Constraints from functional magnetic resonance imaging (fMRI) were used to identify the sources of the visual P300 event-related potential (ERP). Healthy subjects performed a visual three-stimulus oddball paradigm with a difficult discrimination task while fMRI and high-density ERP data were acquired in separate sessions. This paradigm allowed us to differentiate the P3b component of the P300, which has been implicated in the detection of rare events in general (target and distractor), from the P3a component, which is mainly evoked by distractor events. The fMRI-constrained source model explained Ͼ99% of the variance of the scalp ERP for both components. The P3b was mainly produced by parietal and inferior temporal areas, whereas frontal areas and the insula contributed mainly to the P3a. This source model reveals that both higher visual and supramodal association areas contribute to the visual P3b and that the P3a has a strong frontal contribution, which is compatible with its more anterior distribution on the scalp. The results point to the involvement of distinct attentional subsystems in target and distractor processing.

This paper introduces source coherence, a new method for the analysis of cortical coherence using noninvasive EEG and MEG data. Brain electrical source analysis (BESA) is applied to create a discrete multiple source model. This model is used as a source montage to transform the recorded data from sensor level into brain source space. This provides source waveforms of the modeled brain regions as a direct measure for their activities on a single trial basis. The source waveforms are transformed into time-frequency space using complex demodulation. Magnitude-squared coherence between the brain sources reveals oscillatory coupling between sources. This procedure allows one to separate the time-frequency content of different brain regions even if their activities severely overlap at the surface. Thus, source coherence overcomes problems of localization and interpretation that are inherent to coherence analysis at sensor level. The principle of source coherence is illustrated using an EEG recording of an error-related negativity as an example. In this experiment the subject performed a visuo-motor task. Source coherence analysis revealed dynamical linking between posterior and central areas within the gamma-band around the time of button press at a post-stimulus latency of 200-300 ms.

SUMMARY High-frequency oscillations (HFOs) at ≧80 Hz of nonepileptic nature spontaneously emerge from human cerebral cortex. In 10 patients with extra-occipital lobe epilepsy, we compared the spectral-spatial characteristics of HFOs spontaneously arising from the nonepileptic occipital cortex with those of HFOs driven by a visual task as well as epileptogenic HFOs arising from the extra-occipital seizure focus. We identified spontaneous HFOs at ≧80 Hz with a mean duration of 330 msec intermittently emerging from the occipital cortex during interictal slow-wave sleep. The spectral frequency band of spontaneous occipital HFOs was similar to that of visually-driven HFOs. Spontaneous occipital HFOs were spatially sparse and confined to smaller areas, whereas visually-driven HFOs involved the larger areas including the more rostral sites. Neither spectral frequency band nor amplitude of spontaneous occipital HFOs significantly differed from those of epileptogenic HFOs. Spontaneous occipital HFOs were strongly locked to the phase of delta activity, but the strength of delta-phase coupling decayed from 1 to 3 Hz. Conversely, epileptogenic extra-occipital HFOs were locked to the phase of delta activity about equally in the range from 1 to 3 Hz. The occipital cortex spontaneously generates physiological HFOs which may stand out on electrocorticography traces as prominently as pathological HFOs arising from elsewhere; this observation should be taken into consideration during presurgical evaluation. Coupling of spontaneous delta and HFOs may increase the understanding of significance of delta-oscillations during slow-wave sleep. Further studies are warranted to determine whether delta-phase coupling distinguishes physiological from pathological HFOs or simply differs across anatomical locations.

We used the combination of functional magnetic resonance imaging and event-related potentials to decompose the processing stages (mental chronometry) of working memory retrieval. Our results reveal an early transient activation of inferotemporal cortex, which was accompanied by the onset of a sustained activation of posterior parietal cortex. We furthermore observed late transient responses in ventrolateral prefrontal cortex and late sustained activity in medial frontal and premotor areas. We propose that these neural signatures reflect the cognitive stages of task processing, perceptual evaluation (inferotemporal cortex), storage buffer operations (posterior parietal cortex), active retrieval (ventrolateral prefrontal cortex), and action selection (medial frontal and premotor cortex). This is also supported by their differential temporal contribution to specific subcomponents of the P300 cognitive potential.

No abstract