Imagine There Is No Plegia. Mental Motor Imagery Difficulties in Patients with Traumatic Spinal Cord Injury

Gait Training-Based Motor Imagery and EEG Neurofeedback in Lokomat: A Clinical Intervention With Complete Spinal Cord Injury Individuals

Robotic systems, such as Lokomat® have shown promising results in people with severe motor impairments, who suffered a stroke or other neurological damage. Robotic devices have also been used by people with more challenging damages, such as Spinal Cord Injury (SCI), using feedback strategies that provide information about the brain activity in real-time. This study proposes a novel Motor Imagery (MI)-based Electroencephalogram (EEG) Visual Neurofeedback (VNFB) system for Lokomat® to teach individuals how to modulate their own μ (8-12 Hz) and β (15-20 Hz) rhythms during passive walking. Two individuals with complete SCI tested our VNFB system completing a total of 12 sessions, each on different days. For evaluation, clinical outcomes before and after the intervention and brain connectivity were analyzed. As findings, the sensitivity related to light touch and painful discrimination increased for both individuals. Furthermore, an improvement in neurogenic bladder and bowel functions was observed according to the American Spinal Injury Association Impairment Scale, Neurogenic Bladder Symptom Score, and Gastrointestinal Symptom Rating Scale. Moreover, brain connectivity between different EEG locations significantly ( [Formula: see text]) increased, mainly in the motor cortex. As other highlight, both SCI individuals enhanced their μ rhythm, suggesting motor learning. These results indicate that our gait training approach may have substantial clinical benefits in complete SCI individuals.

EEG changes during passive movements improve the motor imagery feature extraction in BCIs-based sensory feedback calibration

Objective.This work proposes a method for two calibration schemes based on sensory feedback to extract reliable motor imagery (MI) features, and provide classification outputs more correlated to the user's intention.Method.After filtering the raw electroencephalogram (EEG), a two-step method for spatial feature extraction by using the Riemannian covariance matrices (RCM) method and common spatial patterns is proposed here. It uses EEG data from trials providing feedback, in an intermediate step composed of bothkth nearest neighbors and probability analyses, to find periods of time in which the user probably performed well the MI task without feedback. These periods are then used to extract features with better separability, and train a classifier for MI recognition. For evaluation, an in-house dataset with eight healthy volunteers and two post-stroke patients that performed lower-limb MI, and consequently received passive movements as feedback was used. Other popular public EEG datasets (such as BCI Competition IV dataset IIb, among others) from healthy subjects that executed upper-and lower-limbs MI tasks under continuous visual sensory feedback were further used.Results.The proposed system based on the Riemannian geometry method in two-steps (RCM-RCM) outperformed significantly baseline methods, reaching average accuracy up to 82.29%. These findings show that EEG data on periods providing passive movement can be used to contribute greatly during MI feature extraction.Significance.Unconscious brain responses elicited over the sensorimotor areas may be avoided or greatly reduced by applying our approach in MI-based brain-computer interfaces (BCIs). Therefore, BCI's outputs more correlated to the user's intention can be obtained.

Locked-in syndrome revisited

The locked-in syndrome (LiS) is characterized by quadriplegia with preserved vertical eye and eyelid movements and retained cognitive abilities. Subcategorization, aetiologies and the anatomical foundation of LiS are discussed. The damage of different structures in the pons, mesencephalon and thalamus are attributed to symptoms of classical, complete and incomplete LiS and the locked-in plus syndrome, which is characterized by additional impairments of consciousness, making the clinical distinction to other chronic disorders of consciousness at times difficult. Other differential diagnoses are cognitive motor dissociation (CMD) and akinetic mutism. Treatment options are reviewed and an early, interdisciplinary and aggressive approach, including the provision of psychological support and coping strategies is favoured. The establishment of communication is a main goal of rehabilitation. Finally, the quality of life of LiS patients and ethical implications are considered. While patients with LiS report a high quality of life and well-being, medical professionals and caregivers have largely pessimistic perceptions. The negative view on life with LiS must be overthought and the autonomy and dignity of LiS patients prioritized. Knowledge has to be disseminated, diagnostics accelerated and technical support system development promoted. More well-designed research but also more awareness of the needs of LiS patients and their perception as individual persons is needed to enable a life with LiS that is worth living.

EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives

Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain's motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10-30% of total channels, provided excellent performance compared to other existing studies.

Cervical Spinal Lesion, Completeness of Injury, Stress, and Depression Reduce the Efficiency of Mental Imagery in People With Spinal Cord Injury

OBJECTIVES

The aims of this study were to assess the relationships of (1) clinical variables (age, level of injury, time since injury [TSI], and completeness of injury) and (2) psychological variables (stress and depression) with mental imagery ability in individuals with spinal cord injury.

STUDY DESIGN

This was a cross-sectional study. Participants with spinal cord injury (N = 130) were requested to fill the Kinesthetic and Visual Imagery Questionnaire and Vividness of Motor Imagery Questionnaire. They also completed the Perceived Stress Scale and Patient Health Questionnaire 9 for the assessment of stress and depression, respectively.

RESULTS

Mental imagery scores were found to be significantly low in cervical injuries (P < 0.001) as compared with thoracic injuries (P < 0.001). Furthermore, higher levels of spinal injuries resulted in lower mental imagery scores. Completeness of injury (according to Asia Impairment Scale) also had a significant relationship (P < 0.001) with the mental imagery ability among spinal cord injury participants. Presence of stress (P < 0.001) and depression (P < 0.001) also associated with reduced efficiency of mental imagery in these individuals.

CONCLUSIONS

Injury type and psychological factors were associated with mental imagery in SCI patients. Imagery-based interventions should be designed after consideration of identified factors yielding effect on their outcomes.

TO CLAIM CME CREDITS

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CME OBJECTIVES

Upon completion of this article, the reader should be able to: (1) Determine the impact of clinical variables such as level of injury, completeness and chronicity of injury on mental imagery ability in spinal cord injury; (2) Discuss the role of stress and depression on mental imagery ability in spinal cord injury; and (3) Describe the various dimensions of mental imagery ability and its variability among individuals who have spinal cord injury.

LEVEL

Advanced.

ACCREDITATION

The Association of Academic Physiatrists is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians.The Association of Academic Physiatrists designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.

Embodiment Comfort Levels During Motor Imagery Training Combined With Immersive Virtual Reality in a Spinal Cord Injury Patient

Brain-machine interfaces combining visual, auditory, and tactile feedback have been previously used to generate embodiment experiences during spinal cord injury (SCI) rehabilitation. It is not known if adding temperature to these modalities can result in discomfort with embodiment experiences. Here, comfort levels with the embodiment experiences were investigated in an intervention that required a chronic pain SCI patient to generate lower limb motor imagery commands in an immersive environment combining visual (virtual reality -VR), auditory, tactile, and thermal feedback. Assessments were made pre-/ post-, throughout the intervention (Weeks 0-5), and at 7 weeks follow up. Overall, high levels of embodiment in the adapted three-domain scale of embodiment were found throughout the sessions. No significant adverse effects of VR were reported. Although sessions induced only a modest reduction in pain levels, an overall reduction occurred in all pain scales (Faces, Intensity, and Verbal) at follow up. A high degree of comfort in the comfort scale for the thermal-tactile sleeve, in both the thermal and tactile feedback components of the sleeve was reported. This study supports the feasibility of combining multimodal stimulation involving visual (VR), auditory, tactile, and thermal feedback to generate embodiment experiences in neurorehabilitation programs.

Cognitive Training Improves Disconnected Limbs' Mental Representation and Peripersonal Space after Spinal Cord Injury

Paraplegia following spinal cord injury (SCI) affects the mental representation and peripersonal space of the paralysed body parts (i.e., lower limbs). Physical rehabilitation programs can improve these aspects, but the benefits are mostly partial and short-lasting. These limits could be due to the absence of trainings focused on SCI-induced cognitive deficits combined with traditional physical rehabilitation. To test this hypothesis, we assessed in 15 SCI-individuals the effects of adding cognitive recovery protocols (motor imagery–MI) to standard physical rehabilitation programs (Motor + MI training) on mental body representations and space representations, with respect to physical rehabilitation alone (control training). Each training comprised at least eight sessions administered over two weeks. The status of participants’ mental body representation and peripersonal space was assessed at three time points: before the training (T0), after the training (T1), and in a follow-up assessment one month later (T2). The Motor + MI training induced short-term recovery of peripersonal space that however did not persist at T2. Body representation showed a slower neuroplastic recovery at T2, without differences between Motor and the Motor + MI. These results show that body and space representations are plastic after lesions, and open new rehabilitation perspectives.

Characterization of kinesthetic motor imagery compared with visual motor imageries

Motor imagery (MI) is the only way for disabled subjects to robustly use a robot arm with a brain-machine interface. There are two main types of MI. Kinesthetic motor imagery (KMI) is proprioceptive (OR somato-) sensory imagination and Visual motor imagery (VMI) represents a visualization of the corresponding movement incorporating the visual network. Because these imagery tactics may use different networks, we hypothesized that the connectivity measures could characterize the two imageries better than the local activity. Electroencephalography data were recorded. Subjects performed different conditions, including motor execution (ME), KMI, VMI, and visual observation (VO). We tried to classify the KMI and VMI by conventional power analysis and by the connectivity measures. The mean accuracies of the classification of the KMI and VMI were 98.5% and 99.29% by connectivity measures (alpha and beta, respectively), which were higher than those by the normalized power (p < 0.01, Wilcoxon paired rank test). Additionally, the connectivity patterns were correlated between the ME-KMI and between the VO-VMI. The degree centrality (DC) was significantly higher in the left-S1 at the alpha-band in the KMI than in the VMI. The MI could be well classified because the KMI recruits a similar network to the ME. These findings could contribute to MI training methods.

Virtual reality for the treatment of neuropathic pain in people with spinal cord injuries: A scoping review

Context: Virtual and augmented imagery are emerging technologies with potential to reduce the severity and impact of neuropathic pain in people with spinal cord injury (SCI).Objective: We aimed to identify and discuss studies using virtual and augmented reality applications for the management of neuropathic pain in people with spinal cord injury.Methods (data sources, data extraction): A systematic literature search was conducted using PRISMA scoping review guidelines. Articles were searched in PubMed, Embase and Web of Science databases using search terms relating to SCI, virtual and augmented reality and neuropathic pain. With no strong evidence for visual imagery in the treatment of pain in SCI patients, we selected exploratory, feasibility and more rigorous methodologies such as randomized controlled trials and case-control studies. We only selected studies evaluating the effects of visual imagery on neuropathic pain at or below the spinal cord injury level.Results: Of 60 articles located, we included nine articles involving 207 participants. All studies were exploratory using head-mounted devices or 3D and 2D screens with virtual walking or limb movement imagery. Outcomes included pain sensitivity, motor function and body ownership. Eight of the nine studies reported significant reductions in neuropathic pain intensity. However, given small sample sizes in all studies, results may be unreliable.Conclusion: Although the number of studies and individual sample sizes are small, these initial findings are promising. Given the limited options available for the effective treatment of neuropathic SCI pain and early evidence of efficacy, they provide valuable incentive for further research.

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs

Motor deficiencies constitute a significant problem affecting millions of people worldwide. Such people suffer from a debility in daily functioning, which may lead to decreased and incoherence in daily routines and deteriorate their quality of life (QoL). Thus, there is an essential need for assistive systems to help those people achieve their daily actions and enhance their overall QoL. This study proposes a novel brain–computer interface (BCI) system for assisting people with limb motor disabilities in performing their daily life activities by using their brain signals to control assistive devices. The extraction of useful features is vital for an efficient BCI system. Therefore, the proposed system consists of a hybrid feature set that feeds into three machine-learning (ML) classifiers to classify motor Imagery (MI) tasks. This hybrid feature selection (FS) system is practical, real-time, and an efficient BCI with low computation cost. We investigate different combinations of channels to select the combination that has the highest impact on performance. The results indicate that the highest achieved accuracies using a support vector machine (SVM) classifier are 93.46% and 86.0% for the BCI competition III–IVa dataset and the autocalibration and recurrent adaptation dataset, respectively. These datasets are used to test the performance of the proposed BCI. Also, we verify the effectiveness of the proposed BCI by comparing its performance with recent studies. We show that the proposed system is accurate and efficient. Future work can apply the proposed system to individuals with limb motor disabilities to assist them and test their capability to improve their QoL. Moreover, the forthcoming work can examine the system’s performance in controlling assistive devices such as wheelchairs or artificial limbs.

Mental Imagery as a Rehabilitative Therapy for Neuropathic Pain in People With Spinal Cord Injury: A Randomized Controlled Trial

BACKGROUND

Pain of neuropathic origin in spinal cord injury (SCI) is unbearable and challenging to treat. Research studies conducted in the past have shown that mental imagery (MI) techniques have a significant impact on the reduction of symptoms of central neuropathic pain in people with SCI.

OBJECTIVES

The objective of this study was to evaluate the effect of MI training on pain intensity, neuropathic pain symptoms, and interference of pain with function in SCI.

METHODS

A total of 42 SCI participants with central neuropathic pain (duration 6-12 months) were recruited and randomly allocated to MI or control groups. A MI training protocol was administered to MI group and for 30 min/d for 5 days. Outcome measures were assessed at baseline and at the end of 4 weeks.

RESULTS

There was significant reduction in differences of mean [95% CI] scores of numeric rating scale (-2.1 [CI -2.78 to -1.41]; P < .001) between groups. Mean [95% CI] total scores of Neuropathic Pain Symptom Inventory declined in MI group as compared with control group (-4.52 [CI -5.86 to -3.18]; P < .001). Similarly, Brief Pain Inventory interference scale total dropped significantly (P < .001) in MI group. Majority of participants in the MI group (55%) reported improvement in scores of Patients' Global Impression of Change scale as compared with control group where most of the participants (52%) reported no change.

CONCLUSIONS

This study shows the effectiveness of the MI protocol developed as a rehabilitative approach in improving central neuropathic pain in SCI. Trial Registration. Clinical Trials Registry-India under Indian Council of Medical Research; CTRI/2018/07/014884. Registered July 16, 2018.

Motor imagery for pain and motor function after spinal cord injury: a systematic review

STUDY DESIGN

Systematic review.

OBJECTIVES

To evaluate the therapeutic benefits of motor imagery (MI) for the people with spinal cord injury (SCI).

SETTING

International.

METHODS

We searched electronic bibliographic databases, trial registers, and relevant reference lists. The review included experimental and quasi-experimental study designs as well as observational studies. For the critical appraisal of the 18 studies retrieved (three RCT, seven quasi-RCT, eight observational), we used instruments from the Joanna Briggs Institute. The primary outcome measure was pain. Secondary outcome measures included motor function and neurophysiological parameters. Adverse effects were extracted if reported in the included studies. Because of data heterogeneity, only a qualitative synthesis is offered.

RESULTS

The included studies involved 282 patients. In most, results were an improvement in motor function and decreased pain; however, some reported no effect or an increase in pain. Although protocols of MI intervention were heterogeneous, sessions of 8-20 min were used for pain treatments, and of 30-60 min were used for motor function improvement. Neurophysiological measurements showed changes in brain region activation and excitability imposed by SCI, which were partially recovered by MI interventions. No serious adverse effects were reported.

CONCLUSIONS

High heterogeneity in the SCI population, MI interventions, and outcomes measured makes it difficult to judge the therapeutic effects and best MI intervention protocol, especially for people with SCI with neuropathic pain. Further clinical trials evaluating MI intervention as adjunct therapy for pain in SCI patients are warranted.

HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury

Brain computer interfaces (BCIs) are thought to revolutionize rehabilitation after SCI, e.g., by controlling neuroprostheses, exoskeletons, functional electrical stimulation, or a combination of these components. However, most BCI research was performed in healthy volunteers and it is unknown whether these results can be translated to patients with spinal cord injury because of neuroplasticity. We sought to examine whether high-density EEG (HD-EEG) could improve the performance of motor-imagery classification in patients with SCI. We recorded HD-EEG with 256 channels in 22 healthy controls and 7 patients with 14 recordings (4 patients had more than one recording) in an event related design. Participants were instructed acoustically to either imagine, execute, or observe foot and hand movements, or to rest. We calculated Fast Fourier Transform (FFT) and full frequency directed transfer function (ffDTF) for each condition and classified conditions pairwise with support vector machines when using only 2 channels over the sensorimotor area, full 10-20 montage, high-density montage of the sensorimotor cortex, and full HD-montage. Classification accuracies were comparable between patients and controls, with an advantage for controls for classifications that involved the foot movement condition. Full montages led to better results for both groups (p < 0.001), and classification accuracies were higher for FFT than for ffDTF (p < 0.001), for which the feature vector might be too long. However, full-montage 10–20 montage was comparable to high-density configurations. Motor-imagery driven control of neuroprostheses or BCI systems may perform as well in patients as in healthy volunteers with adequate technical configuration. We suggest the use of a whole-head montage and analysis of a broad frequency range.