The pedunculopontine nucleus as an additional target for deep brain stimulation

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Abstract

The pedunculopontine nucleus has been suggested as a target for DBS. In this paper we propose a single compartment computational model for a PPN Type I cell and compare its dynamic behavior with experimental data. The model shows bursts after a period of hyperpolarization and spontaneous firing at 8 Hz. Bifurcation analysis of the single PPN cell shows bistability of fast and slow spiking solutions for a range of applied currents. A network model for STN, GPe and GPi produces basal ganglia output that is used as input for the PPN cell. The conductances for projections from the STN and the GPi to the PPN are determined from experimental data. The resulting behavior of the PPN cell is studied under normal and Parkinsonian conditions of the basal ganglia network. The effect of high frequency stimulation of the STN is considered as well as the effect of combined high frequency stimulation of the STN and the PPN at various frequencies. The relay properties of the PPN cell demonstrate that the combined high frequency stimulation of STN and low frequency (10 Hz, 25 Hz, 40 Hz) stimulation of PPN hardly improves the effect of exclusive STN stimulation. Moreover, PPN-DBS at low stimulation amplitude has a better effect than at higher stimulation amplitude. The effect of PPN output on the basal ganglia is investigated, in particular the effect of STN-DBS and/or PPN-DBS on the pathological firing pattern of STN and GPe cells. PPN-DBS eliminates the pathological firing pattern of STN and GPe cells, whereas STN-DBS and combined STN-DBS and PPN-DBS eliminate the pathological firing pattern only from STN cells. © 2011 Elsevier Ltd.
Original languageUndefined
Pages (from-to)617-630
Number of pages14
JournalNeural networks
Volume24
Issue number6
DOIs
Publication statusPublished - Aug 2011

Keywords

  • EWI-20741
  • BSS-Electrical Neurostimulation
  • Deep brain stimulation (DBS)
  • Basal ganglia-brain stem circuit
  • Pedunculopontine nucleus (PPN)
  • METIS-279675
  • IR-78344
  • Parkinson's disease (PD)

Cite this

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title = "The pedunculopontine nucleus as an additional target for deep brain stimulation",
abstract = "The pedunculopontine nucleus has been suggested as a target for DBS. In this paper we propose a single compartment computational model for a PPN Type I cell and compare its dynamic behavior with experimental data. The model shows bursts after a period of hyperpolarization and spontaneous firing at 8 Hz. Bifurcation analysis of the single PPN cell shows bistability of fast and slow spiking solutions for a range of applied currents. A network model for STN, GPe and GPi produces basal ganglia output that is used as input for the PPN cell. The conductances for projections from the STN and the GPi to the PPN are determined from experimental data. The resulting behavior of the PPN cell is studied under normal and Parkinsonian conditions of the basal ganglia network. The effect of high frequency stimulation of the STN is considered as well as the effect of combined high frequency stimulation of the STN and the PPN at various frequencies. The relay properties of the PPN cell demonstrate that the combined high frequency stimulation of STN and low frequency (10 Hz, 25 Hz, 40 Hz) stimulation of PPN hardly improves the effect of exclusive STN stimulation. Moreover, PPN-DBS at low stimulation amplitude has a better effect than at higher stimulation amplitude. The effect of PPN output on the basal ganglia is investigated, in particular the effect of STN-DBS and/or PPN-DBS on the pathological firing pattern of STN and GPe cells. PPN-DBS eliminates the pathological firing pattern of STN and GPe cells, whereas STN-DBS and combined STN-DBS and PPN-DBS eliminate the pathological firing pattern only from STN cells. {\circledC} 2011 Elsevier Ltd.",
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author = "Lourens, {Marcel Antonius Johannes} and Meijer, {Hil Ga{\'e}tan Ellart} and Tjitske Heida and Enrico Marani and {van Gils}, {Stephanus A.}",
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The pedunculopontine nucleus as an additional target for deep brain stimulation. / Lourens, Marcel Antonius Johannes; Meijer, Hil Gaétan Ellart; Heida, Tjitske; Marani, Enrico; van Gils, Stephanus A.

In: Neural networks, Vol. 24, No. 6, 08.2011, p. 617-630.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - The pedunculopontine nucleus as an additional target for deep brain stimulation

AU - Lourens, Marcel Antonius Johannes

AU - Meijer, Hil Gaétan Ellart

AU - Heida, Tjitske

AU - Marani, Enrico

AU - van Gils, Stephanus A.

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N2 - The pedunculopontine nucleus has been suggested as a target for DBS. In this paper we propose a single compartment computational model for a PPN Type I cell and compare its dynamic behavior with experimental data. The model shows bursts after a period of hyperpolarization and spontaneous firing at 8 Hz. Bifurcation analysis of the single PPN cell shows bistability of fast and slow spiking solutions for a range of applied currents. A network model for STN, GPe and GPi produces basal ganglia output that is used as input for the PPN cell. The conductances for projections from the STN and the GPi to the PPN are determined from experimental data. The resulting behavior of the PPN cell is studied under normal and Parkinsonian conditions of the basal ganglia network. The effect of high frequency stimulation of the STN is considered as well as the effect of combined high frequency stimulation of the STN and the PPN at various frequencies. The relay properties of the PPN cell demonstrate that the combined high frequency stimulation of STN and low frequency (10 Hz, 25 Hz, 40 Hz) stimulation of PPN hardly improves the effect of exclusive STN stimulation. Moreover, PPN-DBS at low stimulation amplitude has a better effect than at higher stimulation amplitude. The effect of PPN output on the basal ganglia is investigated, in particular the effect of STN-DBS and/or PPN-DBS on the pathological firing pattern of STN and GPe cells. PPN-DBS eliminates the pathological firing pattern of STN and GPe cells, whereas STN-DBS and combined STN-DBS and PPN-DBS eliminate the pathological firing pattern only from STN cells. © 2011 Elsevier Ltd.

AB - The pedunculopontine nucleus has been suggested as a target for DBS. In this paper we propose a single compartment computational model for a PPN Type I cell and compare its dynamic behavior with experimental data. The model shows bursts after a period of hyperpolarization and spontaneous firing at 8 Hz. Bifurcation analysis of the single PPN cell shows bistability of fast and slow spiking solutions for a range of applied currents. A network model for STN, GPe and GPi produces basal ganglia output that is used as input for the PPN cell. The conductances for projections from the STN and the GPi to the PPN are determined from experimental data. The resulting behavior of the PPN cell is studied under normal and Parkinsonian conditions of the basal ganglia network. The effect of high frequency stimulation of the STN is considered as well as the effect of combined high frequency stimulation of the STN and the PPN at various frequencies. The relay properties of the PPN cell demonstrate that the combined high frequency stimulation of STN and low frequency (10 Hz, 25 Hz, 40 Hz) stimulation of PPN hardly improves the effect of exclusive STN stimulation. Moreover, PPN-DBS at low stimulation amplitude has a better effect than at higher stimulation amplitude. The effect of PPN output on the basal ganglia is investigated, in particular the effect of STN-DBS and/or PPN-DBS on the pathological firing pattern of STN and GPe cells. PPN-DBS eliminates the pathological firing pattern of STN and GPe cells, whereas STN-DBS and combined STN-DBS and PPN-DBS eliminate the pathological firing pattern only from STN cells. © 2011 Elsevier Ltd.

KW - EWI-20741

KW - BSS-Electrical Neurostimulation

KW - Deep brain stimulation (DBS)

KW - Basal ganglia-brain stem circuit

KW - Pedunculopontine nucleus (PPN)

KW - METIS-279675

KW - IR-78344

KW - Parkinson's disease (PD)

U2 - 10.1016/j.neunet.2011.03.007

DO - 10.1016/j.neunet.2011.03.007

M3 - Article

VL - 24

SP - 617

EP - 630

JO - Neural networks

JF - Neural networks

SN - 0893-6080

IS - 6

ER -