Dynamic imaging of coherent sources
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24 DOI 10.1007/s10309-012-0292-0 Published online: 17. Januar 2013 © Springer-Verlag Berlin Heidelberg 2013
1 Clinic for Child Neurology, Department of Neuropediatrics, Christian-Albrechts-University of Kiel 2 University Hospital of Child and Adolescent Psychiatry, Johann Wolfgang
Goethe-University of Frankfurt, Frankfurt am Main 3 Department of Neurology, Christian-Albrechts-University of Kiel
Dynamic imaging of coherent sources Identification of neuronal networks underlying frequency-associated EEG patterns
Good temporal resolution of the electroencephalogram (EEG) permits good interpretation of different parts of neuronal networks, such as the separation of brain areas involved in the generation of epileptic activity from regions of propagation [1, 2, 3]. However, EEG signals measured on the scalp surface do not directly indicate the location of the active neurons in the brain due to the vagueness of the underlying static electromagnetic inverse problem [4]. It is particularly challenging to measure electric sources in deep brain structures: Previous studies on EEG source analyses in generalized spike and wave activity did not detect thalamic sources which, however, a EEG pattern of absence seizure (2-5 Hz spike wave discharges)
is crucially important for the complete representation of the neuronal networks including both cortical and subcortical structures [5, 6, 7]. Recent achievements in the development of EEG inverse and forward solutions substantially improved the localization power of the EEG. Dynamic imaging of coherent sources (DICS) is one of these solutions. DICS is a source analysis method which is able to detect brain regions that are coherent, hence functionally related to each other [8]. DICS works in the frequency domain for EEG and magnetoencephalographic (MEG) data and is able to describe neuronal networks by imaging power and coherence of oscillatory
b
Spatial filter algorithm to identify source with power maximum 2-5Hz
c
brain activity [8] (for illustration of the method please see . Fig. 1). Applied to different types of tremor and voluntary motor control, DICS was able to characterize networks including deep structures, such as thalamus, cerebellum and brainstem in MEG [8, 9, 10, 11, 12, 13] as well as in EEG studies [14, 15, 16]. The DICS method employs a spatial filter algorithm [17] to identify the spatial power maximum or coherence in the brain for a particular frequency band. It uses a regularization parameter which determines the spatial extent of source representation. The brain region representing the strongest power in a specific frequency band can subsequently be used as a ref-
Sources coherent with first source
source 2
source 3
source 4
source 5
source 1
Fig. 1 9 a Typical EEG pattern for absence seizures: generalized, high amplitude 2–5 Hz spike and wave discharges. b Spatial filter is applied in order to identify sources with the maximum power in the specific frequency band. c Subsequent coherent sources in the 2–5 Hz frequency band Zeitschrift für Epileptologie
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