Intermittent neural synchronization in Parkinson's disease - PubMed (original) (raw)
Intermittent neural synchronization in Parkinson's disease
Leonid L Rubchinsky et al. Nonlinear Dyn. 2012.
Abstract
Motor symptoms of Parkinson's disease are related to the excessive synchronized oscillatory activity in the beta frequency band (around 20Hz) in the basal ganglia and other parts of the brain. This review explores the dynamics and potential mechanisms of these oscillations employing ideas and methods from nonlinear dynamics. We present extensive experimental documentation of the relevance of synchronized oscillations to motor behavior in Parkinson's disease, and we discuss the intermittent character of this synchronization. The reader is introduced to novel time-series analysis techniques aimed at the detection of the fine temporal structure of intermittent phase locking observed in the brains of parkinsonian patients. Modeling studies of brain networks are reviewed, which may describe the observed intermittent synchrony, and we discuss what these studies reveal about brain dynamics in Parkinson's disease. The parkinsonian brain appears to exist on the boundary between phase-locked and nonsynchronous dynamics. Such a situation may be beneficial in the healthy state, as it may allow for easy formation and dissociation of transient patterns of synchronous activity which are required for normal motor behavior. Dopaminergic degeneration in Parkinson's disease may shift the brain networks closer to this boundary, which would still permit some motor behavior while accounting for the associated motor deficits. Understanding the mechanisms of the intermittent synchrony in Parkinson's disease is also important for biomedical engineering since efficient control strategies for suppression of pathological synchrony through deep brain stimulation require knowledge of the dynamics of the processes subjected to control.
Figures
Figure 1
Schematics of basal ganglia-thalamocortical circuitry. The basal ganglia receive inputs from large cortical areas to striatum and subthalamic nucleus (STN), which is a major site for surgical interventions and thus the main location from which intraoperative electrophysiological data are recorded. The output nucleus of the basal ganglia, Globus Pallidus pars interna (GPi), sends its projections to the thalamus as well as to the brainstem. Other depicted basal ganglia structures are Globus Pallidus pars externa (GPe), Substantia Nigra pars compacta (SNc) and pars reticulata (SNr), and striatum. Excitatory, inhibitory and modulatory dopaminergic projections are presented in the diagram by circles, bars and arrows respectively. While the complete neuroarchitecture of these networks is more complicated, the diagram presents the major pathways.
Figure 2
Electrical activity of Parkinsonian basal ganglia. A is raw recordings of extracellular unit (spiking signal); C is recordings of LFP signal. The signals are modulated in many different ways. Because of the relationship between beta-band activity and parkinsonian motor symptoms, the episodes with significant oscillatory activity in the beta band were detected and the data from these episodes are band-pass filtered to the beta band only, resulting in a more sine-like LFP signal (B) and spiking signal (D).
Figure 3
Fourier spectra of extracellular spiking unit (A) and LFP (B) recorded in STN of a parkinsonian patient. In both cases, there is a relatively broad peak in the beta frequency band.
Figure 4
Dynamics of synchronous activity in time. Black line is the value of the phase-locking index γ computed over a short time-window with duration of 1s (A) and 1.5s (B). Each point on the graph of γ(t) is the value of γ computed over the time-window preceding this point. The gray line is the 95% significance level estimate, obtained from surrogate date as described in [65,66].
Figure 5
The diagram of the first-return map of the phases {ϕspikes,i}, i = 1,…N. The arrows describe all possible transitions between four regions of the phase space. The expressions next to the arrows are the rates for each particular type of transitions between the regions. The regions are numbered in a clockwise manner, because the dynamics in the (ϕspikes,i, ϕspikes,i +1) space mostly follows clockwise pattern.
Figure 6
An example of a short piece of an episode of synchronous LFP and spiking activity in parkinsonian basal ganglia. (A) The upper and the lower panels contain raw and filtered data. The upper panel contains the spikes (gray line) and the spiking signal band-pass filtered to beta-band band (black line), the lower panel contains raw LFP signal (gray line) and LFP filtered in the same way (black dotted line). The middle panel has the value of the sine of the phases of filtered spiking and LFP signals. There is clearly visible, but not perfect phase synchrony. Star is placed to mark the phase of the filtered spiking signal, when the filtered LFP signal phase crosses zero from below. These marked phases generate the sequence {ϕspikes,i}, i = 1,…N used to construct the first-return map shown in part B. (B) The first-return map (ϕspikes,i, ϕspikes,i +1) generated form the data at the part A. All points are within the first region of the phase space, which corresponds to the phase-locked state. This phase-locking is not perfect, but the phase difference between signals is not changed much during the observation time.
Figure 7
First return map obtained from experimentally recorded extracellular units and LFPs from the subthalamic nucleus of a parkinsonian patient. The dynamics is not perfectly synchronous, as evidenced by a scatter of points, however, the tendency for phase-locking is visible: there is a higher density of points in the first (synchronized) region.
Figure 8
The transition rates _r_1, _r_2, _r_3, _r_4 obtained from data recorded in a sample of parkinsonian patients. Average and standard deviation are shown. Four different bars for each transition rate are the values of that rate computed in different ways. Different selection criteria of synchronized episodes (different length of duration of the running window to compute the phase-locking index) are represented by black/dark gray and light gray/white bars. Different averaging procedures (arithmetic mean value and weighted mean computed with weights proportional to the length of each individual episode from the data) are represented by black/light gray and dark gray/white bars. The rates obtained with different methods are only slightly different from each other. Overall, the rates are not very sensitive to the data selection criteria and averaging technique.
Figure 9
The histogram of the durations of desynchronization events. The white bars correspond to computing the frequencies of duration within each data episode and averaging them across the episode (this corresponds to the arithmetical mean rates in Fig. 8). The gray bars correspond to the averaging the frequencies of desynchronization event durations for all episodes together (this corresponds to the arithmetical mean rates in Fig. 8, each data episode makes an impact proportional to its length). All durations of six cycles of oscillations and longer are pulled together in “>5” group. Similar to the rates (Fig. 8), different averaging techniques give values, which are different only slightly; overall tendency for the largest first bin of the histogram is preserved.
Figure 10
Dynamics of model’s synchronous activity in time. The phase-locking index γ is computed for model spiking and LFP in the same way as it was computed for the real data in Fig. 4. The duration of the time-window used for computation of γ is 1s (A) and 1.5s (B). The gray line is the 95% significance level estimate, obtained from surrogate data.
Figure 11
First return map obtained from the model-generated spiking and LFP signals. Compare with the Fig. 7.
Figure 12
The comparison of the model with experimental dynamics in (gsyn, Iapp) parameter space (follows [87]). The circles indicate the number of principle components capturing the dynamics computed from for the slow variable r of all model STN cells (r is the slow variable, and thus it is more appropriate for the study of synchrony in the slow bursting – beta – frequency band, rather than fast variables related to spikes). The lines represent the contours of the parameter domains, where all four model rates ri are within 0.7SD of the experimental rates. Solid, dotted and dashed lines correspond to different values of the weights used to compute model LFP. Note that Iapp is positive in the model (as it was originally developed in [70], thus the increase of inhibition of GPe by striatum, induced by the lack of dopamine, is represented by a decrease in Iapp.
References
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