Brain networks and their relevance for stroke rehabilitation

• Authors: Adrian G. Guggisberg, University Hospital Geneva; Philipp J. Koch, Swiss Federal Institute of Technology (EPFL); Friedhelm C. Hummel, Swiss Federal Institute of Technology (EPFL); Cathrin Buetefisch, Emory University
• Journal Title: Clinical Neurophysiology
• Volume: Volume 130, Number 7
• Publisher: Elsevier Ireland Ltd. | 2019-07-01, Pages 1098-1124
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1016/j.clinph.2019.04.004
• Subjects: Biology, Neuroscience; Health Sciences, Radiology; Health Sciences, Rehabilitation and Therapy
• Keywords: Science & Technology; Life Sciences & Biomedicine; Clinical Neurology; Neurosciences; Neurosciences & Neurology; Stroke; Plasticity; Network; Rehabilitation; Transcranial magnetic stimulation; Primary mortox stimulation; State functional connectivity; Theta-burst stimulation; Noninvasive cortical stimulation; Neurite Orientation Dispersion and Density Imaging (NODDI); Diffusion tensor imaging; Peripheral nerve injury; Axon diameter distribution

Abstract

Stroke has long been regarded as focal disease with circumscribed damage leading to neurological deficits. However, advances in methods for assessing the human brain and in statistics have enabled new tools for the examination of the consequences of stroke on brain structure and function. Thereby, it has become evident that stroke has impact on the entire brain and its network properties and can therefore be considered as a network disease. The present review first gives an overview of current methodological opportunities and pitfalls for assessing stroke-induced changes and reorganization in the human brain. We then summarize principles of plasticity after stroke that have emerged from the assessment of networks. Thereby, it is shown that neurological deficits do not only arise from focal tissue damage but also from local and remote changes in white-matter tracts and in neural interactions among wide-spread networks. Similarly, plasticity and clinical improvements are associated with specific compensatory structural and functional patterns of neural network interactions. Innovative treatment approaches have started to target such network patterns to enhance recovery. Network assessments to predict treatment response and to individualize rehabilitation is a promising way to enhance specific treatment effects and overall outcome after stroke.