The human brain is adept at anticipating upcoming events, but in a rapidly changing world, it is essential to detect and encode events that violate these expectancies. Unexpected events are more likely to be remembered than predictable events, but the underlying neural mechanisms for these effects remain unclear. We report intracranial EEG recordings from the hippocampus of epilepsy patients, and from the nucleus accumbens of depression patients. We found that unexpected stimuli enhance an early (187 ms) and a late (482 ms) hippocampal potential, and that the late potential is associated with successful memory encoding for these stimuli. Recordings from the nucleus accumbens revealed a late potential (peak at 475 ms), which increases in magnitude during unexpected items, but no subsequent memory effect and no early component. These results are consistent with the hypothesis that activity in a loop involving the hippocampus and the nucleus accumbens promotes encoding of unexpected events.

High-frequency oscillations are promising new biomarkers in epilepsy. This review provides interested researchers and clinicians with a review of current state of the art of recording and identification and potential challenges to clinical translation.

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Although Electroencephalography (EEG) source localization is being widely used in adults, this promising technique has not yet been applied to newborns because of technical difficulties, such as lack of data concerning the newborn skull conductivity, thickness, and homogeneity. Using a new type of EEG headcap molded on each baby's head, we aimed to determine whether this technique could be adapted to neonates, and to evaluate the importance of these technical difficulties. We carried out EEG source reconstruction of the recordings of five neonates using dipole fit algorithm. We used four different head models for each neonate, obtained from individual MRI scans: normal skull thickness and conductivity of 0.0042 S/m; normal thickness and conductivity of 0.33 S/m; increased thickness and conductivity of 0.0042 S/m; and normal thickness and conductivity with a modeled bregma fontanel. Dipole locations were consistent with MRI and clinical data. The mean difference between the dipole locations in the 0.0042 and the 0.33 S/m skull layer models was 11.6 +/- 2.5 mm, with an average 29.7% decrease in magnitude for the 0.33 S/m model but no significant changes for the dipoles orientation. Skull layer thickness had a large influence on magnitude, but no significant effect on position and orientation. The mean difference between the dipole locations induced by the modeled fontanel was 2.0 +/- 2.1 mm, with an average 2.1% increase in magnitude. Our results show that EEG source localization is feasible in neonates. With further development, the technique may prove useful for neurological evaluation of neonates.

Physiologic tremor (PT) consists of a peripheral mechanical oscillation at the limbs' resonance frequency and an independent central component in the 6-15 Hz band. This central component has mainly been attributed to spinal interneuronal systems or subcortical oscillators but more recently also to cortical rhythms. We recorded PT electromyographically and accelerometrically from different parts of the arm in parallel to epicortical recordings from grid electrodes covering the primary sensorimotor areas of the contralateral cortex in six epileptic patients. Previous bipolar electrical stimulation of the cortical electrodes resulted in a somatotopic map of the primary cortex underlying the grid. Spectral and cross-spectral analysis including coherence spectra between epicortical electrodes and EMG and the corresponding phase spectra were performed off-line. We found significant corticomuscular coherence in the 6-15 Hz range in four out of the six patients. This coherence was focal on the cortex and it was distributed somatotopically mainly within the primary motor area. The frequency band of the coherence mostly corresponding to the EMG frequency remained stable with added inertia, while the main accelerometric frequency was clearly reduced following the resonance frequency. The phase spectra between electrocorticogram (ECoG) and EMG showed a clear delay between cortex and muscle in two of the patients, which was compatible with conduction in fast pyramidal pathways. These findings indicate that the 6-15 Hz coherence between cortex and EMG reflects a corticomuscular transmission of the oscillation rather than peripheral feedback to the cortex. We conclude that cortical networks are involved in the generation of physiologic tremor.

SUMMARYPurpose: The study analyzes the occurrence of high frequency oscillations in different types of focal cortical dysplasia in 22 patients with refractory epilepsy. High frequency oscillations are biomarkers for epileptic tissue, but it is unknown whether they can reflect increasingly dysplastic tissue changes as well as epileptic disease activity. Methods: High frequency oscillations (80-450 Hz) were visually marked by two independent reviewers in all channels of intracranial implanted grid, strips, and depth electrodes in patients with focal cortical dysplasia and refractory epilepsy. Rates of high frequency oscillations in patients with pathologically confirmed focal cortical dysplasia of Palmini type 1a and b were compared with those in type 2a and b. Key Findings: Patients with focal cortical dysplasia type 2 had significantly more seizures than those with type 1 (p < 0.001). Rates of high frequency oscillations were significantly higher in patients with focal cortical dysplasia type 2 versus type 1 (p < 0.001). In addition, it could be confirmed that rates of high frequency oscillations were significantly higher in presumed epileptogenic areas than outside (p < 0.001). Significance: Activity of high frequency oscillations mirrors the higher epileptogenicity of focal cortical dysplasia type 2 lesions compared to type 1 lesions. Therefore, rates of high frequency oscillations can reflect disease activity of a lesion. This has implications for the use of high frequency oscillations as biomarkers for epileptogenic areas, because a detailed analysis of their rates may be necessary to use high frequency oscillations as a predictive tool in epilepsy surgery.

SUMMARYObjective: To assess the visibility and detectability in scalp electroencephalography (EEG) of cortical sources in frontal lobe epilepsy (FLE) as to their localization, and the extent and amplitude of activation. Methods: We analyzed the simultaneous subdural and scalp interictal EEG recordings of 14 patients with refractory frontal lobe epilepsy (FLE) associated with focal cortical dysplasia. Subdural spike types were identified and averaged for source localization and detection of their scalp EEG correlates. Both raw and averaged scalp EEG segments were reviewed for spikes, blinded to subdural segments. We further analyzed the correlation of spike-to-background amplitude ratios in subdural and scalp EEG. Results: We identified 36 spike types in subdural EEG, corresponding to 29 distinct sources. Four of 29 sources were visible by visual evaluation of scalp EEG and six additional sources were detectable after averaging: four in the medial frontal, two in the dorsolateral gyri, two in the depth of dorsolateral sulci, and two in the basal frontal region. Cortical sources generating scalp-detectable spikes presented a median of 6 cm 2 of activated cortical convexity surface and a subdural spike-to-backgroundamplitude ratio >8. These sources were associated with a higher number of activated subdural grid contacts and a higher subdural spike-to-background amplitude ratio than sources generating non-scalp-detectable spikes. Significance: Not only dorsolateral but also basal and medial sources can be detectable in FLE. This is the first in vivo demonstration derived from simultaneous subdural and scalp EEG recordings of the complementary significance of extensive source activation and higher subdural spike-to-background amplitude ratio in the detection of cortical sources in FLE.

The goal of this study is to provide a seizure detection algorithm that is relatively simple to implement on a microcontroller, so it can be used for an implantable closed loop stimulation device. We propose a set of 11 simple time domain and power bands features, computed from one intracranial EEG contact located in the seizure onset zone. The classification of the features is performed using a random forest classifier. Depending on the training datasets and the optimization preferences, the performance of the algorithm were: 93.84% mean sensitivity (100% median sensitivity), 3.03 s mean (1.75 s median) detection delays and 0.33/h mean (0.07/h median) false detections per hour.