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Characterizing Deep Brain Stimulation effects in computationally efficient neural network models.

Latteri A, Arena P, Mazzone P - Nonlinear Biomed Phys (2011)

Bottom Line: On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized.For this reason we considered a reduced order model, the Izhikevich one, which is computationally much lighter.Results were compared both with the other mathematical models, using Morris Lecar and Izhikevich neurons, and with simulated Local Field Potentials (LFP).

View Article: PubMed Central - HTML - PubMed

Affiliation: DIEEI - Università di Catania, v,le A, Doria 6, Catania, Italy. alatteri@diees.unict.it.

ABSTRACT

Background: Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized.

Results: To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort to simulate large scale networks. For this reason we considered a reduced order model, the Izhikevich one, which is computationally much lighter. The comparison between neural lattices built using both neuron models provided comparable results, both without traditional stimulation and in presence of all the stimulation protocols. This was a first result toward the study and simulation of the large scale neural networks involved in pathological dynamics.Using the reduced order model an inspection on the activity of two neural lattices was also carried out at the aim to analyze how the stimulation in one area could affect the dynamics in another area, like the usual medical treatment protocols require.The study of population dynamics that was carried out allowed us to investigate, through simulations, the positive effects of the stimulation signals in terms of desynchronization of the neural dynamics.

Conclusions: The results obtained constitute a significant added value to the analysis of synchronization and desynchronization effects due to neural stimulation. This work gives the opportunity to more efficiently study the effect of stimulation in large scale yet computationally efficient neural networks. Results were compared both with the other mathematical models, using Morris Lecar and Izhikevich neurons, and with simulated Local Field Potentials (LFP).

No MeSH data available.


Related in: MedlinePlus

Activity of one neuron described by Izhikevich equations. The membrane potentials v(t) of one neuron shows the characteristic spiking and bursting activity. Typical numerical values for the different variables are as follows. Parameters: Dnoise = 0.00001. n = 0.001; , a = 0.02, b = 0.2;c = 50, d = 0.7, T1 = 22, αs = 0.1; βs = 0.05; θs = 0.2; σs = 0.02;
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Figure 6: Activity of one neuron described by Izhikevich equations. The membrane potentials v(t) of one neuron shows the characteristic spiking and bursting activity. Typical numerical values for the different variables are as follows. Parameters: Dnoise = 0.00001. n = 0.001; , a = 0.02, b = 0.2;c = 50, d = 0.7, T1 = 22, αs = 0.1; βs = 0.05; θs = 0.2; σs = 0.02;

Mentions: The parameters and d control the bursting behaviour and its frequency. Also in this case the spikes that form one burst are from 6 to 10 per burst. The dynamics of a single neuron described by the model presented is depicted in Figure 6.


Characterizing Deep Brain Stimulation effects in computationally efficient neural network models.

Latteri A, Arena P, Mazzone P - Nonlinear Biomed Phys (2011)

Activity of one neuron described by Izhikevich equations. The membrane potentials v(t) of one neuron shows the characteristic spiking and bursting activity. Typical numerical values for the different variables are as follows. Parameters: Dnoise = 0.00001. n = 0.001; , a = 0.02, b = 0.2;c = 50, d = 0.7, T1 = 22, αs = 0.1; βs = 0.05; θs = 0.2; σs = 0.02;
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3104360&req=5

Figure 6: Activity of one neuron described by Izhikevich equations. The membrane potentials v(t) of one neuron shows the characteristic spiking and bursting activity. Typical numerical values for the different variables are as follows. Parameters: Dnoise = 0.00001. n = 0.001; , a = 0.02, b = 0.2;c = 50, d = 0.7, T1 = 22, αs = 0.1; βs = 0.05; θs = 0.2; σs = 0.02;
Mentions: The parameters and d control the bursting behaviour and its frequency. Also in this case the spikes that form one burst are from 6 to 10 per burst. The dynamics of a single neuron described by the model presented is depicted in Figure 6.

Bottom Line: On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized.For this reason we considered a reduced order model, the Izhikevich one, which is computationally much lighter.Results were compared both with the other mathematical models, using Morris Lecar and Izhikevich neurons, and with simulated Local Field Potentials (LFP).

View Article: PubMed Central - HTML - PubMed

Affiliation: DIEEI - Università di Catania, v,le A, Doria 6, Catania, Italy. alatteri@diees.unict.it.

ABSTRACT

Background: Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized.

Results: To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort to simulate large scale networks. For this reason we considered a reduced order model, the Izhikevich one, which is computationally much lighter. The comparison between neural lattices built using both neuron models provided comparable results, both without traditional stimulation and in presence of all the stimulation protocols. This was a first result toward the study and simulation of the large scale neural networks involved in pathological dynamics.Using the reduced order model an inspection on the activity of two neural lattices was also carried out at the aim to analyze how the stimulation in one area could affect the dynamics in another area, like the usual medical treatment protocols require.The study of population dynamics that was carried out allowed us to investigate, through simulations, the positive effects of the stimulation signals in terms of desynchronization of the neural dynamics.

Conclusions: The results obtained constitute a significant added value to the analysis of synchronization and desynchronization effects due to neural stimulation. This work gives the opportunity to more efficiently study the effect of stimulation in large scale yet computationally efficient neural networks. Results were compared both with the other mathematical models, using Morris Lecar and Izhikevich neurons, and with simulated Local Field Potentials (LFP).

No MeSH data available.


Related in: MedlinePlus