Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke

Induced sensorimotor brain plasticity controls pain in phantom limb patients

The cause of pain in a phantom limb after partial or complete deafferentation is an important problem. A popular but increasingly controversial theory is that it results from maladaptive reorganization of the sensorimotor cortex, suggesting that experimental induction of further reorganization should affect the pain, especially if it results in functional restoration. Here we use a brain–machine interface (BMI) based on real-time magnetoencephalography signals to reconstruct affected hand movements with a robotic hand. BMI training induces significant plasticity in the sensorimotor cortex, manifested as improved discriminability of movement information and enhanced prosthetic control. Contrary to our expectation that functional restoration would reduce pain, the BMI training with the phantom hand intensifies the pain. In contrast, BMI training designed to dissociate the prosthetic and phantom hands actually reduces pain. These results reveal a functional relevance between sensorimotor cortical plasticity and pain, and may provide a novel treatment with BMI neurofeedback.

Pain in a phantom limb after limb deafferentation may be due to maladaptive sensorimotor representation. Here the authors find that sensorimotor plasticity induced by BMI training with the phantom hand, contrary to expectation, increased pain while dissociating prosthetic movements from the phantom arm relieved the pain.

Motor imagery-based skill acquisition disrupted following rTMS of the inferior parietal lobule

Motor imagery (MI), the mental rehearsal of motor tasks, has promise as a therapy in post-stroke rehabilitation. The potential effectiveness of MI is attributed to the facilitation of plasticity in numerous brain regions akin to those recruited for physical practice. It is suggested, however, that MI relies more heavily on regions commonly affected post-stroke, including left hemisphere parietal regions involved in visuospatial processes. However, the impact of parietal damage on MI-based skill acquisition that underlies rehabilitation remains unclear. Here, we examine the contribution of the left inferior parietal lobule (IPL) to MI using inhibitory transcranial magnetic stimulation (TMS) and an MI-based implicit sequence learning (ISL) paradigm. Participants (N = 27) completed the MI-based ISL paradigm after receiving continuous theta burst stimulation to the left IPL (TMS), or with the coil angled away from the scalp (sham). Reaction time differences (dRT) and effect sizes between implicit and random sequences assessed success of MI-based learning. Mean dRT for the sham group was 36.1 ± 28.2 ms (d = 0.71). Mean dRT in the TMS group was 7.7 ± 38.5 ms (d = 0.11). These results indicate that inhibition of the left IPL impaired MI-based learning. We conclude that the IPL and likely the visuospatial processes it mediates are critical for MI performance and thus MI-based skill acquisition or learning. Ultimately, these findings have implications for the use of MI in post-stroke rehabilitation.

Brain-computer interfaces for communication and rehabilitation

Brain-computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.

Interfacing brain with computer to improve communication and rehabilitation after brain damage

Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.

Reinforcement learning of self-regulated sensorimotor β-oscillations improves motor performance

Self-regulation of sensorimotor oscillations is currently researched in neurorehabilitation, e.g. for priming subsequent physiotherapy in stroke patients, and may be modulated by neurofeedback or transcranial brain stimulation. It has still to be demonstrated, however, whether and under which training conditions such brain self-regulation could also result in motor gains. Thirty-two right-handed, healthy subjects participated in a three-day intervention during which they performed 462 trials of kinesthetic motor-imagery while a brain-robot interface (BRI) turned event-related β-band desynchronization of the left sensorimotor cortex into the opening of the right hand by a robotic orthosis. Different training conditions were compared in a parallel-group design: (i) adaptive classifier thresholding and contingent feedback, (ii) adaptive classifier thresholding and non-contingent feedback, (iii) non-adaptive classifier thresholding and contingent feedback, and (iv) non-adaptive classifier thresholding and non-contingent feedback. We studied the task-related cortical physiology with electroencephalography and the behavioral performance in a subsequent isometric motor task. Contingent neurofeedback and adaptive classifier thresholding were critical for learning brain self-regulation which, in turn, led to behavioral gains after the intervention. The acquired skill for sustained sensorimotor β-desynchronization correlated significantly with subsequent motor improvement. Operant learning of brain self-regulation with a BRI may offer a therapeutic perspective for severely affected stroke patients lacking residual hand function.

Magnetoencephalography in Stroke Recovery and Rehabilitation

Magnetoencephalography (MEG) is a non-invasive neurophysiological technique used to study the cerebral cortex. Currently, MEG is mainly used clinically to localize epileptic foci and eloquent brain areas in order to avoid damage during neurosurgery. MEG might, however, also be of help in monitoring stroke recovery and rehabilitation. This review focuses on experimental use of MEG in neurorehabilitation. MEG has been employed to detect early modifications in neuroplasticity and connectivity, but there is insufficient evidence as to whether these methods are sensitive enough to be used as a clinical diagnostic test. MEG has also been exploited to derive the relationship between brain activity and movement kinematics for a motor-based brain–computer interface. In the current body of experimental research, MEG appears to be a powerful tool in neurorehabilitation, but it is necessary to produce new data to confirm its clinical utility.

Models to Tailor Brain Stimulation Therapies in Stroke

A great challenge facing stroke rehabilitation is the lack of information on how to derive targeted therapies. As such, techniques once considered promising, such as brain stimulation, have demonstrated mixed efficacy across heterogeneous samples in clinical studies. Here, we explain reasons, citing its one-type-suits-all approach as the primary cause of variable efficacy. We present evidence supporting the role of alternate substrates, which can be targeted instead in patients with greater damage and deficit. Building on this groundwork, this review will also discuss different frameworks on how to tailor brain stimulation therapies. To the best of our knowledge, our report is the first instance that enumerates and compares across theoretical models from upper limb recovery and conditions like aphasia and depression. Here, we explain how different models capture heterogeneity across patients and how they can be used to predict which patients would best respond to what treatments to develop targeted, individualized brain stimulation therapies. Our intent is to weigh pros and cons of testing each type of model so brain stimulation is successfully tailored to maximize upper limb recovery in stroke.

Decoding Sensorimotor Rhythms during Robotic-Assisted Treadmill Walking for Brain Computer Interface (BCI) Applications

Locomotor malfunction represents a major problem in some neurological disorders like stroke and spinal cord injury. Robot-assisted walking devices have been used during rehabilitation of patients with these ailments for regaining and improving walking ability. Previous studies showed the advantage of brain-computer interface (BCI) based robot-assisted training combined with physical therapy in the rehabilitation of the upper limb after stroke. Therefore, stroke patients with walking disorders might also benefit from using BCI robot-assisted training protocols. In order to develop such BCI, it is necessary to evaluate the feasibility to decode walking intention from cortical patterns during robot-assisted gait training. Spectral patterns in the electroencephalogram (EEG) related to robot-assisted active and passive walking were investigated in 10 healthy volunteers (mean age 32.3±10.8, six female) and in three acute stroke patients (all male, mean age 46.7±16.9, Berg Balance Scale 20±12.8). A logistic regression classifier was used to distinguish walking from baseline in these spectral EEG patterns. Mean classification accuracies of 94.0±5.4% and 93.1±7.9%, respectively, were reached when active and passive walking were compared against baseline. The classification performance between passive and active walking was 83.4±7.4%. A classification accuracy of 89.9±5.7% was achieved in the stroke patients when comparing walking and baseline. Furthermore, in the healthy volunteers modulation of low gamma activity in central midline areas was found to be associated with the gait cycle phases, but not in the stroke patients. Our results demonstrate the feasibility of BCI-based robotic-assisted training devices for gait rehabilitation.

Improving Motor Corticothalamic Communication After Stroke Using Real-Time fMRI Connectivity-Based Neurofeedback

BACKGROUND

Two thirds of stroke survivors experience motor impairment resulting in long-term disability. The anatomical substrate is often the disruption of cortico-subcortical pathways. It has been proposed that reestablishment of cortico-subcortical communication relates to functional recovery.

OBJECTIVE

In this study, we applied a novel training protocol to augment ipsilesional cortico-subcortical connectivity after stroke. Chronic stroke patients with severe motor impairment were provided online feedback of blood-oxygenation level dependent signal connectivity between cortical and subcortical regions critical for motor function using real-time functional magnetic resonance imaging neurofeedback.

RESULTS

In this proof of principle study, 3 out of 4 patients learned to voluntarily modulate cortico-subcortical connectivity as intended.

CONCLUSIONS

Our results document for the first time the feasibility and safety for patients with chronic stroke and severe motor impairment to self-regulate and augment ipsilesional cortico-subcortical connectivity through neurofeedback using real-time functional magnetic resonance imaging.

Frontoparietal white matter integrity predicts haptic performance in chronic stroke

Frontoparietal white matter supports information transfer between brain areas involved in complex haptic tasks such as somatosensory discrimination. The purpose of this study was to gain an understanding of the relationship between microstructural integrity of frontoparietal network white matter and haptic performance in persons with chronic stroke and to compare frontoparietal network integrity in participants with stroke and age matched control participants. Nineteen individuals with stroke and 16 controls participated. Haptic performance was quantified using the Hand Active Sensation Test (HASTe), an 18-item match-to-sample test of weight and texture discrimination. Three tesla MRI was used to obtain diffusion-weighted and high-resolution anatomical images of the whole brain. Probabilistic tractography was used to define 10 frontoparietal tracts total; Four intrahemispheric tracts measured bilaterally 1) thalamus to primary somatosensory cortex (T–S1), 2) thalamus to primary motor cortex (T–M1), 3) primary to secondary somatosensory cortex (S1 to SII) and 4) primary somatosensory cortex to middle frontal gyrus (S1 to MFG) and, 2 interhemispheric tracts; S1–S1 and precuneus interhemispheric. A control tract outside the network, the cuneus interhemispheric tract, was also examined. The diffusion metrics fractional anisotropy (FA), mean diffusivity (MD), axial (AD) and radial diffusivity (RD) were quantified for each tract. Diminished FA and elevated MD values are associated with poorer white matter integrity in chronic stroke. Nine of 10 tracts quantified in the frontoparietal network had diminished structural integrity poststroke compared to the controls. The precuneus interhemispheric tract was not significantly different between groups. Principle component analysis across all frontoparietal white matter tract MD values indicated a single factor explained 47% and 57% of the variance in tract mean diffusivity in stroke and control groups respectively. Age strongly correlated with the shared variance across tracts in the control, but not in the poststroke participants. A moderate to good relationship was found between ipsilesional T–M1 MD and affected hand HASTe score (r = − 0.62, p = 0.006) and less affected hand HASTe score (r = − 0.53, p = 0.022). Regression analysis revealed approximately 90% of the variance in affected hand HASTe score was predicted by the white matter integrity in the frontoparietal network (as indexed by MD) in poststroke participants while 87% of the variance in HASTe score was predicted in control participants. This study demonstrates the importance of frontoparietal white matter in mediating haptic performance and specifically identifies that T–M1 and precuneus interhemispheric tracts may be appropriate targets for piloting rehabilitation interventions, such as noninvasive brain stimulation, when the goal is to improve poststroke haptic performance.

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Poststroke participants had a wide range of haptic performance, the majority were impaired.

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A good relationship was found between ipsilesional Thal–M1 integrity and poststroke haptics.

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Around 90% of haptic performance was predicted by frontoparietal white matter integrity.

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Precuneus interhemispheric tract integrity was a strong predictor of haptic performance.

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Diminished integrity across the frontoparietal network suggests a general stroke-related factor.

Brain state-dependent robotic reaching movement with a multi-joint arm exoskeleton: combining brain-machine interfacing and robotic rehabilitation

While robot-assisted arm and hand training after stroke allows for intensive task-oriented practice, it has provided only limited additional benefit over dose-matched physiotherapy up to now. These rehabilitation devices are possibly too supportive during the exercises. Neurophysiological signals might be one way of avoiding slacking and providing robotic support only when the brain is particularly responsive to peripheral input. We tested the feasibility of three-dimensional robotic assistance for reaching movements with a multi-joint exoskeleton during motor imagery (MI)-related desynchronization of sensorimotor oscillations in the β-band. We also registered task-related network changes of cortical functional connectivity by electroencephalography via the imaginary part of the coherence function. Healthy subjects and stroke survivors showed similar patterns—but different aptitudes—of controlling the robotic movement. All participants in this pilot study with nine healthy subjects and two stroke patients achieved their maximum performance during the early stages of the task. Robotic control was significantly higher and less variable when proprioceptive feedback was provided in addition to visual feedback, i.e., when the orthosis was actually attached to the subject’s arm during the task. A distributed cortical network of task-related coherent activity in the θ-band showed significant differences between healthy subjects and stroke patients as well as between early and late periods of the task. Brain-robot interfaces (BRIs) may successfully link three-dimensional robotic training to the participants’ efforts and allow for task-oriented practice of activities of daily living with a physiologically controlled multi-joint exoskeleton. Changes of cortical physiology during the task might also help to make subject-specific adjustments of task difficulty and guide adjunct interventions to facilitate motor learning for functional restoration, a proposal that warrants further investigation in a larger cohort of stroke patients.

Altered Human Memory Modification in the Presence of Normal Consolidation

Following initial learning, the memory is stabilized by consolidation mechanisms, and subsequent modification of memory strength occurs via reconsolidation. Yet, it is not clear whether consolidation and memory modification are the same or different systems-level processes. Here, we report disrupted memory modification in the presence of normal consolidation of human motor memories, which relate to differences in lesioned brain structure after stroke. Furthermore, this behavioral dissociation was associated with macrostructural network architecture revealed by a graph-theoretical approach, and with white-matter microstructural integrity measured by diffusion-weighted MRI. Altered macrostructural network architecture and microstructural integrity of white-matter underlying critical nodes of the related network predicted disrupted memory modification. To the best of our knowledge, this provides the first evidence of mechanistic differences between consolidation, and subsequent memory modification through reconsolidation, in human procedural learning. These findings enable better understanding of these memory processes, which may guide interventional strategies to enhance brain function and resulting behavior.

Improvement in Touch Sensation after Stroke is Associated with Resting Functional Connectivity Changes

Background

Distributed brain networks are known to be involved in facilitating behavioral improvement after stroke, yet few, if any, studies have investigated the relationship between improved touch sensation after stroke and changes in functional brain connectivity.

Objective

We aimed to identify how recovery of somatosensory function in the first 6 months after stroke was associated with functional network changes as measured using resting-state connectivity analysis of functional magnetic resonance imaging (fMRI) data.

Methods

Ten stroke survivors underwent clinical testing and resting-state fMRI scans at 1 and 6 months post-stroke. Ten age-matched healthy participants were included as controls.

Results

Patients demonstrated a wide range of severity of touch impairment 1 month post-stroke, followed by variable improvement over time. In the stroke group, significantly stronger interhemispheric functional correlations between regions of the somatosensory system, and with visual and frontal areas, were found at 6 months than at 1 month post-stroke. Clinical improvement in touch discrimination was associated with stronger correlations at 6 months between contralesional secondary somatosensory cortex (SII) and inferior parietal cortex and middle temporal gyrus, and between contralesional thalamus and cerebellum.

Conclusion

The strength of connectivity between somatosensory regions and distributed brain networks, including vision and attention networks, may change over time in stroke survivors with impaired touch discrimination. Connectivity changes from contralesional SII and contralesional thalamus are associated with improved touch sensation at 6 months post-stroke. These functional connectivity changes could represent future targets for therapy.

Reinforcement learning of self-regulated β-oscillations for motor restoration in chronic stroke

Neurofeedback training of Motor imagery (MI)-related brain-states with brain-computer/brain-machine interfaces (BCI/BMI) is currently being explored as an experimental intervention prior to standard physiotherapy to improve the motor outcome of stroke rehabilitation. The use of BCI/BMI technology increases the adherence to MI training more efficiently than interventions with sham or no feedback. Moreover, pilot studies suggest that such a priming intervention before physiotherapy might—like some brain stimulation techniques—increase the responsiveness of the brain to the subsequent physiotherapy, thereby improving the general clinical outcome. However, there is little evidence up to now that these BCI/BMI-based interventions have achieved operate conditioning of specific brain states that facilitate task-specific functional gains beyond the practice of primed physiotherapy. In this context, we argue that BCI/BMI technology provides a valuable neurofeedback tool for rehabilitation but needs to aim at physiological features relevant for the targeted behavioral gain. Moreover, this therapeutic intervention has to be informed by concepts of reinforcement learning to develop its full potential. Such a refined neurofeedback approach would need to address the following issues: (1) Defining a physiological feedback target specific to the intended behavioral gain, e.g., β-band oscillations for cortico-muscular communication. This targeted brain state could well be different from the brain state optimal for the neurofeedback task, e.g., α-band oscillations for differentiating MI from rest; (2) Selecting a BCI/BMI classification and thresholding approach on the basis of learning principles, i.e., balancing challenge and reward of the neurofeedback task instead of maximizing the classification accuracy of the difficulty level device; and (3) Adjusting the difficulty level in the course of the training period to account for the cognitive load and the learning experience of the participant. Here, we propose a comprehensive neurofeedback strategy for motor restoration after stroke that addresses these aspects, and provide evidence for the feasibility of the suggested approach by demonstrating that dynamic threshold adaptation based on reinforcement learning may lead to frequency-specific operant conditioning of β-band oscillations paralleled by task-specific motor improvement; a proposal that requires investigation in a larger cohort of stroke patients.

Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality

Neurofeedback of self-regulated brain activity in circumscribed cortical regions is used as a novel strategy to facilitate functional restoration following stroke. Basic knowledge about its impact on motor system oscillations and functional connectivity is however scarce. Specifically, a direct comparison between different feedback modalities and their neural signatures is missing. We assessed a neurofeedback training intervention of modulating β-activity in circumscribed sensorimotor regions by kinesthetic motor imagery (MI). Right-handed healthy participants received two different feedback modalities contingent to their MI-associated brain activity in a cross-over design: (I) visual feedback with a brain-computer interface (BCI) and (II) proprioceptive feedback with a brain-robot interface (BRI) orthosis attached to the right hand. High-density electroencephalography was used to examine the reactivity of the cortical motor system during the training session of each task by studying both local oscillatory power entrainment and distributed functional connectivity. Both feedback modalities activated a distributed functional connectivity network of coherent oscillations. A significantly higher skill and lower variability of self-controlled sensorimotor β-band modulation could, however, be achieved in the BRI condition. This gain in controlling regional motor oscillations was accompanied by functional coupling of remote β-band and θ-band activity in bilateral fronto-central regions and left parieto-occipital regions, respectively. The functional coupling of coherent θ-band oscillations correlated moreover with the skill of regional β-modulation thus revealing a motor learning related network. Our findings indicate that proprioceptive feedback is more suitable than visual feedback to entrain the motor network architecture during the interplay between motor imagery and feedback processing thus resulting in better volitional control of regional brain activity.

Predictive structural dynamic network analysis

BACKGROUND

Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data.

NEW METHOD

Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models.

RESULTS

We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity.

COMPARISON WITH EXISTING METHODS

For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002).

CONCLUSIONS

We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders.

Brain-computer interface boosts motor imagery practice during stroke recovery

OBJECTIVE

Motor imagery (MI) is assumed to enhance poststroke motor recovery, yet its benefits are debatable. Brain-computer interfaces (BCIs) can provide instantaneous and quantitative measure of cerebral functions modulated by MI. The efficacy of BCI-monitored MI practice as add-on intervention to usual rehabilitation care was evaluated in a randomized controlled pilot study in subacute stroke patients.

METHODS

Twenty-eight hospitalized subacute stroke patients with severe motor deficits were randomized into 2 intervention groups: 1-month BCI-supported MI training (BCI group, n = 14) and 1-month MI training without BCI support (control group; n = 14). Functional and neurophysiological assessments were performed before and after the interventions, including evaluation of the upper limbs by Fugl-Meyer Assessment (FMA; primary outcome measure) and analysis of oscillatory activity and connectivity at rest, based on high-density electroencephalographic (EEG) recordings.

RESULTS

Better functional outcome was observed in the BCI group, including a significantly higher probability of achieving a clinically relevant increase in the FMA score (p < 0.03). Post-BCI training changes in EEG sensorimotor power spectra (ie, stronger desynchronization in the alpha and beta bands) occurred with greater involvement of the ipsilesional hemisphere in response to MI of the paralyzed trained hand. Also, FMA improvements (effectiveness of FMA) correlated with the changes (ie, post-training increase) at rest in ipsilesional intrahemispheric connectivity in the same bands (p < 0.05).

INTERPRETATION

The introduction of BCI technology in assisting MI practice demonstrates the rehabilitative potential of MI, contributing to significantly better motor functional outcomes in subacute stroke patients with severe motor impairments.

Estimating cognitive load during self-regulation of brain activity and neurofeedback with therapeutic brain-computer interfaces

Neurofeedback (NFB) training with brain-computer interfaces (BCIs) is currently being studied in a variety of neurological and neuropsychiatric conditions in an aim to reduce disorder-specific symptoms. For this purpose, a range of classification algorithms has been explored to identify different brain states. These neural states, e.g., self-regulated brain activity vs. rest, are separated by setting a threshold parameter. Measures such as the maximum classification accuracy (CA) have been introduced to evaluate the performance of these algorithms. Interestingly enough, precisely these measures are often used to estimate the subject's ability to perform brain self-regulation. This is surprising, given that the goal of improving the tool that differentiates between brain states is different from the aim of optimizing NFB for the subject performing brain self-regulation. For the latter, knowledge about mental resources and work load is essential in order to adapt the difficulty of the intervention accordingly. In this context, we apply an analytical method and provide empirical data to determine the zone of proximal development (ZPD) as a measure of a subject's cognitive resources and the instructional efficacy of NFB. This approach is based on a reconsideration of item-response theory (IRT) and cognitive load theory for instructional design, and combines them with the CA curve to provide a measure of BCI performance.

Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

Learned self-regulation of the lesioned brain with epidural electrocorticography

Introduction: Different techniques for neurofeedback of voluntary brain activations are currently being explored for clinical application in brain disorders. One of the most frequently used approaches is the self-regulation of oscillatory signals recorded with electroencephalography (EEG). Many patients are, however, unable to achieve sufficient voluntary control of brain activity. This could be due to the specific anatomical and physiological changes of the patient’s brain after the lesion, as well as to methodological issues related to the technique chosen for recording brain signals.

Methods: A patient with an extended ischemic lesion of the cortex did not gain volitional control of sensorimotor oscillations when using a standard EEG-based approach. We provided him with neurofeedback of his brain activity from the epidural space by electrocorticography (ECoG).

Results: Ipsilesional epidural recordings of field potentials facilitated self-regulation of brain oscillations in an online closed-loop paradigm and allowed reliable neurofeedback training for a period of 4 weeks.

Conclusion: Epidural implants may decode and train brain activity even when the cortical physiology is distorted following severe brain injury. Such practice would allow for reinforcement learning of preserved neural networks and may well provide restorative tools for those patients who are severely afflicted.

Decoding upper limb residual muscle activity in severe chronic stroke

Objective

Stroke is a leading cause of long-term motor disability. Stroke patients with severe hand weakness do not profit from rehabilitative treatments. Recently, brain-controlled robotics and sequential functional electrical stimulation allowed some improvement. However, for such therapies to succeed, it is required to decode patients' intentions for different arm movements. Here, we evaluated whether residual muscle activity could be used to predict movements from paralyzed joints in severely impaired chronic stroke patients.

Methods

Muscle activity was recorded with surface-electromyography (EMG) in 41 patients, with severe hand weakness (Fugl-Meyer Assessment [FMA] hand subscores of 2.93 ± 2.7), in order to decode their intention to perform six different motions of the affected arm, required for voluntary muscle activity and to control neuroprostheses. Decoding of paretic and nonparetic muscle activity was performed using a feed-forward neural network classifier. The contribution of each muscle to the intended movement was determined.

Results

Decoding of up to six arm movements was accurate (>65%) in more than 97% of nonparetic and 46% of paretic muscles.

Interpretation

These results demonstrate that some level of neuronal innervation to the paretic muscle remains preserved and can be used to implement neurorehabilitative treatments in 46% of patients with severe paralysis and extensive cortical and/or subcortical lesions. Such decoding may allow these patients for the first time after stroke to control different motions of arm prostheses through muscle-triggered rehabilitative treatments.

Brain-machine interfaces in neurorehabilitation of stroke

Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke.

Non-invasive brain stimulation in neurorehabilitation: local and distant effects for motor recovery

Non-invasive brain stimulation (NIBS) may enhance motor recovery after neurological injury through the causal induction of plasticity processes. Neurological injury, such as stroke, often results in serious long-term physical disabilities, and despite intensive therapy, a large majority of brain injury survivors fail to regain full motor function. Emerging research suggests that NIBS techniques, such as transcranial magnetic (TMS) and direct current (tDCS) stimulation, in association with customarily used neurorehabilitative treatments, may enhance motor recovery. This paper provides a general review on TMS and tDCS paradigms, the mechanisms by which they operate and the stimulation techniques used in neurorehabilitation, specifically stroke. TMS and tDCS influence regional neural activity underlying the stimulation location and also distant interconnected network activity throughout the brain. We discuss recent studies that document NIBS effects on global brain activity measured with various neuroimaging techniques, which help to characterize better strategies for more accurate NIBS stimulation. These rapidly growing areas of inquiry may hold potential for improving the effectiveness of NIBS-based interventions for clinical rehabilitation.

Shutting down sensorimotor interference unblocks the networks for stimulus processing: an SMR neurofeedback training study

OBJECTIVE

In the present study, we investigated how the electrical activity in the sensorimotor cortex contributes to improved cognitive processing capabilities and how SMR (sensorimotor rhythm, 12-15Hz) neurofeedback training modulates it. Previous evidence indicates that higher levels of SMR activity reduce sensorimotor interference and thereby promote cognitive processing.

METHODS

Participants were randomly assigned to two groups, one experimental (N=10) group receiving SMR neurofeedback training, in which they learned to voluntarily increase SMR, and one control group (N=10) receiving sham feedback. Multiple cognitive functions and electrophysiological correlates of cognitive processing were assessed before and after 10 neurofeedback training sessions.

RESULTS

The experimental group but not the control group showed linear increases in SMR power over training runs, which was associated with behavioural improvements in memory and attentional performance. Additionally, increasing SMR led to a more salient stimulus processing as indicated by increased N1 and P3 event-related potential amplitudes after the training as compared to the pre-test. Finally, functional brain connectivity between motor areas and visual processing areas was reduced after SMR training indicating reduced sensorimotor interference.

CONCLUSIONS

These results indicate that SMR neurofeedback improves stimulus processing capabilities and consequently leads to improvements in cognitive performance.

SIGNIFICANCE

The present findings contribute to a better understanding of the mechanisms underlying SMR neurofeedback training and cognitive processing and implicate that SMR neurofeedback might be an effective cognitive training tool.

Enhancing Hebbian Learning to Control Brain Oscillatory Activity

Sensorimotor rhythms (SMR, 8-15 Hz) are brain oscillations associated with successful motor performance, imagery, and imitation. Voluntary modulation of SMR can be used to control brain-machine interfaces (BMI) in the absence of any physical movements. The mechanisms underlying acquisition of such skill are unknown. Here, we provide evidence for a causal link between function of the primary motor cortex (M1), active during motor skill learning and retention, and successful acquisition of abstract skills such as control over SMR. Thirty healthy participants were trained on 5 consecutive days to control SMR oscillations. Each participant was randomly assigned to one of 3 groups that received either 20 min of anodal, cathodal, or sham transcranial direct current stimulation (tDCS) over M1. Learning SMR control across training days was superior in the anodal tDCS group relative to the other 2. Cathodal tDCS blocked the beneficial effects of training, as evidenced with sham tDCS. One month later, the newly acquired skill remained superior in the anodal tDCS group. Thus, application of weak electric currents of opposite polarities over M1 differentially modulates learning SMR control, pointing to this primary cortical region as a common substrate for acquisition of physical motor skills and learning to control brain oscillatory activity.

An EEG-Based BCI Platform to Improve Arm Reaching Ability of Chronic Stroke Patients by Means of an Operant Learning Training with a Contingent Force Feedback

The Brain Computer Interface platform described in this paper was implemented to enhance neuroplasticity of a stroke-damaged brain in order to promote recovery of motor functions like reaching, fundamentally important in a healthy daily life. To this scope a closed-loop between the stroke patients' brain and a robotic arm is established by means of a real-time identification of the cerebral activity related to the movement and its transformation in a force feedback delivered by the robot. In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.

Reducing Abnormal Muscle Coactivation After Stroke Using a Myoelectric-Computer Interface: A Pilot Study

Background A significant factor in impaired movement caused by stroke is the inability to activate muscles independently. Although the pathophysiology behind this abnormal coactivation is not clear, reducing the coactivation could improve overall arm function. A myoelectric computer interface (MCI), which maps electromyographic signals to cursor movement, could be used as a treatment to help retrain muscle activation patterns. Objective To investigate the use of MCI training to reduce abnormal muscle coactivation in chronic stroke survivors. Methods A total of 5 healthy participants and 5 stroke survivors with hemiparesis participated in multiple sessions of MCI training. The level of arm impairment in stroke survivors was assessed using the upper-extremity portion of the Fugl-Meyer Motor Assessment (FMA-UE). Participants performed isometric activations of up to 5 muscles. Activation of each muscle was mapped to different directions of cursor movement. The MCI specifically targeted 1 pair of muscles in each participant for reduction of coactivation. Results Both healthy participants and stroke survivors learned to reduce abnormal coactivation of the targeted muscles with MCI training. Out of 5 stroke survivors, 3 exhibited objective reduction in arm impairment as well (improvement in FMA-UE of 3 points in each of these patients). Conclusions These results suggest that the MCI was an effective tool in directly retraining muscle activation patterns following stroke.

State-dependent effects of transcranial oscillatory currents on the motor system: what you think matters

Imperceptible transcranial alternating current stimulation (tACS) changes the endogenous cortical oscillatory activity in a frequency-specific manner. In the human motor system, tACS coincident with the idling beta rhythm of the quiescent motor cortex increased the corticospinal output. We reasoned that changing the initial state of the brain (i.e., from quiescence to a motor imagery task that desynchronizes the local beta rhythm) might also change the susceptibility of the corticospinal system to resonance effects induced by beta-tACS. We tested this hypothesis by delivering tACS at different frequencies (theta, alpha, beta, and gamma) on the primary motor cortex at rest and during motor imagery. Motor-evoked potentials (MEPs) were obtained by transcranial magnetic stimulation (TMS) on the primary motor cortex with an online-navigated TMS-tACS setting. During motor imagery, the increase of corticospinal excitability was maximal with theta-tACS, likely reflecting a reinforcement of working memory processes required to mentally process and "execute" the cognitive task. As expected, the maximal MEPs increase with subjects at rest was instead obtained with beta-tACS, substantiating previous evidence. This dissociation provides new evidence of state and frequency dependency of tACS effects on the motor system and helps discern the functional role of different oscillatory frequencies of this brain region. These findings may be relevant for rehabilitative neuromodulatory interventions.

Abnormal organization of white matter network in patients with no dementia after ischemic stroke

Structural changes after ischemic stroke could affect information communication extensively in the brain network. It is likely that the defects in the white matter (WM) network play a key role in information interchange. In this study, we used graph theoretical analysis to examine potential organization alteration in the WM network architecture derived from diffusion tensor images from subjects with no dementia and experienced stroke in the past 5.4-14.8 months (N = 47, Mini-Mental Screening Examination, MMSE range 18-30), compared with a normal control group with 44 age and gender-matched healthy volunteers (MMSE range 26-30). Region-wise connectivity was derived from fiber connection density of 90 different cortical and subcortical parcellations across the whole brain. Both normal controls and patients with chronic stroke exhibited efficient small-world properties in their WM structural networks. Compared with normal controls, topological efficiency was basically unaltered in the patients with chronic stroke, as reflected by unchanged local and global clustering coefficient, characteristic path length, and regional efficiency. No significant difference in hub distribution was found between normal control and patient groups. Patients with chronic stroke, however, were found to have reduced betweenness centrality and predominantly located in the orbitofrontal cortex, whereas increased betweenness centrality and vulnerability were observed in parietal-occipital cortex. The National Institutes of Health Stroke Scale (NIHSS) score of patient is correlated with the betweenness centrality of right pallidum and local clustering coefficient of left superior occipital gyrus. Our findings suggest that patients with chronic stroke still exhibit efficient small-world organization and unaltered topological efficiency, with altered topology at orbitofrontal cortex and parietal-occipital cortex in the overall structural network. Findings from this study could help in understanding the mechanism of cognitive impairment and functional compensation occurred in patients with chronic stroke.

Lateralized alpha-band cortical networks regulate volitional modulation of beta-band sensorimotor oscillations

Sensorimotor rhythms (SMRs) are oscillatory brain activities in the α- and β-bands across the sensorimotor regions of the brain. Each frequency band has its own specific function. The α-band oscillations are closely related to intrinsic cortical networks, whereas oscillations in the β-band are relevant for the information transfer between the cortex and periphery, as well as for visual and proprioceptive feedback. This study aimed to investigate the interaction between these two frequency bands, under the premise that the regional modulation of β-band power is linked to a cortical network in the α-band. We therefore designed a procedure to maximize the modulation of β-band activity over the sensorimotor cortex by combining kinesthetic motor-imagery with closed-loop haptic feedback. The cortical network activity during this procedure was estimated via the phase slope index in electroencephalographic recordings. Analysis of effective connectivity within the α-band network revealed an information flow between the precentral (premotor and primary motor), postcentral (primary somatosensory) and parietal cortical areas. The range of β-modulation was connected to a reduction of an ipsilateral sensorimotor and parietal α-network and, consequently, to a lateralization of this network to the contralateral side. These results showed that regional sensorimotor oscillatory activity in the β-band was regulated by cortical coupling of distant areas in the α-band.

Motor timing deficits in sequential movements in Parkinson disease are related to action planning: a motor imagery study

Timing of sequential movements is altered in Parkinson disease (PD). Whether timing deficits in internally generated sequential movements in PD depends also on difficulties in motor planning, rather than merely on a defective ability to materially perform the planned movement is still undefined. To unveil this issue, we adopted a modified version of an established test for motor timing, i.e. the synchronization–continuation paradigm, by introducing a motor imagery task. Motor imagery is thought to involve mainly processes of movement preparation, with reduced involvement of end-stage movement execution-related processes. Fourteen patients with PD and twelve matched healthy volunteers were asked to tap in synchrony with a metronome cue (SYNC) and then, when the tone stopped, to keep tapping, trying to maintain the same rhythm (CONT-EXE) or to imagine tapping at the same rhythm, rather than actually performing it (CONT-MI). We tested both a sub-second and a supra-second inter-stimulus interval between the cues. Performance was recorded using a sensor-engineered glove and analyzed measuring the temporal error and the interval reproduction accuracy index. PD patients were less accurate than healthy subjects in the supra-second time reproduction task when performing both continuation tasks (CONT-MI and CONT-EXE), whereas no difference was detected in the synchronization task and on all tasks involving a sub-second interval. Our findings suggest that PD patients exhibit a selective deficit in motor timing for sequential movements that are separated by a supra-second interval and that this deficit may be explained by a defect of motor planning. Further, we propose that difficulties in motor planning are of a sufficient degree of severity in PD to affect also the motor performance in the supra-second time reproduction task.

Does motor imagery share neural networks with executed movement: a multivariate fMRI analysis

Introduction: Motor imagery (MI) is the mental rehearsal of a motor first person action-representation. There is interest in using MI to access the motor network after stroke. Conventional fMRI modeling has shown that MI and executed movement (EM) activate similar cortical areas but it remains unknown whether they share cortical networks. Proving this is central to using MI to access the motor network and as a form of motor training. Here we use multivariate analysis (tensor independent component analysis-TICA) to map the array of neural networks involved during MI and EM.

Methods: Fifteen right-handed healthy volunteers (mean-age 28.4 years) were recruited and screened for their ability to carry out MI (Chaotic MI Assessment). fMRI consisted of an auditory-paced (1 Hz) right hand finger-thumb opposition sequence (2,3,4,5; 2…) with two separate runs acquired (MI & rest and EM & rest: block design). No distinction was made between MI and EM until the final stage of processing. This allowed TICA to identify independent-components (IC) that are common or distinct to both tasks with no prior assumptions.

Results: TICA defined 52 ICs. Non-significant ICs and those representing artifact were excluded. Components in which the subject scores were significantly different to zero (for either EM or MI) were included. Seven IC remained. There were IC's shared between EM and MI involving the contralateral BA4, PMd, parietal areas and SMA. IC's exclusive to EM involved the contralateral BA4, S1 and ipsilateral cerebellum whereas the IC related exclusively to MI involved ipsilateral BA4 and PMd.

Conclusion: In addition to networks specific to each task indicating a degree of independence, we formally demonstrate here for the first time that MI and EM share cortical networks. This significantly strengthens the rationale for using MI to access the motor networks, but the results also highlight important differences.

Brain-machine interface in chronic stroke rehabilitation: a controlled study

OBJECTIVE

Chronic stroke patients with severe hand weakness respond poorly to rehabilitation efforts. Here, we evaluated efficacy of daily brain-machine interface (BMI) training to increase the hypothesized beneficial effects of physiotherapy alone in patients with severe paresis in a double-blind sham-controlled design proof of concept study.

METHODS

Thirty-two chronic stroke patients with severe hand weakness were randomly assigned to 2 matched groups and participated in 17.8 ± 1.4 days of training rewarding desynchronization of ipsilesional oscillatory sensorimotor rhythms with contingent online movements of hand and arm orthoses (experimental group, n = 16). In the control group (sham group, n = 16), movements of the orthoses occurred randomly. Both groups received identical behavioral physiotherapy immediately following BMI training or the control intervention. Upper limb motor function scores, electromyography from arm and hand muscles, placebo-expectancy effects, and functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent activity were assessed before and after intervention.

RESULTS

A significant group × time interaction in upper limb (combined hand and modified arm) Fugl-Meyer assessment (cFMA) motor scores was found. cFMA scores improved more in the experimental than in the control group, presenting a significant improvement of cFMA scores (3.41 ± 0.563-point difference, p = 0.018) reflecting a clinically meaningful change from no activity to some in paretic muscles. cFMA improvements in the experimental group correlated with changes in fMRI laterality index and with paretic hand electromyography activity. Placebo-expectancy scores were comparable for both groups.

INTERPRETATION

The addition of BMI training to behaviorally oriented physiotherapy can be used to induce functional improvements in motor function in chronic stroke patients without residual finger movements and may open a new door in stroke neurorehabilitation.

Real-time fMRI neurofeedback: progress and challenges

In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.

Multiscale topological properties of functional brain networks during motor imagery after stroke

In recent years, network analyses have been used to evaluate brain reorganization following stroke. However, many studies have often focused on single topological scales, leading to an incomplete model of how focal brain lesions affect multiple network properties simultaneously and how changes on smaller scales influence those on larger scales. In an EEG-based experiment on the performance of hand motor imagery (MI) in 20 patients with unilateral stroke, we observed that the anatomic lesion affects the functional brain network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of the affected hand (Ahand) elicited a significantly lower smallworldness and local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the abnormal reduction in Eloc significantly depended on the increase in interhemispheric connectivity, which was in turn determined primarily by the rise of regional connectivity in the parieto-occipital sites of the affected hemisphere. Further, in contrast to the Uhand MI, in which significantly high connectivity was observed for the contralateral sensorimotor regions of the unaffected hemisphere, the regions with increased connectivity during the Ahand MI lay in the frontal and parietal regions of the contralaterally affected hemisphere. Finally, the overall sensorimotor function of our patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly predicted by the connectivity of their affected hemisphere. These results improve on our understanding of stroke-induced alterations in functional brain networks.

White matter structural connectivity is associated with sensorimotor function in stroke survivors

Purpose

Diffusion tensor imaging (DTI) provides functionally relevant information about white matter structure. Local anatomical connectivity information combined with fractional anisotropy (FA) and mean diffusivity (MD) may predict functional outcomes in stroke survivors. Imaging methods for predicting functional outcomes in stroke survivors are not well established. This work uses DTI to objectively assess the effects of a stroke lesion on white matter structure and sensorimotor function.

Methods

A voxel-based approach is introduced to assess a stroke lesion's global impact on motor function. Anatomical T1-weighted and diffusion tensor images of the brain were acquired for nineteen subjects (10 post-stroke and 9 age-matched controls). A manually selected volume of interest was used to alleviate the effects of stroke lesions on image registration. Images from all subjects were registered to the images of the control subject that was anatomically closest to Talairach space. Each subject's transformed image was uniformly seeded for DTI tractography. Each seed was inversely transformed into the individual subject space, where DTI tractography was conducted and then the results were transformed back to the reference space. A voxel-wise connectivity matrix was constructed from the fibers, which was then used to calculate the number of directly and indirectly connected neighbors of each voxel. A novel voxel-wise indirect structural connectivity (VISC) index was computed as the average number of direct connections to a voxel's indirect neighbors. Voxel-based analyses (VBA) were performed to compare VISC, FA, and MD for the detection of lesion-induced changes in sensorimotor function. For each voxel, a t-value was computed from the differences between each stroke brain and the 9 controls. A series of linear regressions was performed between Fugl-Meyer (FM) assessment scores of sensorimotor impairment and each DTI metric's log number of voxels that differed from the control group.

Results

Correlation between the logarithm of the number of significant voxels in the ipsilesional hemisphere and total Fugl-Meyer score was moderate for MD (R2 = 0.512), and greater for VISC (R2 = 0.796) and FA (R2 = 0.674). The slopes of FA (p = 0.0036), VISC (p = 0.0005), and MD (p = 0.0199) versus the total FM score were significant. However, these correlations were driven by the upper extremity motor component of the FM score (VISC: R2 = 0.879) with little influence of the lower extremity motor component (FA: R2 = 0.177).

Conclusion

The results suggest that a voxel-wise metric based on DTI tractography can predict upper extremity sensorimotor function of stroke survivors, and that supraspinal intraconnectivity may have a less dominant role in lower extremity function.

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An intrinsic voxel-based structural connectivity metric is proposed.

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The metric enhances the impact of stroke lesions on the distant voxels.

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Whole-brain extralesional anatomical connectivity predicts functional outcome.

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Functional impact of a lesion is determined by residual anatomical connectivity.

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Connectivity to the posterior parietal cortex is a key to sensorimotor function.

Prediction of brain-computer interface aptitude from individual brain structure

OBJECTIVE

Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary.

METHODS

We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance.

RESULTS

Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error).

CONCLUSIONS

Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance.

SIGNIFICANCE

This confirms that structural brain traits contribute to individual performance in BCI use.

Near-infrared spectroscopy-mediated neurofeedback enhances efficacy of motor imagery-based training in poststroke victims: a pilot study

BACKGROUND AND PURPOSE

Despite the findings that motor imagery and execution are supposed to share common neural networks, previous studies using imagery-based rehabilitation have revealed inconsistent results. In the present study, we investigated whether feedback of cortical activities (neurofeedback) using near-infrared spectroscopy could enhance the efficacy of imagery-based rehabilitation in stroke patients.

METHODS

Twenty hemiplegic patients with subcortical stroke received 6 sessions of mental practice with motor imagery of the distal upper limb in addition to standard rehabilitation. Subjects were randomly allocated to REAL and SHAM groups. In the REAL group, cortical hemoglobin signals detected by near-infrared spectroscopy were fed back during imagery. In the SHAM group, irrelevant randomized signals were fed back. Upper limb function was assessed using the finger and arm subscales of the Fugl-Meyer assessment and the Action Research Arm Test.

RESULTS

The hand/finger subscale of the Fugl-Meyer assessment showed greater functional gain in the REAL group, with a significant interaction between time and group (F(2,36)=15.5; P<0.001). A significant effect of neurofeedback was revealed even in severely impaired subjects. Imagery-related cortical activation in the premotor area was significantly greater in the REAL group than in the SHAM group (T(58)=2.4; P<0.05).

CONCLUSIONS

Our results suggest that near-infrared spectroscopy-mediated neurofeedback may enhance the efficacy of mental practice with motor imagery and augment motor recovery in poststroke patients with severe hemiparesis.

Noninvasive brain stimulation in neurorehabilitation

Stroke is the major cause of long-term disability worldwide, with impaired manual dexterity being a common feature. In the past few years, noninvasive brain stimulation (NIBS) techniques, such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), have been investigated as adjuvant strategies to neurorehabilitative interventions. These NIBS techniques can be used to modulate cortical excitability during and for several minutes after the end of the stimulation period. Depending on the stimulation parameters, cortical excitability can be reduced (inhibition) or enhanced (facilitation). Differential modulation of cortical excitability in the affected and unaffected hemisphere of patients with stroke may induce plastic changes within neural networks active during functional recovery. The aims of this chapter are to describe results from these proof-of-principle trials and discuss possible putative mechanisms underlying such effects. Neurophysiological and neuroimaging changes induced by application of NIBS are reviewed briefly.

Cerebral network disorders after stroke: evidence from imaging-based connectivity analyses of active and resting brain states in humans

Stroke causes a sudden disruption of physiological brain function which leads to impairments of functional brain networks involved in voluntary movements. In some cases, the brain has the intrinsic capacity to reorganize itself, thereby compensating for the disruption of motor networks. In humans, such reorganization can be investigated in vivo using neuroimaging. Recent developments in connectivity analyses based on functional neuroimaging data have provided new insights into the network pathophysiology underlying neurological symptoms. Here we review recent neuroimaging studies using functional resting-state correlations, effective connectivity models or graph theoretical analyses to investigate changes in neural motor networks and recovery after stroke. The data demonstrate that network disturbances after stroke occur not only in the vicinity of the lesion but also between remote cortical areas in the affected and unaffected hemisphere. The reorganization of motor networks encompasses a restoration of interhemispheric functional coherence in the resting state, particularly between the primary motor cortices. Furthermore, reorganized neural networks feature strong excitatory interactions between fronto-parietal areas and primary motor cortex in the affected hemisphere, suggesting that greater top-down control over primary motor areas facilitates motor execution in the lesioned brain. In addition, there is evidence that motor recovery is accompanied by a more random network topology with reduced local information processing. In conclusion, Stroke induces changes in functional and effective connectivity within and across hemispheres which relate to motor impairments and recovery thereof. Connectivity analyses may hence provide new insights into the pathophysiology underlying neurological deficits and may be further used to develop novel, neurobiologically informed treatment strategies.