Simultaneous EEG-fMRI during a working memory task: modulations in low and high frequency bands.

Functional Neurosurgery, University Hospital Zürich, Zürich, Switzerland.
PLoS ONE (Impact Factor: 3.53). 04/2010; 5(4):e10298. DOI: 10.1371/journal.pone.0010298
Source: PubMed

ABSTRACT EEG studies of working memory (WM) have demonstrated load dependent frequency band modulations. FMRI studies have localized load modulated activity to the dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), and posterior parietal cortex (PPC). Recently, an EEG-fMRI study found that low frequency band (theta and alpha) activity negatively correlated with the BOLD signal during the retention phase of a WM task. However, the coupling of higher (beta and gamma) frequencies with the BOLD signal during WM is unknown.
In 16 healthy adult subjects, we first investigated EEG-BOLD signal correlations for theta (5-7 Hz), alpha1 (8-10), alpha2 (10-12 Hz), beta1 (13-20), beta2 (20-30 Hz), and gamma (30-40 Hz) during the retention period of a WM task with set size 2 and 5. Secondly, we investigated whether load sensitive brain regions are characterised by effects that relate frequency bands to BOLD signals effects.
We found negative theta-BOLD signal correlations in the MPFC, PPC, and cingulate cortex (ACC and PCC). For alpha1 positive correlations with the BOLD signal were found in ACC, MPFC, and PCC; negative correlations were observed in DLPFC, PPC, and inferior frontal gyrus (IFG). Negative alpha2-BOLD signal correlations were observed in parieto-occipital regions. Beta1-BOLD signal correlations were positive in ACC and negative in precentral and superior temporal gyrus. Beta2 and gamma showed only positive correlations with BOLD, e.g., in DLPFC, MPFC (gamma) and IFG (beta2/gamma). The load analysis revealed that theta and--with one exception--beta and gamma demonstrated exclusively positive load effects, while alpha1 showed only negative effects.
We conclude that the directions of EEG-BOLD signal correlations vary across brain regions and EEG frequency bands. In addition, some brain regions show both load sensitive BOLD and frequency band effects. Our data indicate that lower as well as higher frequency brain oscillations are linked to neurovascular processes during WM.


Available from: Lars Michels, May 29, 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: Functional Magnetic Resonance Imaging (fMRI) has very high spatial resolution as compared to the Electroencephalography (EEG) which on other hand has very high temporal resolution. The pros and cons of the EEG and fMRI are complementary to each other. Simultaneous EEG-fMRI data recording solve the problem to get high spatial and temporal resolution at the same time to study the brain dynamics in efficient manner. EEG-fMRI integration is a new approach to study human brain activity. Recent developments in MRI compatible EEG equipment made this integration more easy and attractive in cognitive neuroscience. The simultaneous EEG-fMRI data acquisition gives us the better information for all activated areas of the brain to understand the cognitive processes. We developed data acquisition setup for simultaneous EEG-fMRI for cognitive tasks. Also data recording has been done for two healthy participants as a pilot study which will be further continued on other healthy participants as well as on patients. The EEG and fMRI data is pre-processed and artefacts are removed. This combined EEG-fMRI data can be further used in multimodal data integration or data fusion which can give the better results and understanding of the cognitive processes of human brain as compared to analysing EEG and fMRI data separately.
    2014 5th International Conference on Intelligent and Advanced Systems (ICIAS); 06/2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We employed spectral Granger causality analysis on a full set of 56 electroencephalographic recordings acquired during the execution of either a 2D movement pointing or a perceptual (yes/no) change detection task with memory and non-memory conditions. On the basis of network characteristics across frequency bands, we provide evidence for the full dissociation of the corresponding cognitive processes. Movement-memory trial types exhibited higher degree nodes during the first 2 s of the delay period, mainly at central, left frontal and right-parietal areas. Change detection-memory trial types resulted in a three-peak temporal pattern of the total degree with higher degree nodes emerging mainly at central, right frontal, and occipital areas. Functional connectivity networks resulting from non-memory trial types were characterized by more sparse structures for both tasks. The movement-memory trial types encompassed an apparent coarse flow from frontal to parietal areas while the opposite flow from occipital, parietal to central and frontal areas was evident for the change detection-memory trial types. The differences among tasks and conditions were more profound in α (8-12 Hz) and β (12-30 Hz) and less in γ (30-45 Hz) band. Our results favor the hypothesis which considers spatial working memory as a by-product of specific mental processes that engages common brain areas under different network organizations.
    Frontiers in Computational Neuroscience 11/2014; 8:146. DOI:10.3389/fncom.2014.00146 · 2.23 Impact Factor