Brain effective connectivity during motor-imagery and execution following stroke and rehabilitation

Impact of Focal Muscle Vibration on Flaccid Upper Limb Motor Paralysis following Acute Brain Disease: A Case Study

Focal muscle vibration (FMV) is increasingly being recognized as a rehabilitative therapy for enhancing motor function in central nervous system (CNS) diseases, particularly in patients with fine motor control deficits stemming from CNS damage. Brain lesions from these diseases disrupt the motor networks, necessitating novel rehabilitation strategies. By applying vibrations to muscles, FMV stimulates sensory fibers to induce cortical activity and kinesthetic illusions. While initial studies have highlighted FMV's role in reducing spasticity, recent evidence points to its potential in treating motor paralysis. However, prior research has been limited by the lack of acute-phase studies and a focus on patients with minimal muscle contraction capability. This report aimed to explore FMV's efficacy on upper limb motor function in patients with flaccid motor paralysis immediately after acute CNS diseases. We report the case of a septuagenarian male with a brain abscess in the right parietal lobe, leading to flaccid motor paralysis. Rehabilitation included 28 sessions of occupational and physical therapy that incorporated FMV. Significant improvements were observed in upper extremity function, with moderate to very large effect sizes, while lower limb function showed lesser improvement without adverse effects. This case suggests the utility of FMV in enhancing upper-limb motor function after acute CNS injuries, potentially serving as a supplementary therapy for spontaneous recovery. This report contributes to emerging evidence on FMV's benefits in acute flaccid motor paralysis, expanding the documented therapeutic scope.

Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization

Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.

Motor imagery-based brain-computer interface rehabilitation programs enhance upper extremity performance and cortical activation in stroke patients

BACKGROUND

The most challenging aspect of rehabilitation is the repurposing of residual functional plasticity in stroke patients. To achieve this, numerous plasticity-based clinical rehabilitation programs have been developed. This study aimed to investigate the effects of motor imagery (MI)-based brain-computer interface (BCI) rehabilitation programs on upper extremity hand function in patients with chronic hemiplegia.

DESIGN

A 2010 Consolidated Standards for Test Reports (CONSORT)-compliant randomized controlled trial.

METHODS

Forty-six eligible stroke patients with upper limb motor dysfunction participated in the study, six of whom dropped out. The patients were randomly divided into a BCI group and a control group. The BCI group received BCI therapy and conventional rehabilitation therapy, while the control group received conventional rehabilitation only. The Fugl-Meyer Assessment of the Upper Extremity (FMA-UE) score was used as the primary outcome to evaluate upper extremity motor function. Additionally, functional magnetic resonance imaging (fMRI) scans were performed on all patients before and after treatment, in both the resting and task states. We measured the amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), z conversion of ALFF (zALFF), and z conversion of ReHo (ReHo) in the resting state. The task state was divided into four tasks: left-hand grasping, right-hand grasping, imagining left-hand grasping, and imagining right-hand grasping. Finally, meaningful differences were assessed using correlation analysis of the clinical assessments and functional measures.

RESULTS

A total of 40 patients completed the study, 20 in the BCI group and 20 in the control group. Task-related blood-oxygen-level-dependent (BOLD) analysis showed that when performing the motor grasping task with the affected hand, the BCI group exhibited significant activation in the ipsilateral middle cingulate gyrus, precuneus, inferior parietal gyrus, postcentral gyrus, middle frontal gyrus, superior temporal gyrus, and contralateral middle cingulate gyrus. When imagining a grasping task with the affected hand, the BCI group exhibited greater activation in the ipsilateral superior frontal gyrus (medial) and middle frontal gyrus after treatment. However, the activation of the contralateral superior frontal gyrus decreased in the BCI group relative to the control group. Resting-state fMRI revealed increased zALFF in multiple cerebral regions, including the contralateral precentral gyrus and calcarine and the ipsilateral middle occipital gyrus and cuneus, and decreased zALFF in the ipsilateral superior temporal gyrus in the BCI group relative to the control group. Increased zReHo in the ipsilateral cuneus and contralateral calcarine and decreased zReHo in the contralateral middle temporal gyrus, temporal pole, and superior temporal gyrus were observed post-intervention. According to the subsequent correlation analysis, the increase in the FMA-UE score showed a positive correlation with the mean zALFF of the contralateral precentral gyrus (r = 0.425, P < 0.05), the mean zReHo of the right cuneus (r = 0.399, P < 0.05).

CONCLUSION

In conclusion, BCI therapy is effective and safe for arm rehabilitation after severe poststroke hemiparesis. The correlation of the zALFF of the contralateral precentral gyrus and the zReHo of the ipsilateral cuneus with motor improvements suggested that these values can be used as prognostic measures for BCI-based stroke rehabilitation. We found that motor function was related to visual and spatial processing, suggesting potential avenues for refining treatment strategies for stroke patients.

TRIAL REGISTRATION

The trial is registered in the Chinese Clinical Trial Registry (number ChiCTR2000034848, registered July 21, 2020).

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.

Case-based report of graded motor imagery experience in traumatic brachial plexus injury: The art of moving without moving

BACKGROUND

We reported a 24-year-old woman who sustained multiple upper limb injuries after a traffic accident in March 2017. She sustained a C7-T1 brachial plexus injury and radial nerve injury on the left side diagnosed in November 2017. The patient underwent radial nerve reconstruction. The patient began her comprehensive therapy program in January 2018.

PURPOSE

To describe the use of graded motor imagery (GMI) and outcomes after traumatic brachial plexus palsy. We presented changes in electromyographic (EMG) activity of target muscles during task execution and functional status following 10-session GMI therapy.

STUDY DESIGN

Case report.

METHODS

The program included 4 sessions of motor imagery and 6 sessions of a combination of motor imagery and mirror therapy.

RESULTS

The patient successfully participated in the program with reported improvements in EMG activity, functional status, emotional well-being, and body awareness.

CONCLUSIONS

GMI therapy appears to have peripheral motor effects, including altered surface EMG activity and contributes to a favorable outcome in the functional level of the affected arm. An improved emotional state and awareness of the affected hand could have a positive effect on function. Future long-term randomized controlled trials are needed to investigate the cumulative peripheral effects of treatment of graded motor imagery and the effects of variables mediating its effects on functional performance in patients with nerve injury.

Neuroimaging prognostic factors for treatment response to motor imagery training after stroke

The efficacy of motor imagery training for motor recovery is well acknowledged, but with substantial inter-individual variability in stroke patients. To help optimize motor imagery training therapy plans and screen suitable patients, this study aimed to explore neuroimaging biomarkers explaining variability in treatment response. Thirty-nine stroke patients were randomized to a motor imagery training group (n = 22, received a combination of conventional rehabilitation therapy and motor imagery training) and a control group (n = 17, received conventional rehabilitation therapy and health education) for 4 weeks of interventions. Their demography and clinical information, brain lesion from structural MRI, spontaneous brain activity and connectivity from rest fMRI, and sensorimotor brain activation from passive motor task fMRI were acquired to identify prognostic factors. We found that the variability of outcomes from sole conventional rehabilitation therapy could be explained by the reserved sensorimotor neural function, whereas the variability of outcomes from motor imagery training + conventional rehabilitation therapy was related to the spontaneous activity in the ipsilesional inferior parietal lobule and the local connectivity in the contralesional supplementary motor area. The results suggest that additional motor imagery training treatment is also efficient for severe patients with damaged sensorimotor neural function, but might be more effective for patients with impaired motor planning and reserved motor imagery.

Upper limb intelligent feedback robot training significantly activates the cerebral cortex and promotes the functional connectivity of the cerebral cortex in patients with stroke: A functional near-infrared spectroscopy study

Background

Upper limb intelligence robots are widely used to improve the upper limb function of patients with stroke, but the treatment mechanism is still not clear. In this study, functional near-infrared spectroscopy (fNIRS) was used to evaluate the concentration changes of oxygenated hemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) in different brain regions and functional connectivity (FC) of the cerebral cortex in patients with stroke.

Method

Twenty post-stroke patients with upper limb dysfunction were included in the study. They all received three different types of shoulder joint training, namely, active intelligent feedback robot training (ACT), upper limb suspension training (SUS), and passive intelligent feedback robot training (PAS). During the training, activation of the cerebral cortex was detected by fNIRS to obtain the concentration changes of hemoglobin and FC of the cerebral cortex. The fNIRS signals were recorded over eight ROIs: bilateral prefrontal cortices (PFC), bilateral primary motor cortices (M1), bilateral primary somatosensory cortices (S1), and bilateral premotor and supplementary motor cortices (PM). For easy comparison, we defined the right hemisphere as the ipsilesional hemisphere and flipped the lesional right hemisphere in the Nirspark.

Result

Compared with the other two groups, stronger cerebral cortex activation was observed during ACT. One-way repeated measures ANOVA revealed significant differences in mean oxy-Hb changes among conditions in the four ROIs: contralesional PFC [F_{(2, 48)} = 6,798, p < 0.01], ipsilesional M1 [F_{(2, 48)} = 6.733, p < 0.01], ipsilesional S1 [F_{(2, 48)} = 4,392, p < 0.05], and ipsilesional PM [F_{(2, 48)} = 3.658, p < 0.05]. Oxy-Hb responses in the contralesional PFC region were stronger during ACT than during SUS (p < 0.01) and PAS (p < 0.05). Cortical activation in the ipsilesional M1 was significantly greater during ACT than during SUS (p < 0.01) and PAS (p < 0.05). Oxy-Hb responses in the ipsilesional S1 (p < 0.05) and ipsilesional PM (p < 0.05) were significantly higher during ACT than during PAS, and there is no significant difference in mean deoxy-Hb changes among conditions. Compared with SUS, the FC increased during ACT, which was characterized by the enhanced function of the ipsilesional cortex (p < 0.05), and there was no significant difference in FC between the ACT and PAS.

Conclusion

The study found that cortical activation during ACT was higher in the contralesional PFC, and ipsilesional M1 than during SUS, and showed tighter cortical FC between the cortices. The activation of the cerebral cortex of ACT was significantly higher than that of PAS, but there was no significant difference in FC. Our research helps to understand the difference in cerebral cortex activation between upper limb intelligent feedback robot rehabilitation and other rehabilitation training and provides an objective basis for the further application of upper limb intelligent feedback robots in the field of stroke rehabilitation.

Neuroimaging alterations of the suicidal brain and its relevance to practice: an updated review of MRI studies

Suicide is a leading cause of death in the United States. Historically, scientific inquiry has focused on psychological theory. However, more recent studies have started to shed light on complex biosignatures using MRI techniques, including task-based and resting-state functional MRI, brain morphometry, and diffusion tensor imaging. Here, we review recent research across these modalities, with a focus on participants with depression and Suicidal Thoughts and Behavior (STB). A PubMed search identified 149 articles specific to our population of study, and this was further refined to rule out more diffuse pathologies such as psychotic disorders and organic brain injury and illness. This left 69 articles which are reviewed in the current study. The collated articles reviewed point to a complex impairment showing atypical functional activation in areas associated with perception of reward, social/affective stimuli, top-down control, and reward-based learning. This is broadly supported by the atypical morphometric and diffusion-weighted alterations and, most significantly, in the network-based resting-state functional connectivity data that extrapolates network functions from well validated psychological paradigms using functional MRI analysis. We see an emerging picture of cognitive dysfunction evident in task-based and resting state fMRI and network neuroscience studies, likely preceded by structural changes best demonstrated in morphometric and diffusion-weighted studies. We propose a clinically-oriented chronology of the diathesis-stress model of suicide and link other areas of research that may be useful to the practicing clinician, while helping to advance the translational study of the neurobiology of suicide.

Neural oscillations during acupuncture imagery partially parallel that of real needling

Introduction

Tasks involving mental practice, relying on the cognitive rehearsal of physical motors or other activities, have been reported to have similar patterns of brain activity to overt execution. In this study, we introduced a novel imagination task called, acupuncture imagery and aimed to investigate the neural oscillations during acupuncture imagery.

Methods

Healthy volunteers were guided to watch a video of real needling in the left and right KI3 (Taixi point). The subjects were then asked to perform tasks to keep their thoughts in three 1-min states alternately: resting state, needling imagery left KI3, and needling imagery right KI3. Another group experienced real needling in the right KI3. A 31-channel-electroencephalography was synchronously recorded for each subject. Microstate analyses were performed to depict the brain dynamics during these tasks.

Results

Compared to the resting state, both acupuncture needling imagination and real needling in KI3 could introduce significant changes in neural dynamic oscillations. Moreover, the parameters involving microstate A of needling imagery in the right KI3 showed similar changes as real needling in the right KI3.

Discussion

These results confirm that needling imagination and real needling have similar brain activation patterns. Needling imagery may change brain network activity and play a role in neural regulation. Further studies are needed to explore the effects of acupuncture imagery and the potential application of acupuncture imagery in disease recovery.

Motor network reorganization after motor imagery training in stroke patients with moderate to severe upper limb impairment

BACKGROUND

Motor imagery training (MIT) has been widely used to improve hemiplegic upper limb function in stroke rehabilitation. The effectiveness of MIT is associated with the functional neuroplasticity of the motor network. Currently, brain activation and connectivity changes related to the motor recovery process after MIT are not well understood.

AIM

We aimed to investigate the neural mechanisms of MIT in stroke rehabilitation through a longitudinal intervention study design with task-based functional magnetic resonance imaging (fMRI) analysis.

METHODS

We recruited 39 stroke patients with moderate to severe upper limb motor impairment and randomly assigned them to either the MIT or control groups. Patients in the MIT group received 4 weeks of MIT therapy plus conventional rehabilitation, while the control group only received conventional rehabilitation. The assessment of Fugl-Meyer Upper Limb Scale (FM-UL) and Barthel Index (BI), and fMRI scanning using a passive hand movement task were conducted on all patients before and after treatment. The changes in brain activation and functional connectivity (FC) were analyzed. Pearson's correlation analysis was conducted to evaluate the association between neural functional changes and motor improvement.

RESULTS

The MIT group achieved higher improvements in FM-UL and BI relative to the control group after the treatment. Passive movement of the affected hand evoked an abnormal bilateral activation pattern in both groups before intervention. A significant Group × Time interaction was found in the contralesional S1 and ipsilesional M1, showing a decrease of activation after intervention specifically in the MIT group, which was negatively correlated with the FM-UL improvement. FC analysis of the ipsilesional M1 displayed the motor network reorganization within the ipsilesional hemisphere, which correlated with the motor score changes.

CONCLUSIONS

MIT could help decrease the compensatory activation at both hemispheres and reshape the FC within the ipsilesional hemisphere along with functional recovery in stroke patients.

Sources of independent mu components reveal different brain areas involved in motor imagery, motor execution, and movement observation

To answer the question of whether the same brain circuit(s) facilitates motor imagery (MI), motor execution (ME), and movement observation (MO), we conducted electroencephalography (EEG) experiment combining the three motor conditions in the same experimental runs. The EEG data were analyzed using two different independent component analysis (ICA) decomposition approaches: a single ICA decomposition on all EEG data combined and separate ICA decomposition on the EEG data obtained from the separate conditions. The results indicated that the separate ICA approach may provide a better fit to the EEG data obtained from the separate conditions to deliver specific independent right mu components with distinct topographies for each of the motor conditions. The topography of the MI condition covered the brain regions posterior to the central sulcus (P4 EEG channel); the ME condition covered the brain regions anterior to the central sulcus (C4 EEG channel), and the MO condition had broader coverage with the main activation in the premotor region (CP4 EEG channel). The source localization results also exhibited significant differences among the motor conditions. In addition, the result of single ICA decomposition resembled the result of separate ICA decomposition on the EEG data of ME with similar topographies and closely located EEG sources. This finding may further indicate that the result of single ICA decomposition may be dominated by the ME motor condition because it manifests higher data variance than the other two motor conditions.

The Effects of Vestibular Rehabilitation on Poststroke Fatigue: A Randomized Controlled Trial Study

Background. A major complication caused by stroke is poststroke fatigue (PSF), and by causing limitations in doing activities of daily living (ADL), it can lower the quality of life. Objective. The present study is an attempt to examine the effects of vestibular rehabilitation on BADL (Basic Activities of Daily Living), fatigue, depression, and Lawton Instrumental Activities of Daily Living (IADL) in patients with stroke. Method. Patients with a history of stroke took part voluntarily in a single-blind clinical trial. The participants were allocated to control and experimental groups randomly. The experimental group attended 24 sessions of vestibular rehabilitation protocol, while the control group received the standard rehabilitation (including three sessions per week each for around 60 min). To measure fatigue, the Fatigue Impact Scale (FIS) and the Fatigue Assessment Scale (FAS) were used. Depression, BADL, and IADL were measured using the Beck Depression Inventory-II (BDI-II), Barthel Index (BI), and Lawton Instrumental Activities of Daily Living, respectively. All changes were measured from the baseline after the intervention. Results. Significant improvement was found in the experimental group compared to the control group ( $p<0.05$ ) in FIS (physical, cognition, and social subscales), FAS, BDI-II, BADL, and IADL. Moreover, the results showed small to medium and large effect sizes for the physical subscale of FIS and FAS scores based on Cohen’s $d$ , respectively; however, no significant difference was found in terms of cognition and social subscales of FIS, BDI-II, BADL, and IADL scores. Conclusion. It is possible to improve fatigue, depression, and independence in BADL and IADL using vestibular rehabilitation. Thus, it is an effective intervention in case of stroke, which is also well tolerated.

Motor Imagery Decoding in the Presence of Distraction Using Graph Sequence Neural Networks

In this study, we propose a graph sequence neural network (GSNN) to accurately decode patterns of motor imagery from electroencephalograms (EEGs) in the presence of distractions. GSNN aims to build subgraphs by exploiting biological topologies among brain regions to capture local and global relationships across characteristic channels. Specifically, we model the similarity between pairwise EEG channels by the adjacency matrix of the graph sequence neural network. In addition, we propose a node domain attention selection network in which the connection and sparsity of the adjacency matrix can be adjusted dynamically according to the EEG signals acquired from different subjects. Extensive experiments on the public Berlin-distraction dataset show that in most experimental settings, our model performs considerably better than the state-of-the-art models. Moreover, comparative experiments indicate that our proposed node domain attention selection network plays a crucial role in improving the sensibility and adaptability of the GSNN model. The results show that the GSNN algorithm obtained superior classification accuracy (The average value of Recall, Precision, and F-score were 80.44%, 81.07% and 80.54%) compared to the state-of-the-art models. Finally, in the process of extracting the intermediate results, the relationships between important brain regions and channels were revealed to different influences in distraction themes.

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.

Synergistic Immediate Cortical Activation on Mirror Visual Feedback Combined With a Soft Robotic Bilateral Hand Rehabilitation System: A Functional Near Infrared Spectroscopy Study

Background

Mirror visual feedback (MVF) has been widely used in neurological rehabilitation. Due to the potential gain effect of the MVF combination therapy, the related mechanisms still need be further analyzed.

Methods

Our self-controlled study recruited 20 healthy subjects (age 22.150 ± 2.661 years) were asked to perform four different visual feedback tasks with simultaneous functional near infrared spectroscopy (fNIRS) monitoring. The right hand of the subjects was set as the active hand (performing active movement), and the left hand was set as the observation hand (static or performing passive movement under soft robotic bilateral hand rehabilitation system). The four VF tasks were designed as RVF Task (real visual feedback task), MVF task (mirror visual feedback task), BRM task (bilateral robotic movement task), and MVF + BRM task (Mirror visual feedback combined with bilateral robotic movement task).

Results

The beta value of the right pre-motor cortex (PMC) of MVF task was significantly higher than the RVF task (RVF task: -0.015 ± 0.029, MVF task: 0.011 ± 0.033, P = 0.033). The beta value right primary sensorimotor cortex (SM1) in MVF + BRM task was significantly higher than MVF task (MVF task: 0.006 ± 0.040, MVF + BRM task: 0.037 ± 0.036, P = 0.016).

Conclusion

Our study used the synchronous fNIRS to compare the immediate hemodynamics cortical activation of four visual feedback tasks in healthy subjects. The results showed the synergistic gain effect on cortical activation from MVF combined with a soft robotic bilateral hand rehabilitation system for the first time, which could be used to guide the clinical application and the future studies.

Brain Connectivity Changes During Bimanual and Rotated Motor Imagery

Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Objective: Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training. This study compared the effects of three MI tasks with different visual feedback. Methods: Fourteen healthy subjects performed single hand motor imagery tasks while watching single static hand (traditional MI), single hand with rotation movement (rmMI), and bimanual coordination with a hand pedal exerciser (bcMI). Functional connectivity is estimated by Transfer Entropy (TE) analysis for brain information flow. Results: Brain connectivity of conducting three MI tasks showed that the bcMI demonstrated increased communications from the parietal to the bilateral prefrontal areas and increased contralateral connections between motor-related zones and spatial processing regions. Discussion/Conclusion: The results revealed bimanual coordination operation events increased spatial information and motor planning under the motor imagery task. And the proposed bimanual coordination MI-BCI (bcMI-BCI) can also achieve the effect of traditional motor imagery tasks and promotes more effective connections with different brain regions to better integrate motor-cortex functions for aiding the development of more effective MI-BCI therapy. Clinical and Translational Impact Statement The proposed bcMI-BCI provides more effective connections with different brain areas and integrates motor-cortex functions to promote motor imagery rehabilitation for patients’ impairment.

MRI markers of functional connectivity and tissue microstructure in stroke-related motor rehabilitation: A systematic review

BACKGROUND

Stroke-related disability is a major problem at individual and socio-economic levels. Neuromotor rehabilitation has a key role for its dual action on affected body segment and brain reorganization. Despite its known efficacy in clinical practice, the extent and type of effect at a brain level, mediated by neuroplasticity, are still under question.

OBJECTIVE

To analyze studies applying MRI markers of functional and structural connectivity in patients affected with stroke undergoing motor rehabilitation, and to evaluate the effect of rehabilitation on brain reorganization.

METHODS

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria were applied to select studies applying quantitative non-conventional MRI techniques on patients undergoing motor rehabilitation, both physical and virtual (virtual reality, mental imagery). Literature search was conducted using MEDLINE (via PubMed), Cochrane Central Register of Controlled Trials (CENTRAL), and EMBASE from inception to 30th June 2020.

RESULTS

Forty-one out of 6983 papers were included in the current review. Selected studies are heterogeneous in terms of patient characteristics as well as type, duration and frequency of rehabilitative approach. Neuromotor rehabilitation promotes neuroplasticity, favoring functional recovery of the ipsilesional hemisphere and activation of anatomically and functionally related brain areas in both hemispheres, to compensate for damaged tissue.

CONCLUSIONS

The evidence derived from the analyzed studies supports the positive impact of rehabilitation on brain reorganization, despite the high data heterogeneity. Advanced MRI techniques provide reliable markers of structural and functional connectivity that may potentially aid in helping to implement the most appropriate rehabilitation intervention.

Behavioural indexes of movement imagery ability are associated with the magnitude of corticospinal adaptation following movement imagery training

Movement imagery (MI) is a cognitive process wherein an individual simulates themselves performing a movement in the absence of physical movement. The current paper reports an examination of the relationship between behavioural indexes of MI ability and the magnitude of corticospinal adaptation following MI training. Behavioural indexes of MI ability included data from a questionnaire (MIQ-3), a mental chronometry task, and a hand laterality judgment task. For the measure of corticospinal adaptation, single-pulse transcranial magnetic stimulation (TMS) was administered to elicit thumb movements to determine the representation of thumb movements before and after MI training. MI training involved participants imagining themselves moving their thumb in the opposite direction to the dominant direction of the TMS-evoked movements prior to training. Pre/post-training changes in the direction and velocity of TMS-evoked thumb movements indicated the magnitude of adaptation following MI training. The two main findings were: 1) a positive relationship was found between the MIQ-3 and the pre/post-training changes in the direction of TMS-evoked thumb movements; and 2) a negative relationship between the mental chronometry measure and both measures of corticospinal adaptation following MI training. These results indicate that both ease of imagery and timing of imagery could predict the magnitude of neuroplastic adaptation following MI training. Thus, both these measures may be considered when assessing imagery ability and determining who might benefit from MI interventions.

Asymmetric directed functional connectivity within the frontoparietal motor network during motor imagery and execution

Both imagery and execution of motor control consist of interactions within a neuronal network, including frontal motor-related and posterior parietal regions. To reveal neural representation in the frontoparietal motor network, two approaches have been proposed thus far: one is decoding of actions/modes related to motor control from the spatial pattern of brain activity; and the other is estimating directed functional connectivity (a directed association between two brain regions within motor areas). However, directed connectivity among multiple regions of the frontoparietal motor network during motor imagery (MI) or motor execution (ME) has not been investigated. Here, we attempted to characterize the directed functional connectivity representing the MI and ME conditions. We developed a delayed sequential movement and imagery task to evoke brain activity associated with ME and MI, which can be recorded by functional magnetic resonance imaging. We applied a causal discovery approach, a linear non-Gaussian acyclic causal model, to identify directed functional connectivity among the frontoparietal motor-related brain regions for each condition. We demonstrated higher directed functional connectivity from the contralateral dorsal premotor cortex (dPMC) to the primary motor cortex (M1) in ME than in MI. We further identified significant direct effects of the dPMC and ventral premotor cortex (vPMC) to the parietal regions. In particular, connectivity from the dPMC to the superior parietal lobule (SPL) in the same hemisphere showed significant positive effects across all conditions, while interlateral connectivities from the vPMC to the SPL showed significantly negative effects across all conditions. Finally, we found positive effects from A1 to M1, that is, the audio-motor pathway, in the same hemisphere. These results indicate that the sources of motor command originating in the d/vPMC influenced the M1 and parietal regions for achieving ME and MI. Additionally, sequential sounds may functionally facilitate temporal motor processes.

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.

Brain-Computer Interface Training With Functional Electrical Stimulation: Facilitating Changes in Interhemispheric Functional Connectivity and Motor Outcomes Post-stroke

While most survivors of stroke experience some spontaneous recovery and receive treatment in the subacute setting, they are often left with persistent impairments in upper limb sensorimotor function which impact autonomy in daily life. Brain-Computer Interface (BCI) technology has shown promise as a form of rehabilitation that can facilitate motor recovery after stroke, however, we have a limited understanding of the changes in functional connectivity and behavioral outcomes associated with its use. Here, we investigate the effects of EEG-based BCI intervention with functional electrical stimulation (FES) on resting-state functional connectivity (rsFC) and motor outcomes in stroke recovery. 23 patients post-stroke with upper limb motor impairment completed BCI intervention with FES. Resting-state functional magnetic resonance imaging (rs-fMRI) scans and behavioral data were collected prior to intervention, post- and 1-month post-intervention. Changes in rsFC within the motor network and behavioral measures were investigated to identify brain-behavior correlations. At the group-level, there were significant increases in interhemispheric and network rsFC in the motor network after BCI intervention, and patients significantly improved on the Action Research Arm Test (ARAT) and SIS domains. Notably, changes in interhemispheric rsFC from pre- to both post- and 1 month post-intervention correlated with behavioral improvements across several motor-related domains. These findings suggest that BCI intervention with FES can facilitate interhemispheric connectivity changes and upper limb motor recovery in patients after stroke.

The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study

Objective.In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. Most of the studies using NF or brain computer interfaces for stroke rehabilitation have assessed treatment effects focusing on successful activation of targeted cortical regions. However, given the crucial role of brain network reorganization for stroke recovery, our broader aim was to assess connectivity changes after an NF training protocol targeting localized motor areas.Approach.We considered changes in fMRI connectivity after a multisession EEG-fMRI NF training targeting ipsilesional motor areas in nine stroke patients. We applied the dynamic causal modeling and parametric empirical Bayes frameworks for the estimation of effective connectivity changes. We considered a motor network including both ipsilesional and contralesional premotor, supplementary and primary motor areas.Main results.Our results indicate that NF upregulation of targeted areas (ipsilesional supplementary and primary motor areas) not only modulated activation patterns, but also had a more widespread impact on fMRI bilateral motor networks. In particular, inter-hemispheric connectivity between premotor and primary motor regions decreased, and ipsilesional self-inhibitory connections were reduced in strength, indicating an increase in activation during the NF motor task.Significance.To the best of our knowledge, this is the first work that investigates fMRI connectivity changes elicited by training of localized motor targets in stroke. Our results open new perspectives in the understanding of large-scale effects of NF training and the design of more effective NF strategies, based on the pathophysiology underlying stroke-induced deficits.

Stroke-induced alteration in multi-layer information transmission of cortico-motor system during elbow isometric contraction modulated by myoelectric-controlled interfaces

Objective. Human movement is a complex process requiring information transmission in inter-cortical, cortico-muscular and inter-muscular networks. Though motor deficits after stroke are associated with impaired networks in the cortico-motor system, the mechanisms underlying these networks are to date not fully understood. The purpose of this study is to investigate the changes in information transmission of the inter-cortical, cortico-muscular and inter-muscular networks after stroke and the effect of myoelectric-controlled interface (MCI) dimensionality on such information transmission in each network.Approach. Fifteen healthy control subjects and 11 post-stroke patients were recruited to perform elbow tracking tasks within different dimensional MCIs in this study. Their electromyography (EMG) and functional near-infrared spectroscopy (fNIRS) signals were recorded simultaneously. Transfer entropy was used to analyse the functional connection that represented the information transmission in each network based on the fNIRS and EMG signals.Main results.The results found that post-stroke patients showed the increased inter-cortical connection versus healthy control subjects, which might be attributed to cortical reorganisation to compensate for motor deficits. Compared to healthy control subjects, a lower strength cortico-muscular connection was found in post-stroke patients due to the reduction of information transmission following a stroke. Moreover, the increased MCI dimensionality strengthened inter-cortical, cortico-muscular and inter-muscular connections because of higher visual information processing demands.Significance. These findings not only provide a comprehensive overview to evaluate changes in the cortico-motor system due to stroke, but also suggest that increased MCI dimensionality may serve as a useful rehabilitation tool for boosting information transmission in the cortico-motor system of post-stroke patients.

Blue-Light Therapy Strengthens Resting-State Effective Connectivity within Default-Mode Network after Mild TBI

Background:

Emerging evidence suggests that post concussive symptoms, including mood changes, may be improved through morning blue-wavelength light therapy (BLT). However, the neurobiological mechanisms underlying these effects remain unknown. We hypothesize that BLT may influence the effective brain connectivity (EC) patterns within the default-mode network (DMN), particularly involving the medial prefrontal cortex (MPFC), which may contribute to improvements in mood.

Methods:

Resting-state functional MRI data were collected from 41 healthy-controls (HCs) and 28 individuals with mild traumatic brain injury (mTBI). Individuals with mTBI also underwent a diffusion-weighted imaging scan and were randomly assigned to complete either 6 weeks of daily morning BLT (N = 14) or amber light therapy (ALT; N = 14). Advanced spectral dynamic causal modeling (sDCM) and diffusion MRI connectometry were used to estimate EC patterns and structural connectivity strength within the DMN, respectively.

Results:

The sDCM analysis showed dominant connectivity pattern following mTBI (pre-treatment) within the hemisphere contralateral to the one observed for HCs. BLT, but not ALT, resulted in improved directional information flow (ie, EC) from the left lateral parietal cortex (LLPC) to MPFC within the DMN. The improvement in EC from LLPC to MPFC was accompanied by stronger structural connectivity between the 2 areas. For the BLT group, the observed improvements in function and structure were correlated (at a trend level) with changes in self-reported happiness.

Conclusions:

The current preliminary findings provide empirical evidence that morning short-wavelength light therapy could be used as a novel alternative rehabilitation technique for mTBI.

Trial registry:

The research protocols were registered in the ClinicalTrials.gov database (CT Identifiers NCT01747811 and NCT01721356).

Effect of Neurofeedback Facilitation on Poststroke Gait and Balance Recovery: A Randomized Controlled Trial

OBJECTIVE

To test the hypothesis that supplementary motor area (SMA) facilitation with functional near-infrared spectroscopy-mediated neurofeedback (fNIRS-NFB) augments poststroke gait and balance recovery, we conducted a 2-center, double-blind, randomized controlled trial involving 54 Japanese patients using the 3-meter Timed Up and Go (TUG) test.

METHODS

Patients with subcortical stroke-induced mild to moderate gait disturbance more than 12 weeks from onset underwent 6 sessions of SMA neurofeedback facilitation during gait- and balance-related motor imagery using fNIRS-NFB. Participants were randomly allocated to intervention (28 patients) or placebo (sham: 26 patients). In the intervention group, the fNIRS signal contained participants' cortical activation information. The primary outcome was TUG improvement 4 weeks postintervention.

RESULTS

The intervention group showed greater improvement in the TUG test (12.84 ± 15.07 seconds, 95% confidence interval 7.00-18.68) than the sham group (5.51 ± 7.64 seconds, 95% confidence interval 2.43-8.60; group difference 7.33 seconds, 95% CI 0.83-13.83; p = 0.028), even after adjusting for covariates (group × time interaction; F _1.23,61.69 = 4.50, p = 0.030, partial η² = 0.083). Only the intervention group showed significantly increased imagery-related SMA activation and enhancement of resting-state connectivity between SMA and ventrolateral premotor area. Adverse effects associated with fNIRS-mediated neurofeedback intervention were absent.

CONCLUSION

SMA facilitation during motor imagery using fNIRS neurofeedback may augment poststroke gait and balance recovery by modulating the SMA and its related network.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that for patients with gait disturbance from subcortical stroke, SMA neurofeedback facilitation improves TUG time (UMIN000010723 at UMIN-CTR; umin.ac.jp/english/).

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.

Investigating the Viability of Motor Imagery as a Physical Rehabilitation Treatment for Patients With Stroke-Induced Motor Cortical Damage

Although around 83% of individuals survive a stroke, they usually experience a significant loss in their motor execution (ME) capabilities due to their acquired cortical infarction. The loss of significant ME capabilities due to stroke damage was previously thought to be irreversible. Active movement therapies show considerable promise but depend on motor performance, excluding many otherwise eligible patients. Motor imagery (MI), a process that involves the use of mirror neurons to imagine motor activity, has emerged as a possible avenue to re-acquire some physical abilities lost to stroke damage. This paper examines previous studies to compare the strength of brain activation and connectivity in individuals who have brain lesions and those who do not as they all attempt ME and MI tasks. This paper reviews case studies investigating the direct effect of motor imagery in conjunction with physical therapy and the limitations of motor imagery based on the location of cortical damage and other variables, such as age. The findings analyzed in this review indicate that MI would serve as a beneficial addition to physical therapy and a viable option to stimulate motor evoked potentials (MEPs) in individuals not capable of pursuing physical therapy due to severe motor impairment. Regardless of the presence of brain lesions, motor imagery has consistently had a positive impact on motor rehabilitation either in boosting treatment or stimulating neuromuscular pathways. Therefore, we have concluded that MI is a viable supplemental treatment plan for motor recovery in most patients with motor cortical atrophy.

Motor Imagery EEG Decoding Method Based on a Discriminative Feature Learning Strategy

With the rapid development of deep learning, more and more deep learning-based motor imagery electroencephalograph (EEG) decoding methods have emerged in recent years. However, the existing deep learning-based methods usually only adopt the constraint of classification loss, which hardly obtains the features with high discrimination and limits the improvement of EEG decoding accuracy. In this paper, a discriminative feature learning strategy is proposed to improve the discrimination of features, which includes the central distance loss (CD-loss), the central vector shift strategy, and the central vector update process. First, the CD-loss is proposed to make the same class of samples converge to the corresponding central vector. Then, the central vector shift strategy extends the distance between different classes of samples in the feature space. Finally, the central vector update process is adopted to avoid the non-convergence of CD-loss and weaken the influence of the initial value of central vectors on the final results. In addition, overfitting is another severe challenge for deep learning-based EEG decoding methods. To deal with this problem, a data augmentation method based on circular translation strategy is proposed to expand the experimental datasets without introducing any extra noise or losing any information of the original data. To validate the effectiveness of the proposed method, we conduct some experiments on two public motor imagery EEG datasets (BCI competition IV 2a and 2b dataset), respectively. The comparison with current state-of-the-art methods indicates that our method achieves the highest average accuracy and good stability on the two experimental datasets.

A novel motor imagery EEG decoding method based on feature separation

OBJECTIVE

Motor imagery electroencephalography (EEG) decoding is a vital technology for the brain-computer interface (BCI) systems and has been widely studied in recent years. However, the original EEG signals usually contain a lot of class-independent information, and the existing motor imagery EEG decoding methods are easily interfered by this irrelevant information, which greatly limits the decoding accuracy of these methods.

APPROACH

To overcome the interference of the class-independent information, a motor imagery EEG decoding method based on feature separation is proposed in this paper. Furthermore, a feature separation network based on adversarial learning (FSNAL) is designed for the feature separation of the original EEG samples. First, the class-related features and class-independent features are separated by the proposed FSNAL framework, and then motor imagery EEG decoding is performed only according to the class-related features to avoid the adverse effects of class-independent features.

MAIN RESULTS

To validate the effectiveness of the proposed motor imagery EEG decoding method, we conduct some experiments on two public EEG datasets (the BCI competition IV 2a and 2b datasets). The experimental results comparison between our method and some state-of-the-art methods demonstrates that our motor imagery EEG decoding method outperforms all the compared methods on the two experimental datasets.

SIGNIFICANCE

Our motor imagery EEG decoding method can alleviate the interference of class-independent features, and it has great application potential for improving the performance of motor imagery BCI systems in the near future.

Assessing the effective connectivity of premotor areas during real vs imagined grasping: a DCM-PEB approach

The parieto-frontal circuit underlying grasping, which requires the serial involvement of the anterior intraparietal area (aIPs) and the ventral premotor cortex (PMv), has been recently extended enlightening the role of the dorsal premotor cortex (PMd). The supplementary motor area (SMA) has been also suggested to encode grip force for grasping actions; furthermore, both PMd and SMA are known to play a crucial role in motor imagery. Here, we aimed at assessing the dynamic couplings between left aIPs, PMv, PMd, SMA and primary motor cortex (M1) by comparing executed and imagined right-hand grasping, using Dynamic Causal Modelling (DCM) and Parametrical Empirical Bayes (PEB) analyses. 24 subjects underwent an fMRI exam (3T) during which they were asked to perform or imagine a grasping movement visually cued by photographs of commonly used objects. We tested whether the two conditions a) exert a modulatory effect on both forward and feedback couplings among our areas of interest, and b) differ in terms of strength and sign of these parameters. Results of the real condition confirmed the serial involvement of aIPs, PMv and M1. PMv also exerted a positive influence on PMd and SMA, but received an inhibitory feedback only from PMd. Our results suggest that a general motor program for grasping is planned by the aIPs-PMv circuit; then, PMd and SMA encode high-level features of the movement. During imagery, the connection strength from aIPs to PMv was weaker and the information flow stopped in PMv; thus, a less complex motor program was planned. Moreover, results suggest that SMA and PMd cooperate to prevent motor execution. In conclusion, the comparison between execution and imagery reveals that during grasping premotor areas dynamically interplay in different ways, depending on task demands.

Modulation of Functional Connectivity and Low-Frequency Fluctuations After Brain-Computer Interface-Guided Robot Hand Training in Chronic Stroke: A 6-Month Follow-Up Study

Hand function improvement in stroke survivors in the chronic stage usually plateaus by 6 months. Brain-computer interface (BCI)-guided robot-assisted training has been shown to be effective for facilitating upper-limb motor function recovery in chronic stroke. However, the underlying neuroplasticity change is not well understood. This study aimed to investigate the whole-brain neuroplasticity changes after 20-session BCI-guided robot hand training, and whether the changes could be maintained at the 6-month follow-up. Therefore, the clinical improvement and the neurological changes before, immediately after, and 6 months after training were explored in 14 chronic stroke subjects. The upper-limb motor function was assessed by Action Research Arm Test (ARAT) and Fugl-Meyer Assessment for Upper-Limb (FMA), and the neurological changes were assessed using resting-state functional magnetic resonance imaging. Repeated-measure ANOVAs indicated that long-term motor improvement was found by both FMA (F_[2,26] = 6.367, p = 0.006) and ARAT (F_[2,26] = 7.230, p = 0.003). Seed-based functional connectivity analysis exhibited that significantly modulated FC was observed between ipsilesional motor regions (primary motor cortex and supplementary motor area) and contralesional areas (supplementary motor area, premotor cortex, and superior parietal lobule), and the effects were sustained after 6 months. The fALFF analysis showed that local neuronal activities significantly increased in central, frontal and parietal regions, and the effects were also sustained after 6 months. Consistent results in FC and fALFF analyses demonstrated the increase of neural activities in sensorimotor and fronto-parietal regions, which were highly involved in the BCI-guided training.

Clinical Trial Registration: This study has been registered at ClinicalTrials.gov with clinical trial registration number NCT02323061.

BCI Training Effects on Chronic Stroke Correlate with Functional Reorganization in Motor-Related Regions: A Concurrent EEG and fMRI Study

Brain–computer interface (BCI)-guided robot-assisted training strategy has been increasingly applied to stroke rehabilitation, while few studies have investigated the neuroplasticity change and functional reorganization after intervention from multimodality neuroimaging perspective. The present study aims to investigate the hemodynamic and electrophysical changes induced by BCI training using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) respectively, as well as the relationship between the neurological changes and motor function improvement. Fourteen chronic stroke subjects received 20 sessions of BCI-guided robot hand training. Simultaneous EEG and fMRI data were acquired before and immediately after the intervention. Seed-based functional connectivity for resting-state fMRI data and effective connectivity analysis for EEG were processed to reveal the neuroplasticity changes and interaction between different brain regions. Moreover, the relationship among motor function improvement, hemodynamic changes, and electrophysical changes derived from the two neuroimaging modalities was also investigated. This work suggested that (a) significant motor function improvement could be obtained after BCI training therapy, (b) training effect significantly correlated with functional connectivity change between ipsilesional M1 (iM1) and contralesional Brodmann area 6 (including premotor area (cPMA) and supplementary motor area (SMA)) derived from fMRI, (c) training effect significantly correlated with information flow change from cPMA to iM1 and strongly correlated with information flow change from SMA to iM1 derived from EEG, and (d) consistency of fMRI and EEG results illustrated by the correlation between functional connectivity change and information flow change. Our study showed changes in the brain after the BCI training therapy from chronic stroke survivors and provided a better understanding of neural mechanisms, especially the interaction among motor-related brain regions during stroke recovery. Besides, our finding demonstrated the feasibility and consistency of combining multiple neuroimaging modalities to investigate the neuroplasticity change.

Cortical reorganization after motor stroke: A pilot study on differences between the upper and lower limbs

Stroke patients suffering from hemiparesis may show substantial recovery in the first months poststroke due to neural reorganization. While reorganization driving improvement of upper hand motor function has been frequently investigated, much less is known about the changes underlying recovery of lower limb function. We, therefore, investigated neural network dynamics giving rise to movements of both the hands and feet in 12 well-recovered left-hemispheric chronic stroke patients and 12 healthy participants using a functional magnetic resonance imaging sparse sampling design and dynamic causal modeling (DCM). We found that the level of neural activity underlying movements of the affected right hand and foot positively correlated with residual motor impairment, in both ipsilesional and contralesional premotor as well as left primary motor (M1) regions. Furthermore, M1 representations of the affected limb showed significantly stronger increase in BOLD activity compared to healthy controls and compared to the respective other limb. DCM revealed reduced endogenous connectivity of M1 of both limbs in patients compared to controls. However, when testing for the specific effect of movement on interregional connectivity, interhemispheric inhibition of the contralesional M1 during movements of the affected hand was not detected in patients whereas no differences in condition-dependent connectivity were found for foot movements compared to controls. In contrast, both groups featured positive interhemispheric M1 coupling, that is, facilitation of neural activity, mediating movements of the affected foot. These exploratory findings help to explain why functional recovery of the upper and lower limbs often develops differently after stroke, supporting limb-specific rehabilitative strategies.

Obstetric Brachial Plexus Palsy: Can a Unilateral Birth Onset Peripheral Injury Significantly Affect Brain Development?

Purpose: Examine brain structure and function in OBPP and relate to clinical outcomes to better understand the effects of decreased motor activity on early brain development. Methods: 9 OBPP, 7 controls underwent structural MRI scans. OBPP group completed evaluations of upper-limb function and functional near-infrared spectroscopy (fNIRS) during motor tasks. Results: Mean primary motor area volume was lower in both OBPP hemispheres. No volume differences across sides seen within groups; however, Asymmetry Ratio in supplementary motor area differed between groups. Greater asymmetry in primary somatosensory area correlated with lower ABILHAND-Kids scores. fNIRS revealed more cortical activity in both hemispheres during affected arm reach. Conclusion: Cortical volume differences or asymmetry were found in motor and sensory regions in OBPP that related to clinical outcomes. Widespread cortical activity in fNIRS during affected arm reach suggests reorganization in both hemispheres and is relevant to rehabilitation of those with developmental peripheral and brain injuries.

Motor Imagery Training After Stroke Increases Slow-5 Oscillations and Functional Connectivity in the Ipsilesional Inferior Parietal Lobule

Background. Reorganization in motor areas have been suggested after motor imagery training (MIT). However, motor imagery involves a large-scale brain network, in which many regions, andnot only the motor areas, potentially constitute the neural substrate for MIT. Objective. This study aimed to identify the targets for MIT in stroke rehabilitation from a voxel-based whole brain analysis of resting-state functional magnetic resonance imaging (fMRI). Methods. Thirty-four chronic stroke patients were recruited and randomly assigned to either an MIT group or a control group. The MIT group received a 4-week treatment of MIT plus conventional rehabilitation therapy (CRT), whereas the control group only received CRT. Before and after intervention, the Fugl-Meyer Assessment Upper Limb subscale (FM-UL) and resting-state fMRI were collected. The fractional amplitude of low-frequency fluctuations (fALFF) in the slow-5 band (0.01-0.027 Hz) was calculated across the whole brain to identify brain areas with distinct changes between 2 groups. These brain areas were then targeted as seeds to perform seed-based functional connectivity (FC) analysis. Results. In comparison with the control group, the MIT group exhibited more improvements in FM-UL and increased slow-5 fALFF in the ipsilesional inferior parietal lobule (IPL). The change of the slow-5 oscillations in the ipsilesional IPL was positively correlated with the improvement of FM-UL. The MIT group also showed distinct alternations in FCs of the ipsilesional IPL, which were correlated with the improvement of FM-UL. Conclusions. The rehabilitation efficiency of MIT was associated with increased slow-5 oscillations and altered FC in the ipsilesional IPL. Clinical Trial Registration. http://www.chictr.org.cn . Unique Identifier. ChiCTR-TRC-08003005.

Investigating Priming Effects of Physical Practice on Motor Imagery-Induced Event-Related Desynchronization

For motor imagery (MI) to be effective, an internal representation of the to-be-imagined movement may be required. A representation can be achieved through prior motor execution (ME), but the neural correlates of MI that are primed by ME practice are currently unknown. In this study, young healthy adults performed MI practice of a unimanual visuo-motor task (Group MI, n = 19) or ME practice combined with subsequent MI practice (Group ME&MI, n = 18) while electroencephalography (EEG) was recorded. Data analysis focused on the MI-induced event-related desynchronization (ERD). Specifically, changes in the ERD and movement times (MT) between a short familiarization block of ME (Block pre-ME), conducted before the MI or the ME combined with MI practice phase, and a short block of ME conducted after the practice phase (Block post-ME) were analyzed. Neither priming effects of ME practice on MI-induced ERD were found nor performance-enhancing effects of MI practice in general. We found enhancements of the ERD and MT in Block post-ME compared to Block pre-ME, but only for Group ME&MI. A comparison of ME performance measures before and after the MI phase indicated however that these changes could not be attributed to the combination of ME and MI practice. The mixed results of this study may be a consequence of the considerable intra- and inter-individual differences in the ERD, introduced by specifics of the experimental setup, in particular the individual and variable task duration, and suggest that task and experimental setup can affect the interplay of ME and MI.

A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol

Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.

Brain-computer-interface technology with multisensory feedback for controlled ideomotor training in the rehabilitation of stroke patients

Motor recovery of the upper limb is a priority in the neurorehabilitation of stroke patients. Advances in the brain-computer interface (BCI) technology have significantly improved the quality of rehabilitation. The aim of this study was to explore the factors affecting the recovery of the upper limb in stroke patients undergoing BCI-based rehabilitation with the robotic hand. The study recruited 24 patients (14 men and 10 women) aged 51 to 62 years with a solitary supratentorial stroke lesion. The lesion was left-hemispheric in 11 (45.6%) patients and right-hemispheric in 13 (54.4%) patients. Time elapsed from stroke was 4.0 months (3.0; 12.0). The median MoCa score was 25.0 (23.0; 27.0). The rehabilitation course consisted of 9.5 sessions (8.0; 10.0). We established a significant moderate correlation between motor imagery performance (the MIQ-RS score) and the efficacy of patient-BCI interaction. Patients with high MIQ-RS scores (47.5 (32.0; 54.0) achieved a better control of the BCI-driven hand exoskeleton (63.0 (54.0; 67.0), R = 0.67; p < 0.05). Recovery dynamics were more pronounced in patients with high MIQ-RS scores: the median score on the Fugl-Meyer Assessment scale was 14 (8.0; 16.0) points vs 10 (6.0; 13.0) points in patients with low MIQ-RS scores. However, the difference was not significant. Thus, we established a correlation between a patient’s ability for motor imagery (MIQ-RS) and the efficacy of patient-BCI interaction. A larger patient sample might be necessary to assess the effect of these factors on motor recovery dynamics.

G-Causality Brain Connectivity Differences of Finger Movements between Motor Execution and Motor Imagery

Motor imagery is one of the classical paradigms which have been used in brain-computer interface and motor function recovery. Finger movement-based motor execution is a complex biomechanical architecture and a crucial task for establishing most complicated and natural activities in daily life. Some patients may suffer from alternating hemiplegia after brain stroke and lose their ability of motor execution. Fortunately, the ability of motor imagery might be preserved independently and worked as a backdoor for motor function recovery. The efficacy of motor imagery for achieving significant recovery for the motor cortex after brain stroke is still an open question. In this study, we designed a new paradigm to investigate the neural mechanism of thirty finger movements in two scenarios: motor execution and motor imagery. Eleven healthy participants performed or imagined thirty hand gestures twice based on left and right finger movements. The electroencephalogram (EEG) signal for each subject during sixty trials left and right finger motor execution and imagery were recorded during our proposed experimental paradigm. The Granger causality (G-causality) analysis method was employed to analyze the brain connectivity and its strength between contralateral premotor, motor, and sensorimotor areas. Highest numbers for G-causality trials of 37 ± 7.3, 35.5 ± 8.8, 36.3 ± 10.3, and 39.2 ± 9.0 and lowest Granger causality coefficients of 9.1 ± 3.2, 10.9 ± 3.7, 13.2 ± 0.6, and 13.4 ± 0.6 were achieved from the premotor to motor area during execution/imagination tasks of right and left finger movements, respectively. These results provided a new insight into motor execution and motor imagery based on hand gestures, which might be useful to build a new biomarker of finger motor recovery for partially or even completely plegic patients. Furthermore, a significant difference of the G-causality trial number was observed during left finger execution/imagery and right finger imagery, but it was not observed during the right finger execution phase. Significant difference of the G-causality coefficient was observed during left finger execution and imagery, but it was not observed during right finger execution and imagery phases. These results suggested that different MI-based brain motor function recovery strategies should be taken for right-hand and left-hand patients after brain stroke.

The Sensitivity of Single-Trial Mu-Suppression Detection for Motor Imagery Performance as Compared to Motor Execution and Motor Observation Performance

Motor imagery (MI) has been widely used to operate brain-computer interface (BCI) systems for rehabilitation and some life assistive devices. However, the current performance of an MI-based BCI cannot fully meet the needs of its in-field applications. Most of the BCIs utilizing a generalized feature for all participants have been found to greatly hamper the efficacy of the BCI system. Hence, some attempts have made on the exploration of subject-dependent parameters, but it remains challenging to enhance BCI performance as expected. To this end, in this study, we used the independent component analysis (ICA), which has been proved capable of isolating the pure motor-related component from non-motor-related brain processes and artifacts and extracting the common motor-related component across MI, motor execution (ME), and motor observation (MO) conditions. Then, a sliding window approach was used to detect significant mu-suppression from the baseline using the electroencephalographic (EEG) alpha power time course and, thus, the success rate of the mu-suppression detection could be assessed on a single-trial basis. By comparing the success rates using different parameters, we further quantified the extent of the improvement in each motor condition to evaluate the effectiveness of both generalized and individualized parameters. The results showed that in ME condition, the success rate under individualized latency and that under generalized latency was 90.0% and 77.75%, respectively; in MI condition, the success rate was 74.14% for individual latency and 58.47% for generalized latency, and in MO condition, the success rate was 67.89% and 61.26% for individual and generalized latency, respectively. As can be seen, the success rate in each motor condition was significantly improved by utilizing an individualized latency compared to that using the generalized latency. Moreover, the comparison of the individualized window latencies for the mu-suppression detection across different runs of the same participant as well as across different participants showed that the window latency was significantly more consistent in the intra-subject than in the inter-subject settings. As a result, we proposed that individualizing the latency for detecting the mu-suppression feature for each participant might be a promising attempt to improve the MI-based BCI performance.

Remote effects on corticospinal excitability during motor execution and motor imagery

We investigated the remote effect on corticospinal excitability of resting left and right hand muscles during motor execution and motor imagery when performing left or right foot plantar flexion. Fifteen right-handed subjects performed two conditions with three tasks: Condition (Motor Execution (ME) vs. Motor Imagery (MI)): Task (Control, Ipsilateral, and Contralateral). From the left and right first dorsal interosseous, motor evoked potentials (MEPs) elicited by a single-pulse transcranial magnetic stimulation (TMS) to the left or right primary motor cortices (M1) were recorded under all six trials. MEP amplitudes were significantly larger under the ME than MI condition, irrespective of hands and tasks. MEP amplitudes were also the largest during the Contralateral tasks, irrespective of the condition and hands. The correlation analysis showed that MEP amplitudes were significantly correlated between ME and MI conditions with both left and right hands. Our results indicate that the magnitude of the remote effect on corticospinal excitability of hand muscles differs between motor execution and motor imagery, and between ipsi- and contralateral limbs, when performing foot plantar flexion.