Abstract

Traditional rehabilitation techniques present limitations and the majority of patients show poor 1-year post-stroke recovery. Thus, Neurofeedback (NF) or Brain-Computer-Interface applications for stroke rehabilitation purposes are gaining increased attention. Indeed, NF has the potential to enhance volitional control of targeted cortical areas and thus impact on motor function recovery. However, current implementations are limited by temporal, spatial or practical constraints of the specific imaging modality used. In this pilot work and for the first time in literature, we applied bimodal EEG-fMRI NF for upper limb stroke recovery on four stroke-patients with different stroke characteristics and motor impairment severity. We also propose a novel, multi-target training approach that guides the training towards the activation of the ipsilesional primary motor cortex. In addition to fMRI and EEG outcomes, we assess the integrity of the corticospinal tract (CST) with tractography. Preliminary results suggest the feasibility of our approach and show its potential to induce an augmented activation of ipsilesional motor areas, depending on the severity of the stroke deficit. Only the two patients with a preserved CST and subcortical lesions succeeded in upregulating the ipsilesional primary motor cortex and exhibited a functional improvement of upper limb motricity. These findings highlight the importance of taking into account the variability of the stroke patients’ population and enabled to identify inclusion criteria for the design of future clinical studies.

Highlights

  • Neurofeedback (NF) consists of training self-regulation of a specific brain function by providing a subject with real-time information about his own brain activity (Sitaram et al, 2017)

  • We propose a novel, multi-target training approach that guides the training towards the activation of the ipsilesional primary motor cortex

  • They were satisfied with the visual feedback appearance and how it translated their motor imagery effort

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Summary

Introduction

Neurofeedback (NF) consists of training self-regulation of a specific brain function by providing a subject with real-time information about his own brain activity (Sitaram et al, 2017). The majority of NF (or braincomputer interfaces, BCIs) approaches have relied solely on one imaging technique, historically on EEG recordings. In applications intended for motor recovery, subjects are usually asked to perform motor imagery since imagining the movement and executing it are considered to involve similar brain areas. This is true if the mental practice is oriented towards a kinesthetic (rather than visual) motor imagery (Solodkin et al, 2004).

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