Abstract
Magnetoencephalography (MEG) records brain activity with excellent temporal and good spatial resolution, while functional magnetic resonance imaging (fMRI) offers good temporal and excellent spatial resolution. The aim of this study is to implement a Bayesian framework to use fMRI data as spatial priors for MEG inverse solutions. We used simulated MEG data with both evoked and induced activity and experimental MEG data from sixteen participants to examine the effectiveness of using fMRI spatial priors in MEG source reconstruction. For simulated MEG data, incorporating the prior information from fMRI increased the spatial resolution of MEG source reconstruction by 3 mm on average. For experimental MEG data, fMRI spatial information reduced the spurious clusters for evoked activity and showed more left-lateralized activation pattern for induced activity. The use of fMRI spatial priors greatly reduced location error for induced source in MEG data. Our results provide empirical evidence that the use of fMRI spatial priors improves the accuracy of MEG source reconstruction. The combined MEG and fMRI approach can provide neuroimaging data with better spatial and temporal resolutions to add another perspective to our understanding of the neurobiology of language. The potential clinical applications include pre-surgical evaluation of language function for epilepsy patients and evaluation of language network for children with language disorders.
Highlights
The last decade has witnessed great advances in multi-modal data fusion techniques (Auranen et al, 2009; Baillet & Garnero, 1997; Baillet et al, 1999; Debener et al, 2006; Friston et al, 2008; Henson et al, 2010; Nummenmaa et al, 2007; Sato et al, 2004; Wipf & Nagarajan, 2009) and increasing interests in studying high-order cognition in the human brain using multi-modal techniques, especially data fusion of functional magnetic resonance imaging and magnetoencephalography (MEG) (Liljestrom et al, 2009; Pang et al, 2010; Vartiainen et al, 2011; Wang et al, 2012)
Note that the receiver operating characteristic (ROC) curves had a non-smooth appearance due to the small number of true positive voxels embedded in the simulated data set relative to true negative voxels
Still the area under curve (AUC) varied with the prior information incorporated in the model and from this parameter we could see that the performance was superior when valid functional magnetic resonance imaging (fMRI) priors or mixture of valid and invalid fMRI priors used in the estimation process
Summary
The last decade has witnessed great advances in multi-modal data fusion techniques (Auranen et al, 2009; Baillet & Garnero, 1997; Baillet et al, 1999; Debener et al, 2006; Friston et al, 2008; Henson et al, 2010; Nummenmaa et al, 2007; Sato et al, 2004; Wipf & Nagarajan, 2009) and increasing interests in studying high-order cognition in the human brain using multi-modal techniques, especially data fusion of functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) (Liljestrom et al, 2009; Pang et al, 2010; Vartiainen et al, 2011; Wang et al, 2012). Our previous study has demonstrated spatial concordance in the left inferior frontal gyrus (IFG) for covert or overt generation versus overt repetition, and bilateral motor cortices when overt generation versus covert generation (Wang et al, 2012), algin with other studies (Pang et al, 2010; Vartiainen et al, 2011). These studies provide evidence that the two modalities are assessing the same language network during language production and comprehension tasks. Several integration schemes have been introduced to combine fMRI and MEG data such as fMRI-guided equivalent current dipole (ECD) fitting (Ahlfors & Simpson, 2004), fMRI-constrained cortical current density imaging (Dale et al, 2000; Liu et al, 2008; Ou et al, 2010), and the recent popularity of Bayesian schemes applied in MEG source inversion
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