No abstract

Similar to the posterior alpha rhythm, pericentral (Rolandic) EEG rhythms in the alpha and beta frequency range are referred to as "idle rhythms" indicating a "resting state" of the respective system. The precise function of these rhythms is not clear. We used simultaneous EEG-fMRI during a bimanual motor task to localize brain areas involved in Rolandic alpha and beta EEG rhythms. The identification of these rhythms in the MR environment was achieved by a blind source separation algorithm. Rhythm "strength", i.e. spectral power determined by wavelet analysis, inversely correlated most strongly with the fMRI-BOLD signal in the postcentral cortex for the Rolandic alpha (mu) rhythm and in the precentral cortex for the Rolandic beta rhythm. FMRI correlates of Rolandic alpha and beta rhythms were distinct from those associated with the posterior "classical" alpha rhythm, which correlated inversely with the BOLD signal in the occipital cortex. An inverse correlation with the BOLD signal in the respective sensory area seems to be a general feature of "idle rhythms".

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...