Neural oscillations during motor imagery of complex gait: an HdEEG study

Muscle activity and lower body kinematics change when performing motor imagery of gait

Motor imagery (MI) is a mental simulation of a movement without its actual execution. Our study aimed to assess how MI of two modalities of gait (normal gait and much more posturally challenging slackline gait) affects muscle activity and lower body kinematics. Electromyography (biceps femoris, gastrocnemius medialis, rectus femoris and tibialis anterior muscles) as well as acceleration and angular velocity (shank, thigh and pelvis segments) data were collected in three tasks for both MI modalities of gait (rest, gait imagery before and after the real execution of gait) in quiet bipedal stance in 26 healthy young adults. No significant change was observed in electromyography activity and lower body kinematics when comparing MI tasks of normal gait. A significantly higher acceleration for the lower limb segments in the vertical direction and for the pelvis in the mediolateral and anteroposterior direction and angular velocity for pelvis in the frontal plane were found during MI of slackline gait after its real execution compared to rest. The results show that MI of normal gait does not lead to any significant changes, while MI of slackline gait affects lower body kinematics parameters.

Neural correlates of motor imagery and execution in real-world dynamic behavior: evidence for similarities and differences

A large body of evidence shows that motor imagery and action execution behaviors result from overlapping neural substrates, even in the absence of overt movement during motor imagery. To date it is unclear how neural activations in motor imagery and execution compare for naturalistic whole-body movements, such as walking. Neuroimaging studies have not directly compared imagery and execution during dynamic walking movements. Here we recorded brain activation with mobile EEG during walking compared to during imagery of walking, with mental counting as a control condition. We asked 24 healthy participants to either walk six steps on a path, imagine taking six steps, or mentally count from one to six. We found beta and alpha power modulation during motor imagery resembling action execution patterns; a correspondence not found performing the control task of mental counting. Neural overlap occurred early in the execution and imagery walking actions, suggesting activation of shared action representations. Remarkably, a distinctive walking-related beta rebound occurred both during action execution and imagery at the end of the action suggesting that, like actual walking, motor imagery involves resetting or inhibition of motor processes. However, we also found that motor imagery elicits a distinct pattern of more distributed beta activity, especially at the beginning of the task. These results indicate that motor imagery and execution of naturalistic walking involve shared motor-cognitive activations, but that motor imagery requires additional cortical resources.

Central imaging based on near-infrared functional imaging technology can be useful to plan management in patients with chronic lateral ankle instability

BACKGROUND

Near infrared brain functional imaging (FNIRS) has been used for the evaluation of brain functional areas, the imaging differences of central activation of cognitive-motor dual tasks between patients with chronic lateral ankle instability (CLAI) and healthy population remain unclear. This study aimed to evaluated the role of central imaging based on FNIRS technology on the plan management in patients with CLAI, to provide insights to the clinical treatment of CLAI.

METHODS

CLAI patients treated in our hospital from January 1, 2021 to June 31, 2022 were selected. Both CLAI patients and health controls were intervened with simple task and cognitive-motor dual task under sitting and walking conditions, and the changes of oxygenated hemoglobin concentration in bilateral prefrontal cortex (PFC), premotor cortex (PMC) and auxiliary motor area (SMA) were collected and compared.

RESULTS

A total of 23 participants were enrolled. There were significant differences in the fNIRS ΔHbO2 of barefoot subtractive walking PFC-R and barefoot subtractive walking SMA-R between experimental and control group (all P < 0.05). There was no significant difference in ΔHbO2 between the experimental group and the control group in other states (P > 0.05). There was no significant difference in ΔHbO2 between the experimental group and the control group in each state of the brain PMC region.

CONCLUSION

Adaptive alterations may occur within the relevant brain functional regions of individuals with CLAI. The differential activation observed between the PFC and the SMA could represent a compensatory mechanism emerging from proprioceptive afferent disruptions following an initial ankle sprain.

Brain Networks Modulation during Simple and Complex Gait: A "Mobile Brain/Body Imaging" Study

Walking encompasses a complex interplay of neuromuscular coordination and cognitive processes. Disruptions in gait can impact personal independence and quality of life, especially among the elderly and neurodegenerative patients. While traditional biomechanical analyses and neuroimaging techniques have contributed to understanding gait control, they often lack the temporal resolution needed for rapid neural dynamics. This study employs a mobile brain/body imaging (MoBI) platform with high-density electroencephalography (hd-EEG) to explore event-related desynchronization and synchronization (ERD/ERS) during overground walking. Simultaneous to hdEEG, we recorded gait spatiotemporal parameters. Participants were asked to walk under usual walking and dual-task walking conditions. For data analysis, we extracted ERD/ERS in α, β, and γ bands from 17 selected regions of interest encompassing not only the sensorimotor cerebral network but also the cognitive and affective networks. A correlation analysis was performed between gait parameters and ERD/ERS intensities in different networks in the different phases of gait. Results showed that ERD/ERS modulations across gait phases in the α and β bands extended beyond the sensorimotor network, over the cognitive and limbic networks, and were more prominent in all networks during dual tasks with respect to usual walking. Correlation analyses showed that a stronger α ERS in the initial double-support phases correlates with shorter step length, emphasizing the role of attention in motor control. Additionally, β ERD/ERS in affective and cognitive networks during dual-task walking correlated with dual-task gait performance, suggesting compensatory mechanisms in complex tasks. This study advances our understanding of neural dynamics during overground walking, emphasizing the multidimensional nature of gait control involving cognitive and affective networks.

Association between visuo-spatial working memory and gait motor imagery

Although motor imagery and working memory (WM) appear to be closely linked, no previous studies have demonstrated direct evidence for the relationship between motor imagery and WM abilities. This study investigated the association between WM and gait motor imagery and focused on the individual differences in young adults. This study included 33 participants (mean age: 22.2 ± 0.9 years). We used two methods to measure the ability of different WM domains: verbal and visuo-spatial WM. Gait motor imagery accuracy was assessed via the mental chronometry paradigm. We measured the times participants took to complete an actual and imagined walk along a 5 m walkway, with three different path widths. The linear mixed effects model analysis revealed that visuo-spatial WM ability was a significant predictor of the accuracy of gait motor imagery, but not of verbal WM ability. Specifically, individuals with lower visuo-spatial WM ability demonstrated more inaccuracies in the difficult path-width conditions. However, gait motor imagery was not as accurate as actual walking even in the easiest path width or in participants with high visuo-spatial WM ability. Further, visuo-spatial WM ability was significantly correlated with mental walking but not with actual walking. These results suggest that visuo-spatial WM is related to motor imagery rather than actual movement.

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.

Posthoc Interpretability of Neural Responses by Grouping Subject Motor Imagery Skills Using CNN-Based Connectivity

Motor Imagery (MI) refers to imagining the mental representation of motor movements without overt motor activity, enhancing physical action execution and neural plasticity with potential applications in medical and professional fields like rehabilitation and education. Currently, the most promising approach for implementing the MI paradigm is the Brain-Computer Interface (BCI), which uses Electroencephalogram (EEG) sensors to detect brain activity. However, MI-BCI control depends on a synergy between user skills and EEG signal analysis. Thus, decoding brain neural responses recorded by scalp electrodes poses still challenging due to substantial limitations, such as non-stationarity and poor spatial resolution. Also, an estimated third of people need more skills to accurately perform MI tasks, leading to underperforming MI-BCI systems. As a strategy to deal with BCI-Inefficiency, this study identifies subjects with poor motor performance at the early stages of BCI training by assessing and interpreting the neural responses elicited by MI across the evaluated subject set. Using connectivity features extracted from class activation maps, we propose a Convolutional Neural Network-based framework for learning relevant information from high-dimensional dynamical data to distinguish between MI tasks while preserving the post-hoc interpretability of neural responses. Two approaches deal with inter/intra-subject variability of MI EEG data: (a) Extracting functional connectivity from spatiotemporal class activation maps through a novel kernel-based cross-spectral distribution estimator, (b) Clustering the subjects according to their achieved classifier accuracy, aiming to find common and discriminative patterns of motor skills. According to the validation results obtained on a bi-class database, an average accuracy enhancement of 10% is achieved compared to the baseline EEGNet approach, reducing the number of "poor skill" subjects from 40% to 20%. Overall, the proposed method can be used to help explain brain neural responses even in subjects with deficient MI skills, who have neural responses with high variability and poor EEG-BCI performance.