Network dynamics engaged in the modulation of motor behavior in stroke patients

Posterior parietal cortical areas and recovery after motor stroke: a scoping review

Brain imaging and electrophysiology have significantly enhanced our current understanding of stroke-related changes in brain structure and function and their implications for recovery processes. In the motor domain, most studies have focused on key motor areas of the frontal lobe including the primary and secondary motor cortices. Time- and recovery-dependent alterations in regional anatomy, brain activity and inter-regional connectivity have been related to recovery. In contrast, the involvement of posterior parietal cortical areas in stroke recovery is poorly understood although these regions are similarly important for important aspects of motor functioning in the healthy brain. Just in recent years, the field has increasingly started to explore to what extent posterior parietal cortical areas might undergo equivalent changes in task-related activation, regional brain structure and inter-regional functional and structural connectivity after stroke. The aim of this scoping review is to give an update on available data covering these aspects and thereby providing novel insights into parieto-frontal interactions for systems neuroscience stroke recovery research in the upper limb motor domain.

Interhemispheric parietal cortex connectivity reflects improvement in post-stroke spasticity due to treatment with botulinum toxin-A

In post-stroke spasticity (PSS), effective treatment with botulinum neurotoxin (BoNT) is associated with transient decrease in activation of the ipsilesional superior parietal lobule (SPL) and intraparietal sulcus (IPS). We hypothesized that this would be reflected in changes in resting-state functional connectivity (rsFC) of the SPL/IPS. Our aim was therefore to assess rsFC of the ipsilesional SPL/IPS in chronic stroke patients with hemiparesis both with and without PSS and to explore the relationship between SPL/IPS rsFC and PSS severity. To this end, fourteen chronic stroke patients with upper limb weakness and PSS (the PSS group) and 8 patients with comparable weakness but no PSS (the control group) underwent clinical evaluation and 3 fMRI examinations, at baseline (W0) and 4 and 11 weeks after BoNT (W4 and W11, respectively). Seed-based rsFC of the atlas-based SPL and IPS was evaluated using a group×time interaction analysis and a correlation analysis with PSS severity (modified Ashworth scale), integrity of the ipsilesional somatosensory afferent pathway (evoked potential N20 latency), and age. In the PSS group, transient improvement in PSS was associated with increase in rsFC between the ipsilesional IPS and the contralesional SPL at W4. The interhemispheric connectivity was negatively correlated with PSS severity at baseline and with PSS improvement at W4. We propose adaptation of the internal forward model as the putative underlying mechanism and discuss its possible association with increased limb use, diminished spastic dystonia, or improved motor performance, as well as its potential contribution to the clinical effects of BoNT.

Predictive Factors and Interventional Modalities of Post-stroke Motor Recovery: An Overview

Stroke is the most common cause of motor impairment worldwide. Therefore, many factors are being investigated for their predictive and facilitatory effects on recovery of motor function after stroke. Motor recovery can be predicted through several factors, such as clinical assessment, clinical biomarkers, and gene-based variations. As for interventions, many methods are under experimental investigation that aim to improve motor recovery, including different types of pharmacological interventions, non-invasive stimulation, and rehabilitation training by inducing cortical reorganization, neuroplasticity, angiogenesis, changing the levels of neurotransmitters in the brain, and altering the inflammatory and apoptotic processes occurring after stroke. Studies have shown that clinical biomarkers combined with clinical assessment and gene-based variations are reliable factors for predicting motor recovery after stroke. Moreover, different types of interventions such as pharmacological agents (selective serotonin reuptake inhibitors {SSRI}, noradrenaline reuptake inhibitors {NARIs}, levodopa, and amphetamine), non-invasive stimulation, and rehabilitation training have shown significant results in improving functional and motor recovery.

Evaluating the Abnormality of Bilateral Motor Cortex Activity in Subacute Stroke Patients Executing a Unimanual Motor Task With Increasing Demand on Precision

Abnormal contralesional M1 activity is consistently reported in patients with compromised upper limb and hand function after stroke. The underlying mechanisms and functional implications of this activity are not clear, which hampers the development of treatment strategies targeting this brain area. The goal of the present study was to determine the extent to which contralesional M1 activity can be explained by the demand of a motor task, given recent evidence for increasing ipsilateral M1 activity with increasing demand in healthy age-matched controls. We hypothesized that higher activity in contralesional M1 is related to greater demand on precision in a hand motor task. fMRI data were collected from 19 patients with ischemic stroke affecting hand function in the subacute recovery phase and 31 healthy, right-handed, age-matched controls. The hand motor task was designed to parametrically modulate the demand on movement precision. Electromyography data confirmed strictly unilateral task performance by all participants. Patients showed significant impairment relative to controls in their ability to perform the task in the fMRI scanner. However, patients and controls responded similarly to an increase in demand for precision, with better performance for larger targets and poorer performance for smaller targets. Patients did not show evidence of elevated ipsilesional or contralesional M1 blood oxygenation level-dependent (BOLD) activation relative to healthy controls and mean BOLD activation levels were not elevated for patients with poorer performance relative to patients with better task performance. While both patients and healthy controls showed demand-dependent increases in BOLD activation in both ipsilesional/contralateral and contralesional/ipsilateral hemispheres, patients with stroke were less likely to show evidence of a linear relationship between the demand on precision and BOLD activation in contralesional M1 than healthy controls. Taken together, the findings suggest that task demand affects the BOLD response in contralesional M1 in patients with stroke, though perhaps less strongly than in healthy controls. This has implications for the interpretation of reported abnormal bilateral M1 activation in patients with stroke because in addition to contralesional M1 reorganization processes it could be partially related to a response to the relatively higher demand of a motor task when completed by patients rather than by healthy controls.

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.

Altered Prefrontal-Basal Ganglia Effective Connectivity in Patients With Poststroke Cognitive Impairment

We investigated the association between poststroke cognitive impairment and a specific effective network connectivity in the prefrontal-basal ganglia circuit. The resting-state effective connectivity of this circuit was modeled by employing spectral dynamic causal modeling in 11 poststroke patients with cognitive impairment (PSCI), 8 poststroke patients without cognitive impairment (non-PSCI) at baseline and 3-month follow-up, and 28 healthy controls. Our results showed that different neuronal models of effective connectivity in the prefrontal-basal ganglia circuit were observed among healthy controls, non-PSCI, and PSCI patients. Additional connected paths (extra paths) appeared in the neuronal models of stroke patients compared with healthy controls. Moreover, changes were detected in the extra paths of non-PSCI between baseline and 3-month follow-up poststroke, indicating reorganization in the ipsilesional hemisphere and suggesting potential compensatory changes in the contralesional hemisphere. Furthermore, the connectivity strengths of the extra paths from the contralesional ventral anterior nucleus of thalamus to caudate correlated significantly with cognitive scores in non-PSCI and PSCI patients. These suggest that the neuronal model of effective connectivity of the prefrontal-basal ganglia circuit may be sensitive to stroke-induced cognitive decline, and it could be a biomarker for poststroke cognitive impairment 3 months poststroke. Importantly, contralesional brain regions may play an important role in functional compensation of cognitive decline.

Scalp electroencephalograms over ipsilateral sensorimotor cortex reflect contraction patterns of unilateral finger muscles

A variety of neural substrates are implicated in the initiation, coordination, and stabilization of voluntary movements underpinned by adaptive contraction and relaxation of agonist and antagonist muscles. To achieve such flexible and purposeful control of the human body, brain systems exhibit extensive modulation during the transition from resting state to motor execution and to maintain proper joint impedance. However, the neural structures contributing to such sensorimotor control under unconstrained and naturalistic conditions are not fully characterized. To elucidate which brain regions are implicated in generating and coordinating voluntary movements, we employed a physiologically inspired, two-stage method to decode relaxation and three patterns of contraction in unilateral finger muscles (i.e., extension, flexion, and co-contraction) from high-density scalp electroencephalograms (EEG). The decoder consisted of two parts employed in series. The first discriminated between relaxation and contraction. If the EEG data were discriminated as contraction, the second stage then discriminated among the three contraction patterns. Despite the difficulty in dissociating detailed contraction patterns of muscles within a limb from scalp EEG signals, the decoder performance was higher than chance-level by 2-fold in the four-class classification. Moreover, weighted features in the trained decoders revealed EEG features differentially contributing to decoding performance. During the first stage, consistent with previous reports, weighted features were localized around sensorimotor cortex (SM1) contralateral to the activated fingers, while those during the second stage were localized around ipsilateral SM1. The loci of these weighted features suggested that the coordination of unilateral finger muscles induced different signaling patterns in ipsilateral SM1 contributing to motor control. Weighted EEG features enabled a deeper understanding of human sensorimotor processing as well as of a more naturalistic control of brain-computer interfaces.

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.

Probing rapid network reorganization of motor and language functions via neuromodulation and neuroimaging

Motor and cognitive functions are organized in large-scale networks in the human brain that interact to enable flexible adaptation of information exchange to ever-changing environmental conditions. In this review, we discuss the unique potential of the consecutive combination of repetitive transcranial magnetic stimulation (rTMS) and functional neuroimaging to probe network organization and reorganization in the healthy and lesioned brain. First, we summarize findings highlighting the flexible (re-)distribution and short-term reorganization in motor and cognitive networks in the healthy brain. Plastic after-effects of rTMS result in large-scale changes on the network level affecting both local and remote activity within the stimulated network as well as interactions between the stimulated and distinct functional networks. While the number of combined rTMS-fMRI studies in patients with brain lesions remains scarce, preliminary evidence suggests that the lesioned brain flexibly (re-)distributes its computational capacities to functionally reorganize impaired brain functions, using a similar set of mechanisms to achieve adaptive network plasticity compared to short-term reorganization observed in the healthy brain after rTMS. In general, both short-term reorganization in the healthy brain and stroke-induced reorganization seem to rely on three general mechanisms of adaptive network plasticity that allow to maintain and recover function: i) interhemispheric changes, including increased contribution of homologous regions in the contralateral hemisphere and increased interhemispheric connectivity, ii) increased interactions between differentially specialized networks and iii) increased contributions of domain-general networks after disruption of more specific functions. These mechanisms may allow for computational flexibility of large-scale neural networks underlying motor and cognitive functions. Future studies should use complementary approaches to address the functional relevance of adaptive network plasticity and further delineate how these general mechanisms interact to enable network flexibility. Besides furthering our neurophysiological insights into brain network interactions, identifying approaches to support and enhance adaptive network plasticity may result in clinically relevant diagnostic and treatment approaches.

Connectivity-Related Roles of Contralesional Brain Regions for Motor Performance Early after Stroke

Hemiparesis after stroke is associated with increased neural activity not only in the lesioned but also in the contralesional hemisphere. While most studies have focused on the role of contralesional primary motor cortex (M1) activity for motor performance, data on other areas within the unaffected hemisphere are scarce, especially early after stroke. We here combined functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) to elucidate the contribution of contralesional M1, dorsal premotor cortex (dPMC), and anterior intraparietal sulcus (aIPS) for the stroke-affected hand within the first 10 days after stroke. We used “online” TMS to interfere with neural activity at subject-specific fMRI coordinates while recording 3D movement kinematics. Interfering with aIPS activity improved tapping performance in patients, but not healthy controls, suggesting a maladaptive role of this region early poststroke. Analyzing effective connectivity parameters using a Lasso prediction model revealed that behavioral TMS effects were predicted by the coupling of the stimulated aIPS with dPMC and ipsilesional M1. In conclusion, we found a strong link between patterns of frontoparietal connectivity and TMS effects, indicating a detrimental influence of the contralesional aIPS on motor performance early after stroke.

Changes in electroencephalography complexity and functional magnetic resonance imaging connectivity following robotic hand training in chronic stroke

Introduction: In recent years, robotic training has been utilized for recovery of motor control in patients with motor deficits. Along with clinical assessment, electrical patterns in the brain have emerged as a marker for studying changes in the brain associated with brain injury and rehabilitation. These changes mainly involve an imbalance between the two hemispheres. We aimed to study the effect of brain computer interface (BCI)-based robotic hand training on stroke subjects using clinical assessment, electroencephalographic (EEG) complexity analysis, and functional magnetic resonance imaging (fMRI) connectivity analysis. Method: Resting-state simultaneous EEG-fMRI was conducted on 14 stroke subjects before and after training who underwent 20 sessions robot hand training. Fractal dimension (FD) analysis was used to assess neuronal impairment and functional recovery using the EEG data, and fMRI connectivity analysis was performed to assess changes in the connectivity of brain networks. Results: FD results indicated a significant asymmetric difference between the ipsilesional and contralesional hemispheres before training, which was reduced after robotic hand training. Moreover, a positive correlation between interhemispheric asymmetry change for central brain region and change in Fugl Meyer Assessment (FMA) scores for upper limb was observed. Connectivity results showed a significant difference between pre-training interhemispheric connectivity and post-training interhemispheric connectivity. Moreover, the change in connectivity correlated with the change in FMA scores. Results also indicated a correlation between the increase in connectivity for motor regions and decrease in FD interhemispheric asymmetry for central brain region covering the motor area. Conclusion: In conclusion, robotic hand training significantly facilitated stroke motor recovery, and FD, along with connectivity analysis can detect neuroplasticity changes.

Recovery from stroke: current concepts and future perspectives

Stroke is a leading cause of acquired, permanent disability worldwide. Although the treatment of acute stroke has been improved considerably, the majority of patients to date are left disabled with a considerable impact on functional independence and quality of life. As the absolute number of stroke survivors is likely to further increase due to the demographic changes in our aging societies, new strategies are needed in order to improve neurorehabilitation. The most critical driver of functional recovery post-stroke is neural reorganization. For developing novel, neurobiologically informed strategies to promote recovery of function, an improved understanding of the mechanisms enabling plasticity and recovery is mandatory. This review provides a comprehensive survey of recent developments in the field of stroke recovery using neuroimaging and non-invasive brain stimulation. We discuss current concepts of how the brain reorganizes its functional architecture to overcome stroke-induced deficits, and also present evidence for maladaptive effects interfering with recovery. We demonstrate that the combination of neuroimaging and neurostimulation techniques allows a better understanding of how brain plasticity can be modulated to promote the reorganization of neural networks. Finally, neurotechnology-based treatment strategies allowing patient-tailored interventions to achieve enhanced treatment responses are discussed. The review also highlights important limitations of current models, and finally closes with possible solutions and future directions.

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models

Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.

Increasing robustness of pairwise methods for effective connectivity in magnetic resonance imaging by using fractional moment series of BOLD signal distributions

Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the dynamic causal modeling (DCM) generative model. The directionality is inferred based on statistical dependencies between the two-node time series, for example, by assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.

The functional role of beta-oscillations in the supplementary motor area during reaching and grasping after stroke: A question of structural damage to the corticospinal tract

Hand motor function is often severely affected in stroke patients. Non-satisfying recovery limits reintegration into normal daily life. Understanding stroke-related network changes and identifying common principles that might underlie recovered motor function is a prerequisite for the development of interventional therapies to support recovery. Here, we combine the evaluation of functional activity (multichannel electroencephalography) and structural integrity (diffusion tensor imaging) in order to explain the degree of residual motor function in chronic stroke patients. By recording neural activity during a reaching and grasping task that mimics activities of daily living, the study focuses on deficit-related neural activation patterns. The study showed that the functional role of movement-related beta desynchronization in the supplementary motor area (SMA) for residual hand motor function in stroke patients depends on the microstructural integrity of the corticospinal tract (CST). In particular, in patients with damaged CST, stronger task-related activity in the SMA was associated with worse residual motor function. Neither CST damage nor functional brain activity alone sufficiently explained residual hand motor function. The findings suggest a central role of the SMA in the motor network during reaching and grasping in stroke patients, the degree of functional relevance of the SMA is depending on CST integrity.

Response Hand and Motor Set Differentially Modulate the Connectivity of Brain Pathways During Simple Uni-manual Motor Behavior

We investigated the flexible modulation of undirected functional connectivity (uFC) of brain pathways during simple uni-manual responding. Two questions were central to our interests: (1) does response hand (dominant vs. non-dominant) differentially modulate connectivity and (2) are these effects related to responding under varying motor sets. fMRI data were acquired in twenty right-handed volunteers who responded with their right (dominant) or left (non-dominant) hand (blocked across acquisitions). Within acquisitions, the task oscillated between periodic responses (promoting the emergence of motor sets) or randomly induced responses (disrupting the emergence of motor sets). Conjunction analyses revealed eight shared nodes across response hand and condition, time series from which were analyzed. For right hand responses connectivity of the M1 ←→ Thalamus and SMA ←→ Parietal pathways was more significantly modulated during periodic responding. By comparison, for left hand responses, connectivity between five network pairs (including M1 and SMA, insula, basal ganglia, premotor cortex, parietal cortex, thalamus) was more significantly modulated during random responding. uFC analyses were complemented by directed FC based on multivariate autoregressive models of times series from the nodes. These results were complementary and highlighted significant modulation of dFC for SMA → Thalamus, SMA → M1, basal ganglia → Insula and basal ganglia → Thalamus. The results demonstrate complex effects of motor organization and task demand and response hand on different connectivity classes of fMRI data. The brain's sub-networks are flexibly modulated by factors related to motor organization and/or task demand, and our results have implications for assessment of medical conditions associated with motor dysfunction.

Network dynamics engaged in the modulation of motor behavior in stroke patients

Stroke patients with motor deficits typically feature enhanced neural activity in several cortical areas when moving their affected hand. However, also healthy subjects may show higher levels of neural activity in tasks with higher motor demands. Therefore, the question arises to what extent stroke-related overactivity reflects performance-level-associated recruitment of neural resources rather than stroke-induced neural reorganization. We here investigated which areas in the lesioned brain enable the flexible adaption to varying motor demands compared to healthy subjects. Accordingly, eleven well-recovered left-hemispheric chronic stroke patients were scanned using functional magnetic resonance imaging. Motor system activity was assessed for fist closures at increasing movement frequencies performed with the affected/right or unaffected/left hand. In patients, an increasing movement rate of the affected hand was associated with stronger neural activity in ipsilesional/left primary motor cortex (M1) but unlike in healthy controls also in contralesional/right dorsolateral premotor cortex (PMd) and contralesional/right superior parietal lobule (SPL). Connectivity analyses using dynamic causal modeling revealed stronger coupling of right SPL onto affected/left M1 in patients but not in controls when moving the affected/right hand independent of the movement speed. Furthermore, coupling of right SPL was positively coupled with the "active" ipsilesional/left M1 when stroke patients moved their affected/right hand with increasing movement frequency. In summary, these findings are compatible with a supportive role of right SPL with respect to motor function of the paretic hand in the reorganized brain.