Motor imagery vividness and symptom severity in Parkinson's disease.

J Neuropsychol

Division of Psychology, Communication and Human Neuroscience, School of Health Sciences., University of Manchester, Manchester, UK.

Published: March 2023

Motor imagery (MI), the mental simulation of movement in the absence of overt motor output, has demonstrated potential as a technique to support rehabilitation of movement in neurological conditions such as Parkinson's disease (PD). Existing evidence suggests that MI is largely preserved in PD, but previous studies have typically examined global measures of MI and have not considered the potential impact of individual differences in symptom presentation on MI. The present study investigated the influence of severity of overall motor symptoms, bradykinesia and tremor on MI vividness scores in 44 individuals with mild to moderate idiopathic PD. Linear mixed effects modelling revealed that imagery modality and the severity of left side bradykinesia significantly influenced MI vividness ratings. Consistent with previous findings, participants rated visual motor imagery (VMI) to be more vivid than kinesthetic motor imagery (KMI). Greater severity of left side bradykinesia (but not right side bradykinesia) predicted increased vividness of KMI, while tremor severity and overall motor symptom severity did not predict vividness of MI. The specificity of the effect of bradykinesia to the left side may reflect greater premorbid vividness for the dominant (right) side or increased attention to more effortful movements on the left side of the body resulting in more vivid motor imagery.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10946738PMC
http://dx.doi.org/10.1111/jnp.12293DOI Listing

Publication Analysis

Top Keywords

motor imagery
20
left side
16
side bradykinesia
12
motor
8
symptom severity
8
parkinson's disease
8
severity motor
8
severity left
8
vividness
6
severity
6

Similar Publications

Background: Hand dexterity is affected by normal aging and neuroinflammatory processes in the brain. Understanding the relationship between hand dexterity and brain structure in neurotypical older adults may be informative about prodromal pathological processes, thus providing an opportunity for earlier diagnosis and intervention to improve functional outcomes.

Methods: this study investigates the associations between hand dexterity and brain measures in neurotypical older adults (≥65 years) using the Nine-Hole Peg Test (9HPT) and magnetic resonance imaging (MRI).

View Article and Find Full Text PDF

Evidence for the dependence of visual and kinesthetic motor imagery on isolated visual and motor practice.

Conscious Cogn

December 2024

School of Kinesiology, University of British Columbia, 210-6081 University Boulevard, Vancouver, BC V6T 1Z1, Canada. Electronic address:

Motor imagery (MI) is a cognitive process believed to rely on the representation developed through experience. The equivalence between MI and execution has been questioned and the relationship between experience types and MI is unclear. We tested how observational and physical practice of hand gesture sequences impacted visual and kinesthetic MI and transfer to the unpracticed effector.

View Article and Find Full Text PDF

Non-invasive brain-computer interfaces (BCI) hold great promise in the field of neurorehabilitation. They are easy to use and do not require surgery, particularly in the area of motor imagery electroencephalography (EEG). However, motor imagery EEG signals often have a low signal-to-noise ratio and limited spatial and temporal resolution.

View Article and Find Full Text PDF

A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.

J Neural Eng

December 2024

West China Hospital of Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Sichuan Province, Chengdu, Sichuan, 610041, CHINA.

Objective: Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a Transformer with modified locally linear embedding and sliding window convolution for EEG decoding.

View Article and Find Full Text PDF

An attention-based motor imagery brain-computer interface system for lower limb exoskeletons.

Rev Sci Instrum

December 2024

School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

Lower-limb exoskeletons have become increasingly popular in rehabilitation to help patients with disabilities regain mobility and independence. Brain-computer interface (BCI) offers a natural control method for these exoskeletons, allowing users to operate them through their electroencephalogram (EEG) signals. However, the limited EEG decoding performance of the BCI system restricts its application for lower limb exoskeletons.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!