Execution Of Motor Tasks Research Articles

Comparative analysis of upper body kinematics in stroke, Parkinson's disease, and healthy subjects: An observational study using IMU-based targeted box and block test

BackgroundThe Box and Block Test (BBT) is an essential and widely used test in rehabilitation for the assessment of gross unilateral manual dexterity. Although it is a valid, simple, and ecological instrument, it does not provide a quantitative measure of the upper limb trajectories during the test. Research questionThe study introduces a new motion-capture-based method (using ecological Inertial Measurement Units - IMUs) to evaluate upper body kinematics while performing a targeted version of BBT (tBBT). MethodsThis observational study compares data from 35 healthy subjects, 35 subjects with Parkinson’s disease, and 35 post-stroke individuals to evaluate upper limb kinematics during tBBT quantitatively. Seven IMUs were placed on the trunk, head, and upper limb of each subject. The joint angles and kinematic scores were calculated and analyzed. Motor task execution time and kinematic scores were statistically correlated with clinical assessment measures. Kruskal-Wallis between groups test and Dunn-Bonferroni post-hoc were used. ResultsThe statistics revealed significant differences (p<0.05) among the three groups. The analyzed joint angles highlight various compensatory strategies in neurological subjects, such as using the trunk to complete a motor task instead of the shoulder and using the wrist instead of the elbow, along with differences in movement fluidity (DimensionLess-Jerk, p<0.05). A positive correlation was found between kinematics and the Fugl-Meyer Assessment-Upper Limb (r=0.7344; p<0.01), and a negative correlation between kinematics and the Unified Parkinson's Disease Rating Scale (r=-0.5286; p<0.01). SignificanceThe quantitative assessments of joint kinematics correlated to clinical assessments could guarantee a new method of assessment of the upper limb in subjects with motor deficits. This would allow to capture new insight into the characteristics of the subject’s disability, with implications for the choice of a personalized rehabilitation treatment focused on the motor recovery of the upper limb.

Altered brain function during movement programming is linked with motor deficits after stroke: a high temporal resolution study.

Stroke leads to motor deficits, requiring rehabilitation therapy that targets mechanisms underlying movement generation. Cortical activity during the planning and execution of motor tasks can be studied using EEG, particularly via the Event Related Desynchronization (ERD). ERD is altered by stroke in a manner that varies with extent of motor deficits. Despite this consensus in the literature, defining precisely the temporality of these alterations during movement preparation and performance may be helpful to better understand motor system pathophysiology and might also inform development of novel therapies that benefit from temporal resolution. Patients with chronic hemiparetic post-stroke (n = 27; age 59 ± 14 years) and age-matched healthy right-handed control subjects (n = 23; 59 ± 12 years) were included. They performed a shoulder rotation task following the onset of a stimulus. Cortical activity was recorded using a 256-electrode EEG cap. ERD was calculated in the beta frequency band (15-30 Hz) in ipsilesional sensorimotor cortex, contralateral to movement. The ERD was compared over time between stroke and control subjects using permutation tests. The correlation between upper extremity motor deficits (assessed by the Fugl-Meyer scale) and ERD over time was studied in stroke patients using Spearman and permutation tests. Patients with stroke showed on average less beta ERD amplitude than control subjects in the time window of -350 to 50 ms relative to movement onset (t(46) = 2.8, p = 0.007, Cohen's d = 0.31, 95% CI [0.22: 1.40]). Beta-ERD values correlated negatively with the Fugl-Meyer score during the time window -200 to 400 ms relative to movement onset (Spearman's r = -0.54, p = 0.003, 95% CI [-0.77 to -0.18]). Our results provide new insights into the precise temporal changes of ERD after hemiparetic stroke and the associations they have with motor deficits. After stroke, the average amplitude of cortical activity is reduced as compared to age-matched controls, and the extent of this decrease is correlated with the severity of motor deficits; both were true during motor programming and during motor performance. Understanding how stroke affects the temporal dynamics of cortical preparation and execution of movement paves the way for more precise restorative therapies. Studying the temporal dynamics of the EEG also strengthens the promising interest of ERD as a biomarker of post-stroke motor function.

Neural basis of lower-limb visual feedback therapy: an EEG study in healthy subjects

BackgroundVideo-feedback observational therapy (VOT) is an intensive rehabilitation technique based on movement repetition and visualization that has shown benefits for motor rehabilitation of the upper and lower limbs. Despite an increase in recent literature on the neurophysiological effects of VOT in the upper limb, there is little knowledge about the cortical effects of visual feedback therapies when applied to the lower limbs. The aim of our study was to better understand the neurophysiological effects of VOT. Thus, we identified and compared the EEG biomarkers of healthy subjects undergoing lower limb VOT during three tasks: passive observation, observation and motor imagery, observation and motor execution.MethodsWe recruited 38 healthy volunteers and monitored their EEG activity while they performed a right ankle dorsiflexion task in the VOT. Three graded motor tasks associated with action observation were tested: action observation alone (O), motor imagery with action observation (OI), and motor execution synchronized with action observation (OM). The alpha and beta event-related desynchronization (ERD) and event-related synchronization (or beta rebound, ERS) rhythms were used as biomarkers of cortical activation and compared between conditions with a permutation test. Changes in connectivity during the task were computed with phase locking value (PLV).ResultsDuring the task, in the alpha band, the ERD was comparable between O and OI activities across the precentral, central and parietal electrodes. OM involved the same regions but had greater ERD over the central electrodes. In the beta band, there was a gradation of ERD intensity in O, OI and OM over central electrodes. After the task, the ERS changes were weak during the O task but were strong during the OI and OM (Cz) tasks, with no differences between OI and OM.ConclusionAlpha band ERD results demonstrated the recruitment of mirror neurons during lower limb VOT due to visual feedback. Beta band ERD reflects strong recruitment of the sensorimotor cortex evoked by motor imagery and action execution. These results also emphasize the need for an active motor task, either motor imagery or motor execution task during VOT, to elicit a post-task ERS, which is absent during passive observation.Trial Registration NCT05743647

Time synchronization between parietal–frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks

BackgroundIn post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.MethodsTen participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson’s correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification.ResultsParietal–frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients.ConclusionsIn this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.

EEG connectivity in functional brain networks supporting visuomotor integration processes in dominant and non-dominant hand movements

Objective. This study explores the changes in the organization of functional brain networks induced by performing a visuomotor integration task, as revealed by noninvasive spontaneous electroencephalographic traces (EEG). Approach. EEG data were acquired during the execution of the Nine Hole Peg Test (NHPT) with the dominant and non-dominant hands in a group of 44 right-handed volunteers. Both spectral analysis and phase-based connectivity analysis were performed in the theta (ϑ), mu ( μ ) and beta (ß) bands. Graph Theoretical Analysis (GTA) was also performed to investigate the topological reorganization induced by motor task execution. Main results. Spectral analysis revealed an increase of frontoparietal ϑ power and a spatially diffused reduction of µ and ß contribution, regardless of the hand used. GTA showed a significant increase in network integration induced by movement performed with the dominant limb compared to baseline in the ϑ band. The µ and ß bands were associated with a reduction in network integration during the NHPT. In the µ rhythm, this result was more evident for the right-hand movement, while in the ß band, results did not show dependence on the laterality. Finally, correlation analysis highlighted an association between frequency-specific topology measures and task performance for both hands. Significance. Our results show that functional brain networks reorganize during visually guided movements in a frequency-dependent manner, differently depending on the hand used (dominant/non dominant).

An Analytical Approach for Naturalistic Cooperative and Competitive EEG-Hyperscanning Data: A Proof-of-Concept Study.

Investigating the neural mechanisms underlying both cooperative and competitive joint actions may have a wide impact in many social contexts of human daily life. An effective pipeline of analysis for hyperscanning data recorded in a naturalistic context with a cooperative and competitive motor task has been missing. We propose an analytical pipeline for this type of joint action data, which was validated on electroencephalographic (EEG) signals recorded in a proof-of-concept study on two dyads playing cooperative and competitive table tennis. Functional connectivity maps were reconstructed using the corrected imaginary part of the phase locking value (ciPLV), an algorithm suitable in case of EEG signals recorded during turn-based competitive joint actions. Hyperbrain, within-, and between-brain functional connectivity maps were calculated in three frequency bands (i.e., theta, alpha, and beta) relevant during complex motor task execution and were characterized with graph theoretical measures and a clustering approach. The results of the proof-of-concept study are in line with recent findings on the main features of the functional networks sustaining cooperation and competition, hence demonstrating that the proposed pipeline is promising tool for the analysis of joint action EEG data recorded during cooperation and competition using a turn-based motor task.

Involvement of primary somatosensory cortex in motor learning and task execution

The primary somatosensory cortex (S1) is responsible for processing information related to tactile stimulation, motor learning and control. Despite its significance, the connection between S1 and the primary motor cortex (M1), as well as its role in motor learning, remains a topic of ongoing exploration. In the present study, we silenced S1 by the GABA receptor agonist muscimol to study the potential roles of S1 in motor learning and task execution. Our results show that the inhibition of S1 leads to an immediate impairment in performance during the training session and also a substantial reduction in performance improvement during post-test session on the subsequent day. To understand the underlying mechanism, we used intravital two-photon imaging to investigate the dynamics of dendritic spines of layer V pyramidal neurons and the calcium activities of pyramidal neurons in M1 after inhibition of S1. Notably, S1 inhibition reduces motor training-induced spine formation and facilitates the elimination of existing spines of layer V pyramidal neurons in M1. The calcium activities in M1 exhibit a significant decrease during both resting and running periods following S1 inhibition. Furthermore, inhibition of S1, but not M1, significantly impairs the execution of the acquired motor task in the well-trained animals. Together, these findings reveal that S1 plays important roles in motor learning and task execution.

A three degrees of freedom switchable impedance myoelectric prosthetic wrist

Wrist mobility contributes significantly to the execution of upper limb motor tasks. Despite this, current prosthetic wrists are far less advanced than other artificial joints. Typically, prosthetic wrists offer limited degrees of freedom, if any, which forces users to execute compensatory movements during task performance. This addition increases weight and complexity, two unwelcome factors in upper limb prostheses. This article presents the design of a 3-degree-of-freedom friction-lockable prosthetic wrist actuated by a single motor. The design features adaptable behavior when unlocked, promoting a gentle interaction with the environment, and enables users to adjust the hand configuration during pre-grasping phases. The proposed system was tested, combined with a hand prosthesis, and compared to a commercial rotational wrist during the execution of functional movements. Experiments involved nine able-bodied subjects and one prosthesis user. Participants also performed the experiments with their biological wrist (the intact wrist for the prosthesis user) as a control. Results showed that the lockable wrist was used actively 20% more often than the commercial solution without compromising users’ execution time. Interaction tests reveal that compensatory movements are reduced when using the proposed design, resulting in closer resemblance to the control wrist’s performance. The average satisfaction and usability scores were significantly higher for the proposed wrist, indicating its potential acceptance. Finally, the system was validated in a set of activities of daily living performed by the prosthesis user. The study contributes to the development of more intuitive and adaptable prostheses that can improve the quality of life of amputees.

Distinct patterns of metabolic motor cortex activity for phantom and residual limb pain in people with amputations: A functional near-infrared spectroscopy study

BackgroundPhantom pain limb (PLP) has gained more attention due to the large number of people with amputations around the world and growing knowledge of the pain process, although its mechanisms are not completely understood. ObjectivesThe aim of this study was to understand, in patients with amputations, the association between PLP and residual limb pain (RLP), and the brain metabolic response in cortical motor circuits, using functional near-infrared spectroscopy (fNIRS). MethodsSixty participants were recruited from the rehabilitation program in São Paulo, Brazil. Included patients were aged over 18 years, with traumatic unilateral lower-limb amputation, with PLP for at least 3 months after full recovery from amputation surgery. PLP and RLP levels were measured using visual analogue scales. fNIRS was performed during motor execution and motor mirror tasks for 20 s. In order to highlight possible variables related to variation in pain measures, univariate linear regression analyses were performed for both experimental conditions, resulting in four fNIRS variables (two hemispheres x two experimental conditions). Later, in order to test the topographic specificity of the models, eight multivariate regression analyses were performed (two pain scales x two experimental conditions x two hemispheres), including the primary motor cortex (PMC) related channel as an independent variable as well as five other channels related to the premotor area, supplementary area, and somatosensory cortex. All models were controlled for age, sex, ethnicity, and education. ResultsWe found that: i) there is an asymmetric metabolic activation during motor execution and mirror task between hemispheres (with a predominance that is ipsilateral to the amputated limb), ii) increased metabolic response in the PMC ipsilateral to the amputation is associated with increased PLP (during both experimental tasks), while increased metabolic response in the contralateral PMC is associated with increased RLP (during the mirror motor task only); ii) increased metabolic activity of the ipsilateral premotor region is associated with increased PLP during the motor mirror task; iii) RLP was only associated with higher metabolic activity in the contralateral PMC and lower metabolic activity in the ipsilateral inferior frontal region during motor mirror task, but PLP was associated with higher metabolic activity during both tasks. ConclusionThese results suggest there is both task and region specificity for the association between the brain metabolic response and the two different types of post-amputation pain. The metabolic predominance that is ipsilateral to the amputated limb during both tasks was associated with higher levels of PLP, suggesting a cortical motor network activity imbalance due to potential interhemispheric compensatory mechanisms. The present work contributes to the understanding of the underlying topographical patterns in the motor-related circuits associated with pain after amputations.

Supplementary motor area is deactivated during mental rotation tasks with biomechanical constraints in fMRI.

Mental rotation (MR) tasks of body parts involve anatomically interconnected brain systems. The systems are implicated in sensorimotor information integration and activate cortical motor-related areas, corresponding to the execution of similar motor tasks. In this study, we aimed to investigate the effect of varying the angle in the hand MR task on cerebral activation of the motor-related areas. Twenty healthy right-handed participants were recruited. We investigated cerebral activation while each participant decided whether a hand-palm image, rotated by 0°, 90°, 180°, and 270°, was a right or left hand. A significant negative correlation between the angle and brain activity was observed in the right and left supplementary motor area (SMA) and right posterior anterior cingulate gyrus. The SMA was inactivated with 180°- or 270°-rotated images in the regions of interest analysis. 180°- and 270°-rotated palms would be biomechanically difficult to position; thus, SMA deactivation may be closely associated with biomechanical constraints. This study provided novel findings regarding the neurophysiological mechanisms of motor imagery and may be useful in developing treatment plans using MR tasks during patient rehabilitation.

Fusion of Deep Features from 2D-DOST of fNIRS Signals for Subject-Independent Classification of Motor Execution Tasks

Functional near-infrared spectroscopy (fNIRS) is a low-cost and noninvasive method to measure the hemodynamic responses of cortical brain activities and has received great attention in brain-computer interface (BCI) applications. In this paper, we present a method based on deep learning and the time-frequency map (TFM) of fNIRS signals to classify the three motor execution tasks including right-hand tapping, left-hand tapping, and foot tapping. To simultaneously obtain the TFM and consider the correlation among channels, we propose to utilize the two-dimensional discrete orthonormal Stockwell transform (2D-DOST). The TFMs for oxygenated hemoglobin (HbO), reduced hemoglobin (HbR), and two linear combinations of them are obtained and then we propose three fusion schemes for combining their deep information extracted by the convolutional neural network (CNN). Two CNNs, LeNet and MobileNet, are considered and their structures are modified to maximize the accuracy. Due to the lack of enough signals for training CNNs, data augmentation based on the Wasserstein generative adversarial network (WGAN) is performed. Several simulations are performed to assess the performance of the proposed method in three-class and binary scenarios. The results present the efficiency of the proposed method in different scenarios. Also, the proposed method outperforms the recently introduced methods.

Explainable cross-task adaptive transfer learning for motor imagery EEG classification

Objective. In the field of motor imagery (MI) electroencephalography (EEG)-based brain-computer interfaces, deep transfer learning (TL) has proven to be an effective tool for solving the problem of limited availability in subject-specific data for the training of robust deep learning (DL) models. Although considerable progress has been made in the cross-subject/session and cross-device scenarios, the more challenging problem of cross-task deep TL remains largely unexplored. Approach. We propose a novel explainable cross-task adaptive TL method for MI EEG decoding. Firstly, similarity analysis and data alignment are performed for EEG data of motor execution (ME) and MI tasks. Afterwards, the MI EEG decoding model is obtained via pre-training with extensive ME EEG data and fine-tuning with partial MI EEG data. Finally, expected gradient-based post-hoc explainability analysis is conducted for the visualization of important temporal-spatial features. Main results. Extensive experiments are conducted on one large ME EEG High-Gamma dataset and two large MI EEG datasets (openBMI and GIST). The best average classification accuracy of our method reaches 80.00% and 72.73% for OpenBMI and GIST respectively, which outperforms several state-of-the-art algorithms. In addition, the results of the explainability analysis further validate the correlation between ME and MI EEG data and the effectiveness of ME/MI cross-task adaptation. Significance. This paper confirms that the decoding of MI EEG can be well facilitated by pre-existing ME EEG data, which largely relaxes the constraint of training samples for MI EEG decoding and is important in a practical sense.

Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface

Background and ObjectiveThe brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features. MethodsIn the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME). ResultsOur results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks. ConclusionsThese results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.

Brain activation by a VR-based motor imagery and observation task: An fMRI study.

Training motor imagery (MI) and motor observation (MO) tasks is being intensively exploited to promote brain plasticity in the context of post-stroke rehabilitation strategies. This may benefit from the use of closed-loop neurofeedback, embedded in brain-computer interfaces (BCI's) to provide an alternative non-muscular channel, which may be further augmented through embodied feedback delivered through virtual reality (VR). Here, we used functional magnetic resonance imaging (fMRI) in a group of healthy adults to map brain activation elicited by an ecologically-valid task based on a VR-BCI paradigm called NeuRow, whereby participants perform MI of rowing with the left or right arm (i.e., MI), while observing the corresponding movement of the virtual arm of an avatar (i.e., MO), on the same side, in a first-person perspective. We found that this MI-MO task elicited stronger brain activation when compared with a conventional MI-only task based on the Graz BCI paradigm, as well as to an overt motor execution task. It recruited large portions of the parietal and occipital cortices in addition to the somatomotor and premotor cortices, including the mirror neuron system (MNS), associated with action observation, as well as visual areas related with visual attention and motion processing. Overall, our findings suggest that the virtual representation of the arms in an ecologically-valid MI-MO task engage the brain beyond conventional MI tasks, which we propose could be explored for more effective neurorehabilitation protocols.

The effects of age and postural constraints on prehension.

Older adults adapt the execution of complex motor tasks to use compensatory strategies in the reaching-to-grasping (i.e., prehension) movement. The presence of postural constraints may exacerbate these compensatory strategies. Therefore, we investigated the reach-to-grasp action with different postural constraints (sitting, standing, and walking) in younger and older people and evaluated the postural stability during the reach-to-grasp action. Thirty individuals (15 younger and 15 older adults) performed the prehension under three postural tasks: sitting, standing, and walking. The reaching movement was slower in the walking task than in the other two postural tasks; however, there was no difference between the age groups. For the grasping action, the older adults presented a larger grip aperture, and the peak grip aperture occurred earlier during hand transportation in sitting and standing tasks. In the standing task, the margin of stability was smaller for older adults. In the walking task, there was no difference between the groups for the margin of stability. Also, prehension during sitting and standing tasks were similar, and both differed from walking across age groups. Finally, older adults reduced their margin of stability compared to younger adults, but only in the standing task. The margin of stability was similar between age groups during the walking task. We concluded that age affected grasping (distal component) but not reaching (proximal component), suggesting that healthy older adults have more difficulty controlling distal than proximal body segments.

Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets.

The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain-computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.

Electroencephalographic-based wearable instrumentation to monitor the executive functions during gait: a feasibility study

A feasibility study on electroencephalographic monitoring of executive functions during dual (motor and cognitive) task execution is presented. Electroencephalographic (EEG) signals are acquired by means of a wearable device with few channels and dry electrodes. The light weight and wireless device allow for walking in a natural way. The most significant EEG features are investigated to classify different levels of activation for two fundamental Executive Functions (EF) both in sitting and walking conditions. Power spectral density in the gamma band resulted in the most relevant feature in discriminating low and high levels of Inhibition. Power spectral density in the beta and gamma bands resulted the most discriminating the level of activation of Working Memory. The study poses the basis for (i) monitoring the activation levels of EF during Gait allowing loss prevention in the elderly and (ii) specific cognitive rehabilitation aimed at the most relevant executive functions during walking.

Técnicas de Neuroimagenología en la Cuantificación de la Neuroplasticidad en Pacientes con Enfermedad Vascular Cerebral

Neuroimaging techniques provide relevant information of the functional and anatomical status of the human brain. This information is of particular importance when a pathology, like stroke, produces a brain injury. In stroke patients, it has been determined that neuroplasticity is the primary recovery mechanism of the lost motor function. Due to worldwide high prevalence, especially in developing countries, it is necessary to continue the research of the recovery mechanisms involved in this pathology. To this end, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are two of the most used neuroimaging techniques. In stroke patients, fMRI allows the analysis of the neural activity produced by the execution of motor tasks, whereas DTI provides structural information of the brain anatomy. In this narrative review, multiple studies that employ these neuroimaging techniques for quantification of neuroplasticity changes in stroke patients after undergoing a neurorehabilitation program are presented. Better understanding of these neuroplasticity changes would allow researchers to design and provide more beneficial rehabilitation schemes to stroke patients.

The interaction between changes of muscle activation and cortical network dynamics during isometric elbow contraction: a sEMG and fNIRS study.

Objective: The relationship between muscle activation during motor tasks and cerebral cortical activity remains poorly understood. The aim of this study was to investigate the correlation between brain network connectivity and the non-linear characteristics of muscle activation changes during different levels of isometric contractions. Methods: Twenty-one healthy subjects were recruited and were asked to perform isometric elbow contractions in both dominant and non-dominant sides. Blood oxygen concentrations in brain from functional Near-infrared Spectroscopy (fNIRS) and surface electromyography (sEMG) signals in the biceps brachii (BIC) and triceps brachii (TRI) muscles were recorded simultaneously and compared during 80% and 20% of maximum voluntary contraction (MVC). Functional connectivity, effective connectivity, and graph theory indicators were used to measure information interaction in brain activity during motor tasks. The non-linear characteristics of sEMG signals, fuzzy approximate entropy (fApEn), were used to evaluate the signal complexity changes in motor tasks. Pearson correlation analysis was used to examine the correlation between brain network characteristic values and sEMG parameters under different task conditions. Results: The effective connectivity between brain regions in motor tasks in dominant side was significantly higher than that in non-dominant side under different contractions (p < 0.05). The results of graph theory analysis showed that the clustering coefficient and node-local efficiency of the contralateral motor cortex were significantly varied under different contractions (p < 0.01). fApEn and co-contraction index (CCI) of sEMG under 80% MVC condition were significantly higher than that under 20% MVC condition (p < 0.05). There was a significant positive correlation between the fApEn and the blood oxygen value in the contralateral brain regions in both dominant or non-dominant sides (p < 0.001). The node-local efficiency of the contralateral motor cortex in the dominant side was positively correlated with the fApEn of the EMG signals (p < 0.05). Conclusion: In this study, the mapping relationship between brain network related indicators and non-linear characteristic of sEMG in different motor tasks was verified. These findings provide evidence for further exploration of the interaction between the brain activity and the execution of motor tasks, and the parameters might be useful in evaluation of rehabilitation intervention.

Functional correlates of neurological soft signs in heavy cannabis users.

Sensorimotor dysfunction has been previously reported in persons with cannabis dependence. Such individuals can exhibit increased levels of neurological soft signs (NSS), particularly involving motor coordination, sensorimotor integration and complex motor task performance. Abnormal NSS levels can also be detected in non-dependent individuals with heavy cannabis use (HCU), yet very little is known about the functional correlates underlying such deficits. Here, we used resting-state functional magnetic resonance imaging (MRI) to investigate associations between NSS and intrinsic neural activity (INA) in HCU (n= 21) and controls (n= 26). Compared with controls, individuals with HCU showed significantly higher NSS across all investigated subdomains. Three of these subdomains, that is, motor coordination, sensorimotor integration and complex motor task behaviour, were associated with specific use-dependent variables, particularly age of onset of cannabis use and current cannabis use. Between-group comparisons of INA revealed lower regional homogeneity (ReHo) in left precentral gyrus, left inferior occipital gyrus, right triangular pat of the inferior frontal gyrus and right precentral gyrus in HCU compared with controls. In addition, HCU showed also higher ReHo in right cerebellum and left postcentral gyrus compared with controls. Complex motor task behaviour in HCU was significantly related to INA in postcentral, inferior frontal and occipital cortices. Our findings indicate abnormal ReHo in HCU in regions associated with sensorimotor, executive control and visuomotor-integration processes. Importantly, we show associations between ReHo, cannabis-use behaviour and execution of complex motor tasks. Given convergent findings in manifest psychotic disorders, this study suggests an HCU endophenotype that may present with a cumulative risk for psychosis.