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A Neuronal Network Model for Simulating the Effects of Repetitive Transcranial Magnetic Stimulation on Local Field Potential Power Spectra

Overview of attention for article published in PLOS ONE, November 2012
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Title
A Neuronal Network Model for Simulating the Effects of Repetitive Transcranial Magnetic Stimulation on Local Field Potential Power Spectra
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0049097
Pubmed ID
Authors

Alina Bey, Stefan Leue, Christian Wienbruch

Abstract

Repetitive transcranial magnetic stimulation (rTMS) holds promise as a non-invasive therapy for the treatment of neurological disorders such as depression, schizophrenia, tinnitus, and epilepsy. Complex interdependencies between stimulus duration, frequency and intensity obscure the exact effects of rTMS stimulation on neural activity in the cortex, making evaluation of and comparison between rTMS studies difficult. To explain the influence of rTMS on neural activity (e.g. in the motor cortex), we use a neuronal network model. The results demonstrate that the model adequately explains experimentally observed short term effects of rTMS on the band power in common frequency bands used in electroencephalography (EEG). We show that the equivalent local field potential (eLFP) band power depends on stimulation intensity rather than on stimulation frequency. Additionally, our model resolves contradictions in experiments.

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Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Serbia 1 2%
Canada 1 2%
Unknown 62 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 18%
Student > Master 10 15%
Student > Ph. D. Student 9 14%
Student > Doctoral Student 5 8%
Student > Bachelor 5 8%
Other 14 22%
Unknown 10 15%
Readers by discipline Count As %
Medicine and Dentistry 19 29%
Neuroscience 10 15%
Psychology 6 9%
Agricultural and Biological Sciences 5 8%
Nursing and Health Professions 2 3%
Other 6 9%
Unknown 17 26%