Functional connectivity in relation to motor performance and recovery after stroke.

Plasticity after stroke has traditionally been studied by observing changes only in the spatial distribution and laterality of focal brain activation during affected limb movement. However, neural reorganization is multifaceted and our understanding may be enhanced by examining dynamics of activity within large-scale networks involved in sensorimotor control of the limbs. Here, we review functional connectivity as a promising means of assessing the consequences of a stroke lesion on the transfer of activity within large-scale neural networks. We first provide a brief overview of techniques used to assess functional connectivity in subjects with stroke. Next, we review task-related and resting-state functional connectivity studies that demonstrate a lesion-induced disruption of neural networks, the relationship of the extent of this disruption with motor performance, and the potential for network reorganization in the presence of a stroke lesion. We conclude with suggestions for future research and theories that may enhance the interpretation of changing functional connectivity. Overall findings suggest that a network level assessment provides a useful framework to examine brain reorganization and to potentially better predict behavioral outcomes following stroke

Brain networks under attack : robustness properties and the impact of lesions

A growing number of studies approach the brain as a complex network, the so-called ‘connectome’. Adopting this framework, we examine what types or extent of damage the brain can withstand—referred to as network ‘robustness’—and conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimer’s disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regions—and especially those connecting different subnetworks—was found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research

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

Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

Visual feedback alters force control and functional activity in the visuomotor network after stroke.

Modulating visual feedback may be a viable option to improve motor function after stroke, but the neurophysiological basis for this improvement is not clear. Visual gain can be manipulated by increasing or decreasing the spatial amplitude of an error signal. Here, we combined a unilateral visually guided grip force task with functional MRI to understand how changes in the gain of visual feedback alter brain activity in the chronic phase after stroke. Analyses focused on brain activation when force was produced by the most impaired hand of the stroke group as compared to the non-dominant hand of the control group. Our experiment produced three novel results. First, gain-related improvements in force control were associated with an increase in activity in many regions within the visuomotor network in both the stroke and control groups. These regions include the extrastriate visual cortex, inferior parietal lobule, ventral premotor cortex, cerebellum, and supplementary motor area. Second, the stroke group showed gain-related increases in activity in additional regions of lobules VI and VIIb of the ipsilateral cerebellum. Third, relative to the control group, the stroke group showed increased activity in the ipsilateral primary motor cortex, and activity in this region did not vary as a function of visual feedback gain. The visuomotor network, cerebellum, and ipsilateral primary motor cortex have each been targeted in rehabilitation interventions after stroke. Our observations provide new insight into the role these regions play in processing visual gain during a precisely controlled visuomotor task in the chronic phase after stroke

Specific subsystems of the inferior parietal lobule are associated with hand dysfunction following stroke: A cross-sectional resting-state fMRI study

Aim The inferior parietal lobule (IPL) plays important roles in reaching and grasping during hand movements, but how reorganizations of IPL subsystems underlie the paretic hand remains unclear. We aimed to explore whether specific IPL subsystems were disrupted and associated with hand performance after chronic stroke. Methods In this cross-sectional study, we recruited 65 patients who had chronic subcortical strokes and 40 healthy controls from China. Each participant underwent the Fugl-Meyer Assessment of Hand and Wrist and resting-state fMRI at baseline. We mainly explored the group differences in resting-state effective connectivity (EC) patterns for six IPL subregions in each hemisphere, and we correlated these EC patterns with paretic hand performance across the whole stroke group and stroke subgroups. Moreover, we used receiver operating characteristic curve analysis to distinguish the stroke subgroups with partially (PPH) and completely (CPH) paretic hands. Results Stroke patients exhibited abnormal EC patterns with ipsilesional PFt and bilateral PGa, and five sensorimotor-parietal/two parietal–temporal subsystems were positively or negatively correlated with hand performance. Compared with CPH patients, PPH patients exhibited abnormal EC patterns with the contralesional PFop. The PPH patients had one motor-parietal subsystem, while the CPH patients had one sensorimotor-parietal and three parietal-occipital subsystems that were associated with hand performance. Notably, the EC strength from the contralesional PFop to the ipsilesional superior frontal gyrus could distinguish patients with PPH from patients with CPH. Conclusions The IPL subsystems manifest specific functional reorganization and are associated with hand dysfunction following chronic stroke.Natural Science Foundation of Zhejiang Province, Grant/Award Number: LGF19H270001; Shanghai Sailing Program, Grant/Award Number: 20YF144510

Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches

The motor system comprises a network of cortical and subcortical areas interacting via excitatory and inhibitory circuits, thereby governing motor behaviour. The balance within the motor network may be critically disturbed after stroke when the lesion either directly affects any of these areas or damages-related white matter tracts. A growing body of evidence suggests that abnormal interactions among cortical regions remote from the ischaemic lesion might also contribute to the motor impairment after stroke. Here, we review recent studies employing models of functional and effective connectivity on neuroimaging data to investigate how stroke influences the interaction between motor areas and how changes in connectivity relate to impaired motor behaviour and functional recovery. Based on such data, we suggest that pathological intra- and inter-hemispheric interactions among key motor regions constitute an important pathophysiological aspect of motor impairment after subcortical stroke. We also demonstrate that therapeutic interventions, such as repetitive transcranial magnetic stimulation, which aims to interfere with abnormal cortical activity, may correct pathological connectivity not only at the stimulation site but also among distant brain regions. In summary, analyses of connectivity further our understanding of the pathophysiology underlying motor symptoms after stroke, and may thus help to design hypothesis-driven treatment strategies to promote recovery of motor function in patients

Transcranial magnetic stimulation combined with functional magnetic resonance imaging: From target identification to prediction of therapeutic effects in stroke patients

Repetitive transcranial magnetic stimulation (rTMS), particularly theta-burst stimulation (TBS), can be applied to modulate cortical excitability beyond the period of stimulation (Huang et al., 2005). Consequently, rTMS is regarded to have high therapeutic potential for treatment of various psychiatric and neurological diseases related to cortical hypo- or hyperexcitability such as stroke (Ridding & Rothwell, 2007). Whether rTMS induced effects are sufficiently robust to be useful in clinical settings is currently under intense investigation. The most challenging problem appears to be considerably high variability in rTMS induced effects both, across studies (Hoogendam et al., 2010) and individual patients (Ameli et al., 2009). Hence, the major goal of the present thesis was to improve rTMS intervention strategies in stroke patients suffering from chronic motor hand deficits by multimodal uses of (repetitive) TMS with state-of-the-art neuroimaging techniques. Sources of variance across studies are likely to be methodological in origin. They might result from different strategies to identify the cortical rTMS target position. Individual functional magnetic resonance (fMRI) data have been demonstrated to yield best spatial approximations of the most excitable TMS position compared to other techniques (Sparing et al., 2008). However, there is still a considerably large spatial mismatch between the cortical position showing highest movement-related fMRI signal and the cortical position yielding highest muscle responses when stimulated with TMS of up to 14 mm (Bastings et al., 1998; Boroojerdi et al., 1999; Herwig et al., 2002; Krings et al., 1997; Lotze et al., 2003; Sparing et al., 2008; Terao et al., 1998). The underlying cause of this spatial mismatch is unknown. Hence, the aim of the first study (Study I) of the present thesis was to test the hypothesis that the spatial mismatch between positions with highest fMRI signal change and positions with highest TMS excitability might be caused by the widely-used Gradient-Echo blood oxygenation level dependent (GRE-BOLD) fMRI technique. GRE-BOLD signal has been demonstrated to occur further downstream from the site of neural activity in large veins running on the cerebral surface (Uludag et al., 2009). Consequently, we tested the hypothesis that alternative fMRI sequences may localize neural activity (i) closer to the anatomical motor hand area, i.e. Brodmann Area 4 (BA4), and (ii) closer to the optimal TMS position than GRE-BOLD. The following alternative fMRI techniques were tested: (i) Spin-Echo (SE-BOLD) assessing blood oxygenation level dependent signal changes with decreased sensitivity for the macrovasculature at high magnetic fields (≥ 3 Tesla, Uludag et al., 2009) and (ii) arterial spin labelling (ASL), assessing local changes in cerebral blood flow (ASL-CBF) which have been shown to occur in close proximity to synaptic activity (Duong et al., 2000). GRE-BOLD, SE-BOLD, and ASL-CBF signal changes during right thumb abductions were obtained from 15 healthy young subjects at 3 Tesla. In 12 subjects, brain tissue at fMRI peak voxel coordinates was stimulated with neuronavigated TMS to investigate whether spatial differences between fMRI techniques are functionally relevant, i.e. impact on motor-evoked potentials (MEPs) recorded from a contralateral target muscle, which is involved in thumb abductions. A systematic TMS motor mapping was performed to identify the most excitable TMS position (i.e. the TMS hotspot) and the centre-of-gravity (i.e. the TMS CoG), which considers the spatial distribution of excitability in the pericentral region. Euclidean distances between TMS and fMRI positions were calculated for each fMRI technique. Results indicated that highest SE-BOLD and ASL-CBF signal changes occurred in the anterior wall of the central sulcus (BA4), whereas highest GRE-BOLD signal changes occurred significantly closer to the gyral surface where most large draining veins are located. fMRI techniques were not significantly different from each other in Euclidean distances to optimal TMS positions since optimal TMS positions were located considerably more anterior (and slightly surprisingly in premotor cortex (BA6) and not BA4). Stimulation of brain tissue at GRE-BOLD peak voxel coordinates with TMS resulted in significantly higher MEPs (compared to SE-BOLD and ASL-CBF coordinates). This was probably the case because GRE-BOLD positions tended to be located at the gyral crown, which was slightly (but not significantly) closer to the TMS hotspot position. Taken together, findings of Study I suggest that spatial differences between fMRI and TMS positions are not caused by spatial unspecificity of the widely-used GRE-BOLD fMRI technique. Hnece, other factors such as complex interactions between brain tissue and the TMS induced electric field (Opitz et al., 2011), could be the underlying cause. Identification of the cortical rTMS target position is particularly challenging in stroke patients since reorganization processes after stroke may shift both, fMRI and TMS positions in unknown direction and extend (Rossini et al., 1998). In the second study (Study II) of the present thesis, we therefore tested whether findings obtained from healthy young subjects in Study I do also apply to chronic stroke patients and older (i.e. age-matched) healthy control subjects. In this study, arterial spin labelling (ASL) was used to assess CBF and BOLD signal changes simultaneously during thumb abductions with the affected/non-dominant and the unaffected/dominant hand in 15 chronic stroke patients and 13 age-matched healthy control subjects at 3 Tesla. Brain tissue at fMRI peak voxel coordinates was stimulated with neuronavigated TMS to test whether spatial differences are functionally relevant and impact on MEPs. Systematic TMS motor mappings were performed for both hemispheres in overall 12 subjects (6 stroke patients and 6 healthy subjects). Euclidean distances between fMRI and TMS positions were calculated for each hemisphere and fMRI technique. In line with results of Study I, highest ASL-CBF signal changes were located in the anterior wall of the central sulcus (BA4), whereas highest ASL-BOLD signal changes occurred significantly closer to the gyral surface. In contrast to Study I, there were no significant differences between ASL-CBF and ASL-BOLD positions in MEPs when stimulated with neuronavigated TMS, which suggests that spatial differences (in depth) were not functionally relevant for TMS applications. In line with Study I, there were no significant differences between fMRI techniques in Euclidean distances to optimal TMS positions, since optimal TMS positions were located considerably more anterior than fMRI positions (in premotor cortex, i.e. BA6). Stroke patients showed overall larger displacements (between fMRI and TMS positions) on the ipsilesional (but not the contralesional) hemisphere compared to healthy subjects. However, none of the fMRI techniques yielded positions significantly closer to the optimal TMS position. Hence, functional reorganization may impact on spatial congruence between fMRI and TMS, but the effect is similar for ASL-CBF and ASL-BOLD. Pathomechanisms underlying stroke induced motor deficits are still poorly understood but a simplified model of hemispheric competition has been suggested, which proposes relative hypoexcitability of the ipsilesional hemisphere and hyperexcitability of the contralesional hemisphere leading to pathologically increased interhemispheric inhibition from the contralesional onto the ipsilesional hemisphere during movements of the paretic hand (Duque et al., 2005; Grefkes et al., 2008b, 2010; Murase et al., 2004). In line with the model of hemispheric competition, both increasing excitability of the ipsilesional hemisphere (Khedr et al., 2005; Talelli et al., 2007) as well as decreasing excitability of the contralesional hemisphere (Fregni et al., 2006; Di Lazzaro et al., 2008a) have been demonstrated to normalize cortical excitability towards physiological levels and/or ameliorate motor performance of the stroke affected hand. However, there is considerably high inter-individual variance and some patients may even show deteriorations of motor performance after rTMS (Ameli et al., 2009). Therefore, the aim of the third study (Study III) was to identify reliable predictors for TBS effects on motor performance of the affected hand in stroke patients, which appears essential for successful implementation of TBS in neurorehabilitation. Overall, 13 chronic stroke patients with unilateral motor hand deficit and 12 age-matched healthy control subjects were included in the study. All patients received 3 different TBS interventions on 3 different days: (i) intermittent TBS (iTBS, facilitatory) over the primary motor cortex (M1) of the ipsilesional hemisphere, (ii) continuous TBS (cTBS, inhibitory) over M1 of the contralesional hemisphere, and (iii) either iTBS or cTBS over a control stimulation site (to control for placebo effects). Motor performance was measured before and after each TBS session with 3 different motor tasks and an overall motor improvement score was calculated. All subjects participated in an fMRI experiment, in which they performed rhythmic fist closures with their affected/non-dominant and unaffected/dominant hand. A laterality index (LI), reflecting laterality of fMRI signal in cortical motor areas was calculated. Effective connectivity, i.e. the direct or indirect causal influence that activity in one area exerts on activity of another area (Friston et al., 1993a), was inferred from fMRI data by means of dynamic causal modelling (DCM). Due to relatively high inter-individual variance, neither iTBS nor cTBS was significantly different from control TBS in terms of average behavioural (or electrophysiological) changes over the group of patients. However, beneficial effects of iTBS over the ipsilesional hemisphere were predicted by a unilateral fMRI activation pattern during movements of the affected hand and by the integrity of the cortical motor network. The more pronounced the promoting influence from the ipsilesional supplementary motor area (SMA) onto ipsilesional M1 and the more pronounced the inhibitory effect originating from ipsilesional M1 onto contralesional M1, the better was the behavioural response to facilitatory iTBS applied to the ipsilesional hemisphere. No significant correlations were found for behavioural improvements following cTBS or behavioural changes of the unaffected hand. Taken together, Study III yielded promising results indicating that laterality of fMRI signal and integrity of the motor network architecture constitute promising predictors for response to iTBS. In patients in whom the connectivity pattern of the ipsilesional motor network resembled physiological network connectivity patterns (i.e. preserved inhibition of the contralesional hemisphere and supportive role of the SMA of the ipsilesional hemisphere), beneficial effects of iTBS over the ipsilesional hemisphere could be observed. In contrast, patients with severely disturbed motor networks did not respond to iTBS or even deteriorated