The Impact of Neurofeedback on Effective Connectivity Networks in Chronic Stroke Patients

Stroke is a complex motor disease that not only affects perilesional areas but also global brain networks in both hemispheres. Neurofeedback (NF) is a promising technique to enhance neural plasticity and support functional improvement after stroke by means of brain self-regulation. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on motor outcomes and successful activation of targeted cortical regions. However, given the crucial role of large-scale networks reorganization for stroke recovery, it is now believed that assessment of brain connectivity is central to predict treatment response and to individualize rehabilitation therapies. In this study, we assessed the impact of EEG-fMRI NF training on connectivity strength and direction using a Dynamic Causal Modeling approach. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas. Our results in nine chronic stroke patients indicate that NF upregulation of targeted areas (ipsilesional SMA and M1) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task. To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.