Fitting local field potentials generating model of the basal ganglia to actual recorded signals

Tsirogiannis, GL; Tagaris, GA; Sakas, D; Nikita, KS

dc.contributor.author	Tsirogiannis, GL	en
dc.contributor.author	Tagaris, GA	en
dc.contributor.author	Sakas, D	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:45:16Z
dc.date.available	2014-03-01T02:45:16Z
dc.date.issued	2008	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32252
dc.subject	Critical Parameter	en
dc.subject	Deep Brain Stimulation	en
dc.subject	Frequency Domain	en
dc.subject	Generic Model	en
dc.subject	Local Field Potential	en
dc.subject	Optimal Method	en
dc.subject	Subthalamic Nucleus	en
dc.subject	Basal Ganglia	en
dc.subject.other	Basal ganglia	en
dc.subject.other	Critical parameter	en
dc.subject.other	Deep brain stimulation	en
dc.subject.other	Frequency domains	en
dc.subject.other	High beta	en
dc.subject.other	Local field potentials	en
dc.subject.other	Microelectrode recording	en
dc.subject.other	Optimization method	en
dc.subject.other	Parkinson's disease	en
dc.subject.other	Pathophysiology	en
dc.subject.other	Population levels	en
dc.subject.other	Recorded signals	en
dc.subject.other	Subthalamic nucleus	en
dc.subject.other	Synaptic strengths	en
dc.subject.other	Bioinformatics	en
dc.subject.other	Independent component analysis	en
dc.title	Fitting local field potentials generating model of the basal ganglia to actual recorded signals	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/BIBE.2008.4696825	en
heal.identifier.secondary	http://dx.doi.org/10.1109/BIBE.2008.4696825	en
heal.identifier.secondary	4696825	en
heal.publicationDate	2008	en
heal.abstract	A population level model of the basal ganglia has been shown to reliably reproduce the local field potential (LFP) activity recorded from subthalamic nucleus (STN) during typical microelectrode recording sessions. The purpose of the present work is to investigate optimization methods that can be used to fit that model to actual recorded LFPs. For that, we utilize data derived from seven parkinsonian subjects prior to the permanent implantation of the deep brain stimulation (DBS) electrode. For the fitting, five optimization methods are used, combined with two methods for estimating the error between the actual recorded and the model predicted LFP signals in the frequency domain. The procedures are focused on re-generating the characteristic beta peak of the STN LFP. The results indicate that the model is able to reproduce the beta peak in various frequencies in the range of both low and high beta, while at the same time, the values of the critical parameters bringing the model in that area of behavior reveal the crucial role of the synaptic strengths in Parkinson's disease pathophysiology.	en
heal.journalName	8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008	en
dc.identifier.doi	10.1109/BIBE.2008.4696825	en