The role of alpha oscillations in premotor-cerebellar connectivity in motor sequence learning: Insights from transcranial alternating current stimulation

Alpha oscillations (8-13 Hz) have been suggested to play an important role in dynamic neural processes underlying learning and memory. The goal of this work was to scrutinize the role of alpha oscillations in communication within a cortico-cerebellar network implicated in motor sequence learning. To this end, we conducted two EEG experiments using a serial reaction time task. In the first experiment, we explored changes in alpha power and cross-channel alpha coherence as subjects learned a motor sequence. We found a gradual decrease in spectral alpha power over left premotor cortex (PMC) and sensorimotor cortex (SM1) during learning blocks. In addition, alpha coherence between left PMC/SM1 and left cerebellar crus I was specifically decreased during sequence learning, possibly reflecting a functional decoupling in the broader motor learning network. In the second experiment in a different cohort, we applied 10Hz transcranial alternating current stimulation (tACS), a method shown to entrain local oscillatory activity, to left M1 (lM1) and right cerebellum (rCB) during sequence learning. We observed a tendency for diminished learning following rCB tACS compared to sham, but not following lM1 tACS. Learning-related alpha power following rCB tACS was increased in left PMC, possibly reflecting increase in local inhibitory neural activity. Importantly, learning-specific alpha coherence between left PMC and right cerebellar lobule VIIb was enhanced following rCB tACS. These findings provide strong evidence for a causal role of alpha oscillations in controlling information transfer in a premotor-cerebellar loop during motor sequence learning. Our findings are consistent with a model in which sequence learning may be impaired by enhancing premotor cortical alpha oscillation via external modulation of cerebellar oscillations.:1 List of Abbreviations 2 Introduction 2.1 Motor Learning Stages 2.2 Motor Learning Tasks 2.3 Motor Learning Network 2.4 Theoretical Models of Motor Learning 2.5 Functional Connectivity of Motor Brain Regions 2.6 Effective Connectivity of Motor Brain Regions 2.7 Oscillations in Neuronal Communication 2.8 Alpha Oscillations 2.8.1 Role of Alpha Oscillations in Motor Sequence Learning 2.9 Transcranial Electric Stimulation 2.9.1 Transcranial Alternating Current Stimulation (tACS) 2.10 Summary of Study Rationale 3 Publication 4 Summary 5 List of References 6 Supplementary Materials 7 Contribution of Authors / Darstellung des eigenen Beitrags 8 Declaration of Authorship 9 Curriculum Vitae 10 Publication and Presentation 11 Acknowledgement / Danksagun

Neural oscillations during conditional associative learning.

Associative learning requires mapping between complex stimuli and behavioural responses. When multiple stimuli are involved, conditional associative learning is a gradual process with learning based on trial and error. It is established that a distributed network of regions track associative learning, however the role of neural oscillations in human learning remains less clear. Here we used scalp EEG to test how neural oscillations change during learning of arbitrary visuo-motor associations. Participants learned to associative 48 different abstract shapes to one of four button responses through trial and error over repetitions of the shapes. To quantify how well the associations were learned for each trial, we used a state-space computational model of learning that provided a probability of each trial being correct given past performance for that stimulus, that we take as a measure of the strength of the association. We used linear modelling to relate single-trial neural oscillations to single-trial measures of association strength. We found frontal midline theta oscillations during the delay period tracked learning, where theta activity was strongest during the early stages of learning and declined as the associations were formed. Further, posterior alpha and low-beta oscillations in the cue period showed strong desynchronised activity early in learning, while stronger alpha activity during the delay period was seen as associations became well learned. Moreover, the magnitude of these effects during early learning, before the associations were learned, related to improvements in memory seen on the next presentation of the stimulus. The current study provides clear evidence that frontal theta and posterior alpha/beta oscillations play a key role during associative memory formation

Cortical motor network modulation: Common mechanisms parallel efficient motor integration in implicit motor learning in healthy subjects and subthalamic neurostimulation in Parkinson’s disease

On the one hand, the neuronal circuitry and connectivity of the large-scale motor network play an important role in many human cognitive functions, i. e. in implicit motor learning. On the other hand, alterations in connectivity of the motor network are also a hallmark in the pathophysiology of a variety of psychological and neurological diseases, such as Parkinson’s disease. Here, we set out to study the motor network activity (more exactly the cortical and spinal aspects of it) under two different aspects: in healthy controls during implicit motor learning and in Parkinson’s disease patients in the conditions ‘stimulation off’ and ‘stimulation on’. To this end, 12 healthy controls and 20 Parkinson’s disease patients performed externally paced right finger movements with simultaneous recordings of a 64-channel EEG and EMG of the forearm muscles. The healthy controls performed the serial reaction time task. Parkinson’s disease patients conducted the baseline of this task with only random trials in the two conditions ‘stimulation off’ and ‘stimulation on ‘. Cortical and muscular activity was analyzed by time-frequency movement-related spectral perturbations and by power spectral density and corticospinal synchronization was assessed by time-frequency cross-spectra coherence. Clinically, Parkinson’s disease patients improved significantly with deep brain stimulation, assessed by the Unified Parkinson’s Disease Rating Scale III score, the reaction time and the error ratio. Deep brain stimulation lead to an increased cortical beta-band movement-related desynchronization, which was topographically spread over a wider cortical area. Besides, in ‘stimulation off’ after finger tap we found a premature beta-band rebound of the corticomuscular coherence to the extensor digitorum over the primary sensorimotor cortex, which was suppressed with stimulation on. The healthy controls presented with significantly reduced reaction times in the ‘sequence blocks’ compared to ‘random blocks’. In ‘sequence blocks’, power spectral density increased mainly over the right posterior parietal cortex but also over a larger left-hemispheric cortical area in alpha and low beta band. Alpha and beta band movement-related desynchronization presented most pronounced over the bilateral prefrontal, fronto-central and central channels. The movement-related desynchronization was significantly modulated over the course of implicit motor learning. The present findings reveal the impressive modulation of the motor network activity including cortical activations and corticospinal synchronizations introduced by deep brain stimulation therapy of the subthalamic nucleus in Parkinson’s disease

Beta oscillations underlie top-down, feedback control while gamma oscillations reflect bottom-up, feedforward influences

Prefrontal cortex (PFC) is critical to behavioral flexibility and, hence, the top-down control over bottom-up sensory information. The mechanisms underlying this capacity have been hypothesized to involve the propagation of alpha/beta (8-30 Hz) oscillations via feedback connections to sensory regions. In contrast, gamma (30-160 Hz) oscillations are thought to arise as a function of bottom-up, feedforward stimulation. To test the hypothesis that such oscillatory phenomena embody such functional roles, we assessed the performance of nine monkeys on tasks of learning, categorization, and working memory concurrent with recording of local field potentials (LFPs) from PFC. The first set of tasks consisted of two classes of learning: one, explicit and, another, implicit. Explicit learning is a conscious process that demands top-down control, and in these tasks alpha/beta oscillations tracked learning. In contrast, implicit learning is an unconscious process that is automatic (i.e. bottom up), and in this task alpha/beta oscillations did not track learning. We next looked at dot-pattern categorization. In this task, category exemplars were generated by jittering the dot locations of a prototype. By chance, some of these exemplars were similar to the prototype (low distortion), and others were not (high distortion). Behaviorally, the monkeys performed well on both distortion levels. However, alpha/beta band oscillations carried more category information at high distortions, while gamma-band category information was greatest on low distortions. Overall, the greater the need for top-down control (i.e. high distortion), the greater the beta, and the lesser the need (i.e. low distortion), the greater the gamma. Finally, laminar electrodes were used to record from animals trained on working memory tasks. Each laminar probe was lowered so that its set of contacts sampled all cortical layers. During these tasks, gamma oscillations peaked in superficial layers, while alpha/beta peaked in deep layers. Moreover, these deep-layer alpha/beta oscillations entrained superficial alpha/beta, and modulated the amplitude of superficial-layer gamma oscillations. These laminar distinctions are consistent with anatomy: feedback neurons originate in deep layers and feedforward neurons in superficial layers. In summary, alpha/beta oscillations reflect top-down control and feedback connectivity, while gamma oscillations reflect bottom-up processes and feedforward connectivity

The Role of the Dorsal Premotor and Superior Parietal Cortices in Decoupled Visuomotor Transformations

In order to successfully interact with objects located within our environment, the brain must be capable of combining visual information with the appropriate felt limb position (i.e. proprioception) in order compute an appropriate coordinated muscle plan for accurate motor control. Eye-hand coordination is essential to our independence as a species and relies heavily on the reciprocally-connected regions of the parieto-frontal reach network. The dorsal premotor cortex (PMd) and the superior parietal lobule (SPL) remain prime candidates within this network for controlling the transformations required during visually-guided reaching movements. Our brains are primed to reach directly towards a viewed object, a situation that has been termed a “standard” or coupled reach. Such direct eye-hand coordination is common across species and is crucial for basic survival. Humans, however, have developed the capacity for tool-use and thus have learned to interact indirectly with an object. In such “non-standard” or decoupled situations, the directions of gaze and arm movement have been spatially decoupled and rely on both the implementation of a cognitive rule and on online feedback of the decoupled limb. The studies included within this dissertation were designed to further characterize the role of PMd and SPL during situations in which when a reach requires a spatial transformation between the actions of the eyes and the hand. More specifically, we were interested in examining whether regions within PMd (PMdr, PMdc) and SPL (PEc, MIP) responded differently during coupled versus decoupled visuomotor transformations. To address the relative contribution of these various cortical regions during decoupled reaching movements, we trained two female rhesus macaques on both coupled and decoupled visually-guided reaching tasks. We recorded the neural activity (single units and local field potentials) within each region while the animals performed each condition. We found that two separate networks emerged each contributing in a distinct ways to the performance of coupled versus decoupled eye-hand reaches. While PMdr and PEc showed enhanced activity during decoupled reach conditions, PMdc and MIP were more enhanced during coupled reaches. Taken together, these data presented here provide further evidence for the existence of alternate task-dependent neural pathways for visuomotor integration

The neuropsychological measure (EEG) of flow under conditions of peak performance

Flow is a mental state characterised by a feeling of energised focus, complete involvement and success when fully immersed in an activity. The dimensions of and the conditions required for flow to occur have been explored in a broad spectrum of situational contexts. The close relationship between flow and peak performance sparked an interest in ways to induce flow. However, any process of flow induction requires a measure to trace the degree to which flow is in fact occurring. Self-reports of the flow experience are subjective and provide ad hoc information. Psycho-physiological measures, such as EEG, can provide objective and continuous indications of the degree to which flow is occurring. Unfortunately few studies have explored the relationships between psycho-physiological measures and flow. The present study was an attempt to determine the EEG correlates of flow under conditions of peak performance. Twenty participants were asked to perform a continuous visuomotor task 10 times. Time taken per task was used as an indicator of task performance. EEG recordings were done concurrently. Participants completed an Abbreviated Flow Questionnaire (AFQ) after each task and a Game Flow Inventory (GFI) after having finished all 10 tasks. On completion, performance times and associated flow scores were standardised where after the sample was segmented into a high flow - peak performance and a low flow - low performance level. Multi-variate analysis of variance (MANOVA) was conducted on the performance, flow and EEG data to establish that a significant difference existed between the two levels. In addition, a one-way analysis of variance between high and low flow data was conducted for all variables and main effects were established. Inter-correlations of all EEG data at both levels were then conducted across four brain sites (F3, C3, P3, O1). In high flow only, results indicated increased lobeta power in the sensorimotor cortex together with a unique EEG pattern showing beta band synchronisation between the prefrontal and sensori-motor areas and de-synchronisation between all other areas, while all other frequencies (delta, theta, alpha, lobeta, hibeta, and gamma) remained synchronised across all scalp locations. These findings supported a theoretical neuropsychological model of flow.PsychologyD. Com. (Consulting Psychology

Neural basis of implicit motor sequence learning : Modulation of cortical power

Implicit sequence learning describes the acquisition of serially ordered movements and sequentially structured cognitive information, that occurs without awareness. Theta, alpha and beta cortical oscillations are present during implicit motor sequence learning, but their role in this process is unclear. The current study addressed this gap in the literature. A total of 50 healthy adults aged between 19 and 37 years participated in the study. Implicit motor sequence learning was examined using the Serial Reaction Time task where participants unknowingly repeat a sequence of finger movements in response to a visual stimulus. Sequence learning was examined by comparing reaction times and oscillatory power between sequence trials and a set of control trials comprising random stimulus presentations. Electroencephalography was recorded as participants completed the task. Analyses of the behavioral data revealed participants learnt the sequence. Analyses of oscillatory activity, using permutation testing, revealed sequence learning was associated with a decrease in theta band (4–7 Hz) power recorded over frontal and central electrode sites. Sequence learning effects were not observed in the alpha (7–12 Hz) or beta bands (12–20 Hz). Even though alpha and beta power modulations have long been associated with executing a motor response, it seems theta power is a correlate of sequence learning in the manual domain. Theta power modulations on the serial reaction time task may reflect disengagement of attentional resources, either promoting or occurring as a consequence of implicit motor sequence learnin

Advances in Clinical Neurophysiology

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Towards cerebral and behavioral representations of motor learning and its interaction with interference and sleep

Das Lernen neuer Bewegungen spielt eine fundamentale Rolle in der Natur. Menschliche Bewegungen sind in der Regel sehr komplex, auch wenn es auf den ersten Blick nicht so erscheint. Die meisten dieser komplexen Bewegungen werden im Laufe des Lebens erlernt und erfordern somit eine Lernfähigkeit des Individuums. Wie diese motorischen Lernprozesse funktionieren, ist im Detail bisher nicht bekannt. Dennoch ist ein tieferes Verständnis dieser Prozesse notwendig, um Trainingsprotokolle in der Rehabilitation oder im Sport zu verbessern. In der wissenschaftlichen Literatur liegen zahlreiche Befunde vor, die darauf hindeuten. dass Interferenzen im Üben einen Einfluss auf die spätere Gedächtnisleistung und somit die Konsolidierung haben. Solche Einflüsse können sowohl positiv als auch negativ sein, in Abhängigkeit davon, ob Interferenzen während des Übens oder aber in unterschiedlichen Übungseinheiten mit entgegengesetzten kinematischen oder dynamischen Bedingungen stattfinden. Die neuronalen Mechanismen, die entweder zu einem Vorteil oder einen Nachteil führen, sind bisher unbekannt. Das Ziel dieser Dissertationsschrift war es deshalb, in drei empirischen Arbeiten die neuronalen Grundlagen zu untersuchen, die entweder positive oder negative Effekte nach einem interferenzhaltigen Üben hervorrufen. Dabei wurde neben der motorischen Adaptation an Kraftfeldbedingungen auch das Elektroenzephalogramm (EEG) aufgezeichnet. Die erste Studie untersuchte, ob sehr instabile im Vergleich zu eher stabilen Übungsbedingungen zu einer besseren Konsolidierungsleistung führten. Dieses Phänomen, welches bereits als Kontextinterferenzeffekt bekannt ist, konnte hier mit einer motorischen Adaptationsaufgabe an veränderte dynamische Bedingungen bestätigt werden. Zusätzliche EEG-Analysen zeigten, dass diese positiven Effekte auf der Verhaltensebene mit Veränderungen in den Alpha-Frequenzen des parietalen Kortex in Verbindung zu stehen scheinen. Tiefgreifende Analysen deuten an, dass die positiven Befunde variablen Übens durch effektive Korrekturen auf Grund von Feedbackmechanismen hervorgerufen wurden. Jedoch sind zukünftige Studien notwendig, um diese Annahme zu bestätigen. Die Befunde aus der ersten Studie konnten auch in der zweiten Studie dieser Dissertationsschrift reproduziert werden. Zusätzlich wurde in dieser zweiten Studie untersucht, ob Schlaf einen Einfluss auf die Konsolidierungsfähigkeit einer Kraftfeldadaptationsaufgabe hat und ob solch ein Einfluss durch instabile Übungsbedingungen beeinflusst wird. Dazu wurden drei unterschiedliche Methoden kombiniert, nämlich: das Kraftfeldparadigma, das EEG und das Polysomnogramm. Die Ergebnisse zeigten keinen Hinweis auf einen Schlafeffekt, unabhängig von den Übungsbedingungen. Dennoch konnte im Polysomnogramm eine erhöhte Spindelaktivität in den niedrigen Frequenzbändern gefunden werden, welche mit der motorischen Leistung korrelierte. Die dritte Studie untersuchte, ob eine separate Übungseinheit, 24 Stunden nach der ersten Übungseinheit, einen negativen Einfluss auf den Wiederabruf der motorischen Aufgabe hat. Dabei bestand die Interferenz darin, dass das zu adaptierende Kraftfeld in der separaten Übungseinheit dem Kraftfeld der ersten Übungseinheit räumlich entgegengesetzt war. Dieses so genannte \u27ABA\u27 Paradigma führte zu einer verringerten motorischen Leistung im Wiederabruf der Aufgabe aus der ersten Übungseinheit, verglichen mit einer Kontrollgruppe, die keine separate Übungseinheit hatte. Die vergleichsweise bessere Leistung der Kontrollgruppe beim Wiederabruf trat zusammen mit einer erhöhten Leistung der Gamma-Frequenzen im EEG über frontalen Gehirnarealen auf. Dieser Effekt war jedoch nur von kurzer Dauer und verringerte sich während der Nachtestung. Insgesamt konnte diese Dissertationsschrift zeigen, dass EEG Aufnahmen während motorischer Adaptationsaufgaben an Robotermanipulanda möglich sind. Hohe Interferenzen während der Übungseinheit können einen positiven Einfluss auf die Konsolidierungsleistung haben, welche mit den Alpha-Frequenzen über parietalen Gehirnarealen in Verbindung steht. Es ist möglich, dass die positiven Befunde auf der Verhaltensebene durch Feedback-Korrekturmaßnahmen während der Bewegungsdurchführung hervorgerufen wurden. Außerdem konnte die zweite Studie zeigen, dass die Leistung im EEG Alpha Band die behaviorale Konsolidierungsleistung prädizieren kann, sofern das Üben unter eher stabilen Bedingungen stattfand. Wenn Interferenzen jedoch durch unterschiedliche Übungseinheiten mit gegen-sätzlichen Kraftfeldrichtung hervorgerufen werden, verringert diese Interferenz die motorische Leistung beim Wiederabruf. Dieser verringerte Wiederabruf fällt jedoch weniger stark aus, wenn das Üben mittels eingestreuter \u27catch trials\u27 variabler gestaltet wird. Somit scheint die Konsolidierung motorischer Erinnerungen nicht nur auf den Faktor \u27Zeit\u27 zu beruhen. Unterschiedliche Resultate ähnlicher Studien lassen vermuten, dass \u27Zeit\u27, aber nicht \u27Schlaf\u27, ein wichtiger Faktor für die Konsolidierung von Adaptationsaufgabe darstellt, es jedoch noch weitere Faktoren wie zum Beispiel die Übungsvariabilität gibt, die den uneinheitlichen Forschungsstand hervorrufen