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result(s) for
"Motor imagery"
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Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Increases Posterior Theta Rhythm and Reduces Latency of Motor Imagery
Experiments show activation of the left dorsolateral prefrontal cortex (DLPFC) in motor imagery (MI) tasks, but its functional role requires further investigation. Here, we address this issue by applying repetitive transcranial magnetic stimulation (rTMS) to the left DLPFC and evaluating its effect on brain activity and the latency of MI response. This is a randomized, sham-controlled EEG study. Participants were randomly assigned to receive sham (15 subjects) or real high-frequency rTMS (15 subjects). We performed EEG sensor-level, source-level, and connectivity analyses to evaluate the rTMS effects. We revealed that excitatory stimulation of the left DLPFC increases theta-band power in the right precuneus (PrecuneusR) via the functional connectivity between them. The precuneus theta-band power negatively correlates with the latency of the MI response, so the rTMS speeds up the responses in 50% of participants. We suppose that posterior theta-band power reflects attention modulation of sensory processing; therefore, high power may indicate attentive processing and cause faster responses.
Journal Article
Extracting electrophysiological correlates of functional magnetic resonance imaging data using the canonical polyadic decomposition
The relation between electrophysiology and BOLD‐fMRI requires further elucidation. One approach for studying this relation is to find time‐frequency features from electrophysiology that explain the variance of BOLD time‐series. Convolution of these features with a canonical hemodynamic response function (HRF) is often required to model neurovascular coupling mechanisms and thus account for time shifts between electrophysiological and BOLD‐fMRI data. We propose a framework for extracting the spatial distribution of these time‐frequency features while also estimating more flexible, region‐specific HRFs. The core component of this method is the decomposition of a tensor containing impulse response functions using the Canonical Polyadic Decomposition. The outputs of this decomposition provide insight into the relation between electrophysiology and BOLD‐fMRI and can be used to construct estimates of BOLD time‐series. We demonstrated the performance of this method on simulated data while also examining the effects of simulated measurement noise and physiological confounds. Afterwards, we validated our method on publicly available task‐based and resting‐state EEG‐fMRI data. We adjusted our method to accommodate the multisubject nature of these datasets, enabling the investigation of inter‐subject variability with regards to EEG‐to‐BOLD neurovascular coupling mechanisms. We thus also demonstrate how EEG features for modelling the BOLD signal differ across subjects. We show that EEG features of BOLD‐fMRI dynamics can be obtained using the canonical polyadic decomposition. Hemodynamic response functions are also estimated in the process, which allows one to model BOLD‐fMRI signals using EEG signals. We employ our method on simulated data, as well as publicly available task‐based and resting‐state EEG‐fMRI data.
Journal Article
Towards the integration of mental practice in rehabilitation programs. A critical review
Many clinical studies have investigated the use of mental practice (MP) through motor imagery (MI) to enhance functional recovery of patients with diverse physical disabilities. Although beneficial effects have been generally reported for training motor functions in persons with chronic stroke (e.g., reaching, writing, walking), attempts to integrate MP within rehabilitation programs have been met with mitigated results. These findings have stirred further questioning about the value of MP in neurological rehabilitation. In fact, despite abundant systematic reviews, which customarily focused on the methodological merits of selected studies, several questions about factors underlying observed effects remain to be addressed. This review discusses these issues in an attempt to identify factors likely to hamper the integration of MP within rehabilitation programs. First, the rationale underlying the use of MP for training motor function is briefly reviewed. Second, three modes of MI delivery are proposed based on the analysis of the research protocols from 27 studies in persons with stroke and Parkinson's disease. Third, for each mode of MI delivery, a general description of MI training is provided. Fourth, the review discusses factors influencing MI training outcomes such as: the adherence to MI training, the amount of training and the interaction between physical and mental rehearsal; the use of relaxation, the selection of reliable, valid and sensitive outcome measures, the heterogeneity of the patient groups, the selection of patients and the mental rehearsal procedures. To conclude, the review proposes a framework for integrating MP in rehabilitation programs and suggests research targets for steering the implementation of MP in the early stages of the rehabilitation process. The challenge has now shifted towards the demonstration that MI training can enhance the effects of regular therapy in persons with subacute stroke during the period of spontaneous recovery.
Journal Article
Evaluation of the Effectiveness of Control Using a Brain–Computer Interface in Training to Upper and Lower Limb Motor Imagery
The effectiveness of control using a brain–computer interface (BCI) and the success of motor imagery of the upper and lower limbs was assessed in terms of the accuracy of recognition of brain EEG signals (classification accuracy) on hand, foot, and locomotion motor imagery during 10 days of training in 10 volunteers. On training day 1, mean classification accuracy was higher for locomotion imagery than foot movement imagery, while accuracy on day 2 was better for hand imagery than locomotion imagery and accuracy on day 5 was better for foot imagery than hand imagery. On average, there was a significant increase in group mean classification accuracy by training day 3 in motor imagery of the hands and feet; as training continued, classification accuracy then decreased and again increased. Classification accuracy did not change significantly during training to locomotion imagery. Assessment of the dynamics of individual changes in classification accuracy using linear trend analysis showed that training led to increased classification accuracy for three participants (hand movements and locomotion in one, feet in two) and to decreased classification accuracy in three (hand movements and locomotion in one, locomotion in the second, and foot movements in the third). Four participants – like the group mean – showed no significant changes. These results are discussed in terms of changes in the activity of brain structures during training in relation to types of motor imagery.
Journal Article
Motor imagery ability scores are related to cortical activation during gait imagery
Motor imagery (MI) is the mental execution of actions without overt movements that depends on the ability to imagine. We explored whether this ability could be related to the cortical activity of the brain areas involved in the MI network. To this goal, brain activity was recorded using high-density electroencephalography in nineteen healthy adults while visually imagining walking on a straight path. We extracted Event-Related Desynchronizations (ERDs) in the θ, α, and β band, and we measured MI ability via (i) the Kinesthetic and Visual Imagery Questionnaire (KVIQ), (ii) the Vividness of Movement Imagery Questionnaire-2 (VMIQ), and (iii) the Imagery Ability (IA) score. We then used Pearson’s and Spearman’s coefficients to correlate MI ability scores and average ERD power (
avgERD
). Positive correlations were identified between VMIQ and
avgERD
of the middle cingulum in the β band and with
avgERD
of the left insula, right precentral area, and right middle occipital region in the θ band. Stronger activation of the MI network was related to better scores of MI ability evaluations, supporting the importance of testing MI ability during MI protocols. This result will help to understand MI mechanisms and develop personalized MI treatments for patients with neurological dysfunctions.
Journal Article
Motor Imagery: How to Assess, Improve Its Performance, and Apply It for Psychosis Diagnostics
With this review, we summarize the state-of-the-art of scientific studies in the field of motor imagery (MI) and motor execution (ME). We composed the brain map and description that correlate different brain areas with the type of movements it is responsible for. That gives a more complete and systematic picture of human brain functionality in the case of ME and MI. We systematized the most popular methods for assessing the quality of MI performance and discussed their advantages and disadvantages. We also reviewed the main directions for the use of transcranial magnetic stimulation (TMS) in MI research and considered the principal effects of TMS on MI performance. In addition, we discuss the main applications of MI, emphasizing its use in the diagnostics of various neurodegenerative disorders and psychoses. Finally, we discuss the research gap and possible improvements for further research in the field.
Journal Article
Can a Subjective Questionnaire Be Used as Brain-Computer Interface Performance Predictor?
Predicting a subject's ability to use a Brain Computer Interface (BCI) is one of the major issues in the BCI domain. Relevant applications of forecasting BCI performance include the ability to adapt the BCI to the needs and expectations of the user, assessing the efficiency of BCI use in stroke rehabilitation, and finally, homogenizing a research population. A limited number of recent studies have proposed the use of subjective questionnaires, such as the Motor Imagery Questionnaire Revised-Second Edition (MIQ-RS). However, further research is necessary to confirm the effectiveness of this type of subjective questionnaire as a BCI performance estimation tool. In this study we aim to answer the following questions: can the MIQ-RS be used to estimate the performance of an MI-based BCI? If not, can we identify different markers that could be used as performance estimators? To answer these questions, we recorded EEG signals from 35 healthy volunteers during BCI use. The subjects had previously completed the MIQ-RS questionnaire. We conducted an offline analysis to assess the correlation between the questionnaire scores related to Kinesthetic and Motor imagery tasks and the performances of four classification methods. Our results showed no significant correlation between BCI performance and the MIQ-RS scores. However, we reveal that BCI performance is correlated to habits and frequency of practicing manual activities.
Journal Article
Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients
Motor imagery (MI) based brain-computer interface (BCI) has been developed as an alternative therapy for stroke rehabilitation. However, experimental evidence demonstrates that a significant portion (10-50%) of subjects are BCI-inefficient users (accuracy less than 70%). Thus, predicting BCI performance prior to clinical BCI usage would facilitate the selection of suitable end-users and improve the efficiency of stroke rehabilitation. In the current study, we proposed two physiological variables, i.e., laterality index (LI) and cortical activation strength (CAS), to predict MI-BCI performance. Twenty-four stroke patients and 10 healthy subjects were recruited for this study. Each subject was required to perform two blocks of left- and right-hand MI tasks. Linear regression analyses were performed between the BCI accuracies and two physiological predictors. Here, the predictors were calculated from the electroencephalography (EEG) signals during paretic hand MI tasks (5 trials; approximately 1 min). LI values exhibited a statistically significant correlation with two-class BCI (left vs. right) performance (r = -0.732,
< 0.001), and CAS values exhibited a statistically significant correlation with brain-switch BCI (task vs. idle) performance (
= 0.641,
< 0.001). Furthermore, the BCI-inefficient users were successfully recognized with a sensitivity of 88.2% and a specificity of 85.7% in the two-class BCI. The brain-switch BCI achieved a sensitivity of 100.0% and a specificity of 87.5% in the discrimination of BCI-inefficient users. These results demonstrated that the proposed BCI predictors were promising to promote the BCI usage in stroke rehabilitation and contribute to a better understanding of the BCI-inefficiency phenomenon in stroke patients.
Journal Article
Effectiveness of movement representation techniques in non-specific shoulder pain: a systematic review and meta-analysis
This systematic review and meta-analysis aims to assess the effects of movement representation techniques (MRT) on pain, range of motion, functional outcomes, and pain-related fear in patients with non-specific shoulder pain (NSSP). A literature search conducted in PubMed, PEDro, EBSCO, Scopus, Cochrane Library, ScienceDirect, and gray literature on April 31, 2023. We selected seven randomized controlled trials based on the PICOS framework. Incomplete data or non-NSSP excluded. Study quality was assessed using the PEDro scale (mean score = 6.43), and certainty of evidence was evaluated with the GRADE approach. MRT demonstrated a large effect size for pain reduction (high heterogeneity, I2 = 85.2%, Hedges’g = 1.324, 95% CI = 0.388–2.260,
P
= 0.006), functional improvement (moderate heterogeneity, I2 = 70.82%, Hedges’g = 1.263, 95% CI = 0.622–1.904,
P
< 0.001), and reduction of pain-related fear (moderate heterogeneity, I2 = 70.86%, Hedges’g = 0.968, 95% CI = 0.221–1.716,
P
< 0.001). MRT also showed significant benefits for range of motion, particularly in flexion (low heterogeneity, I2 = 26.38%, Hedges’g = 0.683), abduction (low heterogeneity, I2 = 33.27%, Hedges’g = 0.756), and external rotation (low heterogeneity, I2 = 48.33%, Hedges’g = 0.542) (
P
< 0.001 for all), while no significant effect was found for internal rotation (
P
> 0.05). No publication bias was detected. While limited evidence and methodological concerns necessitate further research, MRT appears to positively impact pain, range of motion, functional outcomes, and pain-related fear in NSSP patients.
Journal Article