Open AccessA novel method for integrating MEG and BOLD fMRI signals with the linear convolution model in human primary somatosensory cortex
Author(s) -
Nangini Cathy,
Tam Fred,
Graham Simon J.
Publication year - 2008
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20361
Subject(s) - magnetoencephalography , functional magnetic resonance imaging , somatosensory system , neuroimaging , neuroscience , computer science , linear model , artificial intelligence , context (archaeology) , convolution (computer science) , functional imaging , psychology , pattern recognition (psychology) , artificial neural network , electroencephalography , machine learning , paleontology , biology
Abstract Characterizing the neurovascular coupling between hemodynamic signals and their neural origins is crucial to functional neuroimaging research, even more so as new methods become available for integrating results from different functional neuroimaging modalities. We present a novel method to relate magnetoencephalography (MEG) and BOLD fMRI data from primary somatosensory cortex within the context of the linear convolution model. This model, which relates neural activity to BOLD signal change, has been widely used to predict BOLD signals but typically lacks experimentally derived measurements of neural activity. In this study, an fMRI experiment is performed using variable‐duration (≤1 s) vibrotactile stimuli applied at 22 Hz, analogous to a previously published MEG study (Nangini et al., [ 2006]: Neuroimage 33:252–262), testing whether MEG source waveforms from the previous study can inform the convolution model and improve BOLD signal estimates across all stimulus durations. The typical formulation of the convolution model in which the input is given by the stimulus profile is referred to as Model 1. Model 2 is based on an energy argument relating metabolic demand to the postsynaptic currents largely responsible for the MEG current dipoles, and uses the energy density of the estimated MEG source waveforms as input to the convolution model. It is shown that Model 2 improves the BOLD signal estimates compared to Model 1 under the experimental conditions implemented, suggesting that MEG energy density can be a useful index of hemodynamic activity. Hum Brain Mapp 2008. © 2007 Wiley‐Liss, Inc.