Driving human motor cortical oscillations leads to behaviorally relevant changes in local GABAA inhibition: a tACS-TMS study

Beta and gamma oscillations are the dominant oscillatory activity in the human motor cortex (M1). However, their physiological basis and precise functional significance remain poorly understood. Here, we used transcranial magnetic stimulation (TMS) to examine the physiological basis and behavioral relevance of driving beta and gamma oscillatory activity in the human M1 using transcranial alternating current stimulation (tACS). tACS was applied using a sham-controlled crossover design at individualized intensity for 20 min and TMS was performed at rest (before, during, and after tACS) and during movement preparation (before and after tACS). We demonstrated that driving gamma frequency oscillations using tACS led to a significant, duration-dependent decrease in local resting-state GABAA inhibition, as quantified by short interval intracortical inhibition. The magnitude of this effect was positively correlated with the magnitude of GABAA decrease during movement preparation, when gamma activity in motor circuitry is known to increase. In addition, gamma tACS-induced change in GABAA inhibition was closely related to performance in a motor learning task such that subjects who demonstrated a greater increase in GABAA inhibition also showed faster short-term learning. The findings presented here contribute to our understanding of the neurophysiological basis of motor rhythms and suggest that tACS may have similar physiological effects to endogenously driven local oscillatory activity. Moreover, the ability to modulate local interneuronal circuits by tACS in a behaviorally relevant manner provides a basis for tACS as a putative therapeutic intervention.SIGNIFICANCE STATEMENT Gamma oscillations have a vital role in motor control. Using a combined tACS-TMS approach, we demonstrate that driving gamma frequency oscillations modulates GABAA inhibition in the human motor cortex. Moreover, there is a clear relationship between the change in magnitude of GABAA inhibition induced by tACS and the magnitude of GABAA inhibition observed during task-related synchronization of oscillations in inhibitory interneuronal circuits, supporting the hypothesis that tACS engages endogenous oscillatory circuits. We also show that an individual's physiological response to tACS is closely related to their ability to learn a motor task. These findings contribute to our understanding of the neurophysiological basis of motor rhythms and their behavioral relevance and offer the possibility of developing tACS as a therapeutic tool

Age-dependent modulation of motor network connectivity for skill acquisition, consolidation and interlimb transfer after motor practice

Objective: Age-related differences in neural strategies for motor learning are not fully understood. We determined the effects of age on the relationship between motor network connectivity and motor skill acquisition, consolidation, and interlimb transfer using dynamic imaging of coherent sources. Methods: Healthy younger (n = 24, 18-24 y) and older (n = 24, 65-87 y) adults unilaterally practiced a visuomotor task and resting-state electroencephalographic data was acquired before and after practice as well as at retention. Results: The results showed that right-hand skill acquisition and consolidation did not differ between age groups. However, age affected the ability to transfer the newly acquired motor skill to the non-practiced limb. Moreover, strengthened left- and right-primary motor cortex-related beta conectivity was negatively and positively associated with right-hand skill acquisition and left-hand skill consolidation in older adults, respectively. Conclusion: Age-dependent modulations of bilateral resting-state motor network connectivity indicate age-specific strategies for the acquisition, consolidation, and interlimb transfer of novel motor tasks. Significance: The present results provide insights into the mechanisms underlying motor learning that are important for the development of interventions for patients with unilateral injuries. (C) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved

Neurophysiological Biomarkers of Motor Recovery in Severe Chronic Stroke

Schlaganfälle kommen jährlich millionenfach und weltweit vor. Die Patienten leiden fast immer unter verschiedensten Behinderungen, und mehr als die Hälfte entwickelt chronische Lähmungen. Für diese Gruppe der Patienten gibt es keine wirksame standardisierte Therapie. Die Vorgänge im Gehirn der Patienten, die ihre Bewegungsfähigkeit nach dem Schlaganfall wieder verbessern, versteht man auch nach Jahrzehnten der Forschung nur teilweise. Das Finden, Beobachten und Überwachen von neurophysiologischen Korrelaten des Erholungsvorgangs könnte helfen, die Gehirnaktivität vor und während einer solchen Behandlung besser zu verstehen. Durch die Entdeckung solcher “Biomarker” und davon abgeleiteter Vorhersagemodelle könnten neuartige Therapien entwickelt und gezieltere klinische Studien entworfen werden.\\ In dieser Dissertation werden mehrere Analysen von neurophysiologischen Daten der Gehirn- und Muskelaktivität von Gesunden und schwerst gelähmten chronischen Schlaganfallpatienten vorgestellt. Das Potential verschiedener Biomarker wird auf Basis dieser Daten bewertet. Zunächst wurde die Desynchronisierung des Sensorimotorischen Rhythmus’ untersucht. Die Gehirnaktivität von chronisch gelähmten Schlaganfallpatienten wurde während der Ausführung von Bewegungen des gelähmten Arms analysiert. Es ergab sich eine Korrelation zwischen der Erholung der Bewegungsfähigkeit und dem Verlauf der Desynchronisierung des Sensorimotorischen Rhythmus’ sowie dessen Lateralisation zwischen beiden Hemisphären. Die Position der Läsion scheint zudem einen Einfluss auf die Stärke der Desynchronisierung zu haben. Außerdem gab es beim Vergleich von Schlaganfallpatienten und Gesunden Unterschiede in der individuellen dominanten Frequenz des Rhythmus’. In einer weiteren Analyse wurde die Gehirnaktivität in niedrigen Frequenzen des Aktivitätsspektrums untersucht. Diese erhöhte sich bei den Patienten über der Läsion, was Ergebnisse früherer Arbeiten mit Tiermodellen und weniger stark gelähmten Schlaganfallpatienten bestätigt. Darüberhinaus wird eine Vorgehensweise für die Analyse von kohärenten Oszillationen im Gehirn während der Ausführung von Bewegungen vorgestellt. Erste Ergebnisse zeigen, dass sich die Verbindungen innerhalb der beschädigten Gehirnhälfte und zwischen den beiden Gehirnhälften mit der Therapie verstärken. Zuletzt wird ein Experiment vorgestellt, worin die möglichen Effekte der intensiven robotischen Therapie auf die Muskelaktivität der Patienten untersucht werden. Die Ergebnisse zeigten keinen ermüdenden Effekt des Trainings auf die Muskeln.\\ Im letzten Kapitel werden weiterführende Arbeiten für die Erweiterung von bereits existierenden Brain-Machine interface-Therapien vorgestellt. Ein virtuelles Exoskelett wurde zur Verbesserung des visuellen Feedbacks, das die Patienten während des Bewegungstraining mit dem Roboter erhalten, implementiert. Außerdem wurde hier ein umfassendes Training in eine “gamifizierte” Rehabilitationsumgebung integriert, um die Immersion und damit die Motivation der Patienten zu erhöhen.\\ Die hier vorgestellten Biomarker könnten als Bausteine für Modelle von Neurorehabilitation dienen und den Trainingsfortschritt der Patienten in solchen neuartigen Rehabilitationsumgebungen beobachtbar und visualisierbar machen. Die Übertragung der hier vorgestellten Forschungsergebnisse in neue oder verbesserte moderne Behandlungsmethoden könnten Millionen von Schlaganfallpatienten helfen, in Zukunft ihre Bewegungsfähigkeit wiederzuerlangen.The most common repercussion of stroke is disability, affecting millions of survivors globally each year. More than half of the victims develop chronic motor impairments. There is no efficient standardized therapy for this group. The processes in the brain of patients who recover their motor ability are poorly understood, even after decades of research. Finding, observing and tracking neurophysiological correlates of stroke and recovery could facilitate the interpretation of the brain activity before and during treatment. New therapies could be devised and studies designed, based on models and predicitions derived from these biomarkers. This thesis presents analyses of neurophysiological data of brain and muscles of healthy individuals and chronic stroke patients with severe motor impairments. Candidates for biomarkers are presented and their potential evaluated. First, desynchronization of the Sensorimotor Rhythm was considered. The brain activity of chronic stroke patients was analyzed during movement attempts of the paretic arm. There was a correlation between motor impairment and the evolution of desynchronization strength as well as the hemispheric laterality of the desynchronization. Moreover, the location of the lesion might have an effect on the strength of desynchronization. Furthermore, there was a significant difference of the peak center frequency of the rhythm compared between stroke patients and healthy individuals. Secondly, low-frequency activity of the brain during movement attempts of the stroke patients increased from before to after the intervention, confirming previous work in animal models and patients with less severe impairment. Thirdly, a methodology for the analysis of coherent brain oscillations during movement attempts is presented. First results show that connectivity within the hemisphere of the lesion as well as the communication between the hemispheres increased with the therapy. Finally, an experiment investigating potential effects of intensive robot-based interventions on muscle activity is presented. Results from four stroke patients did not indicate that the rehabilitation training induces muscular fatigue. The final chapter presents complementary work on enhancing existing Brain-Machine interface therapies. Sensory feedback to the patient is improved by way of displaying a virtual representation of an exoskeleton for training of movements of hand and arm. Furthermore, a rehabilitation training was embedded in a gamified environment for maximizing immersion and motivation of the patient. The biomarkers presented in these studies could serve as building blocks for modeling neurorehabilitation and to track and visualize recovery in such enriched training environments. The transition of this research to new or improved modern therapeutic approaches in clinical practice could serve millions of stroke victims