Neural network dynamics in Parkinson's disease

Lourens, M.A.J.

doi:10.3990/1.9789036535076

Cited by 6 publications

(8 citation statements)

References 218 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4). This shows the adequate uncorrelation within STN neurons, i.e., the fairly desynchronizing state, which is consistent with the previous works Lourens, 2013). In addition, the influence of synaptic plasticity on the reliability of thalamic relay is also shown in Fig.…”

Section: Open-loop Crs With Synaptic Plasticitysupporting

confidence: 86%

“…In addition, model-based simulation analysis (Schroll et al, 2014) has also confirmed that both the motor impairments and pathway imbalances emerging in Parkinson's disease can indeed be explained by the dysfunctional synaptic plasticity. Interestingly, Lourens et al have used a biologically plausible STN-GPe network model with the synaptic plasticity to investigate the effect of continuous stimulation and CR-stimulation on the Parkinson's disease Lourens, 2013). Their results show that synaptic plasticity can facilitate the training of the network to fire in a less synchronized manner as a short-duration desynchronizing stimulation is applied sufficiently long, and has sufficiently high amplitude.…”

Section: Open-loop Crs With Synaptic Plasticitymentioning

confidence: 97%

See 1 more Smart Citation

Improving desynchronization of parkinsonian neuronal network via triplet-structure coordinated reset stimulation

Fan

2015

Journal of Theoretical Biology

View full text Add to dashboard Cite

Section: Open-loop Crs With Synaptic Plasticitysupporting

confidence: 86%

Section: Open-loop Crs With Synaptic Plasticitymentioning

confidence: 97%

Improving desynchronization of parkinsonian neuronal network via triplet-structure coordinated reset stimulation

Fan

2015

Journal of Theoretical Biology

View full text Add to dashboard Cite

“…The inhibitory input from the striatum to the GPe and GPi was uncorrelated white noise. To quantify synchrony, we performed principal component analysis on spike activity as described in Lourens . In short, we calculated the number of principal components (PCs) needed to describe 95% of the information contained in the spike times for all 16 cells in each nucleus.…”

Section: Methodsmentioning

confidence: 99%

Pallidal gap junctions‐triggers of synchrony in Parkinson's disease?

et al. 2014

View full text Add to dashboard Cite

Although increased synchrony of the neural activity in the basal ganglia may underlie the motor deficiencies exhibited in Parkinson's disease (PD), how this synchrony arises, propagates through the basal ganglia, and changes under dopamine replacement remains unknown. Gap junctions could play a major role in modifying this synchrony, because they show functional plasticity under the influence of dopamine and after neural injury. In this study, confocal imaging was used to detect connexin-36, the major neural gap junction protein, in postmortem tissues of PD patients and control subjects in the putamen, subthalamic nucleus (STN), and external and internal globus pallidus (GPe and GPi, respectively). Moreover, we quantified how gap junctions affect synchrony in an existing computational model of the basal ganglia. We detected connexin-36 in the human putamen, GPe, and GPi, but not in the STN. Furthermore, we found that the number of connexin-36 spots in PD tissues increased by 50% in the putamen, 43% in the GPe, and 109% in the GPi compared with controls. In the computational model, gap junctions in the GPe and GPi strongly influenced synchrony. The basal ganglia became especially susceptible to synchronize with input from the cortex when gap junctions were numerous and high in conductance. In conclusion, connexin-36 expression in the human GPe and GPi suggests that gap junctional coupling exists within these nuclei. In PD, neural injury and dopamine depletion could increase this coupling. Therefore, we propose that gap junctions act as a powerful modulator of synchrony in the basal ganglia.

show abstract

“…Consequently, the hyper‐activity is resulted in the GPi by the delivered signal in both pathways. When the imbalanced transmission signal occurs in the direct and indirect pathways, a tremor with frequency of 4–6 Hz and high amplitude is expected [9, 10].…”

Section: Introductionmentioning

confidence: 99%

Design of robust adaptive controller and feedback error learning for rehabilitation in Parkinson's disease: a simulation study

et al. 2017

View full text Add to dashboard Cite

Deep brain stimulation (DBS) is an efficient therapy to control movement disorders of Parkinson's tremor. Stimulation of one area of basal ganglia (BG) by DBS with no feedback is the prevalent opinion. Reduction of additional stimulatory signal delivered to the brain is the advantage of using feedback. This results in reduction of side effects caused by the excessive stimulation intensity. In fact, the stimulatory intensity of controllers is decreased proportional to reduction of hand tremor. The objective of this study is to design a new controller structure to decrease three indicators: (i) the hand tremor; (ii) the level of delivered stimulation in disease condition; and (iii) the ratio of the level of delivered stimulation in health condition to disease condition. For this purpose, the authors offer a new closed-loop control structure to stimulate two areas of BG simultaneously. One area (STN: subthalamic nucleus) is stimulated by an adaptive controller with feedback error learning. The other area (GPi: globus pallidus internal) is stimulated by a partial state feedback (PSF) controller. Considering the three indicators, the results show that, stimulating two areas simultaneously leads to better performance compared with stimulating one area only. It is shown that both PSF and adaptive controllers are robust regarding system parameter uncertainties. In addition, a method is proposed to update the parameters of the BG model in real time. As a result, the parameters of the controllers can be updated based on the new parameters of the BG model.

show abstract

Neural network dynamics in Parkinson's disease

Cited by 6 publications

References 218 publications

Improving desynchronization of parkinsonian neuronal network via triplet-structure coordinated reset stimulation

Improving desynchronization of parkinsonian neuronal network via triplet-structure coordinated reset stimulation

Pallidal gap junctions‐triggers of synchrony in Parkinson's disease?

Design of robust adaptive controller and feedback error learning for rehabilitation in Parkinson's disease: a simulation study

Contact Info

Product

Resources

About