Spatiotemporal Activity of a Cortical Network for Processing Visual Motion Revealed by MEG and fMRI

Abstract

A sudden change in the direction of motion is a particularly salient and relevant feature of visual information. Extensive research has identified cortical areas responsive to visual motion and characterized their sensitivity to different features of motion, such as directional specificity. However, relatively little is known about responses to sudden changes in direction. Electrophysiological data from animals and functional imaging data from humans suggest a number of brain areas responsive to motion, presumably working as a network. Temporal patterns of activity allow the same network to process information in different ways. The present study in humans sought to determine which motion-sensitive areas are involved in processing changes in the direction of motion and to characterize the temporal patterns of processing within this network of brain regions. To accomplish this, we used both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). The fMRI data were used as supplementary information in the localization of MEG sources. The change in the direction of visual motion was found to activate a number of areas, each displaying a different temporal behavior. The fMRI revealed motion-related activity in areas MT+ (the human homologue of monkey middle temporal area and possibly also other motion sensitive areas next to MT), a region near the posterior end of the superior temporal sulcus (pSTS), V3A, and V1/V2. The MEG data suggested additional frontal sources. An equivalent dipole model for the generators of MEG signals indicated activity in MT+, starting at 130 ms and peaking at 170 ms after the reversal of the direction of motion, and then again at approximately 260 ms. Frontal activity began 0-20 ms later than in MT+, and peaked approximately 180 ms. Both pSTS and FEF+ showed long-duration activity continuing over the latency range of 200-400 ms. MEG responses in the region of V3A and V1/V2 were relatively small, and peaked at longer latencies than the initial peak in MT+. These data revealed characteristic patterns of activity in this cortical network for processing sudden changes in the direction of visual motion.

fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints

“…Depending on different EEG source models, the fMRI map can be used to constrain the locations of multiple current dipoles, namely the fMRI-constrained dipole fitting (Ahlfors et al, 1999;Korvenoja et al, 1999;Fujimaki et al, 2002;Vanni et al, 2004), or to constrain the distributed source distribution over the folded cortical surface or in the 3-D brain volume, namely the fMRI-constrained current density imaging (George et al, 1995;Liu et al, 1998;Dale et al, 2000;Wagner et al, 2000;Babiloni et al, 2005;Ahlfors and Simpson, 2004;Sato et al, 2004;Phillips et al, 2005;Liu et al, 2006b;Mattout et al, 2006).…”

Automatic fMRI‐guided MEG multidipole localization for visual responses

“…Since the MEG/EEG inverse problem is illposed [Sarvas, 1987], spatial fMRI information is potentially useful in resolving the ambiguity. Often, because of difficulties in the true multimodal integration of MEG/ EEG and fMRI, the suggested solutions have been for instance direct comparison of the separate results [e.g., Ahlfors et al, 1999], using fMRI data as a basis to adjust the source variance parameters [Dale et al, 2000;Liu et al, 1998], utilization of the functional results for constraining the possible source positions [e.g., Korvenoja et al, 1999], or by directly seeding the fMRI locations and cortical orientations to be optimized with a suitable EEG source dipole model [Vanni et al, 2004a,b]. Lately, there has been great interest in Bayesian methods utilizing fMRI prior information, for instance, with distributed linear solutions of the MEG/EEG inverse problem [e.g., Dale et al, 2000;Phillips et al, 2005] and also on determining the relevance of the fMRI prior information included in the inverse solution [Daunizeau et al, 2005].…”

MRI detection of weak magnetic fields due to an extended current dipole in a conducting sphere: A model for direct detection of neuronal currents in the brain

“…The resulting data could also be straightforwardly integrated with data from magnetoencephalography (MEG) to combine the favorable spatial resolution of fMRI with the excellent temporal resolution of MEG. Although this is possible with BOLD fMRI, confounding effects may result from the occurrence of BOLD contrast at some distance from the underlying neuronal activity (15)(16)(17).…”