| Synchronization and Multiscale Complex Dynamics in the Brain (BSYNC09) MPIPKS, Dresden, 2-6 November 2009 Graph theoretical analysis of functional brain networks: application to neurological disorders C.J. Stam Department of Clinical Neurophysiology; VU University Medical Center; P.O. Box 7057; 1007 MB Amsterdam; The Netherlands. e-mail: CJ.Stam@VUmc.nl Normal brain function requires both local processing of information as well as efficient exchange of information between widely distributed specialized brain areas. A central question in neuroscience is how this information exchange takes place, and how it breaks down in various neuropsychiatric disorders. Interactions between distributed brain regions can be studied by computing statistical interdependencies between time series of neural activity recorded over these areas; this concept is known as 'functional connectivity'. Synchronization of neural activity in different frequency bands turns out to be a major principle of information exchange. Large arrays of correlations between pair wise channels of EEG or MEG can be further analyzed by approaching them as complex networks, with each channel representing a network node, and each correlation between two nodes a network link. The theory of complex networks has made enormous progress since the discovery of small-world and scale-free networks as paradigm models of complex systems. Application of modern network analysis to neuroscience data has shown that healthy brains are likely to be small-world networks characterized by a combination of high clustering and short path lengths (Reijneveld et al., 2007). This is evident from the neuronal up to the macroscopic level, and is true for anatomical as well as neurophysiologial data. Network structure as determined from EEG is strongly determined by genes, and may gradually become more complex during normal development. More recently graph analysis has been applied to various neuropsychiatric conditions as well. In patients with epilepsy interictal network structure may be abnormally random and may change towards a small-world structure during the seizure; this has now been demonstrated both in intracranial as well as scalp recordings. In schizophrenia, resting-state EEG networks show a loss of the normal small-world architecture. Similar patterns have been observed in patients with brain tumours and patients with Alzheimer's disease, both with EEG and with MEG. Network changes in Alzheimer's disease can be replicated in a simple model assuming that the disease process attacks primarily critical connections between clusters (Stam et al., 2008). References: Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. The application of graph theoretical analysis to complex networks in the brain. Clin Neurophysiol. 2007; 118: 2317-2331. Stam CJ, de Haan W, Daffertshofer A, Jones BF, Manshanden I, van Cappellen van Walsum AM, Montez T, Verbunt JP, de Munck JC, van Dijk BW, Berendse HW, Scheltens P. Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease. Brain. 2008 Oct 24. |
|