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Failure of delayed feedback deep brain stimulation for intermittent pathological synchronization in Parkinson's disease.

Dovzhenok A, Park C, Worth RM, Rubchinsky LL - PLoS ONE (2013)

Bottom Line: We employ a computational model of the basal ganglia networks which reproduces experimentally observed fine temporal structure of the synchronous dynamics.When the parameters of our model are such that the synchrony is unphysiologically strong, the feedback exerts a desynchronizing action.However, when the network is tuned to reproduce the highly variable temporal patterns observed experimentally, the same kind of delayed feedback may actually increase the synchrony.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Sciences and Center for Mathematical Biosciences, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America. andrey.dovzhenok@uc.edu

ABSTRACT
Suppression of excessively synchronous beta-band oscillatory activity in the brain is believed to suppress hypokinetic motor symptoms of Parkinson's disease. Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). This type of synchrony control was shown to destabilize the synchronized state in networks of simple model oscillators as well as in networks of coupled model neurons. However, the dynamics of the neural activity in Parkinson's disease exhibits complex intermittent synchronous patterns, far from the idealized synchronous dynamics used to study the delayed feedback stimulation. This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. We employ a computational model of the basal ganglia networks which reproduces experimentally observed fine temporal structure of the synchronous dynamics. When the parameters of our model are such that the synchrony is unphysiologically strong, the feedback exerts a desynchronizing action. However, when the network is tuned to reproduce the highly variable temporal patterns observed experimentally, the same kind of delayed feedback may actually increase the synchrony. As network parameters are changed from the range which produces complete synchrony to those favoring less synchronous dynamics, desynchronizing delayed feedback may gradually turn into synchronizing stimulation. This suggests that delayed feedback DBS in Parkinson's disease may boost rather than suppress synchronization and is unlikely to be clinically successful. The study also indicates that delayed feedback stimulation may not necessarily exhibit a desynchronization effect when acting on a physiologically realistic partially synchronous dynamics, and provides an example of how to estimate the stimulation effect.

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Maximum improvement in the number of principal components during stimulation.The spatial electrode setups are the same as in Figure 6. These results are overall similar to those in Figure 7. Dashed contours represent parameter values for which the model network synchronization dynamics is close to the experimental dynamics as analyzed in [26] for the weight parameter w1 = 0.3. A, B) 30% or 50% of STN neurons are directly affected by the stimulation current, correspondingly. Electrode is placed near the 5th STN neuron in the array. C, D) 40% or 60% of STN neurons are directly affected by the stimulation current. Electrodes are placed near the 5th and 6th STN neurons. E, F) 70% or 90% of STN neurons are directly affected by the stimulation current. Electrodes are placed near the 5th, 7th and 9th STN neurons. Weight parameters are w1 = 0.3, A, C, E) w2 = 0; B, D, F) w2 = 0.1.
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pone-0058264-g008: Maximum improvement in the number of principal components during stimulation.The spatial electrode setups are the same as in Figure 6. These results are overall similar to those in Figure 7. Dashed contours represent parameter values for which the model network synchronization dynamics is close to the experimental dynamics as analyzed in [26] for the weight parameter w1 = 0.3. A, B) 30% or 50% of STN neurons are directly affected by the stimulation current, correspondingly. Electrode is placed near the 5th STN neuron in the array. C, D) 40% or 60% of STN neurons are directly affected by the stimulation current. Electrodes are placed near the 5th and 6th STN neurons. E, F) 70% or 90% of STN neurons are directly affected by the stimulation current. Electrodes are placed near the 5th, 7th and 9th STN neurons. Weight parameters are w1 = 0.3, A, C, E) w2 = 0; B, D, F) w2 = 0.1.

Mentions: Similar to Figure 7, the effect of spatial electrode arrangements considered in Figure 6 is summarized in Figure 8. Therefore, while the delayed feedback stimulation produces reliable synchrony suppression in the case of strongly correlated activity, it frequently fails to destroy synchronized activity in networks with intermittent synchrony regimes like those observed in PD patients.


Failure of delayed feedback deep brain stimulation for intermittent pathological synchronization in Parkinson's disease.

Dovzhenok A, Park C, Worth RM, Rubchinsky LL - PLoS ONE (2013)

Maximum improvement in the number of principal components during stimulation.The spatial electrode setups are the same as in Figure 6. These results are overall similar to those in Figure 7. Dashed contours represent parameter values for which the model network synchronization dynamics is close to the experimental dynamics as analyzed in [26] for the weight parameter w1 = 0.3. A, B) 30% or 50% of STN neurons are directly affected by the stimulation current, correspondingly. Electrode is placed near the 5th STN neuron in the array. C, D) 40% or 60% of STN neurons are directly affected by the stimulation current. Electrodes are placed near the 5th and 6th STN neurons. E, F) 70% or 90% of STN neurons are directly affected by the stimulation current. Electrodes are placed near the 5th, 7th and 9th STN neurons. Weight parameters are w1 = 0.3, A, C, E) w2 = 0; B, D, F) w2 = 0.1.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585780&req=5

pone-0058264-g008: Maximum improvement in the number of principal components during stimulation.The spatial electrode setups are the same as in Figure 6. These results are overall similar to those in Figure 7. Dashed contours represent parameter values for which the model network synchronization dynamics is close to the experimental dynamics as analyzed in [26] for the weight parameter w1 = 0.3. A, B) 30% or 50% of STN neurons are directly affected by the stimulation current, correspondingly. Electrode is placed near the 5th STN neuron in the array. C, D) 40% or 60% of STN neurons are directly affected by the stimulation current. Electrodes are placed near the 5th and 6th STN neurons. E, F) 70% or 90% of STN neurons are directly affected by the stimulation current. Electrodes are placed near the 5th, 7th and 9th STN neurons. Weight parameters are w1 = 0.3, A, C, E) w2 = 0; B, D, F) w2 = 0.1.
Mentions: Similar to Figure 7, the effect of spatial electrode arrangements considered in Figure 6 is summarized in Figure 8. Therefore, while the delayed feedback stimulation produces reliable synchrony suppression in the case of strongly correlated activity, it frequently fails to destroy synchronized activity in networks with intermittent synchrony regimes like those observed in PD patients.

Bottom Line: We employ a computational model of the basal ganglia networks which reproduces experimentally observed fine temporal structure of the synchronous dynamics.When the parameters of our model are such that the synchrony is unphysiologically strong, the feedback exerts a desynchronizing action.However, when the network is tuned to reproduce the highly variable temporal patterns observed experimentally, the same kind of delayed feedback may actually increase the synchrony.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematical Sciences and Center for Mathematical Biosciences, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America. andrey.dovzhenok@uc.edu

ABSTRACT
Suppression of excessively synchronous beta-band oscillatory activity in the brain is believed to suppress hypokinetic motor symptoms of Parkinson's disease. Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). This type of synchrony control was shown to destabilize the synchronized state in networks of simple model oscillators as well as in networks of coupled model neurons. However, the dynamics of the neural activity in Parkinson's disease exhibits complex intermittent synchronous patterns, far from the idealized synchronous dynamics used to study the delayed feedback stimulation. This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. We employ a computational model of the basal ganglia networks which reproduces experimentally observed fine temporal structure of the synchronous dynamics. When the parameters of our model are such that the synchrony is unphysiologically strong, the feedback exerts a desynchronizing action. However, when the network is tuned to reproduce the highly variable temporal patterns observed experimentally, the same kind of delayed feedback may actually increase the synchrony. As network parameters are changed from the range which produces complete synchrony to those favoring less synchronous dynamics, desynchronizing delayed feedback may gradually turn into synchronizing stimulation. This suggests that delayed feedback DBS in Parkinson's disease may boost rather than suppress synchronization and is unlikely to be clinically successful. The study also indicates that delayed feedback stimulation may not necessarily exhibit a desynchronization effect when acting on a physiologically realistic partially synchronous dynamics, and provides an example of how to estimate the stimulation effect.

Show MeSH
Related in: MedlinePlus