Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI

Abstract

The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (oddball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se, single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology.multimodal imaging ͉ P3 pattern learning ͉ target detection F unctional MRI (fMRI) of the blood oxygenation leveldependent (BOLD) response (BOLD-fMRI) measures local changes in brain hemodynamics associated with a cognitive process noninvasively with a high spatial resolution. However, an unsolved issue in fMRI research is the insufficient temporal resolution of the BOLD response. In contrast to the spatial resolution of BOLDfMRI, event-related potentials (ERP) access the current induced by synaptic activity instantaneously, with an effective temporal resolution on the order of tens to hundreds of milliseconds in case of long-latency cortical responses. However, the location of underlying generators cannot be inferred with certainty. In combination, these two complementary noninvasive methods would allow for joint high-resolution spatial and temporal mapping of the mental process under investigation and add to a more complete understanding of the neural correlates of perception and cognition (1-3). In humans, this integrated spatial and temporal precision could so far be obtained only in direct intracranial recordings, usually performed in patients receiving brain surgery for treatment of epilepsy (4-7).There are basically three approaches to multimodal integration: (i) through fusion, usually referring to the use of a common forward or generative model that can explain both the electroencephalogram (EEG) and fMRI data (8, 9); (ii) through constraints, where spatial information from the fMRI is used for a (spatiotemporal) source reconstruction of the EEG (10-12); and (iii) through prediction, where the fMRI signal is modeled as some measure of the EEG convolved with a hemodynamic response function, a principle used in our study.Invasive r...

Unmixing concurrent EEG-fMRI with parallel independent component analysis

“…Debener, et al, 2006). One such improvement is to unmix both modalities in parallel at the single-trial level, which follows naturally from the recent work (Calhoun, et al, 2006c;Debener, et al, 2005b;Eichele, et al, 2005) and the reasoning laid out above.…”

“…In the case of concurrent EEG-fMRI recordings, one can complement the temporal resolution provided by scalp potentials with the spatial precision of fMRI. This can be done for example by finding correlations between single-trial modulation at a selected time latency in the event-related EEG and activation in the fMRI volume employing mass univariate voxelby-voxel analysis (Benar, et al, 2007;Debener, et al, 2005b;Eichele, et al, 2005). Implicit in this approach is the critical assumption that the scalp EEG data from a selected channel and latency can predict the fMRI activation in single voxels (Friston, et al, 1995;Friston, 2005b).…”

“…Also, this applies to the case where the EEG feature captures a single source, but the fMRI activity at corresponding locations is buried in the spread of other, unrelated sources, leading to underestimation of the spatial extent of the response. Although denoising and inclusion of parametric modulations into the stimulus paradigm (Eichele, et al, 2005), and temporal unmixing of the EEG (Debener, et al, 2005b) solve parts of the problem and make way for refined spatiotemporal mapping, there is still need for improvement of the analysis tools for integration of concurrent recordings (cf. Debener, et al, 2006).…”

“…Another option is to predict the fMRI data with the measured EEG single trial amplitudes, assuming that some EEG timepoints and channels represent functional processes in some voxels without much overlap, representing a point-to-point correlation between mixtures (Benar, et al, 2007;Eichele, et al, 2005). A third solution is to unmix the EEG and predict the fMRI mixture with the modulation of a temporally independent component (Debener, et al, 2005b;Feige, et al, 2005).…”

Frontal theta EEG activity correlates negatively with the default mode network in resting state

“…For instance, quite some effort has been devoted to identify BOLD correlates of epileptoform EEG activity (Aghakhani et al, 2004;Al-Asmi et al, 2003;Benar et al, 2006;Lemieux et al, 2001;Salek-Haddadi et al, 2002). More recently, simultaneous EEG/ fMRI has been successfully applied to investigate cognitive phenomena such as performance monitoring (Debener et al, 2005) and sustained attention (Eichele et al, 2005). Apart from epilepsy research most studies have focused on the BOLD correlates of ongoing oscillatory activity during resting state (i.e., recorded while the subject was quietly lying in the scanner, with no explicit task), focusing on the posterior alpha rhythm and, to a lesser extent, the beta rhythm (Feige et al, 2005;Goldman et al, 2002;Goncalves et al, 2006;Laufs et al, 2006Laufs et al, , 2003aMoosmann et al, 2003).…”