A method for spatiotemporal mapping of event-related modulation of cortical rhythmic activity

• Published in: NeuroImage (Orlando, Fla.) Vol. 42; no. 1; pp. 207 - 217
• Main Authors: Laaksonen, H., Kujala, J., Salmelin, R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2008; Elsevier Limited
• Subjects: Algorithms; Biological Clocks - physiology; Brain; Brain Mapping - methods; Electroencephalography; Evoked Potentials, Motor - physiology; Humans; Magnetoencephalography - methods; Medical imaging; Methods; Motor Cortex - physiology; Nerve Net - physiology; Noise; Periodicity
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2008.04.175

Cortical rhythmic activity can be systematically modulated by stimuli or tasks and may thus provide relevant information about brain function. Meaningful use of those phenomena requires characterization of both locations and time courses of event-related suppressions and increases of oscillatory activity. However, localization of the neural sources of cortical rhythms during intervals of very low levels of activity, and within short time intervals, is not a trivial matter. Hence, event-related modulation of rhythmic activity has typically been described at the level of magnetoencephalography (MEG) sensors or electroencephalography (EEG) electrodes, without reaching into the brain. Here, we introduce erDICS, an event-related version of Dynamic Imaging of Coherent Sources that allows spatial mapping of the level of oscillatory activity in the brain as a function of time, with respect to stimulus or task timing. By utilizing a time-resolved frequency-domain beamformer, erDICS yields the spatial distribution of both power suppressions and power increases. Permutation tests further reveal areas and time windows in which the modulations of oscillatory power are statistically significant, in individual subjects. We demonstrate the usability of erDICS on simulated and real MEG data. From the erDICS maps we identify areas showing salient event-related changes of rhythmic activity, represent them with equivalent current dipoles and calculate their contribution to the measured signal. Comparison of this multidipole model with the original signal yields a quantitative measure of goodness for the identified source areas and the analysis approach in general.