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

Department of Biological and Medical Psychology, University of Bergen, 5009 Bergen, Norway. tom.eichele@
Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 01/2006; 102(49):17798-803. DOI: 10.1073/pnas.0505508102
Source: PubMed

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 (odd-ball) 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.


Available from: Tom Eichele, Jun 03, 2015
  • [Show abstract] [Hide abstract]
    ABSTRACT: The functional magnetic resonance imaging (fMRI) research on face processing have found that the significant activation by face stimuli mainly locailized at the occipital temporal lobe especilly the fusiform gyrus. However, fMRI cannot reflect the face processing as time changes. Event-related potential (ERP) can record electrophysiological changes induced by neuronal activation in time, but spatial information is not well localized. Fusing fMRI and ERP data can perform that how the fMRI activation changes as time move at each ERP time point. Although most of fuse methods perform to analysis by constraint ERP or fMRI data, joint independent component analysis (jICA) method can equally use the ERP and fMRI data and simultaneously examine electrophysiologic and hemodynamic response. In this paper, we use jICA method to analysis two modalities in common data space in order to examine the dynamics of face stimuli response. The results showed that the ERP component N170 response associated with middle occipital gyrus, fusiform gyrus, inferior occipital gyrus, superior temporal gyrus and parahippocampa gyrus for face. Likewise, for non-face, the N170 component was mainly related to parahippocampa gyrus, middle occipital gyrus and inferior occipital gyrus. Further studying on the correlation of the localized ERP response and corresponding average ERP, it was also concluded that the spatial activations related to N170 response induced by face stimulus located in fusiform gyrus, and that induced by non-face stimulus located in parahippocampa gyrus. From the result, fusing fMRI and ERP data by jICA not only provides the time information on fMRI and the spatial source of ERP component, but also reflects spatiotemporal change during face processing.
    SPIE Medical Imaging; 03/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Many studies have reported that discrete cortical areas in the ventral temporal cortex of humans were correlated with the perception of pictures visual stimuli. Moreover, event-related potentials caused by different kinds of picture stimuli showed different amplitude levels of N170 which was maximal over occipito-temporal electrode sites. However, the phenomenon which is mentioned above may be correlated with some local bold signal change, and where is the change happened is still unclear. Recently, research for EEG-fMRI has been widely performed through General Linear Model (GLM) to find the relationship between some feature of the ERP component and the activation of local brain area. In our study, we dealt with the simultaneously recorded EEG-fMRI data of picture stimuli to find the correlation between the change of the N170’s amplitude and the BOLD signal. The amplitudes of the N170 component from the average ERPs of 4 different kinds of picture stimuli were extracted from the EEG data and the activation map for the same stimuli was provided based on the fMRI data. GLM was performed including regressors that could represent the change of the N170’s amplitude. Our result showed that fusiform and occipital gyrus were activated by the parametric design and were overlapped by the activation map of the common fMRI design. Thus we might infer that these regions had relationship with the change of the amplitudes of N170. Our research may contribute to location of the source of N170 and bring a new approach for the parameter design of the fMRI signal in EEG-fMRI analysis.
    SPIE Medical Imaging; 03/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Most studies involving simultaneous electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data rely on the first-order, affine-linear correlation of EEG and fMRI features within the framework of the general linear model. An alternative is the use of information-based measures such as mutual information and entropy, which can also detect higher-order correlations present in the data. The estimate of information-theoretic quantities might be influenced by several parameters, such as the numerosity of the sample, the amount of correlation between variables, and the discretization (or binning) strategy of choice. While these issues have been investigated for invasive neurophysiological data and a number of bias-correction estimates have been developed, there has been no attempt to systematically examine the accuracy of information estimates for the multivariate distributions arising in the context of EEG-fMRI recordings. This is especially important given the differences between electrophysiological and EEG-fMRI recordings. In this study, we drew random samples from simulated bivariate and trivariate distributions, mimicking the statistical properties of EEG-fMRI data. We compared the estimated information shared by simulated random variables with its numerical value and found that the interaction between the binning strategy and the estimation method influences the accuracy of the estimate. Conditional on the simulation assumptions, we found that the equipopulated binning strategy yields the best and most consistent results across distributions and bias correction methods. We also found that within bias correction techniques, the asymptotically debiased (TPMC), the jackknife debiased (JD), and the best upper bound (BUB) approach give similar results, and those are consistent across distributions.
    Neural Computation 12/2014; 27(2):1-25. DOI:10.1162/NECO_a_00695 · 1.69 Impact Factor