Electrophysiological correlates of object-repetition effects: sLORETA imaging with 64-channel EEG and individual MRI.

BACKGROUND: We investigated the electrophysiological correlates of object-repetition effects using an object categorization task, standardized low-resolution electromagnetic tomography (sLORETA), and individual magnetic resonance imaging. Sixteen healthy adults participated, and a total of 396 line drawings of living and non-living objects were used as stimuli. Of these stimuli, 274 were presented only once, and 122 were repeated after one to five intervening pictures. Participants were asked to categorize the objects as living or non-living things by pressing one of two buttons. RESULTS: The old/new effect (i.e., a faster response time and more positive potentials in response to repeated stimuli than to stimuli initially presented) was observed at 350-550 ms post-stimulus. The distributions of cortical sources for the old and new stimuli were very similar at 250-650 ms after stimulus-onset. Activation in the right middle occipital gyrus/cuneus, right fusiform gyrus, left superior temporal gyrus, and right inferior frontal gyrus was significantly reduced in response to old compared with new stimuli at 250-350, 350-450, 450-550, and 550-650 ms after stimulus-onset, respectively. Priming in response time was correlated with the electrophysiological priming at left parietal area and repetition suppression at left superior temporal gyrus in 450-550 ms. CONCLUSIONS: These results suggest processing of repeated objects is facilitated by sharpening perceptual representation and by efficient detection or attentional control of repeated objects.

Ictal high-gamma oscillation (60-99 Hz) in intracranial electroencephalography and postoperative seizure outcome in neocortical epilepsy.

OBJECTIVE: High-gamma oscillations (HGOs) (60-99 Hz) have been suggested to correlate with seizure onset zones and seizure outcomes. We investigated the correlation between the extent of removal of ictal HGO generating areas and postoperative seizure outcome in neocortical epilepsy (NE). METHODS: Twenty three patients with medically intractable NE underwent chronic intracranial electroencephalography (iEEG) using subdural electrodes. Ictal HGOs and superimposed undersampled ripples within ±3 s of video-iEEG ictal onset were extracted by wavelet clustering and thresholding. Cluster epileptogenicity indices (CEIs) were calculated. The temporal analysis window was locked to the timing of the maximum CEI wavecluster. Root mean square amplitudes, cross-correlation synchronies and the local focus indices within the temporal window were calculated. RESULTS: Percentages of resected maximum CEI waveclusters and HGO zones with high standardised amplitudes (>3), high cross-correlation synchronies (>0.9) and high local focus indices (>2) were significantly higher in the seizure-free group compared to the not seizure-free group (p=0.036, p=0.018, and p=0.026, respectively). CONCLUSIONS: The automatic quantitative ictal HGO analysis may be effective in delineating the epileptogenic zone. SIGNIFICANCE: HGO analysis may be helpful for improving post-resection seizure outcome in NE in the future.

An EEG-based real-time cortical rhythmic activity monitoring system: a pilot study.

This paper introduces an electroencephalography (EEG)-based, real-time, cortical rhythmic activity monitoring system which can monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, with a high temporal resolution. In the monitoring system, a frequency domain inverse operator is preliminarily constructed, based on the subject's anatomical information and sensor arrangement, and then spectral current power at each cortical vertex is calculated for the Fourier transforms of successive sections of continuous data, when a particular frequency band is given. The first pilot system was applied to two human experiments: (1)cortical alpha rhythm changes induced by opening and closing eyes and (2) cortical mu rhythm changes originated from arm movements, demonstrating the feasibility of the system.

Extraction of qEEG variables to diagnose early dementia.

Although it is difficult to definitely diagnose dementia with noninvasive methods such as EEG and MEG, early detection of subjects with probable dementia is of importance to apply currently available treatment strategies. In the present study, we have extracted featured qEEG variables, which can be potentially used to diagnose early dementia, from resting eyes-closed continuous EEGs of 22 early dementia patients and 27 age-matched healthy controls. Among 1680 candidate qEEG variables, about 160 variables showed statistically significant difference (p < 0.01) between early dementia group and control subject group, when mean phase coherence as well as EEG coherence was evaluated for all possible combinations of electrode pairs. Some preliminary trials to discriminate the two groups with the extracted qEEG variables demonstrated that the use of mean phase coherence as a supplementary or alternative measure for the EEG coherence may enhance the accuracy of diagnosis of early dementia.

An EEG-based real-time cortical rhythmic activity monitoring system.

In the present study, we introduce an electroencephalography (EEG)-based, real-time, cortical rhythmic activity monitoring system which can monitor spatiotemporal changes of cortical rhythmic activity on a subject's cortical surface, not on the subject's scalp surface, with a high temporal resolution. In the monitoring system, a frequency domain inverse operator is preliminarily constructed, considering the subject's anatomical information and sensor configurations, and then the spectral current power at each cortical vertex is calculated for the Fourier transforms of successive sections of continuous data, when a particular frequency band is given. A preliminary offline simulation study using four sets of artifact-free, eye-closed, resting EEG data acquired from two dementia patients and two normal subjects demonstrates that spatiotemporal changes of cortical rhythmic activity can be monitored at the cortical level with a maximal delay time of about 200 ms, when 18 channel EEG data are analyzed under a Pentium4 3.4 GHz environment. The first pilot system is applied to two human experiments-(1) cortical alpha rhythm changes induced by opening and closing eyes and (2) cortical mu rhythm changes originated from the arm movements-and demonstrated the feasibility of the developed system.