About this Research Topic
The World Health Organization (WHO) defines stroke as “the rapid development of clinical signs and symptoms of a focal neurological disturbance lasting more than 24 hours or leading to death with no apparent cause other than vascular origin”. Worldwide, 15 million people suffer a stroke every year, and stroke is the second leading cause of disability after dementia (http://www.who.int/topics/cerebrovascular_accident/en/). Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. Therefore, in addition to facilitating beneficial neuroplasticity, it is necessary to reduce maladaptive plasticity, which compromises re-gain of function via implementation of sub-optimal compensatory strategies. Non-invasive brain stimulation (NIBS) - an intervention at the central nervous system level - might be a promising tool in facilitating motor learning in the context of rehabilitation. It has been shown that beneficial neuroplastic changes may be facilitated with NIBS such as transcranial direct current stimulation (tDCS) which results in improved motor functions. The key principles underlying the effects of tDCS on motor learning and control are, (1) neuronal firing rates are increased by anodal polarization and decreased by cathodal polarization, (2) anodal polarization strengthens newly formed associations, and (3) polarization modulates the memory of new/preferred firing patterns.
Therefore, it is important that functionally beneficial associations are formed during neurorehabilitation based on residual motor function such that they can be strengthened and consolidated using NIBS. If we can identify intact and/or viable alternative motor pathways based on neuroimaging then it may be possible using computational modeling to appropriately focus NIBS. Here, it was recently proposed that over-reliance on the interhemispheric competition and vicariation models of recovery for designing NIBS protocols led to failures since they do not apply to all patients with stroke.
Therefore, in this Frontiers Research Topic, we wish to highlight neuroimaging and computational modeling approaches to develop post-stroke patient-specific NIBS protocols that are in various stages of bench-to-beside translation. Specifically, the neuroimaging and computational modeling approaches can link interhemispheric balancing and functional recovery to the structural reserve spared by the lesion. We aim to produce a series of important and high caliber publications useful to both researchers and clinicians alike. In doing so, this compilation will hopefully lead to further research on novel and innovative approaches to tailor NIBS to the needs of individual patients based on neuroimaging and computational modeling.
Therefore, it is important that functionally beneficial associations are formed during neurorehabilitation based on residual motor function such that they can be strengthened and consolidated using NIBS. If we can identify intact and/or viable alternative motor pathways based on neuroimaging then it may be possible using computational modeling to appropriately focus NIBS. Here, it was recently proposed that over-reliance on the interhemispheric competition and vicariation models of recovery for designing NIBS protocols led to failures since they do not apply to all patients with stroke.
Therefore, in this Frontiers Research Topic, we wish to highlight neuroimaging and computational modeling approaches to develop post-stroke patient-specific NIBS protocols that are in various stages of bench-to-beside translation. Specifically, the neuroimaging and computational modeling approaches can link interhemispheric balancing and functional recovery to the structural reserve spared by the lesion. We aim to produce a series of important and high caliber publications useful to both researchers and clinicians alike. In doing so, this compilation will hopefully lead to further research on novel and innovative approaches to tailor NIBS to the needs of individual patients based on neuroimaging and computational modeling.
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