Author: Kujala J1, Sudre G2, Vartiainen J3, Liljeström M3, Mitchell T4, Salmelin R3.
Affiliation: 1Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, FI-00076 Aalto, Finland; MEG Core and Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University, FI-00076 Aalto, Finland. Electronic address: jan.kujala@aalto.fi. 2Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA. 3Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, FI-00076 Aalto, Finland; MEG Core and Advanced Magnetic Imaging Centre, Aalto NeuroImaging, Aalto University, FI-00076 Aalto, Finland. 4Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Conference/Journal: Neuroimage.
Date published: 2014 Feb 8
Other:
Pages: S1053-8119(14)00088-3 , Special Notes: doi: 10.1016/j.neuroimage.2014.01.057 , Word Count: 183
Animal and human studies have frequently shown that in primary sensory and motor regions the BOLD signal correlates positively with high-frequency and negatively with low-frequency neuronal activity. However, recent evidence suggests that this relationship may also vary across cortical areas. Detailed knowledge of the possible spectral diversity between electrophysiological and hemodynamic responses across the human cortex would be essential for neural-level interpretation of fMRI data and for informative multimodal combination of electromagnetic and hemodynamic imaging data, especially in cognitive tasks. We applied multivariate partial least squares correlation analysis to MEG-fMRI data recorded in a reading paradigm to determine the correlation patterns between the data types, at once, across the cortex. Our results revealed heterogeneous patterns of high-frequency correlation between MEG and fMRI responses, with marked dissociation between lower and higher order cortical regions. The low-frequency range showed substantial variance, with negative and positive correlations manifesting at different frequencies across cortical regions. These findings demonstrate the complexity of the neurophysiological counterparts of hemodynamic fluctuations in cognitive processing.
Copyright © 2014. Published by Elsevier Inc.
KEYWORDS:
Magnetoencephalography, blood-oxygen-level dependent, correlation, functional magnetic resonance imaging, multivariate analysis
PMID: 24518260