Objective:Motor recovery after stroke shows a high inter-subject variability. The brain's potential to form new connections determines individual levels of recovery of motor function. Most of our daily activities require visuomotor integration, which engages parietal areas. Compared to the frontal motor system, less is known about the parietal motor system's reconfiguration related to stroke recovery. Here, we tested if functional connectivity among parietal and frontal motor areas undergoes plastic changes after stroke and assessed the behavioral relevance for motor function after stroke.Methods:We investigated stroke lesion-induced changes in functional connectivity by measuring high-density electroencephalography (EEG) and assessing task-related changes in coherence during a visually guided grip task with the paretic hand in 30 chronic stroke patients with variable motor deficits and 19 healthy control subjects. Quantitative changes in task-related coherence in sensorimotor rhythms were compared to the residual motor deficit.Results:Parietofrontal coupling was significantly stronger in patients compared to controls. Whereas motor network coupling generally increased during the task in both groups, the task-related coherence between the parietal and primary motor cortex in the stroke lesioned hemisphere showed increased connectivity across a broad range of sensorimotor rhythms. Particularly the parietofrontal task-induced coupling pattern was significantly and positively related to residual impairment in the Nine-Hole Peg Test performance and grip force.Interpretation:These results demonstrate that parietofrontal motor system integration during visually guided movements is stronger in the stroke-lesioned brain. The correlation with the residual motor deficit could either indicate an unspecific marker of motor network damage or it might indicate that upregulated parietofrontal connectivity has some impact on post-stroke motor function.

Neuromuscular electrical stimulation (NMES) has been applied as one rehabilitative treatment option in stroke patients (Quandt and Hummel, 2014) and a meta-analysis recently showed its positive effects on motor recovery (Stein et al., 2015). One major challenge in stroke patients, especially severely impaired, is the opening of the hand due to extensor weakness accompanied by flexor spasticity. Here, NMES could provide assistance and substitute lost function. Studies in healthy controls have shown, however, that the rapid onset of muscle fatigue is a critical limitation for practical use. Here, we measure muscle fatigue over time during repetitive NMES in stroke patients and evaluate the impact of NMES on spasticity. NMES was applied in a heterogeneous group of 7 chronic stroke patients (FM μ = 34/66, ran 13–51, ARAT μ = 13/57, ran 3–34) with different degrees of spasticity (MAS 0–4) over the extensor digitorum muscles using the MotionStim8, controlled via PC (25 Hz, 25 mA, 200–300 μs). The protocol comprised 6 blocks of stimulation with increasing pulse width (50 μs). Each block consisted of 10 alternating 10 s rest and 10 s stimulation periods. The next block was initiated 3 min later. To measure the impact of stimulation on finger extension, but not on wrist movement, stimulation was combined with an orthosis to stabilize the wrist. Effect of stimulation on finger movement was measured using a force gauge and a bend sensor. The median of the relative stimulation effect (stimulation period – following rest period) was taken and data were normalized between 0 and 1 to allow for comparison across patients. Statistical significance was assessed by modeling a simple linear regression for each patient. The regression coefficients βI were tested in a Students t-test against zero. The stimulation induced strength and movement showed a significant reduction over time (force, p = 0.004, sensor deviation, p = 0.017), even though pulse width was increased (Fig. 1). Moreover, a reduction was already detected within a block. Even though a slight recovery occurs after the 3-minute break, break time was not sufficient to recover and reduce muscle fatigue completely. Spasticity of the flexor muscle was reduced after stimulation as measured by the MAS (μprä = 2.6, μpost = 1.6, p = 0.05). The effect of NMES on force production and finger extension did significantly decrease over a short period of time. The immediate decline after few hand openings critically underlines the need for measures to reduce muscle fatigue. Increased muscle fatigue during NMES compared to physiological movement could be a result of synchronous fiber stimulation and attempts of spatially distributed stimulation have been made to reduce fatigue in healthy controls (Sayenko et al., 2014).

* Postervortrag auf der 59. Jahrestagung der Deutschen Gesellschaft für Klinische Neurophysiologie und Funktionelle Bildgebung, 18.–21.03.2015, Tübingen; international publiziert [1]

Dynamic causal modeling (DCM) based on functional magnetic resonance imaging (FMRI) and magneto-/electroencephalographic (MEEG) data has increasingly extended the understanding of intrinsic brain network dynamics in a variety of functional systems (Friston et al., Curr Opin Neurobiol, 2013). In the motor system, several studies have used DCM to detect causal information flow from secondary to primary motor areas during simple movements and bimanual coordination (Grefkes et al., Neuroimage, 2008). There is also evidence for non-linear cross-frequency interactions among motor areas (Chen et al., J of Neuroscience, 2010; Herz et al., Neuroimage, 2012). As such, data based on MEEG and FMRI provide complementary and synergistic insights into the dynamics of motor networks due to the higher spatial resolution of brain activity by FMRI versus the frequency-resolved coupling revealed by MEEG. A study combining FMRI- and EEG-based DCM for induced responses (DCM-IR) using the same task in the same participants to directly compare network architectures deriving from BOLD response and spectral neuroelectric dynamics has not been conducted so far. Specifically, such an approach is of interest as both modalities share principal similarities in the formulation of the DCM. We hypothesized that DCM based on induced responses and the BOLD-signal would reveal a similar network architecture. To test this, we measured 14 young healthy individuals during a simple isometric hand grip task using FMRI and EEG separately, set up a common model space of six equally plausible models and modeled the coupling parameters within a core motor network during the right hand grip. Bayesian Model Selection revealed strongest evidence for a fully connected model in DCM for FMRI and a sparsely connected model in DCM-IR, comparing the individual interregional coupling parameters revealed interesting similarities: First, both modalities showed a significant grip-related increase in facilitatory coupling from the left SMA onto the left M1. Second, the left PMv also exerted a positive coupling onto the left M1 with a significant result for DCM-IR and a trend of significance for DCM for fMRI. Frequency-resolved coupling showed that the information flow from SMA to M1 was a linear alpha to alpha interaction but also a nonlinear cross-frequency interaction between faster oscillations in SMA (18–25 Hz) and full range alpha to beta (9–22 Hz) in left M1. Coupling between PMv and M1 was found from upper alpha (10–13 Hz) to lower beta (14–22 Hz). The strategy of informing EEG source space configurations with FMRI location priors, cross-validating basic connectivity maps and then looking at the details of frequency coding allows for a deeper insight into the motor network architecture in the human brain. These results thus undermine the importance of these functional connections and validate the methodological approach of DCM to explore network architecture. Furthermore, extending findings from previous studies showing a covariation of the BOLD-signal and alpha/beta band power (Ritter et al., Human Brain Mapping, 2009; Yuan et al., Neuroimage, 2010), we here present evidence that these two signals not only share similarities in local activation patterns but also in the connectivity domain.

Spontaneous recovery of motor deficits after stroke evolve at a rather unpredictable fashion regarding the time and extend of skill reacquisition (Langhorne et al., Lancet, 2011). Previous longitudinal studies investigating brain activity during recovery from hand motor deficits point to an early overactivation of the motor network with a decrease back to near normal patterns later after stroke (Rehme et al., Neuroimage, 2012). Since patients regain force and skill during recovery, changes in neural activation over time could be explained by a decreased relative task effort over time. Such a behavioral bias could affect neuroimaging findings and their interpretation in regard to recovery, a question not or not sufficiently accounted for in previous studies. The aim of the present study was to systematically investigate the influence of task effort on recovery-related brain activation. We addressed the question whether keeping either task effort or task output constant leads to differing patterns of recovery-related brain activation over time within a “constant output-constant effort” design. We speculated that due to the recovery-related increase in manual power, the two experimental conditions show different evolutions of brain activity patterns with constant output reflecting stronger changes over time. Since electroencephalography and functional magnetic resonance imaging measure different aspects of brain activation, we used a multimodal approach. We assessed brain activity with functional magnetic resonance imaging (FMRI, blood oxygenation level dependent signal (BOLD)) and EEG based task-related spectral-power within lower alpha, upper alpha, beta band, in a longitudinal design covering the acute (3–5 d post stroke), subacute (30 days) and early chronic (90 days) phase after an ischemic infarction causing a hand motor deficit. At every time point, patients (n = 12 (EEG)/8 (FMRI), m = 6, age 66.67 ± 9.03 (mean ± std), 4 left hemispheric lesions) performed whole hand grips with both 5 kg constant output and 20% of the current individual maximal force (constant effort). Patients showed significant recovery over time (increase in grip force over time, p < 0.001). In parallel, we found a significant reduction in task-related brain activity during recovery (p < 0.01). Relative task effort had no significant impact on the evolution of task-related brain activation over time (TASK × TIME, n.s.). This proved to be equally applicable to FMRI and EEG data (TASK × TIME × METHOD, n.s.; TASK × METHOD n.s.; TIME × METHOD, n.s.). This longitudinal study demonstrates in mildly affected patients that task-related brain activity significantly decreases over time as suggested previously. The main finding was that the dynamic change of brain activation over time did not relate to the effort necessary to perform the task at each measurement time point. In detail, this shows that the task effort had no significant effect on the slope of brain activity decrease in the course of recovery. These data suggest that the decline of motor task-related brain activity during recovery is largely reflecting adaptive processes in the course of neuronal reorganization and is not influenced significantly by behavioral/cognitive differences, such as effort, over time. Using multimodal imaging, two independent measures of neural activity (BOLD, EEG power) support this finding, mutually validating methodological assessment of brain activation and augmenting the explanatory power.

In einer Studie mit 12 gesunden Probanden evaluierten wir ein EEG-Verarbeitungssystem zur Echtzeitphasendetektion, das auf Grundlage eines kürzlich vorgestellten EEG-Aufnahme-Geräts von uns entwickelt wurde [1]. Ziel der Studie war es, mittels paralleler Aufnahmen mit einem konventionellen EEG-System die Güte der Echtzeitphasendetektion zu prüfen. Zielfrequenzen waren dabei die okzipitalen Alpha-Oszillationen der Versuchspersonen. Die Ergebnisse unsere Studie zeigen, dass die von uns avisierte Phasendetektion in allen Probanden erfolgreich war.

Complex movements require the interplay of local activation and interareal communication of sensorimotor brain regions. This is reflected in a decrease of task-related spectral power over the sensorimotor cortices and an increase in functional connectivity predominantly in the upper alpha band in the electroencephalogram (EEG). In the present study, directionality of information flow was investigated using EEG recordings to gain better understanding about the network architecture underlying the performance of complex sequential finger movements. This was assessed by means of Granger causality-derived directed transfer function (DTF). As DTF measures the influence one signal exerts on another based on a time lag between them, it allows implications to be drawn on causal relationships. To reveal causal connections between brain regions that are specifically modulated by task complexity, we contrasted the performance of right-handed sequential finger movements of different complexities (simple, scale, complex) that were either pre-learned (memorized) or novel instructed. A complexity-dependent increase in information flow from mesial frontocentral to the left motor cortex and, less pronounced, also to the right motor cortex specifically in the upper alpha range was found. Effective coupling during sequences of high complexity was larger for memorized sequences compared with novel sequences (P=0.0037). These findings further support the role of mesial frontocentral areas in directing the primary motor cortex in the process of orchestrating complex movements and in particular learned sequences.