Magnetoencephalography (MEG) is an excellent tool for noninvasive investigation of neuronal activity from outside of the head. It provides millisecond temporal accuracy and good spatial resolution. The classical measure of task-related brain activity is the evoked response that is phase-locked to task or stimulus. But the brain also exhibits spontaneous oscillations, or rhythmic activity, that has been observed to be modulated during task or in response to stimuli. This Thesis mainly focuses on investigations of cortical brain rhythms, developing methods for analyzing them and probing their relationship with evoked responses. While the sensor level MEG signal can be used successfully for studying evoked responses and cortical rhythmic activity, a proper evaluation of the data requires localization of the active brain areas. For this purpose, a beamforming technique called Dynamic Imaging of Coherent Sources (DICS) was used and modified in this Thesis. With DICS, it is possible to examine both power level modulations of rhythmic activity and functional connectivity between different brain regions conveyed by rhythmic activity. In this Thesis, an event-related version of a beamformer method DICS was implemented to assist the modeling of rhythmic activity. The feasibility of this new method, erDICS, was shown with simulations and real MEG data. The method was further applied to compare evoked responses and rhythmic activity in a high-level cognitive task of picture naming, with the conclusion that the two measures of cortical processes are largely detached and that both measures are needed for an accurate portrayal of brain activity. With another data set from a word priming study, erDICS was used to investigate connections between the left superior temporal cortex and other cortical regions. The method revealed different brain networks for phonological and semantic priming.
|Translated title of the contribution||Aivokuoren rytmit hermostollisen käsittelyn merkkinä|
|Publication status||Published - 2012|
|MoE publication type||G5 Doctoral dissertation (article)|
- evoked response
- functional connectivity