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Department VUmc:Clinical Neurophysiology
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Name            : Cornelis Jan Stam
Department  : department of Clinical Neurophysiology, VUmc
Position        : professor of Clinical Neurophysiology
Website        : http://home.kpn.nl/stam7883/index.html

CURRICULUM VITAE 
- Kees Stam (1959) obtained his medical  degree in 1985. Following his medicals studies he did basic research on the mesocortical dopaminergic system of the rat in the Netherlands Institute for Brain Research in Amsterdam. During his training as a neurologist he did his PhD studies on neurophysiological mechanisms of cognitive dysfunction in Parkinson’s disease. He defended his PhD thesis ‘Frontal attentional disturbances in Parkinson’s disease’ at the Vrije Universiteit in Amsterdam in 1992. The thesis was awarded with the Lilly CVS award in 1994. He worked as a neurologist and clinical neurophysiologist in the Leyenburg hospital in The Hague between 1992-2000. During this period his research interests were focused on the neurophysiological mechanisms of behavioural and cognitive disturbances, especially in Alzheimer’s and Parkinson’s disease. Since 2000 he is a full professor of clinical neurophysiology at the department of clinical neurophysiology of the VU University Medical Hospital in Amsterdam. His current research interests involve the development and use of nonlinear time series analysis of EEG and MEG signals to study normal and disturbed cognitive function in a broad range of neurological disorders such as Alzheimer’s and Parkinson’s disease, multiple sclerosis and brain tumour patients. Kees Stam is a member of the science board of the VU, and scientific counsels of the Finish academy of science and the Belgium FWO. He is a member of the editorial board of Human Brain Mapping and the Journal of Neurology, Neurosurgery and Psychiatry, and associate editor of Clinical Neurophysiology.

CURRENT PROJECTS
-Current project are centered around the question how we can understand complex brain networks in health and disease using advanced nonlinear time series analysis and modern graph theory. More specifically, four questions are addressed:
1.What are the characteristics of complex functional brain networks?
2.How do these characteristics arise during development?
3.How do these characteristics determine normal (higher) brain function?
4.How does neurological disease affect network structure and function?
Three approaches are used:
1.Development of new tools to quantify synchronization and graph theoretical network properties of EEG, MEG and more recently MRI; these methods are incorporated in my own software (DIGEEGXP); currently this software is transformed to a platform independent Java version (BrainWave).
2.modeling of large scale brain networks to understand the synchronization and network properties of EEG, MEG and fMRI studies in patients
3.Empirical studies of synchronization and complex networks in various cohorts of healthy subjects (AGGO cohort; twin cohorts of Boomsma and de Geus) and patients, including: Alzheimer’s disease, frontotemporal lobe dementia; Parkinson’s disease, brain tumours, multiple sclerosis, diabetes.

KEY PUBLICATIONS

  • Stam CJ, Dijk BW van. Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets. Physica D 2002; 163: 236-241. 
  • Stam CJ. Nonlinear dynamical analysis of EEG and MEG: review of an emerging field. Clin Neurophysiol 2005; 116: 2266-2301. 
  • Stam CJ, Nolte G, Daffertshofer A. Phase lag index: assessment of functional connectivity from multichannel EEG and MEG with diminished bias from common sources. Human Brain Mapping 2007; 28: 1178-1193. 
  • Stam CJ, Reijneveld JC. Graph theoretical analysis of complex networks in the brain. Nonlinear Biomedical Physics 2007; 1: 3 doi: 10.1186/1753-4631-1-3
  • Stam CJ, de Haan W, Daffertshofer A, Jones BF, Manshanden I, van Cappellen van Walsum AM, Montez T, Verbunt JPA, de Munck JC, van Dijk BW, Berendse HW, Scheltens P. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 2009; doi: 10.1093/brain/awn262.


 

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