KNAW

Organisation

Spinoza Centre for Neuroimaging

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Part of Koninklijke Nederlandse Akademie van Wetenschappen - KNAW
Address Roeterseiland Building D / Nieuwe Achtergracht 129, 1018 WS Amsterdam
Get directions Google Maps
Phone +31-20-5251499
URL http://www.spinozacenter.nl/
Email H. S. Scholte -at- uva. nl
Mission The Spinoza Center is a research facility dedicated to neuroscientific research. It is the center's mission to both excel in science and to utilize the resulting knowledge. The center is a unique collaboration of the University of Amsterdam's psychology and science departments, the Academic Medical Center (AMC), the Vrije Universiteit Medical Center (VUmc) and the Netherlands Institute for Neuroscience (NIN). The center is an institute of the Netherlands Royal Academy of Arts and Sciences. Philips Achieva 3T MRI scanner Currently the Spinoza center operates a research-dedicated 3T scanner in the inner city of Amsterdam (REC complex), and several state-of-the-art neurophysiological instruments such as EEG, TMS and eyetracking systems. In the near future, it is the center's ambition to host multiple MRI scanners including a 7T at the location of the Netherlands Institute for Neuroscience. The day to day operation of the 3T scanner at the Spinoza Center's Amsterdam location is in the hands of a group of dedicated professionals under the direction of dr. H. Steven Scholte. The actual scanning is performed by a group of experienced research assistants.
Addition Het Spinozacentrum is opgericht per 1 januari 2008. Het centrum is een samenwerkingsverband van het Academisch Medisch Centrum, de Faculteit Maatschappij- & Gedragswetenschappen van de Universiteit van Amsterdam, de Faculteit Natuurwetenschappen, Wiskunde en Informatica van de Universiteit van Amsterdam, de Vrije Universiteit, het Vrije Universiteit Medisch Centrum, de KNAW, het Nederlands Instituut voor Neurowetenschappen (NIN-KNAW).

People

Management Dr. R. van der Neut

Current research

(the most recent research is at the top)
Secretariat Testing the feasibility of Massive MRI data analysis

Classification


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