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.
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.
N170 is an important neurophysiological index to study the underlying mechanisms of face and object perception. In this
study, we proposed a mean-sensitive spatial filtering (MSF) method for linear transformation of event-related potentials
(ERP) that is sensitive to mean differences between stimuli conditions and applied it to N170 component to extract
category-specific spatio-temporal features contained in EEG. MSF method estimated a set of optimal projecting vectors
according to the spatial distribution patterns of N170 means. Then, we applied these spatial filters to single-trial ERP
data and perform classification on the extracted features. In this way, the presence of a larger negative component in
EEG time courses evoked by faces can be detected robustly in single trial EEG, and hereby we can infer the category of
every presented stimulus from faces and objects. Furthermore, we also successfully extracted the unobvious distinct
spatial patterns between cars and cats with MSF and separated them correctly. Our remarkable and robust classification
performances suggest that MSF works well in extracting stable spatial patterns from N170. Therefore, MSF provides a
promising solution for decoding presented visual information basing on single-trial N170 component.
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