INFRA-SLOW FLUCTUATIONS IN SIMULTANEOUS EEG-FMRI, ACTA UNIVERSITATIS OULUENSIS D Medica 1393
|Kustantaja:||Oulun yliopisto|| |
|Painos:||Osajulkaisuväitöskirjan yhteenveto-osa|| |
|Sijainti:||Print Tietotalo|| |
|Tekijät:||KEINÄNEN TUIJA|| |
Brain activity fluctuations occur in multiple spatial and temporal scales. Functional magnetic
resonance imaging (fMRI) has shown that infra slow fluctuations (ISF) of blood oxygen leveldependent
signal (BOLD) are organized into well-defined areas called resting state networks
(RSN). ISFs have also been detected in full-band EEG (fbEEG) data and in recent years, many
have combined these two modalities to enable more accurate measurements of brain fluctuations.
In simultaneous EEG-fMRI measurements the ISFs of BOLD signal have been found to be
correlated with amplitude envelopes of faster electrophysiological data, suggesting the same
underlying neuronal dynamics. Also direct correlations have been found in task related studies but
not previously in resting state studies. Understanding the relation between EEG and BOLD signal
in resting state might prove beneficial in the research of baseline activity fluctuations of the brain.
Functional connectivity (FC) of the RSNs has been found to vary in different tasks and in some
diseases, but also in resting state in healthy people. Despite numerous studies, no clear cause for
these variations has yet been found. To research these open questions we performed simultaneous
fbEEG-fMRI studies. The measurements from both modalities were analyzed with independent
component analysis to improve the comparability of these results. Correlation analysis revealed
that the EEG ISFs correlate with BOLD signal both temporally and spatially. These correlations
showed spatiotemporal variability that was related to the strength of RSN functional connectivity.
These results indicate that the ISFs of EEG and BOLD reflect a common source of fluctuations.
The understanding of the correlations between ISFs in EEG and fMRI BOLD signals gives
basic information of brain dynamics and of the variables that affect it. A better understanding of
the background of brain activity helps in the development of more effective treatments for various
neurological diseases as the knowledge of the mechanisms behind them grows. The ability to
measure RSN activity with EEG more accurately can help in the development of new methods for
early diagnosis of diseases.