Quantitative EEG and Verbal Fluency in DBS Patients: Comparison of Stimulator-On and -Off Conditions

Introduction: Deep brain stimulation of the subthalamic nucleus (STN-DBS) ameliorates motor function in patients with Parkinson's disease and allows reducing dopaminergic therapy. Beside effects on motor function STN-DBS influences many non-motor symptoms, among which decline of verbal fluency test performance is most consistently reported. The surgical procedure itself is the likely cause of this decline, while the influence of the electrical stimulation is still controversial. STN-DBS also produces widespread changes of cortical activity as visualized by quantitative EEG. The present study aims to link an alteration in verbal fluency performance by electrical stimulation of the STN to alterations in quantitative EEG.Methods: Sixteen patients with STN-DBS were included. All patients had a high density EEG recording (256 channels) while testing verbal fluency in the stimulator on/off situation. The phonemic, semantic, alternating phonemic and semantic fluency was tested (Regensburger Wortflüssigkeits-Test).Results: On the group level, stimulation of STN did not alter verbal fluency performance. EEG frequency analysis showed an increase of relative alpha2 (10–13 Hz) and beta (13–30 Hz) power in the parieto-occipital region (p ≤ 0.01). On the individual level, changes of verbal fluency induced by stimulation of the STN were disparate and correlated inversely with delta power in the left temporal lobe (p < 0.05).Conclusion: STN stimulation does not alter verbal fluency performance in a systematic way at group level. However, when in individual patients an alteration of verbal fluency performance is produced by electrical stimulation of the STN, it correlates inversely with left temporal delta power

Frequency-specific network activity predicts bradykinesia severity in Parkinson's disease

Objective Bradykinesia has been associated with beta and gamma band interactions in the basal ganglia-thalamo-cortical circuit in Parkinson’s disease. In this present cross-sectional study, we aimed to search for neural networks with electroencephalography whose frequency-specific actions may predict bradykinesia. Methods Twenty Parkinsonian patients treated with bilateral subthalamic stimulation were first prescreened while we selected four levels of contralateral stimulation (0: OFF, 1–3: decreasing symptoms to ON state) individually, based on kinematics. In the screening period, we performed 64-channel electroencephalography measurements simultaneously with electromyography and motion detection during a resting state, finger tapping, hand grasping tasks, and pronation-supination of the arm, with the four levels of contralateral stimulation. We analyzed spectral power at the low (13–20 Hz) and high (21–30 Hz) beta frequency bands and low (31–60 Hz) and high (61–100 Hz) gamma frequency bands using the dynamic imaging of coherent sources. Structural equation modelling estimated causal relationships between the slope of changes in network beta and gamma activities and the slope of changes in bradykinesia measures. Results Activity in different subnetworks, including predominantly the primary motor and premotor cortex, the subthalamic nucleus predicted the slopes in amplitude and speed while switching between stimulation levels. These subnetwork dynamics on their preferred frequencies predicted distinct types and parameters of the movement only on the contralateral side. Discussion Concurrent subnetworks affected in bradykinesia and their activity changes in the different frequency bands are specific to the type and parameters of the movement; and the primary motor and premotor cortex are common nodes

Magnetoencephalography in cognitive neuroscience: a primer

Magnetoencephalography (MEG) is an invaluable tool to study the dynamics and connectivity of large-scale brain activity and their interactions with the body and the environment in functional and dysfunctional body and brain states. This primer introduces the basic concepts of MEG, discusses its strengths and limitations in comparison to other brain imaging techniques, showcases interesting applications, and projects exciting current trends into the near future, in a way that might more fully exploit the unique capabilities of MEG

How Does Technology Development Influence the Assessment of Parkinson’s Disease? A Systematic Review

abstract: Parkinson’s disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The pathology for PD is difficult and expensive. Furthermore, it depends on patient diaries and the neurologist’s subjective assessment of clinical scales. Objective, accurate, and continuous patient monitoring have become possible with the advancement in mobile and portable equipment. Consequently, a significant amount of work has been done to explore new cost-effective and subjective assessment methods or PD symptoms. For example, smart technologies, such as wearable sensors and optical motion capturing systems, have been used to analyze the symptoms of a PD patient to assess their disease progression and even to detect signs in their nascent stage for early diagnosis of PD. This review focuses on the use of modern equipment for PD applications that were developed in the last decade. Four significant fields of research were identified: Assistance diagnosis, Prognosis or Monitoring of Symptoms and their Severity, Predicting Response to Treatment, and Assistance to Therapy or Rehabilitation. This study reviews the papers published between January 2008 and December 2018 in the following four databases: Pubmed Central, Science Direct, IEEE Xplore and MDPI. After removing unrelated articles, ones published in languages other than English, duplicate entries and other articles that did not fulfill the selection criteria, 778 papers were manually investigated and included in this review. A general overview of PD applications, devices used and aspects monitored for PD management is provided in this systematic review.Dissertation/ThesisMasters Thesis Computer Engineering 201

Unilateral deep brain stimulation suppresses alpha and beta oscillations in sensorimotor cortices

Deep brain stimulation (DBS) is an established therapy to treat motor symptoms in movement disorders such as Parkinson's disease (PD). The mechanisms leading to the high therapeutic effectiveness of DBS are poorly understood so far, but modulation of oscillatory activity is likely to play an important role. Thus, investigating the effect of DBS on cortical oscillatory activity can help clarifying the neurophysiological mechanisms of DBS. Here, we aimed at scrutinizing changes of cortical oscillatory activity by DBS at different frequencies using magneto-encephalography (MEG)

Ruhenetzwerke von Parkinsonpatienten – Effekte der Dopamintherapie

Die Motivation der hier vorliegenden Studie war es, Ruhenetzwerke von Parkinsonpatienten mit der Elektroenzephalographie (EEG) auf einer globalen Hirnnetzwerkebene zu analysieren. Als übergreifendes Ziel der Arbeit galt der vertiefte Einblick in pathophysiologische Mechanismen und Effekte der dopaminergen Therapie. Dabei wurden im Einzelnen folgende Hypothesen untersucht: 1) Die Extraktion von Ruhenetzwerken in einem globalen Analyseansatz ohne a priori Annahmen wurde bislang für magnetenzephalographische (MEG) Daten demonstriert. Es wurde angenommen, dass sich dieser Ansatz auch auf EEG Daten übertragen lässt und robuste Ergebnisse generieren würde. 2) Das Verständnis von Morbus Parkinson geht über eine reine Bewegungsstörung weit hinaus. Als Ausdruck einer solchen globalen Neurodegeneration waren daher pathologische Netzwerkveränderungen im Vergleich von Patienten und Gesunden zu erwarten, die sich nicht nur auf motorische Netzwerke beschränken würden. 3) Die dopaminerge Therapie stellt unverändert den zentralen Baustein der Behandlung der Parkinsonerkrankung dar. Als Ausdruck der resultierenden klinischen Besserung waren auch auf der Netzwerkebene spezifische Therapieeffekte zu erwarten. Inwiefern dies durch Restitution physiologischer Netzwerkmuster oder Etablierung einer alternativen Netzwerkstruktur erfolgen würde, sollte näher untersucht werden. ad 1) In der Literatur mit funktioneller Magnetresonanztomographie (fMRT) gut untersuchte und als etabliert geltende Ruhenetzwerke konnten auch in den EEG Daten identifiziert werden. Dabei wurde die eigentliche Netzwerkextraktion mittels einer Independent Component Analysis (ICA) durch Lösung des inversen Problems im Quellenraum lokalisiert. So konnte neben der im EEG grundsätzlich guten zeitlichen Auflösung auch die räumliche Auflösung optimiert werden. ad 2) Bei den näher untersuchten Ruhenetzwerken ließen sich spezifische räumliche und frequenzbezogene Veränderungen feststellen, welche in die bestehende Forschungsliteratur eingegliedert werden konnten und gleichzeitig das Verständnis dieser Veränderungen erweiterten. Insbesondere für den Bereich von motorischen Arealen zeigte sich ein präzises pathologisches Korrelat im b-Frequenzband, was erneut die Schlüsselrolle von b-Oszillationen betonte. Desweiteren zeigten sich Veränderungen des Default Mode Network (DMN) und des visuellen Netzwerks mit aktuell unklarer klinischer Relevanz. ad 3) Im Bereich der motorischen Kortexareale zeigte das supplementär motorische Areal (SMA) im Sinne einer Restitution auf nahezu physiologische räumliche Netzwerkparameter unmittelbare Effekte der medikamentösen Therapie. Dies war im Einklang mit einer wachsenden Evidenz vor allem aus der fMRT Literatur. Als neuer Aspekt ergab sich nun der offenbar spezifische Effekt im g-Frequenzband

A Novel Neurorehabilitation Model Designed to Examine the Neural Plasticity Involved in Disease

Parkinson's disease (PD) is a progressive neurodegenerative disorder that that is most often characterised for its motor impairments. However, people with PD (PwPD) often experience a range of mental health and non-motor issues alongside their physical symptoms. Exercise has shown to positively impact and improve PD motor symptoms, less research observations have been shown in PD mental health and non-motor symptoms. Dance is a great form of exercise which provides both aerobic and anaerobic movements. Dance is constantly changing providing a creative outlet, dance provides flexibility and balance/coordination, develops social skills thereby improving mental health, and lastly dance with music combination allows this form of exercise to be unique in that it encompasses a multisensory component that exercise alone cannot provide. My dissertation aims to understand how dance impacts PD motor, non-motor symptoms and if the changes are associated to specific brain related alterations. Using behavioral, motor and EEG approaches, I will present three separate experiments to test the effects of dance on people with PD by first studying the potential impacts of dance on short-term behavioral changes in PwPD and their overall Quality of Life (QoL) after a 12-week dance intervention. Second I will present a novel examination of the interaction of dance on both behavioural measures and electroencephalography (EEG) activity before and after the short-term (1.25 hour) course of a single dance class. The third study is a novel examination of the interaction of dance on the progression of both behavioural measures and non-motor symptoms over the long-term course of participating in multiple dance classes over a 3-year period of time. Finally, EEG activity changes over the long-term course of participating in multiple dance classes over a 3-year period of time is presented. The results of these studies strengthen the idea of dance being an alternative or additional therapy for PwPD and also provides putative neuroplastic changes in the diseased brain

Clinical applications of real-time FMRI neurofeedback training – premises, promises, and pitfalls

Neurofeedback training represents a form of biofeedback training with a history of over 50 years. During neurofeedback training participants aim to gain control over a feedback signal that represents the activity of a brain region or network of interest. As such, it holds promise for clinical translation as an add-on treatment for psychiatric and neurological conditions. Yet, currently available evidence for its therapeutic efficacy remains limited. Originally provided based on cortical signals measured with electroencephalography (EEG), methodological developments have allowed providing neurofeedback based on (cortical and subcortical) brain signals acquired from functional magnetic resonance imaging (fMRI). The aim of this thesis was to test the feasibility and clinical efficacy of fMRI neurofeedback (fMRI-NF) training in a psychiatric population and to develop protocols that allow translating the technique to motor rehabilitation. Specifically, this thesis summarises the clinical and neuroimaging results from a randomised controlled trial conducted in patients suffering from depression. Depression represents a leading cause of disability in adults and epidemiological data indicates that up to one third of patients remain depressed after treatment. Another focus was the development of a motor imagery-based fMRI-NF protocol in healthy participants. This work has informed a proof-of-concept study for motor rehabilitation in stroke survivors, for which the methodology was preregistered on a public platform before data collection started to increase transparency. The thesis aims to address problematic research practices that have been attributed to the replication crisis in many areas of science, including a clear separation of planned and exploratory hypotheses and the use and adaptation of alternative statistical methods. A review chapter discusses potential electrophysiological target signatures for EEG-NF to improve motor symptoms in Parkinson’s disease patients. The thesis concludes with a discussion of current premises, promises, and pitfalls in clinical applications of neurofeedback training and considerations for clinical trials development