD for curative interventions, such as stem cell transplant or gene therapy trials.…”

Dynamic functional connectivity analysis reveals transiently increased segregation in patients with severe stroke

“…ref. 36 for a comparable approach) or dynamic estimates (dwell times for State 1–3, the acute dynamic model). We did not integrate fraction times in addition to dwell times to reduce probable collinearity of input variables.…”

“…With regard to the cognitive control and default mode networks: These domains are well known to encompass cortical hubs that are globally well-connected and essential to orchestrate various brain functions 50,51,52 . Recent stroke studies relying on structural MRI data already indicated worse stroke outcomes the more of such well-connected areas were affected by stroke lesions 36,53 . Previous functional MRI studies have also reported disturbed functional connectivity within the default mode network when comparing subacute ischemic stroke patients and healthy controls, independent of any specific deficits or stroke severity ( 54 : decreased left medial temporal lobe, posterior cingulate and medial prefrontal cortical areas, 10 : decreased posterior cingulate cortex/precuneus, increased medial prefrontal cortex, left hippocampus).…”

“…Previous studies have already begun to highlight the value of MRI derived estimates. Examples of such predictors comprise the number of lesioned rich club regions, as obtained from structural MRI data 53 , lesion topographies as inferred from acute diffusion tensor imaging 56 , static connectivity between motor areas 8 and information on the network topology 36 , as characterized by functional MRI data.…”

“…With regard to the cognitive control and default mode networks: These domains are well known to encompass cortical hubs that are globally well-connected and essential to orchestrate various brain functions 50,51,52 . Recent stroke studies relying on structural MRI data already indicated worse stroke outcomes the more of such well-connected areas were affected by stroke lesions 36,53 . Previous functional MRI studies .…”