Changes in network connectivity during motor imagery and execution

Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion

Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain-computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time-frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions.

Connectivity of neural signals to the primary motor area during preparatory periods for movement following external and internal cues

PURPOSE

We investigated the connectivity of neural signals from movement-related cortical areas to the primary motor area (M1) in the hemisphere contralateral to the movement side during the period of movement-related magnetic fields before movement.

MATERIALS AND METHODS

Participants were 13 healthy adults, and nerual signals were recorded using magnetoencephalography. Spontaneous extension of the right wrist was performed at the participant's own pace and following a visual cue in internal (IC) and external (EC) cue tasks. The connectivity of neural signals to M1 from each movement-related motor area was assessed by Granger causality analysis (GCA). The GCA was performed on the neural activity elicited in a frequency band between 7.8 and 46.9 Hz during the pre-movement periods, which occurred durng the readiness field (RF) and the negative slope prime (NSp). F-values, as connectivity values obtained by GCA, were compared between the EC and IC cue tasks.

RESULTS

For NSp periods, the connectivity of neural signals from the left superior frontal area (SF-L) to M1 was dominant in the IC task, whereas that from the left superior parietal area (SP-L) to M1 was dominant in the EC task. The F value in the GCA from SP-L to M1 was greater in the EC task during RF than in the IC task during equivalent periods.

CONSLUSIONS

In the present study, there were differences in the connectivity of neural signals to M1 between IC and EC tasks. The present results suggested that the pattern of pre-movement neural activity that resulted in a movement was not uniform but differed between movement tasks just before the movement.

The polarity of high-definition transcranial direct current stimulation affects the planning and execution of movement sequences

Noninvasive brain stimulation of the primary motor cortex has been shown to alter therapeutic outcomes in stroke and other neurological conditions, but the precise mechanisms remain poorly understood. Determining the impact of such neurostimulation on the neural processing supporting motor control is a critical step toward further harnessing its therapeutic potential in multiple neurological conditions affecting the motor system. Herein, we leverage the excellent spatio-temporal precision of magnetoencephalographic (MEG) imaging to identify the spectral, spatial, and temporal effects of high-definition transcranial direct current stimulation (HD-tDCS) on the neural responses supporting motor control. Participants (N = 67) completed three HD-tDCS visits (anode, cathode, sham), with each involving 20 min of left primary motor cortex stimulation and performance of a simple/complex motor sequencing task during MEG. Whole-brain statistical analyses of beta oscillatory responses revealed stimulation-by-task interaction effects in the left primary motor cortex, right occipitotemporal, and the right dorsolateral prefrontal cortices. Broadly, anodal stimulation induced significantly stronger beta oscillatory responses in these regions during simple movement sequences, while neural responses to complex sequences were not affected by stimulation. En masse, these data suggest that the beta oscillations serving motor planning (i.e., pre-movement) are particularly sensitive to the polarity of noninvasive stimulation and that the impact varies based on the difficulty of the movement sequence.

Motor imagery perspective and brain oscillations characteristics: Differences between right- and left-handers

Motor imagery (MI) encompasses kinesthetic motor imagery (KMI), internal visual-motor motor imagery (IVMI), and external visual-motor motor imagery (EVMI). This study explored α/β oscillations during MI of left-/right-hand movement from KMI/IVMI/EVMI perspectives in a group of left- (N = 20) and right-handed (N = 20), volunteers selected based on their laterality quotient (RH > 80; LH > -80). We analyzed changes in the power of α/β oscillations from visual- and motor-related clusters of independent components, connectivity (imaginary part of coherence; ICOH) between electroencephalographic activity from selected regions of interest (ROIs), and the correctness of the MI. The left-handed individuals showed more robust α/β activity in visual-related ROI during EVMI for their non-dominant hand, compared during IVMI. However, they did not show a difference in brain activity for EVMI/IVMI compared with KMI and connectivity variance across EVMI/IVMI and KMI. The right-handed individuals exhibited visual area suppression during KMI, which could signify focusing on internal sensations and blocking visual processing in this condition. ICOH connectivity in the RH group varied depending on the task and hand involved, with more robust connections for EVMI of the non-dominant hand compared with IVMI and stronger connections for EVMI and KMI of the non-dominant hand compared with the dominant hand. The results suggest that left-handed individuals rely more on visual representations during MI, especially for their non-dominant hand. At the same time, right-handed people may create more multimodal images during the same tasks.

The Brain Activation of Two Motor Imagery Strategies in a Mental Rotation Task

Background: Motor imagery includes visual imagery and kinesthetic imagery, which are two strategies that exist for mental rotation and are currently widely studied. However, different mental rotation tests can lead to different strategic performances. There are also many research results where two different strategies appear simultaneously under the same task. Previous studies on the comparative brain mechanisms of kinesthetic imagery and visual imagery have not adopted consistent stimulus images or mature mental rotation paradigms, making it difficult to effectively compare these types of imagery. Methods: In this study, we utilized functional near-infrared spectroscopy (fNIRS) to investigate the brain activation of sixty-seven young right-handed participants with different strategy preferences during hand lateral judgment tasks (HLJT). Results: The results showed that the accuracy of the kinesthetic imagery group was significantly higher than that of the visual imagery group, and the reaction time of the kinesthetic imagery group was significantly shorter than that of the visual imagery group. The areas significantly activated in the kinesthetic imagery group were wider than those in the visual imagery group, including the dorsolateral prefrontal cortex (BA9, 46), premotor cortex (BA6), supplementary motor area (SMA), primary motor cortex (BA4), and parietal cortex (BA7, 40). It is worth noting that the activation levels in the frontal eye fields (BA8), primary somatosensory cortex (BA1, 2, 3), primary motor cortex (BA4), and parietal cortex (BA40) of the kinesthetic imagery group were significantly higher than those in the visual imagery group. Conclusion: Therefore, we speculate that kinesthetic imagery has more advantages than visual imagery in the mental rotation of egocentric transformations.

EEG-based action anticipation in human-robot interaction: a comparative pilot study

As robots become integral to various sectors, improving human-robot collaboration is crucial, particularly in anticipating human actions to enhance safety and efficiency. Electroencephalographic (EEG) signals offer a promising solution, as they can detect brain activity preceding movement by over a second, enabling predictive capabilities in robots. This study explores how EEG can be used for action anticipation in human-robot interaction (HRI), leveraging its high temporal resolution and modern deep learning techniques. We evaluated multiple Deep Learning classification models on a motor imagery (MI) dataset, achieving up to 80.90% accuracy. These results were further validated in a pilot experiment, where actions were accurately predicted several hundred milliseconds before execution. This research demonstrates the potential of combining EEG with deep learning to enhance real-time collaborative tasks, paving the way for safer and more efficient human-robot interactions.

Connectivity study on resting-state EEG between motor imagery BCI-literate and BCI-illiterate groups

Objective.Although motor imagery-based brain-computer interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity's significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state electroencephalography (EEG) network differs between BCI-literacy and -illiteracy.Approach.To address the issues above, we analyzed three large public EEG datasets using three functional connectivity and three effective connectivity metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for multivariate Granger causality. The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.Significance.Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction's accuracy.

A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection

Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this problem will greatly affect real-world applications. Most of the proposed methods are based on raw signal processing techniques. Known as prior knowledge, the structural-functional information and interregional connections can improve signal processing accuracy. It is possible to correctly perceive the generated signals by considering the brain structure (i.e. anatomical units), the source of signals, and the structural-functional dependence of different brain regions (i.e. effective connection) that are the semantic generators of signals. This study employed electroencephalograph (EEG) signals based on the activity of brain regions (cortex) and effective connections between brain regions based on dynamic causal modeling to solve the MI problem. EEG signals, as well as effective connections between brain regions to improve the interpretability of MI action, were fed into the architecture of Graph Convolutional Neural Network (GCN). The proposed model allowed GCN to extract more discriminative features. The results indicated that the proposed method was successful in developing a model with a MI detection accuracy of 93.73%.

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.

Unraveling the Left-Right Judgment Task in Chronic Low Back Pain: Insights Through Behavioral, Electrophysiological, Motor Imagery, and Bodily Disruption Perspectives

Bodily disruptions have been consistently demonstrated in individuals with chronic low back pain. The performance on the left-right judgment task has been purposed as an indirect measure of the cortical proprioceptive representation of the body. It has been suggested to be dependent on implicit motor imagery, although the available evidence is conflicting. Hence, the aim of this case-control observational study was to examine the performance (accuracy and reaction times) and event-related potentials while performing the left-right judgment task for back and hand images in individuals with chronic low back pain versus healthy controls, along with its relationship with self-reported measurements and quantitative sensory testing. While self-reported data suggested bodily disruptions in the chronic low back pain sample, this was not supported by quantitative sensory testing. Although both groups displayed the same performance, our results suggested an increased attentional load on participants with chronic low back pain to achieve equal performance, measured by a higher N1 peak amplitude in occipital electrodes, especially when the effect of contextual images arises. The absence of differences in the reaction times for the left-right judgment task between both groups, along with inconsistencies in self-reported and quantitative sensory testing data, could question the involvement of implicit motor imagery in solving the task. In conclusion, our results suggest disrupted attentional processing in participants with chronic low back pain to solve the left-right judgment task. PERSPECTIVE: Although there are no differences in the performance of the left-right judgment task (hits, reaction times) between chronic low back pain patients and controls, the analysis of event-related potentials revealed that patients require a higher cognitive load, measured by N1 peak amplitude.

Combined non-invasive neuromodulation using transcranial direct current stimulation, motor imagery and action observation for motor, cognitive and functional recovery in cortico-basal degeneration: a single case study

This case report presents a comprehensive assessment and therapeutic intervention using non-invasive motor cortex neuromodulation for a 70-year-old female patient diagnosed with corticobasal degeneration (CBD). The study followed the CARE guidelines. The patient meets the criteria for probable CBD, with neuroimaging evidence of exclusively cortical impairment. The patient underwent a non-invasive neuromodulation protocol involving transcranial direct current stimulation (tDCS) and action observation plus motor imagery (AO+MI). The neuromodulation protocol comprised 20 sessions involving tDCS over the primary motor cortex and combined AO+MI. Anodal tDCS was delivered a 2 mA excitatory current for 20 minutes. AO+MI focused on lower limb movements, progressing over four weeks with video observation and gradual execution, both weekly and monthly. The neuromodulation techniques were delivered online (i.e. applied simultaneously in each session). Outcome measures were obtained at baseline, post-intervention and follow-up (1 month later), and included motor (lower limb), cognitive/neuropsychological and functional assessments. Walking speed improvements were not observed, but balance (Berg Balance Scale) and functional strength (Five Times Sit-to-Stand Test) improved post-treatment. Long-term enhancements in attentional set-shifting, inhibitory control, verbal attentional span, and working memory were found. There was neurophysiological evidence of diminished intracortical inhibition. Functional changes included worsening in Cortico Basal Ganglia Functional Scale score. Emotional well-being and general health (SF-36) increased immediately after treatment but were not sustained, while Falls Efficacy Scale International showed only long-term improvement. The findings suggest potential benefits of the presented neuromodulation protocol for CBD patients, highlighting multifaceted outcomes in motor, cognitive, and functional domains.

Variation in brain connectivity during motor imagery and motor execution in stroke patients based on electroencephalography

Objective

The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, aiming to explore suitable forms of treatment and potential therapeutic targets.

Methods

Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared.

Results

PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area.

Conclusion

Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke Through EMG-Driven Robot Hand Training: Insights From Dynamic Causal Modeling

EMG-driven robot hand training can facilitate motor recovery in chronic stroke patients by restoring the interhemispheric balance between motor networks. However, the underlying mechanisms of reorganization between interhemispheric regions remain unclear. This study investigated the effective connectivity (EC) between the ventral premotor cortex (PMv), supplementary motor area (SMA), and primary motor cortex (M1) using Dynamic Causal Modeling (DCM) during motor tasks with the paretic hand. Nineteen chronic stroke subjects underwent 20 sessions of EMG-driven robot hand training, and their Action Reach Arm Test (ARAT) showed significant improvement ( β =3.56, [Formula: see text]). The improvement was correlated with the reduction of inhibitory coupling from the contralesional M1 to the ipsilesional M1 (r=0.58, p=0.014). An increase in the laterality index was only observed in homotopic M1, but not in the premotor area. Additionally, we identified an increase in resting-state functional connectivity (FC) between bilateral M1 ( β =0.11, p=0.01). Inter-M1 FC demonstrated marginal positive relationships with ARAT scores (r=0.402, p=0.110), but its changes did not correlate with ARAT improvements. These findings suggest that the improvement of hand functions brought about by EMG-driven robot hand training was driven explicitly by task-specific reorganization of motor networks. Particularly, the restoration of interhemispheric balance was induced by a reduction in interhemispheric inhibition from the contralesional M1 during motor tasks of the paretic hand. This finding sheds light on the mechanistic understanding of interhemispheric balance and functional recovery induced by EMG-driven robot training.

Brief Visual Deprivation Effects on Brain Oscillations During Kinesthetic and Visual-motor Imagery

It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.

Effects of trunk training using motor imagery on trunk control ability and balance function in patients with stroke

OBJECTIVE

To explore the effects of trunk training using motor imagery on trunk control and balance function in patients with stroke.

METHODS

One hundred eligible stroke patients were randomly divided into a control group and trial group. The control group was given routine rehabilitation therapy, while the trial group was given routine rehabilitation therapy and trunk training using motor imagery.

RESULTS

Prior to treatment, there was no significant difference between the two groups (P > 0.05) in Sheikh's trunk control ability, Berg rating scale (BBS), Fugl-Meyer assessment (FMA), movement length, movement area, average front-rear movement speed, average left-right movement speed, and surface electromyography (sEMG) signal of the bilateral erector spinae and rectus abdominis. After treatment, Sheikh's trunk control ability, FMA, and BBS in the two groups were significantly higher than those before treatment (P < 0.05). The movement length, movement area, the average front-rear movement speed, and the average left-right movement speed in the two groups decreased significantly (P < 0.05). The differences of these indicators between the two groups were statistically significant (P < 0.05). After treatment, the rectus abdominis and erector spinae on the affected side of the two groups improved when compared with those before treatment (P < 0.05). The rectus abdominis and erector spinae on the healthy side of the trial group descended after treatment (P < 0.05), while little changes were observed on the healthy side of the control group after treatment (P > 0.05). The rectus abdominis and erector spinae on the affected side of the trial group improved when compared with those in the control group (P < 0.05). There was no significant difference between the two groups in the decline of abdominalis rectus and erector spinal muscle on the healthy side.

CONCLUSION

Trunk training using motor imagery can significantly improve the trunk control ability and balance function of stroke patients and is conducive to promoting the recovery of motor function.

Development and Validation of the Body Cognition Assessment System

Body awareness, which comprises the sense of body possession and action ownership, is essential for the adaptive movement of humans in response to external environments. However, existing body cognition assessments include many overt elements of cognitive functional activity, but no assessment captures the latent body cognition necessary for exercise and daily life activities. Therefore, this study aimed to devise a body cognition assessment system (BCAS) to examine the functional basis of body cognition in healthy participants and investigate its usefulness. The BCAS was used to assess body cognition on three occasions, and BCAS values were calculated from the results of the assessment. The intraclass correlation coefficient (ICC) was used to determine reproducibility. Neural activity in the brain during somatocognition assessment while conducting the BCAS was measured by electroencephalogram. Moreover, the functional basis for somatocognition with the BCAS was also investigated. The results demonstrated that the BCAS values varied across the three administrations (ICC (1.3) = 0.372), and changes in the state of neural activity in the brain were observed. The results suggest that assessment using the BCAS may be a new indicator of ever-changing body cognition.

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.

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.

Spared Premotor Areas Undergo Rapid Nonlinear Changes in Functional Organization Following a Focal Ischemic Infarct in Primary Motor Cortex of Squirrel Monkeys

Recovery of motor function after stroke is accompanied by reorganization of movement representations in spared cortical motor regions. It is widely assumed that map reorganization parallels recovery, suggesting a causal relationship. We examined this assumption by measuring changes in motor representations in eight male and six female squirrel monkeys in the first few weeks after injury, a time when motor recovery is most rapid. Maps of movement representations were derived using intracortical microstimulation techniques in primary motor cortex (M1), ventral premotor cortex (PMv), and dorsal premotor cortex (PMd) in 14 adult squirrel monkeys before and after a focal infarct in the M1 distal forelimb area. Maps were derived at baseline and at either 2 (n = 7) or 3 weeks (n = 7) postinfarct. In PMv the forelimb maps remained unchanged at 2 weeks but contracted significantly (-42.4%) at 3 weeks. In PMd the forelimb maps expanded significantly (+110.6%) at 2 weeks but contracted significantly (-57.4%) at 3 weeks. Motor deficits were equivalent at both time points. These results highlight two features of plasticity after M1 lesions. First, significant contraction of distal forelimb motor maps in both PMv and PMd is evident by 3 weeks. Second, an unpredictable nonlinear pattern of reorganization occurs in the distal forelimb representation in PMd, first expanding at 2 weeks, and then contracting at 3 weeks postinjury. Together with previous results demonstrating reliable map expansions in PMv several weeks to months after M1 injury, the subacute time period may represent a critical window for the timing of therapeutic interventions.SIGNIFICANCE STATEMENT The relationship between motor recovery and motor map reorganization after cortical injury has rarely been examined in acute/subacute periods. In nonhuman primates, premotor maps were examined at 2 and 3 weeks after injury to primary motor cortex. Although maps are known to expand late after injury, the present study demonstrates early map expansion at 2 weeks (dorsal premotor cortex) followed by contraction at 3 weeks (dorsal and ventral premotor cortex). This nonlinear map reorganization during a time of gradual behavioral recovery suggests that the relationship between map plasticity and motor recovery is much more complex than previously thought. It also suggests that rehabilitative motor training may have its most potent effects during this early dynamic phase of map reorganization.

Neuropsychological Evidence Underlying Counterclockwise Bias in Running: Electroencephalography and Functional Magnetic Resonance Imaging Studies of Motor Imagery

We aimed to answer the question "why do people run the track counterclockwise (CCW)?" by investigating the neurophysiological differences in clockwise (CW) versus CCW direction using motor imagery. Three experiments were conducted with healthy adults. Electroencephalography (EEG) was used to examine hemispheric asymmetries in the prefrontal, frontal, and central regions during CW and CCW running imagery (n = 40). We also evaluated event-related potential (ERP) N200 and P300 amplitudes and latencies (n = 66) and conducted another experiment using functional magnetic resonance imaging (fMRI) (n = 30). EEG data indicated greater left frontal cortical activation during CCW imagery, whereas right frontal activation was more dominant during CW imagery. The prefrontal and central asymmetries demonstrated greater left prefrontal activation during both CW and CCW imagery, with CCW rotation exhibiting higher, though statistically insignificant, asymmetry scores than CW rotation. As a result of the fMRI experiment, greater activation was found during CW than during CCW running imagery in the brain regions of the left insula, Brodmann area 18, right caudate nucleus, left dorsolateral prefrontal cortex, left superior parietal cortex, and supplementary motor area. In the ERP experiment, no significant differences were found depending on direction. These findings suggest that CCW rotation might be associated with the motivational approach system, behavioral activation, or positive affect. However, CW rotation reflects withdrawal motivation, behavioral inhibition, or negative affect. Furthermore, CW rotation is understood to be associated with neural inefficiency, increased task difficulty, or unfamiliarity.

Postural control of Parkinson's disease: A visualized analysis based on Citespace knowledge graph

Postural control impairment is one of the primary motor symptoms in patients with Parkinson's disease, leading to an increased risk of falling. Several studies have been conducted on postural control disorders in Parkinson's disease patients, but no relevant bibliometric analysis has been found. In this paper, the Web of Science Core Collection database was searched for 1,295 relevant papers on postural control in Parkinson's disease patients from December 2011 to December 2021. Based on the Citespace knowledge graph, these relevant papers over the last decade were analyzed from the perspectives of annual publication volume, countries and institutes cooperation, authors cooperation, dual-map overlay of journals, co-citation literature, and keywords. The purpose of this study was to explore the current research status, research hotspots, and frontiers in this field, and to provide a reference for further promoting the research on postural control in Parkinson's disease patients.

EEG Correlation Coefficient Change with Motor Task Activation Can Be a Predictor of Functional Recovery after Hemiparetic Stroke

BACKGROUND

Recently, it was reported that the extent of cortico-cortical functional connections can be estimated by the correlation coefficient based on electroencephalography (EEG) monitoring. We aimed to investigate whether the EEG correlation coefficient change with motor task activation can predict the functional outcomes of hemiparetic stroke patients.

METHODS

Sixteen post-stroke hemiparetic patients admitted to our rehabilitation ward were studied. On admission, EEG recording to calculate the correlation coefficient was performed at rest and during motor task activation. For the analysis of the EEG data, the program software FOCUS (NIHON KOHDEN, Japan) was used. The motor function of paretic limbs was evaluated with the Fugl-Meyer Assessment (FMA) on admission and 4 weeks after admission.

RESULTS

Significant increases in the correlation coefficient with motor task activation were noted in C3-F3 or C4-F4, C3-F7 or C4-F8, and F3-F7 or F4-F8 of the lesional hemisphere. Among them, the rate of the correlation coefficient change in F3-F7 or F4-F8 in the lesional hemisphere was significantly correlated with the rate of the upper-limb FMA score change.

CONCLUSION

The extent of the EEG correlation coefficient change with motor task activation in F3-F7 or F4-F8 of the lesional hemisphere may help predict the motor functional outcomes of hemiparetic upper limbs after stroke.

Functional near-infrared spectroscopy during motor imagery and motor execution in healthy adults

OBJECTIVES

Studies on the influence of motor imagery (MI) on brain structure and function are limited to traditional imaging techniques and the mechanism for MI therapy is not clear. By observing the brain activation mode during MI and motor execution (ME) in healthy adults, this study aims to use near-infrared brain imaging technology to provide theoretical basis for the treatment of MI.

METHODS

A total of 30 healthy adults recruited to the public from June 2021 to August 2021. The MI and ME of the right knee movement served as the task mode. Block design was repeated 5 times alternately in a 20 s task period and a 30 s resting period. The activation patterns of brain regions were compared between the 2 tasks, and the regression coefficient was calculated to reflect the activation intensity of each brain region by Nirspark and SPSS 23.0 softwares.

RESULTS

Lane 2, 3, 4, 5, 7, 9, 19, 20, 21, 24, 25, 26, 27, 32, 33, and 34 were significantly activated during the ME task (P<0.05, corrected by FDR) and lane 2, 5, 9, 16, 27, 29, 33, 34, and 35 were significantly activated during the MI task (P<0.05, corrected by FDR). According to the channel brain region registration information, the brain region activation pattern was similar during both MI and ME tasks in healthy adults, including left primary motor cortex (LM1), left primary sensory cortex (LS1), prefrontal pole, Broca area, and right supramarginal gyrus. Both LM1 and left pre-motor cortex (LPMC) were activated during MI in healthy adults, whereas dorsolateral prefrontal cortex (DLPFC) and only LM1 of the motor region were activated during ME. Compared to MI, the activation intensity of left sensory and left motor cortex was significantly enhanced in ME, and that of left and right prefrontal cortex especially left and right pars triangularis Broca's area (P<0.001, corrected by FDR) were significantly enhanced.

CONCLUSIONS

The rationality of MI therapy is proved by functional near-infrared spectroscopy. The involvement of DLPFC in motor decision-making may regulate the two-way feedback of premoter cortex-M1 during ME; and Broca area, closely related to the motor program understanding, participates in MI and ME.

The Effect of Non-immersive Virtual Reality Exergames Versus Band Stretching on Cardiovascular and Cerebral Hemodynamic Response: A Functional Near-Infrared Spectroscopy Study

Background

Exercise is one of the effective ways to improve cognition. Different forms of exercises, such as aerobic exercise, resistance exercise, and coordination exercise, have different effects on the improvement of cognitive impairment. In recent years, exergames based on Non-Immersive Virtual Reality (NIVR-Exergames) have been widely used in entertainment and have gradually been applied to clinical rehabilitation. However, the mechanism of NIVR-Exergames on improving motor cognition has not been clarified. Therefore, the aim of this study is to find whether NIVR-Exergames result in a better neural response mechanism to improve the area of the cerebral cortex related to motor cognition under functional near-infrared spectroscopy (fNIRS) dynamic monitoring in comparison with resistance exercise (resistance band stretching).

Methods

A cross-over study design was adopted in this study, and 15 healthy young subjects (18–24 years old) were randomly divided into group A (n = 8) and group B (n = 7) according to a computerized digital table method. Task 1 was an NIVR-Exergame task, and Task 2 was resistance band stretching. Group A first performed Task 1, rested for 30 min (i.e., a washout period), and then performed Task 2. Group B had the reverse order. The fNIRS test was synchronized in real time during exercise tasks, and heart rate measurements, blood pressure measurements, and 2-back task synchronization fNIRS tests were performed at baseline, Post-task 1, and Post-task 2. The primary outcomes were beta values from the general linear model (GLM) in different regions of interest (ROIs), and the secondary outcomes were heart rate, blood pressure, reaction time of 2-back, and accuracy rate of 2-back.

Results

The activation differences of Task 1 and Task 2 in the right premotor cortex (PMC) (P = 0.025) and the left PMC (P = 0.011) were statistically significant. There were statistically significant differences in the activation of the right supplementary motor area (SMA) (P = 0.007), left dorsolateral prefrontal cortex (DLPFC) (P = 0.031), left and right PMC (P = 0.005; P = 0.002) between baseline and Post-task 1. The differences in systolic pressure (SBP) between the two groups at three time points among women were statistically significant (P1 = 0.009, P2 < 0.001, P3 = 0.044).

Conclusion

In this study, we found that NIVR-Exergames combined with motor and challenging cognitive tasks can promote the activation of SMA, PMC and DLPFC in healthy young people compared with resistance exercise alone, providing compelling preliminary evidence of the power for the rehabilitation of motor and cognitive function in patients with central nervous system diseases.

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern

To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.

Brain Function and Upper Limb Deficit in Stroke With Motor Execution and Imagery: A Cross-Sectional Functional Magnetic Resonance Imaging Study

Background

Motor imagery training might be helpful in stroke rehabilitation. This study explored if a specific modulation of movement-related regions is related to motor imagery (MI) ability.

Methods

Twenty-three patients with subcortical stroke and 21 age-matched controls were recruited. They were subjectively screened using the Kinesthetic and Visual Imagery Questionnaire (KVIQ). They then underwent functional magnetic resonance imaging (fMRI) while performing three repetitions of different motor tasks (motor execution and MI). Two separate runs were acquired [motor execution tasks (ME and rest) and motor imagery (MI and rest)] in a block design. For the different tasks, analyses of cerebral activation and the correlation of motor/imagery task-related activity and KVIQ scores were performed.

Results

During unaffected hand (UH) active grasp movement, we observed decreased activations in the contralateral precentral gyrus (PreCG), contralateral postcentral gyrus (PoCG) [p &lt; 0.05, family wise error (FWE) corrected] and a positive correlation with the ability of FMA-UE (PreCG: r = 0.46, p = 0.028; PoCG: r = 0.44, p = 0.040). During active grasp of the affected hand (AH), decreased activation in the contralateral PoCG was observed (p &lt; 0.05, FWE corrected). MI of the UH induced significant activations of the contralateral superior frontal gyrus, opercular region of the inferior frontal gyrus, and ipsilateral ACC and deactivation in the ipsilateral supplementary motor area (p &lt; 0.05, AlphaSim correction). Ipsilateral anterior cingulate cortex (ACC) activity negatively correlated with MI ability (r = =–0.49, p = 0.022). Moreover, we found significant activation of the contralesional middle frontal gyrus (MFG) during MI of the AH.

Conclusion

Our results proved the dominant effects of MI dysfunction that exist in stroke during the processing of motor execution. In the motor execution task, the enhancement of the contralateral PreCG and PoCG contributed to reversing the motor dysfunction, while in the MI task, inhibition of the contralateral ACC can increase the impaired KVIQ ability. The bimodal balance recovery model can explain our results well. Recognizing neural mechanisms is critical to helping us formulate precise strategies when intervening with electrical or magnetic stimulation.

Motor Impairment in Stroke Patients is Associated with Network Properties During Consecutive Motor Imagery

OBJECTIVE

Our study aimed to predict the Fugl-Meyer assessment (FMA) upper limb using network properties during motor imagery using electroencephalography (EEG) signals.

METHODS

The subjects performed a finger tapping imagery task according to consecutive cues. We measured the weighted phase lag index (wPLI) as functional connectivity and directed transfer function (DTF) as causal connectivity in healthy controls and stroke patients. The network properties based on the wPLI and DTF were calculated. We predicted the FMA upper limb using partial least squares regression.

RESULTS

A higher DTF in the mu band was observed in stroke patients than in healthy controls. Notably, the difference in local properties at node F3 was negatively correlated with motor impairment in stroke patients. Finally, using significant network properties based on the wPLI and DTF, we predicted motor impairments using the FMA upper limb with a root-mean-square error of 1.68 (R² = 0.97). This outperformed the state-of-the-art predictors.

CONCLUSION

These findings demonstrate that network properties based on functional and causal connectivity were highly associated with motor function in stroke patients.

SIGNIFICANCE

Our network properties can help calculate the predictor of motor impairments in stroke rehabilitation and provide insight into the neural correlates related to motor function based on EEG after reorganization induced by stroke.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Transcranial Direct Current Stimulation on Parkinson's Disease: Systematic Review and Meta-Analysis

Background: Clinical impact of transcranial direct current stimulation (tDCS) alone for Parkinson's disease (PD) is still a challenge. Thus, there is a need to synthesize available results, analyze methodologically and statistically, and provide evidence to guide tDCS in PD.

Objective: Investigate isolated tDCS effect in different brain areas and number of stimulated targets on PD motor symptoms.

Methods: A systematic review was carried out up to February 2021, in databases: Cochrane Library, EMBASE, PubMed/MEDLINE, Scopus, and Web of science. Full text articles evaluating effect of active tDCS (anodic or cathodic) vs. sham or control on motor symptoms of PD were included.

Results: Ten studies (n = 236) were included in meta-analysis and 25 studies (n = 405) in qualitative synthesis. The most frequently stimulated targets were dorsolateral prefrontal cortex and primary motor cortex. No significant effect was found among single targets on motor outcomes: Unified Parkinson's Disease Rating Scale (UPDRS) III – motor aspects (MD = −0.98%, 95% CI = −10.03 to 8.07, p = 0.83, I² = 0%), UPDRS IV – dyskinesias (MD = −0.89%, CI 95% = −3.82 to 2.03, p = 0.55, I² = 0%) and motor fluctuations (MD = −0.67%, CI 95% = −2.45 to 1.11, p = 0.46, I² = 0%), timed up and go – gait (MD = 0.14%, CI 95% = −0.72 to 0.99, p = 0.75, I² = 0%), Berg Balance Scale – balance (MD = 0.73%, CI 95% = −1.01 to 2.47, p = 0.41, I² = 0%). There was no significant effect of single vs. multiple targets in: UPDRS III – motor aspects (MD = 2.05%, CI 95% = −1.96 to 6.06, p = 0.32, I² = 0%) and gait (SMD = −0.05%, 95% CI = −0.28 to 0.17, p = 0.64, I² = 0%). Simple univariate meta-regression analysis between treatment dosage and effect size revealed that number of sessions (estimate = −1.7, SE = 1.51, z-score = −1.18, p = 0.2, IC = −4.75 to 1.17) and cumulative time (estimate = −0.07, SE = 0.07, z-score = −0.99, p = 0.31, IC = −0.21 to 0.07) had no significant association.

Conclusion: There was no significant tDCS alone short-term effect on motor function, balance, gait, dyskinesias or motor fluctuations in Parkinson's disease, regardless of brain area or targets stimulated.

A neurophysiologically interpretable deep neural network predicts complex movement components from brain activity

The effective decoding of movement from non-invasive electroencephalography (EEG) is essential for informing several therapeutic interventions, from neurorehabilitation robots to neural prosthetics. Deep neural networks are most suitable for decoding real-time data but their use in EEG is hindered by the gross classes of motor tasks in the currently available datasets, which are solvable even with network architectures that do not require specialized design considerations. Moreover, the weak association with the underlying neurophysiology limits the generalizability of modern networks for EEG inference. Here, we present a neurophysiologically interpretable 3-dimensional convolutional neural network (3D-CNN) that captured the spatiotemporal dependencies in brain areas that get co-activated during movement. The 3D-CNN received topography-preserving EEG inputs, and predicted complex components of hand movements performed on a plane using a back-drivable rehabilitation robot, namely (a) the reaction time (RT) for responding to stimulus (slow or fast), (b) the mode of movement (active or passive, depending on whether there was an assistive force provided by the apparatus), and (c) the orthogonal directions of the movement (left, right, up, or down). We validated the 3D-CNN on a new dataset that we acquired from an in-house motor experiment, where it achieved average leave-one-subject-out test accuracies of 79.81%, 81.23%, and 82.00% for RT, active vs. passive, and direction classifications, respectively. Our proposed method outperformed the modern 2D-CNN architecture by a range of 1.1% to 6.74% depending on the classification task. Further, we identified the EEG sensors and time segments crucial to the classification decisions of the network, which aligned well with the current neurophysiological knowledge on brain activity in motor planning and execution tasks. Our results demonstrate the importance of biological relevance in networks for an accurate decoding of EEG, suggesting that the real-time classification of other complex brain activities may now be within our reach.

A New Framework to Interpret Individual Inter-Hemispheric Compensatory Communication after Stroke

Stroke constitutes the main cause of adult disability worldwide. Even after application of standard rehabilitation protocols, the majority of patients still show relevant motor impairment. Outcomes of standard rehabilitation protocols have led to mixed results, suggesting that relevant factors for brain re-organization after stroke have not been considered in explanatory models. Therefore, finding a comprehensive model to optimally define patient-dependent rehabilitation protocols represents a crucial topic in clinical neuroscience. In this context, we first report on the rehabilitation models conceived thus far in the attempt of predicting stroke rehabilitation outcomes. Then, we propose a new framework to interpret results in stroke literature in the light of the latest evidence regarding: (1) the role of the callosum in inter-hemispheric communication, (2) the role of prefrontal cortices in exerting a control function, and (3) diaschisis mechanisms. These new pieces of evidence on the role of callosum can help to understand which compensatory mechanism may take place following a stroke. Moreover, depending on the individual impairment, the prefrontal control network will play different roles according to the need of high-level motor control. We believe that our new model, which includes crucial overlooked factors, will enable clinicians to better define individualized motor rehabilitation protocols.

A comparison of directed functional connectivity among fist-related brain activities during movement imagery, movement execution, and movement observation

Brain-computer interface (BCI) has been widely used in sports training and rehabilitation training. It is primarily based on action simulation, including movement imagery (MI) and movement observation (MO). However, the development of BCI technology is limited due to the challenge of getting an in-depth understanding of brain networks involved in MI, MO, and movement execution (ME). To better understand the brain activity changes and the communications across various brain regions under MO, ME, and MI, this study conducted the fist experiment under MO, ME, and MI. We recorded 64-channel electroencephalography (EEG) from 39 healthy subjects (25 males, 14 females, all right-handed) during fist tasks, obtained intensities and locations of sources using EEG source imaging (ESI), computed source activation modes, and finally investigated the brain networks using spectral Granger causality (GC). The brain regions involved in the three motor conditions are similar, but the degree of participation of each brain region and the network connections among the brain regions are different. MO, ME, and MI did not recruit shared brain connectivity networks. In addition, both source activation modes and brain network connectivity had lateralization advantages.

A Novel Method to Assess Motor Cortex Connectivity and Event Related Desynchronization Based on Mass Models

Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. The model considers six cortical regions of interest (ROIs) involved in hand movement. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during resting condition, movement of the affected, and movement of the unaffected hand. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network and allows brain rhythms and desynchronization to be assessed on a quantitative basis.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Neural mechanism by which physical fatigue sensation suppresses physical performance: a magnetoencephalography study

The right dorsolateral prefrontal cortex (DLPFC) has been proposed to be the brain region regulating performance through fatigue sensation in fatigue, but direct evidence has been lacking for right DLPFC activation when physical performance is suppressed in the presence of fatigue sensation. We examined whether the right DLPFC is activated when physical performance is suppressed by remembering a physical fatigue sensation. Eighteen healthy male volunteers participated. They performed a rest session followed by a handgrip session to induce physical fatigue sensation. Then, they were instructed to remember the state of the right hand with (i.e., the target condition) and without (i.e., the control condition) fatigue sensation as experienced in the handgrip and rest sessions, respectively while performing motor imagery of maximum handgrip of the right hand. Neural activity during both conditions was recorded using magnetoencephalography. The level of fatigue sensation was higher in the target condition than in the control condition. Decreases of handgrip strength and beta band power in the right Brodmann's area 46 were observed in the target condition, suggesting that the right DLPFC is involved in the regulation of physical performance through fatigue sensation. These findings may help elucidate the neural mechanisms regulating performance under fatigue conditions.

Weighted neurofeedback facilitates greater self-regulation of functional connectivity between the primary motor area and cerebellum

Objective.Brain-computer interface (BCI) is a tool that can be used to train brain self-regulation and influence specific activity patterns, including functional connectivity, through neurofeedback. The functional connectivity of the primary motor area (M1) and cerebellum play a critical role in motor recovery after a brain injury, such as stroke. The objective of this study was to determine the feasibility of achieving control of the functional connectivity between M1 and the cerebellum in healthy subjects. Additionally, we aimed to compare the brain self-regulation of two different feedback modalities and their effects on motor performance.Approach.Nine subjects were trained with a real-time functional magnetic resonance imaging BCI system. Two groups were conformed: equal feedback group (EFG), which received neurofeedback that weighted the contribution of both regions of interest (ROIs) equally, and weighted feedback group (WFG) that weighted each ROI differentially (30% cerebellum; 70% M1). The magnitude of the brain activity induced by self-regulation was evaluated with the blood-oxygen-level-dependent (BOLD) percent change (BPC). Functional connectivity was assessed using temporal correlations between the BOLD signal of both ROIs. A finger-tapping task was included to evaluate the effect of brain self-regulation on motor performance.Main results.A comparison between the feedback modalities showed that WFG achieved significantly higher BPC in M1 than EFG. The functional connectivity between ROIs during up-regulation in WFG was significantly higher than EFG. In general, both groups showed better tapping speed in the third session compared to the first. For WFG, there were significant correlations between functional connectivity and tapping speed.Significance.The results show that it is possible to train healthy individuals to control M1-cerebellum functional connectivity with rtfMRI-BCI. Besides, it is also possible to use a weighted feedback approach to facilitate a higher activity of one region over another.

Neural and biomechanical tradeoffs associated with human-exoskeleton interactions

Industrial passive low-back exoskeletons have gained recent attention as ergonomic interventions to manual handling tasks. This research utilized a two-armed experimental approach (single vs dual-task paradigms) to quantify neural and biomechanical tradeoffs associated with short-term human-exoskeleton interaction (HEI) during asymmetrical lifting in twelve healthy adults balanced by gender. A dynamic, electromyography-assisted spine model was employed that indicated statistical, but marginal, biomechanical benefits of the tested exoskeleton, which diminished with the introduction of the cognitive dual-task. Using Near Infrared Spectroscopy (fNIRS)-based brain connectivity analyses, we found that the tested exoskeleton imposed greater neurocognitive and motor adaptation efforts by engaging action monitoring and error processing brain networks. Collectively, these findings indicate that a wearer's biomechanical response to increased cognitive demands in the workplace may offset the mechanical advantages of exoskeletons. We also demonstrate the utility of ambulatory fNIRS to capture the neural cost of HEI without the need for elaborate dual-task manipulations.

BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors

Brain–computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex’s electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55–63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient’s perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient’s performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject’s MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.

Effects of Frontal Theta Rhythms in a Prior Resting State on the Subsequent Motor Imagery Brain-Computer Interface Performance

Dealing with subjects who are unable to attain a proper level of performance, that is, those with brain–computer interface (BCI) illiteracy or BCI inefficients, is still a major issue in human electroencephalography (EEG) BCI systems. The most suitable approach to address this issue is to analyze the EEG signals of individual subjects independently recorded before the main BCI tasks, to evaluate their performance on these tasks. This study mainly focused on non-linear analyses and deep learning techniques to investigate the significant relationship between the intrinsic characteristics of a prior idle resting state and the subsequent BCI performance. To achieve this main objective, a public EEG motor/movement imagery dataset that constituted two individual EEG signals recorded from an idle resting state and a motor imagery BCI task was used in this study. For the EEG processing in the prior resting state, spectral analysis but also non-linear analyses, such as sample entropy, permutation entropy, and recurrent quantification analyses (RQA), were performed to obtain individual groups of EEG features to represent intrinsic EEG characteristics in the subject. For the EEG signals in the BCI tasks, four individual decoding methods, as a filter-bank common spatial pattern-based classifier and three types of convolution neural network-based classifiers, quantified the subsequent BCI performance in the subject. Statistical linear regression and ANOVA with post hoc analyses verified the significant relationship between non-linear EEG features in the prior resting state and three types of BCI performance as low-, intermediate-, and high-performance groups that were statistically discriminated by the subsequent BCI performance. As a result, we found that the frontal theta rhythm ranging from 4 to 8 Hz during the eyes open condition was highly associated with the subsequent BCI performance. The RQA findings that higher determinism and lower mean recurrent time were mainly observed in higher-performance groups indicate that more regular and stable properties in the EEG signals over the frontal regions during the prior resting state would provide a critical clue to assess an individual BCI ability in the following motor imagery task.

Handedness effects on motor imagery during kinesthetic and visual-motor conditions

Recent studies show that during a simple movement imagery task, the power of sensorimotor rhythms differs according to handedness. However, the effects of motor imagery perspectives on these differences have not been investigated yet. Our study aimed to check how handedness impacts the activity of alpha (8-13 Hz) and beta (15-30 Hz) oscillations during creating a kinesthetic (KMI) or visual-motor (VMI) representation of movement. Forty subjects (20 right-handed and 20 left-handed) who participated in the experiment were tasked with imagining sequential finger movement from a visual or kinesthetic perspective. Both the electroencephalographic (EEG) activity and behavioral correctness of the imagery task performance were measured. After the registration, we used independent component analysis (ICA) on EEG data to localize visual- and motor-related EEG sources of activity shared by both motor imagery conditions. Significant differences were obtained in the visual cortex (the occipital ICs cluster) and the right motor-related area (right parietal ICs cluster). In comparison to right-handers who, regardless of the task, demonstrated the same pattern in the visual area, left-handers obtained higher power in the alpha waves in the VMI task and better performance in this condition. On the other hand, only the right-handed showed different patterns in the alpha waves in the right motor cortex during the KMI condition. The results indicate that left-handers imagine movement differently than right-handers, focusing on visual experience. This provides new empirical evidence on the influence of movement preferences on imagery processes and has possible future implications for research in the area of neurorehabilitation and motor imagery-based brain-computer interfaces (MI-BCIs).

Effects of visual-motor illusion on functional connectivity during motor imagery

This study aimed to verify whether visual-motor illusion changes the functional connectivity during kinesthetic motor imagery and the vividness of kinesthetic motor imagery. Twelve right-handed healthy adults participated in this study. All participants randomly performed both the illusion and observation conditions in 20 min, respectively. Illusion condition was induced kinesthetic illusion by viewing own finger movement video. Observation condition was observed own finger movement video. Before and after each condition, the brain activity of kinesthetic motor imagery was measured using functional near-infrared spectroscopy. The measure of brain activity under kinesthetic motor imagery was executed in five sets using block design. Under the kinesthetic motor imagery, participants were asked to imagine the movement of their right finger. Functional connectivity was analyzed during the kinesthetic motor imagery. In addition, after performing the task under kinesthetic motor imagery, the vividness of the kinesthetic motor imagery was measured using a visual analog scale. Furthermore, after each condition, the degree of kinesthetic illusion and sense of body ownership measured based on a seven-point Likert scale. Our results indicated that the functional connectivity during kinesthetic motor imagery was changed in the frontal-parietal network of the right hemisphere. The vividness of the kinesthetic motor imagery was significantly higher with the illusion condition compared with the observation condition. The degree of kinesthetic illusion and sense of body ownership were significantly higher with the illusion condition compared with the observation condition. In conclusion, the visual-motor illusion changes the functional connectivity during kinesthetic motor imagery and influences the vividness of kinesthetic motor imagery. The visual-motor illusion provides evidence that it improves motor imagery ability. VMI may be used in patients with impaired motor imagery.

The Relationship between Oscillations in Brain Regions and Functional Connectivity: A Critical Analysis with the Aid of Neural Mass Models

Propagation of brain rhythms among cortical regions is a relevant aspect of cognitive neuroscience, which is often investigated using functional connectivity (FC) estimation techniques. The aim of this work is to assess the relationship between rhythm propagation, FC and brain functioning using data generated from neural mass models of connected Regions of Interest (ROIs). We simulated networks of four interconnected ROIs, each with a different intrinsic rhythm (in θ, α, β and γ ranges). Connectivity was estimated using eight estimators and the relationship between structural connectivity and FC was assessed as a function of the connectivity strength and of the inputs to the ROIs. Results show that the Granger estimation provides the best accuracy, with a good capacity to evaluate the connectivity strength. However, the estimated values strongly depend on the input to the ROIs and hence on nonlinear phenomena. When a population works in the linear region, its capacity to transmit a rhythm increases drastically. Conversely, when it saturates, oscillatory activity becomes strongly affected by rhythms incoming from other regions. Changes in functional connectivity do not always reflect a physical change in the synapses. A unique connectivity network can propagate rhythms in very different ways depending on the specific working conditions.

Simultaneous spatial-temporal decomposition for connectome-scale brain networks by deep sparse recurrent auto-encoder

Exploring the spatial patterns and temporal dynamics of human brain activity has been of great interest, in the quest to better understand connectome-scale brain networks. Though modeling spatial and temporal patterns of functional brain networks have been researched for a long time, the development of a unified and simultaneous spatial-temporal model has yet to be realized. For instance, although some deep learning methods have been proposed recently in order to model functional brain networks, most of them can only represent either spatial or temporal perspective of functional Magnetic Resonance Imaging (fMRI) data and rarely model both domains simultaneously. Due to the recent success in applying sequential auto-encoders for brain decoding, in this paper, we propose a deep sparse recurrent auto-encoder (DSRAE) to be applied unsupervised to learn spatial patterns and temporal fluctuations of brain networks at the same time. The proposed DSRAE was evaluated and validated based on three tasks of the publicly available Human Connectome Project (HCP) fMRI dataset, resulting with promising evidence. To the best of our knowledge, the proposed DSRAE is among the early efforts in developing unified models that can extract connectome-scale spatial-temporal networks from 4D fMRI data simultaneously.

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.

Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

Translation and Validation of the Spanish Movement Imagery Questionnaire Revised Second Version (MIQ-RS)

BACKGROUND

Motor imagery, which emphasizes mental rehearsal of motor skills to improve function, is frequently used in clinical practice. Because of its increasing use, reliable and valid tools are necessary to evaluate motor imagery abilities. However, there are few questionnaires translated and validated into Spanish language.

OBJECTIVE

To translate, transculturally adapt, and validate the Spanish version of the Movement Imagery Questionnaire-Revised Second Version (MIQ-RS).

DESIGN

A single-center observational study.

SETTING

University community.

PARTICIPANTS

One hundred fifty-five healthy participants were recruited.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Spanish translation of the MIQ-RS and psychometric performances of the questionnaire were tested using concurrent-criterion and content validity, construct validity, internal consistency, and test-rest reliability. Internal consistency, concurrent-criterion validity, construct validity, and test-rest reliability were assessed with Cronbach´s alpha, Spearman´s correlation coefficient, confirmatory factor analysis, and intraclass correlation coefficient (ICC), respectively.

RESULTS

Results showed satisfactory internal consistency (Cronbach α = 0.90), test-rest reliability (ICC for visual items = 0.844 and for kinesthetic items = 0.70) and content and criterion-concurrent validity (Spearman´s correlation coefficient for visual items, 0.60 and for kinesthetic items, 0.81) of the MIQ-RS Spanish version. The two-factor structure was supported by confirmatory factor analysis. Statistically significant gender differences were observed in mean kinesthetic motor imagery scores and in mean visual motor imagery scores according to sports practice. No significant differences for gender, age, and sports, musical, and dance practice were reported.

CONCLUSIONS

The Spanish version of the MIQ-RS is a valid and reliable tool to assess motor imagery abilities in healthy young people.

Scalp Acupuncture Enhances the Functional Connectivity of Visual and Cognitive-Motor Function Network of Patients with Acute Ischemic Stroke

Design

A parallel-group randomized controlled trial. Participants. 30 hemiplegic patients with middle cerebral artery acute infarction of the dominant hemisphere. Interventions. 30 patients were divided into 2 groups randomly. 15 patients in the treatment group (TG) were treated with ISSA, needling at the parietal midline (MS5) and left anterior/posterior parietal-temporal oblique lines (MS6 and MS7), combined with western routine treatment. While another 15 patients in the control group (CG) received routine treatment only. Main Outcome Measures. (1) Functional connectivity (FC): patients received brain scan using 3.0 T MRI after the treatment for 1 week. Based on the Matlab2012a platform, SPM12 software and DPABI software were used to process the scanning data and finally the functional connectivity of the brain was obtained. (2) National Institute of Health Stroke Scale (NIHSS) score.

Results

The difference in the NIHSS score between the two groups of patients before and after treatment was statistically significant (tNIHSS = 2.225; PNIHSS = 0.038), indicating that TG had a better effect. Centered to the seed region of the left supplementary motor area (SMA) (-5.32, 4.85, 61.38), FC increased at the left middle cerebellar peduncle, left cerebellum posterior lobe (uvula and declive), vermis, fusiform gyrus, lingual gyrus, inferior occipital gyrus, calcarine, cuneus, precuneus, BA7, BA18 and BA19, etc. Centered to the seed region of the left parahippocampal gyrus (PG) (-21.17, -15.95, -20.70), FC increased at the left precuneus, inside-paracingulate, inferior parietal gyrus, paracentral lobule, BA5, BA6, BA7, and BA40, right median cingulate, precuneus, BA19, BA23, and BA31, etc.

Conclusions

It is indicated that ISSA can regulate the brain functional connection in patients with middle cerebral artery acute infarction in the dominant hemisphere and specifically strengthen the connections between visual, cognitive, motor control, and planning-related brain regions, which may be related to the recovery of movement in the mechanism. This trial is registered with ChiCTR-IOR-15007672.

Early classification of motor tasks using dynamic functional connectivity graphs from EEG

OBJECTIVE

Classification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.

APPROACH

The proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classification.

MAIN RESULTS

Features extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data.

SIGNIFICANCE

Our results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using a short interval of the data in the order of hundreds of milliseconds (e.g. 500 ms). This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.