Motor imagery and gait control in Parkinson’s disease: techniques and new perspectives in neurorehabilitation

ABSTRACT

Introduction

Motor imagery (MI), defined as the ability to mentally represent an action without actual movement, has been used to improve motor function in athletes and, more recently, in neurological disorders such as Parkinson’s disease (PD). Several studies have investigated the neural correlates of motor imagery, which change also depending on the action imagined.

Areas covered

This review focuses on locomotion, which is a crucial activity in everyday life and is often impaired by neurological conditions. After a general discussion on the neural correlates of motor imagery and locomotion, we review the evidence highlighting the abnormalities in gait control and gait imagery in PD patients. Next, new perspectives and techniques for PD patients’ rehabilitation are discussed, namely Brain Computer Interfaces (BCIs), neurofeedback, and virtual reality (VR).

Expert opinion

Despite the few studies, the literature review supports the potential beneficial effects of motor imagery interventions in PD focused on locomotion. The development of new technologies could empower the administration of training based on motor imagery locomotor tasks, and their application could lead to new rehabilitation protocols aimed at improving walking ability in patients with PD.

KEYWORDS:

• Parkinson’s disease
• motor imagery
• gait control
• neuro-rehabilitation

Article highlights

• There are many studies investigating motor imagery ability in patients with PD, some of them related to locomotor acts

• There are still few studies investigating the efficacy of a motor imagery training on walking ability in patients with PD

• An extensive evaluation in terms of cognitive and functional capability of the patient prior to the start of the training is fundamental

• Training should be assisted by a physiotherapist, in addition to conventional training and integrated to a personalized neurorehabilitation program.

• Motor imagery can be empowered by the use of new technologies according to PETTLEP model factors

Declaration of interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership, or options, expert testimony, grants, or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers in this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.