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Miguel Valencia

Researcher at University of Navarra

Publications -  96
Citations -  4284

Miguel Valencia is an academic researcher from University of Navarra. The author has contributed to research in topics: Subthalamic nucleus & Deep brain stimulation. The author has an hindex of 28, co-authored 91 publications receiving 3716 citations. Previous affiliations of Miguel Valencia include Chartered Institute of Management Accountants & Centre national de la recherche scientifique.

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Journal ArticleDOI

Coupling between Beta and High-Frequency Activity in the Human Subthalamic Nucleus May Be a Pathophysiological Mechanism in Parkinson's Disease

Jon López-Azcárate, +9 more
- 12 May 2010 - 
TL;DR: The findings suggest that nonlinear coupling between frequencies may not only be a physiological mechanism but also that it may participate in the pathophysiology of parkinsonism.
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Slow oscillatory activity and levodopa-induced dyskinesias in Parkinson's disease.

Fernando Alonso-Frech, +8 more
- 01 Jul 2006 - 
TL;DR: Recordings of local field potentials from macroelectrodes implanted in the subthalamic nucleus of 14 patients with Parkinson's disease suggest that the 4-10 Hz oscillation is associated with the expression of LID in Parkinson's Disease.
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Human cerebral activation during steady-state visual-evoked responses.

Maria A. Pastor, +4 more
- 17 Dec 2003 - 
TL;DR: This study showed that visual stimulation at 40 Hz causes selective activation of the macular region of the visual cortex, and that a region in the dorsal aspect of the Crus I lobule of the left cerebellar hemisphere is activated during repetitive visual stimulation.
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Remote synchronization reveals network symmetries and functional modules.

Vincenzo Nicosia, +5 more
- 25 Apr 2013 - 
TL;DR: A Kuramoto model in which the oscillators are associated with the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization is studied, suggesting that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.
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Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study.

Jorge Iriarte, +6 more
- 01 Jul 2003 - 
TL;DR: ICA was applied to standard EEG recordings to eliminate well-known artifacts, thus quantifying its efficacy in an objective way, and proved to be a useful tool to clean artifacts in short EEG samples, without having the disadvantages associated with the digital filters.