Target-oriented motor imagery for grasping action: different characteristics of brain activation between kinesthetic and visual imagery

The multisensory nature of human action imagery

Imagination can appeal to all our senses and may, therefore, manifest in very different qualities (e.g., visual, tactile, proprioceptive, or kinesthetic). One line of research addresses action imagery that refers to a process by which people imagine the execution of an action without actual body movements. In action imagery, visual and kinesthetic aspects of the imagined action are particularly important. However, other sensory modalities may also play a role. The purpose of the paper will be to address issues that include: (i) the creation of an action image, (ii) how the brain generates images of movements and actions, (iii) the richness and vividness of action images. We will further address possible causes that determine the sensory impression of an action image, like task specificity, instruction and experience. In the end, we will outline open questions and future directions.

The lateralized effects of Parkinson's Disease on motor imagery: Evidence from mental chronometry

Alterations to the content of action representations may contribute to the movement challenges that characterize Parkinson's Disease (PD). One way to investigate action representations is through motor imagery. As PD motor symptoms typically have a unilateral onset, disease-related deficits related to action representations may follow a similarly lateralized pattern. The present study examined if temporal accuracy of motor imagery in individuals with PD differed according to the side of the body involved in the task. Thirty-eight participants with PD completed a mental chronometry task using their more affected and less affected side. Participants had significantly shorter mental versus physical movement times for the more affected. Higher imagery vividness in the kinaesthetic domain predicted shorter mental versus physical movement times for the more affected side, as did lower imagery vividness in the visual domain and poorer cognitive function. These results indicate that people with PD imagine movements differently when the target actions their more affected versus less affected side. It is additionally possible that side-specific deficits in the accurate processing of kinaesthetic information lead to an increased reliance on visual processes and cognitive resources to successfully execute motor imagery involving the more affected side.

Characterization and classification of kinesthetic motor imagery levels

Objective.Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals.Approach.A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN).Main results.A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN.Significance.Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Kinesthetic vs. visual focus: No evidence for effects of practice modality in representation types after action imagery practice and action execution practice

Action-imagery practice (AIP) is assumed to result in partly different action representations than action-execution practice (AEP). The present study investigated whether focusing on either kinesthetic or visual aspects of a task during practice amplifies or diminishes such differences between AIP and AEP. In ten sessions, four groups, using either AIP or AEP with either kinesthetic or visual focus, practiced a twelve-element sequence in a unimanual serial reaction time task. Tests involved the practice sequence, a mirror sequence, and a different sequence, each performed with the practice and transfer hand. In AIP and AEP, in both hands, reaction times (RTs) were shorter in the practice sequence than in the different sequence, indicating effector-independent visual-spatial sequence representations. Further, RTs were shorter in the practice hand than in the transfer hand in the practice sequence (but not in the different sequence), indicating effector-dependent representations in AEP and AIP. Although the representation types did not differ, learning effects were stronger in AEP than in AIP. Thus, although to a lower extent than in AEP, effector-dependent representations can be acquired using AIP. Contrary to the expectations, the focus manipulation did not have an impact on the acquired representation types. Hence, modality instructions in AIP may not have such a strong impact as commonly assumed, at least in implicit sequence learning.

Neuropsychological Activations and Networks While Performing Visual and Kinesthetic Motor Imagery

This study aimed to answer the questions 'What are the neural networks and mechanisms involved in visual and kinesthetic motor imagery?', and 'Is part of cognitive processing included during visual and kinesthetic motor imagery?' by investigating the neurophysiological networks and activations during visual and kinesthetic motor imagery using motor imagery tasks (golf putting). The experiment was conducted with 19 healthy adults. Functional magnetic resonance imaging (fMRI) was used to examine neural activations and networks during visual and kinesthetic motor imagery using golf putting tasks. The findings of the analysis on cerebral activation patterns based on the two distinct types of motor imagery indicate that the posterior lobe, occipital lobe, and limbic lobe exhibited activation, and the right hemisphere was activated during the process of visual motor imagery. The activation of the temporal lobe and the parietal lobe were observed during the process of kinesthetic motor imagery. This study revealed that visual motor imagery elicited stronger activation in the right frontal lobe, whereas kinesthetic motor imagery resulted in greater activation in the left frontal lobe. It seems that kinesthetic motor imagery activates the primary somatosensory cortex (BA 2), the secondary somatosensory cortex (BA 5 and 7), and the temporal lobe areas and induces human sensibility. The present investigation evinced that the neural network and the regions of the brain that are activated exhibit variability contingent on the category of motor imagery.

Deciphering Functional Connectivity Differences Between Motor Imagery and Execution of Target-Oriented Grasping

This study aimed to delineate overlapping and distinctive functional connectivity in visual motor imagery, kinesthetic motor imagery, and motor execution of target-oriented grasping action of the right hand. Functional magnetic resonance imaging data were obtained from 18 right-handed healthy individuals during each condition. Seed-based connectivity and multi-voxel pattern analyses were employed after selecting seed regions with the left primary motor cortex and supplementary motor area. There was equivalent seed-based connectivity during the three conditions in the bilateral frontoparietal and temporal areas. When the seed region was the left primary motor cortex, increased connectivity was observed in the left cuneus and superior frontal area during visual and kinesthetic motor imageries, respectively, compared with that during motor execution. Multi-voxel pattern analyses revealed that each condition was differentiated by spatially distributed connectivity patterns of the left primary motor cortex within the right cerebellum VI, cerebellum crus II, and left lingual area. When the seed region was the left supplementary motor area, the connectivity patterns within the right putamen, thalamus, cerebellar areas IV-V, and left superior parietal lobule were significantly classified above chance level across the three conditions. The present findings improve our understanding of the spatial representation of functional connectivity and its specific patterns among motor imagery and motor execution. The strength and fine-grained connectivity patterns of the brain areas can discriminate between motor imagery and motor execution.

EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients' Rehabilitation

The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain-computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as irregularity parameters. The MI-based BCI features were then statistically retrieved from each participant using two-way analysis of variance (ANOVA) to demonstrate the individuals' performances from four classes (left hand, right hand, foot, and tongue). The dimensionality reduction algorithm, Laplacian Eigenmap (LE), was used to enhance the MI-based BCI classification performance. Utilizing k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) classifiers, the groups of post-stroke patients were ultimately determined. The findings show that LE with RF and KNN obtained 74.48% and 73.20% accuracy, respectively; therefore, the integrated set of the proposed features along with ICA denoising technique can exactly describe the proposed MI framework, which may be used to explore the four classes of MI-based BCI rehabilitation. This study will help clinicians, doctors, and technicians make a good rehabilitation program for people who have had a stroke.

Imagined Timed Up and Go test (iTUG) in people with Parkinson's Disease: test-retest reliability and validity

PURPOSE

To determine the test-retest reliability and validity of the Imagined Timed Up and Go Test (iTUG) as a Motor Imagery measure of temporal accuracy in people with Parkinson's Disease (PD).

MATERIALS AND METHODS

A descriptive study was conducted following the GRRAS recommendations. Thirty-two people with idiopathic, mild to moderate PD (Hoehn and Yahr I-III), without cognitive impairment (MMSE ≥ 24), were assessed twice (7-15 days apart) with the iTUG. The absolute unadjusted difference in seconds, and the absolute adjusted difference as percentage of estimation error, between real and imagined TUG times, were calculated as outcome measures. Test-retest reliability was assessed using a two-way mixed-effects model of the ICC. Construct validity was tested with the Imagined Box and Blocks Test (iBBT) and convergent validity with clinical characteristics of PD, using the Spearman's rank correlation coefficient.

RESULTS

The ICC for the unadjusted and adjusted measures of the iTUG was ICC = 0.61 and ICC = 0.55, respectively. Correlations between iTUG and iBBT were not statistically significant. The iTUG was partially correlated to clinical characteristics of PD.

CONCLUSIONS

Test-retest reliability of the iTUG was moderate. Construct validity between iTUG and iBBT was poor, so caution should be taken when using them concurrently to assess imagery's temporal accuracy.

Consistent under-reporting of task details in motor imagery research

Motor Imagery is a subject of longstanding scientific interest. However, critical details of motor imagery protocols are not always reported in full, hampering direct replication and translation of this work. The present review provides a quantitative assessment of the prevalence of under-reporting in the recent motor imagery literature. Publications from the years 2018-2020 were examined, with 695 meeting the inclusion criteria for further examination. Of these studies, 64% (445/695) did not provide information about the modality of motor imagery (i.e., kinesthetic, visual, or a mixture of both) used in the study. When visual or mixed imagery was specified, the details of the visual perspective to be used (i.e., first person, third person, or combinations of both) were not reported in 24% (25/103) of studies. Further analysis indicated that studies using questionnaires to assess motor imagery reported more information than those that did not. We conclude that studies using motor imagery consistently under-report key details of their protocols, which poses a significant problem for understanding, replicating, and translating motor imagery effects.

Neural Activity During Imagery Supports Three Imagery Abilities as Measured by the Movement Imagery Questionnaire-3

Self-report and neural data were examined in 14 right-handed college-age males screened from a pool of 200 to verify neural activity during imagery and that the neural activity (area of brain) varies as a function of the imagery type. Functional magnetic resonance imaging data collected during real-time imagery of the three Movement Imagery Questionnaire-3 abilities indicated frontal areas, motor areas, and cerebellum active during kinesthetic imagery, motor areas, and superior parietal lobule during internal visual imagery, and parietal lobule and occipital cortex during external visual imagery. Central and imagery-specific neural patterns were found providing further biological validation of kinesthetic, internal visual, and external visual complementing results on females. Next, research should (a) compare neural activity between male participants screened by self-reported imagery abilities to determine if good imagers have more efficient neural networks than poor imagers and (b) determine if there is a statistical link between participants' neural activity during imagery and self-report Movement Imagery Questionnaire-3 scores.

Differential Influence of the Dorsal Premotor and Primary Somatosensory Cortex on Corticospinal Excitability during Kinesthetic and Visual Motor Imagery: A Low-Frequency Repetitive Transcranial Magnetic Stimulation Study

Consistent evidence suggests that motor imagery involves the activation of several sensorimotor areas also involved during action execution, including the dorsal premotor cortex (dPMC) and the primary somatosensory cortex (S1). However, it is still unclear whether their involvement is specific for either kinesthetic or visual imagery or whether they contribute to motor activation for both modalities. Although sensorial experience during motor imagery is often multimodal, identifying the modality exerting greater facilitation of the motor system may allow optimizing the functional outcomes of rehabilitation interventions. In a sample of healthy adults, we combined 1 Hz repetitive transcranial magnetic stimulation (rTMS) to suppress neural activity of the dPMC, S1, and primary motor cortex (M1) with single-pulse TMS over M1 for measuring cortico-spinal excitability (CSE) during kinesthetic and visual motor imagery of finger movements as compared to static imagery conditions. We found that rTMS over both dPMC and S1, but not over M1, modulates the muscle-specific facilitation of CSE during kinesthetic but not during visual motor imagery. Furthermore, dPMC rTMS suppressed the facilitation of CSE, whereas S1 rTMS boosted it. The results highlight the differential pattern of cortico-cortical connectivity within the sensorimotor system during the mental simulation of the kinesthetic and visual consequences of actions.

The Effects of Instruction Manipulation on Motor Performance Following Action Observation

The effects of action observation (AO) on motor performance can be modulated by instruction. The effects of two top-down aspects of the instruction on motor performance have not been fully resolved: those related to attention to the observed task and the incorporation of motor imagery (MI) during AO. In addition, the immediate vs. 24-h retention test effects of those instruction’s aspects are yet to be elucidated. Forty-eight healthy subjects were randomly instructed to: (1) observe reaching movement (RM) sequences toward five lighted units with the intention of reproducing the same sequence as fast and as accurate as possible (Intentional + Attentional group; AO+At); (2) observe the RMs sequence with the intention of reproducing the same sequence as fast and as accurate as possible and simultaneously to the observation to imagine performing the RMs (Intentional + attentional + MI group; AO+At+MI); and (3) observe the RMs sequence (Passive AO group). Subjects’ performance was tested before and immediately after the AO and retested after 24 h. During each of the pretest, posttest, and retest, the subject performed RMs toward the units that were activated in the same order as the observed sequence. Occasionally, the sequence order was changed by beginning the sequence with a different activated unit. The outcome measures were: averaged response time of the RMs during the sequences, difference between the response time of the unexpected and expected RMs and percent of failures to reach the target within 1 s. The averaged response time and the difference between the response time of the unexpected and expected RMs were improved in all groups at posttest compared to pretest, regardless of instruction. Averaged response time was improved in the retest compared to the posttest only in the Passive AO group. The percent of failures across groups was higher in pretest compared to retest. Our findings suggest that manipulating top-down aspects of instruction by adding attention and MI to AO in an RM sequence task does not improve subsequent performance more than passive observation. Off-line learning of the sequence in the retention test was improved in comparison to posttest following passive observation only.