Stroke is a leading cause of disability. For prognosis, infarct size and location are used. Recent evidence suggests that measures of functional connectivity may provide additional insights. Resting state functional MRI (rs-fMRI) can measure features of functional networks including their “modularity” (network segregation). Modularity and other network measures may be important for characterizing outcome differences associated with interventions including mechanical thrombectomy (MT). We measured brain network modularity longitudinally in patients treated with MT.

This study was approved by the University of Nebraska Medical Center IRB. MT treated stroke patients were recruited. A 3T Siemens Prisma MRI scanner was used to collect data with a protocol adapted from the Human Connectome Project (HCP). The protocol included structural measures (T1-/T2-weighted) as well as rs-fMRI measures (multiband EPI). Images were acquired twice: within the first 2-weeks after stroke; and after three months. rs-fMRI data were processed with HCP pipelines. Network modularity was measured using a previously reported cortical parcellation.

We observed increases in brain network modularity between sub-acute and chronic epochs in two out of three patients treated with MT. We hypothesize that increased modularity may reflect restoration or reorganization of brain networks to support normal brain function.  Longitudinal cohort studies could help understand functional brain network disruption, track recovery, guide interventions, provide insight into cognitive deficits, and improve prognostication.