Functional brain networks assessed with surface electroencephalography for predicting motor recovery in a neural guided intervention for chronic stroke

Predicting whether a chronic stroke patient is likely to benefit from a specific intervention can help patients establish reasonable expectations. It also provides the basis for candidates selecting for the intervention. Recent convergent evidence supports the value of network-based approach for understanding the relationship between dysfunctional neural activity and motor deficits after stroke. In this study, we applied resting-state brain connectivity networks to investigate intervention-specific predictive biomarkers of motor improvement in 22 chronic stroke participants who received either combined action observation with EEG-guided robothand training (Neural Guided-Action Observation Group, n=12, age: 34-68 years) or robot-hand training without action observation and EEG guidance (non-Neural Guided-text group, n=10, age: 42-57 years). The robot hand in Neural Guided-Action Observation training was activated only when significant mu suppression (8-12 Hz) was detected from participant's EEG signals in ipsilesional hemisphere while it was randomly activated in non-Neural Guided-text training. Only the Neural Guided-Action Observation group showed a significant long-term improvement in their upper-limb motor functions (P<0.5). In contrast, no significant training effect on the paretic motor functions was found in the non-Neural Guided-text group (P>0.5). The results of brain connectivity estimated via EEG coherence showed that the pre-training interhemispheric connectivity of delta, theta, alpha and contralesional connectivity of beta were motor improvement related in the Neural Guided-Action Observation group. They can not only differentiate participants with good and poor recovery (interhemispheric delta: P=0.047, Hedges' g=1.409; interhemispheric theta: P=0.046, Hedges' g=1.333; interhemispheric alpha: P=0.038, Hedges' g=1.536; contralesional beta: P=0.027, Hedges' g=1.613) but also significantly correlated with post-training intervention gains (interhemispheric delta: r = -0.901, P<0.05; interhemispheric theta: r = -0.702, P<0.05; interhemispheric alpha: r = -0.641, P<0.05; contralesional beta: r = -0.729, P<0.05). In contrast, no EEG coherence was significantly correlated with intervention gains in the non-Neural Guided-text group (all Ps > 0:05). Partial least square regression showed that the combination of pre-training interhemispheric and contralesional local connectivity could precisely predict intervention gains in the Neural Guided-Action Observation group with a strong correlation between predicted and observed intervention gains (r = 0.82r = 0:82) and between predicted and observed intervention outcomes (r = 0.90r = 0:90). In summary, EEG-based resting-state brain connectivity networks may serve clinical decision- making by offering an approach to predicting Neural Guided-Action Observation training-induced motor improvement.