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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

Spectrogram in dB. LFP of a not stimulated population of 100 Morris Lecar neurons.
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Figure 17: Spectrogram in dB. LFP of a not stimulated population of 100 Morris Lecar neurons.

Mentions: The dynamics of the non stimulated network for the Moris Lecar model, in terms of LFP is reported (through the spectrogram in dB) in Figure 17. From this plot we can appreciate the presence of a high synchronization at low frequencies: this is the key characteristic of a pathological network, according to the neuronal gate theory. It is to be outlined that we conducted the study considering a very low number of neurons with respect to the actual case. Moreover relevant parameters, like the noise level and sources and the details of the network topology, in the in vivo case, were mostly unknown. Under this perspective, the results obtained can be considered relevant, above all in view of the effect of stimulation.


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

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

Spectrogram in dB. LFP of a not stimulated population of 100 Morris Lecar neurons.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 17: Spectrogram in dB. LFP of a not stimulated population of 100 Morris Lecar neurons.
Mentions: The dynamics of the non stimulated network for the Moris Lecar model, in terms of LFP is reported (through the spectrogram in dB) in Figure 17. From this plot we can appreciate the presence of a high synchronization at low frequencies: this is the key characteristic of a pathological network, according to the neuronal gate theory. It is to be outlined that we conducted the study considering a very low number of neurons with respect to the actual case. Moreover relevant parameters, like the noise level and sources and the details of the network topology, in the in vivo case, were mostly unknown. Under this perspective, the results obtained can be considered relevant, above all in view of the effect of stimulation.

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