Context: Patients who have suffered from persistent symptoms often undergo lumbar spinal surgery (LSS). Motor imagery should be added to postoperative home exercises to reduce patient complaints.
Objective: The aim of this study was to compare the effects of home exercise plus motor imagery and only home exercise in patients undergoing LSS.
Design: A randomized controlled study.
Settings: This study was designed by researchers at Dokuz Eylul University.
Participants: Thirty-seven patients undergoing LSS were randomized to motor imagery group (n = 19) and control group (n = 18).
Main Outcome Measures: Pain was measured by Visual Analogue Scale, disability related to low back pain by Oswestry Disability Index, pain-related fear by Tampa Scale of Kinesiophobia, depression by Beck Depression Inventory, quality of life by World Health Organization Quality of Life Scale-Short Form (WHOQOL-BREF). All assessments were repeated in the preoperative period, three weeks after and six weeks after the surgery.
Interventions: Motor imagery group underwent home exercise plus motor imagery program applied by voice recording. Control group underwent only home exercise program. Exercise program compliance was monitored by exercise diary and telephone calls once every week.
Results: There was a significant improvement in pain at rest and during activity, disability, kinesiophobia, depression, physical health and psychological sub-parameters of WHOQOL-BREF between preoperative period, and the third week and sixth week in both groups (p < 0.05). When comparing groups for gain scores, there was a more significant improvement in pain during activity in motor imagery group (p < 0.05). Motor imagery should be addressed as an effective treatment after LSS.
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http://dx.doi.org/10.1016/j.explore.2020.02.001 | DOI Listing |
Neuroscience
January 2025
Kansai University of Health Sciences, Faculty of Health Sciences, Department of Physical Therapy, 2-11-1 Wakaba Sennangun Kumatori, Osaka 590-0482, Japan; Graduate School of Kansai University of Health Sciences, Graduate School of Health Sciences, 2-11-1 Wakaba Sennangun Kumatori, Osaka 590-0482, Japan.
Elderly adults may have poorer recall ability than young adults and may not fully enjoy the effects of motor imagery. To understand the age bias of the effect of motor imagery on hand dexterity, we evaluated brain activation and spinal motor nerve excitability. Brain activation was evaluated from changes in oxygenated hemoglobin concentration, while spinal motor nerve excitability was evaluated from F-waves in eight young (mean age 21.
View Article and Find Full Text PDFClin EEG Neurosci
January 2025
Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
Motor Imagery (MI) electroencephalographic (EEG) signal classification is a pioneer research branch essential for mobility rehabilitation. This paper proposes an end-to-end hybrid deep network "Spatio Temporal Inception Transformer Network (STIT-Net)" model for MI classification. Discrete Wavelet Transform (DWT) is used to derive the alpha (8-13) Hz and beta (13-30) Hz EEG sub bands which are dominant during motor tasks to enhance the performance of the proposed work.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Shanghai Dianji University, shnaghai, Shanghai, Shanghai, 201306, CHINA.
Objective: Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain.
View Article and Find Full Text PDFThe complementary strengths of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have driven extensive research into integrating these two noninvasive modalities to better understand the neural mechanisms underlying cognitive, sensory, and motor functions. However, the precise neural patterns associated with motor functions, especially imagined movements, remain unclear. Specifically, the correlations between electrophysiological responses and hemodynamic activations during executed and imagined movements have not been fully elucidated at a whole-brain level.
View Article and Find Full Text PDFCogn Neurodyn
December 2025
School of Computer Science, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.
The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance.
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