The role of synaptic parameters in a parkinsonian population level model of the basal ganglia

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	2014-03-01T02:52:12Z
dc.date.available	2014-03-01T02:52:12Z
dc.date.issued	2009	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/35865
dc.subject	Basal ganglia	en
dc.subject	Beta band peak	en
dc.subject	Dopamine modulation	en
dc.subject	Local field potentials	en
dc.subject	Parkinson's disease	en
dc.subject	Population level models	en
dc.subject.other	Basal ganglia	en
dc.subject.other	Beta band peak	en
dc.subject.other	Dopamine modulation	en
dc.subject.other	Local field potentials	en
dc.subject.other	Parkinson's disease	en
dc.subject.other	Population level models	en
dc.subject.other	Brain	en
dc.subject.other	Independent component analysis	en
dc.subject.other	Power spectral density	en
dc.subject.other	Dispensers	en
dc.title	The role of synaptic parameters in a parkinsonian population level model of the basal ganglia	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/NER.2009.5109363	en
heal.identifier.secondary	http://dx.doi.org/10.1109/NER.2009.5109363	en
heal.identifier.secondary	5109363	en
heal.publicationDate	2009	en
heal.abstract	A persistent hallmark of Parkinson's disease is a beta band peak in the power spectral density function of the Local Field Potential (LFP) signals, derived from basal ganglia's subthalamic nucleus microelectrode recordings. The peak's role in parkinsonian pathophysiology is thought to be central. A new proposed population level model of the basal ganglia generates parkinsonian LFPs by expressing a beta band peak. For that to take place, the neurophysiological parameters that suffer pathogenic modification are shown to be both the amplitude and the duration of the post-synaptic potentials. ©2009 IEEE.	en
heal.journalName	2009 4th International IEEE/EMBS Conference on Neural Engineering, NER '09	en
dc.identifier.doi	10.1109/NER.2009.5109363	en
dc.identifier.spage	581	en
dc.identifier.epage	584	en