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

The Effects of Dance on Motor and Non-Motor Functions, and Resting State Electroencephalography in Individuals With Parkinson's Disease and Age-Matched Controls

We investigated the effects of a single Dancing with Parkinson’s (DwP) class on behavior (balance, walking speed, depression) and electroencephalography (resting state - rsEEG) in individuals with Parkinson’s disease (PD) and age-matched controls (CONs). Following a single 75-minute DwP class, individuals with PD demonstrated significant improvements in balance and depression, and CONs showed improvements in walking speed. The rsEEG also showed significant changes in both individual alpha peak frequency (iAPF) and individual alpha peak power (iAPP). CONs showed a global increase in iAPF during eyes open (EO) rsEEG and in iAPP during both eyes closed (EC) and EO conditions. Individuals with PD showed an increase in iAPP lateralized to right frontal areas, while this increase was lateralized to the left in CONs. We provide novel evidence for change in motor and non-motor functions with modulation of rsEEG alpha activity following dance class in individuals with PD and CONs

The Effects of Dance on Motor and Non-Motor Functions, and Resting State Electroencephalography in Individuals With Parkinson's Disease and Age-Matched Controls

We investigated the effects of a single Dancing with Parkinson’s (DwP) class on behavior (balance, walking speed, depression) and electroencephalography (resting state - rsEEG) in individuals with Parkinson’s disease (PD) and age-matched controls (CONs). Following a single 75-minute DwP class, individuals with PD demonstrated significant improvements in balance and depression, and CONs showed improvements in walking speed. The rsEEG also showed significant changes in both individual alpha peak frequency (iAPF) and individual alpha peak power (iAPP). CONs showed a global increase in iAPF during eyes open (EO) rsEEG and in iAPP during both eyes closed (EC) and EO conditions. Individuals with PD showed an increase in iAPP lateralized to right frontal areas, while this increase was lateralized to the left in CONs. We provide novel evidence for change in motor and non-motor functions with modulation of rsEEG alpha activity following dance class in individuals with PD and CONs

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities

Shaking hands:establishing objective parameters to differentiate between essential tremor and Parkinson's disease

In 1817, James Parkinson was the first physician to publish his observations about the shaking palsy (later: Parkinson’s disease (PD)) and its differences compared to other tremulous disorders [1]. Nowadays, more than 200 years later, a lot more is known about neurodegenerative disorders. However, the exact pathophysiology is yet unknown. Furthermore, differentiation from other tremulous movement disorders, such as essential tremor (ET)], remains difficult due to overlapping symptoms such as tremor or timing deficits during voluntary movement and common diagnostic tools are often either invasive, time consuming, subjective, expensive and/or not widely available.Therefore, in my research I focused on finding objective parameters to differentiate PD from ET that can be measured with commonly available tools. For this purpose we simultaneously measured movement of the hands, using accelerometers, and brain activity using EEG and functional MRI to:1. quantify tremor occurrence and identifying corresponding cortical activity.2. quantify timing deficits during voluntary movement and identifying corresponding neuronal networks.Analyzing cortical activity during tremor revealed cortical involvement in tremor occurrence during rest in PD but not ET. A postural task revealed involvement of the associate and primary visual cortex in ET suggesting that these patients rely on visual guidance for maintaining a posture during tremor. To analyze timing deficits in ET and PD, subjects were asked to perform a bimanual motor task with an without an external cue. In both patient groups areas of motor planning, movement initiation, maintenance and coordination were active. However, activation of additional areas was found in both patient groups.From the results we can conclude that objective differentiation between ET and PD might be possible in the future using only commonly available tools. However, there is still a lot of work that needs to be done

Mean-field analysis of basal ganglia and thalamocortical dynamics

When modeling a system as complex as the brain, considerable simplifications are inevitable. The nature of these simplifications depends on the available experimental evidence, and the desired form of model predictions. A focus on the former often inspires models of networks of individual neurons, since properties of single cells are more easily measured than those of entire populations. However, if the goal is to describe the processes responsible for the electroencephalogram (EEG), such models can become unmanageable due to the large numbers of neurons involved. Mean-field models in which assemblies of neurons are represented by their average properties allow activity underlying the EEG to be captured in a tractable manner. The starting point of the results presented here is a recent physiologically-based mean-field model of the corticothalamic system, which includes populations of excitatory and inhibitory cortical neurons, and an excitatory population representing the thalamic relay nuclei, reciprocally connected with the cortex and the inhibitory thalamic reticular nucleus. The average firing rates of these populations depend nonlinearly on their membrane potentials, which are determined by afferent inputs after axonal propagation and dendritic and synaptic delays. It has been found that neuronal activity spreads in an approximately wavelike fashion across the cortex, which is modeled as a two-dimensional surface. On the basis of the literature, the EEG signal is assumed to be roughly proportional to the activity of cortical excitatory neurons, allowing physiological parameters to be extracted by inverse modeling of empirical EEG spectra. One objective of the present work is to characterize the statistical distributions of fitted model parameters in the healthy population. Variability of model parameters within and between individuals is assessed over time scales of minutes to more than a year, and compared with the variability of classical quantitative EEG (qEEG) parameters. These parameters are generally not normally distributed, and transformations toward the normal distribution are often used to facilitate statistical analysis. However, no single optimal transformation exists to render data distributions approximately normal. A uniformly applicable solution that not only yields data following the normal distribution as closely as possible, but also increases test-retest reliability, is described in Chapter 2. Specialized versions of this transformation have been known for some time in the statistical literature, but it has not previously found its way to the empirical sciences. Chapter 3 contains the study of intra-individual and inter-individual variability in model parameters, also providing a comparison of test-retest reliability with that of commonly used EEG spectral measures such as band powers and the frequency of the alpha peak. It is found that the combined model parameters provide a reliable characterization of an individual's EEG spectrum, where some parameters are more informative than others. Classical quantitative EEG measures are found to be somewhat more reproducible than model parameters. However, the latter have the advantage of providing direct connections with the underlying physiology. In addition, model parameters are complementary to classical measures in that they capture more information about spectral structure. Another conclusion from this work was that a few minutes of alert eyes-closed EEG already contain most of the individual variability likely to occur in this state on the scale of years. In Chapter 4, age trends in model parameters are investigated for a large sample of healthy subjects aged 6-86 years. Sex differences in parameter distributions and trends are considered in three age ranges, and related to the relevant literature. We also look at changes in inter-individual variance across age, and find that subjects are in many respects maximally different around adolescence. This study forms the basis for prospective comparisons with age trends in evoked response potentials (ERPs) and alpha peak morphology, besides providing a standard for the assessment of clinical data. It is the first study to report physiologically-based parameters for such a large sample of EEG data. The second main thrust of this work is toward incorporating the thalamocortical system and the basal ganglia in a unified framework. The basal ganglia are a group of gray matter structures reciprocally connected with the thalamus and cortex, both significantly influencing, and influenced by, their activity. Abnormalities in the basal ganglia are associated with various disorders, including schizophrenia, Huntington's disease, and Parkinson's disease. A model of the basal ganglia-thalamocortical system is presented in Chapter 5, and used to investigate changes in average firing rates often measured in parkinsonian patients and animal models of Parkinson's disease. Modeling results support the hypothesis that two pathways through the basal ganglia (the so-called direct and indirect pathways) are differentially affected by the dopamine depletion that is the hallmark of Parkinson's disease. However, alterations in other components of the system are also suggested by matching model predictions to experimental data. The dynamics of the model are explored in detail in Chapter 6. Electrophysiological aspects of Parkinson's disease include frequency reduction of the alpha peak, increased relative power at lower frequencies, and abnormal synchronized fluctuations in firing rates. It is shown that the same parameter variations that reproduce realistic changes in mean firing rates can also account for EEG frequency reduction by increasing the strength of the indirect pathway, which exerts an inhibitory effect on the cortex. Furthermore, even more strongly connected subcircuits in the indirect pathway can sustain limit cycle oscillations around 5 Hz, in accord with oscillations at this frequency often observed in tremulous patients. Additionally, oscillations around 20 Hz that are normally present in corticothalamic circuits can spread to the basal ganglia when both corticothalamic and indirect circuits have large gains. The model also accounts for changes in the responsiveness of the components of the basal ganglia-thalamocortical system, and increased synchronization upon dopamine depletion, which plausibly reflect the loss of specificity of neuronal signaling pathways in the parkinsonian basal ganglia. Thus, a parsimonious explanation is provided for many electrophysiological correlates of Parkinson's disease using a single set of parameter changes with respect to the healthy state. Overall, we conclude that mean-field models of brain electrophysiology possess a versatility that allows them to be usefully applied in a variety of scenarios. Such models allow information about underlying physiology to be extracted from the experimental EEG, complementing traditional measures that may be more statistically robust but do not provide a direct link with physiology. Furthermore, there is ample opportunity for future developments, extending the basic model to encompass different neuronal systems, connections, and mechanisms. The basal ganglia are an important addition, not only leading to unified explanations for many hitherto disparate phenomena, but also contributing to the validation of this form of modeling