Recent resting-state functional MRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity between homotopic regions of the same network, and an abnormal increase of ipsi-lesional functional connectivity between task-negative and task-positive resting-state networks. Whole-brain computational modeling studies, at the individual subject level, using undirected effective connectivity derived from empirically measured functional connectivity, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional effective connectivity from zero-lagged and lagged covariance matrices, then, to compare it to empirically measured functional connectivity for predicting stroke vs. healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of functional connectivity vs. model effective connectivity in predicting the long-term outcome from acute measures.

Both functional and effective connectivity predicted healthy from stroke individuals significantly better than the chance-level, however, accuracy for the effective connectivity was significantly higher than for functional connectivity at 1–2 weeks, three-months, and one-year post-stroke. Predictive functional connections mainly included those reported in previous studies (within-network inter-hemispheric, and between task-positive and -negative networks intra-hemispherically). Predictive effective connections included additional between-network links. Effective connectivity was a better predictor than functional connectivity of the number of behavioral domains in which patients suffered deficits, both at two-weeks and one-year post onset of stroke. Interestingly, patient deficits at one-year time-point were better predicted by effective connectivity values at two-weeks rather than at one-year time-point. Our results thus demonstrate that the second-order statistics of functional MRI resting-state activity at an early stage of stroke, derived from a whole-brain effective connectivity, estimated in a model fitted to reproduce the propagation of neuronal activity, has pertinent information for clinical prognosis.