Summary Modern electroencephalographic (EEG) technology contributed to the appreciation that the EEG signal outside the classical Berger frequency band contains important information. In epilepsy, research of the past decade focused particularly on interictal high-frequency oscillations (HFOs) > 80 Hz. The first large application of HFOs was in the context of epilepsy surgery. This is now followed by other applications such as assessment of epilepsy severity and monitoring of antiepileptic therapy. This article reviews the evidence on the clinical use of HFOs in epilepsy with an emphasis on the latest developments. It highlights the growing literature on the association between HFOs and post-surgical seizure outcome. A recent meta-analysis confirmed a higher resection ratio for HFOs in seizure-free versus non–seizure-free patients. Residual HFOs in the postoperative electrocorticogram were shown to predict epilepsy surgery outcome better than preoperative HFO rates. The review further discusses the different attempts to separate physiological from epileptic HFOs, as this might increase the specificity of HFOs. As an example, analysis of sleep microstructure demonstrated a different coupling between HFOs inside and outside the epileptogenic zone. Moreover, there is increasing evidence that HFOs are useful to measure disease activity and assess treatment response using noninvasive EEG and magnetoencephalography. This approach is particularly promising in children, because they show high scalp HFO rates. HFO rates in West syndrome decrease after adrenocorticotropic hormone treatment. Presence of HFOs at the time of rolandic spikes correlates with seizure frequency. The time-consuming visual assessment of HFOs, which prevented their clinical application in the past, is now overcome by validated computer-assisted algorithms. HFO research has considerably advanced over the past decade, and use of noninvasive methods will make HFOs accessible to large numbers of patients. Prospective multicenter trials are awaited to gather information over long recording periods in large patient samples.

EMSE explained individual mortality in almost 90 % of cases, and performed significantly better than previous scores. This explorative study needs external prospective corroboration. EMSE may be a valuable tool for risk stratification in interventional studies in the future.

High frequency oscillations (HFOs) are estimated as a potential marker for epileptogenicity. Current research strives for valid evidence that these HFOs could aid the delineation of the to-be resected area in patients with refractory epilepsy and improve surgical outcomes. In the present meta-analysis, we evaluated the relation between resection of regions from which HFOs can be detected and outcome after epilepsy surgery. We conducted a systematic review of all studies that related the resection of HFO-generating areas to postsurgical outcome. We related the outcome (seizure freedom) to resection ratio, that is, the ratio between the number of channels on which HFOs were detected and, among these, the number of channels that were inside the resected area. We compared the resection ratio between seizure free and not seizure free patients. In total, 11 studies were included. In 10 studies, ripples (80–200 Hz) were analyzed, and in 7 studies, fast ripples (>200 Hz) were studied. We found comparable differences (dif) and largely overlapping confidence intervals (CI) in resection ratios between outcome groups for ripples (dif = 0.18; CI: 0.10–0.27) and fast ripples (dif = 0.17; CI: 0.01–0.33). Subgroup analysis showed that automated detection (dif = 0.22; CI: 0.03–0.41) was comparable to visual detection (dif = 0.17; CI: 0.08–0.27). Considering frequency of HFOs (dif = 0.24; CI: 0.09–0.38) was related more strongly to outcome than considering each electrode that was showing HFOs (dif = 0.15; CI = 0.03–0.27). The effect sizes found in the meta-analysis are small but significant. Automated detection and application of a detection threshold in order to detect channels with a frequent occurrence of HFOs is important to yield a marker that could be useful in presurgical evaluation. In order to compare studies with different methodological approaches, detailed and standardized reporting is warranted.

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SUMMARYObjective: In patients with epilepsy, seizure relapse and behavioral impairments can be observed despite the absence of interictal epileptiform discharges (IEDs). Therefore, the characterization of pathologic networks when IEDs are not present could have an important clinical value. Using Granger-causal modeling, we investigated whether directed functional connectivity was altered in electroencephalography (EEG) epochs free of IED in left and right temporal lobe epilepsy (LTLE and RTLE) compared to healthy controls. Methods: Twenty LTLE, 20 RTLE, and 20 healthy controls underwent a resting-state high-density EEG recording. Source activity was obtained for 82 regions of interest (ROIs) using an individual head model and a distributed linear inverse solution. Grangercausal modeling was applied to the source signals of all ROIs. The directed functional connectivity results were compared between groups and correlated with clinical parameters (duration of the disease, age of onset, age, and learning and mood impairments). Results: We found that: (1) patients had significantly reduced connectivity from regions concordant with the default-mode network; (2) there was a different network pattern in patients versus controls: the strongest connections arose from the ipsilateral hippocampus in patients and from the posterior cingulate cortex in controls; (3) longer disease duration was associated with lower driving from contralateral and ipsilateral mediolimbic regions in RTLE; (4) aging was associated with a lower driving from regions in or close to the piriform cortex only in patients; and (5) outflow from the anterior cingulate cortex was lower in patients with learning deficits or depression compared to patients without impairments and to controls. Significance: Resting-state network reorganization in the absence of IEDs strengthens the view of chronic and progressive network changes in TLE. These resting-state connectivity alterations could constitute an important biomarker of TLE, and hold promise for using EEG recordings without IEDs for diagnosis or prognosis of this disorder.

Recovery of consciousness has been associated with connectivity in the frontal cortex and parietal regions modulated by the thalamus. To examine this model and to relate alterations to deficits in cognitive functioning and conscious processing, we investigated topological network properties in patients with chronic disorders of consciousness recovered from coma.Resting state fMRI data of 34 patients with unresponsive wakefulness syndrome and 25 in minimally conscious state were compared to 28 healthy controls. We investigated global and local network characteristics. Additionally, behavioral measures were correlated with the local metrics of 28 regions within the fronto-parietal network and the thalamus.In chronic disorders of consciousness, modularity at the global level was reduced suggesting a disturbance in the optimal balance between segregation and integration. Moreover, network properties were altered in several regions which are associated with conscious processing (particularly, in medial parietal, and frontal regions, as well as in the thalamus). Between minimally conscious and unconscious patients the local efficiency of medial parietal regions differed. Alterations in the thalamus were particularly evident in non-conscious patients. Most of the regions affected in patients with impaired consciousness belong to the so-called ‘rich club’ of highly interconnected central nodes. Disturbances in their topological characteristics have severe impact on information integration and are reflected in deficits in cognitive functioning probably leading to a total breakdown of consciousness.

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The intrinsic connectivity of the default mode network has been associated with the level of consciousness in patients with severe brain injury. Especially medial parietal regions are considered to be highly involved in impaired consciousness. To better understand what aspect of this intrinsic architecture is linked to consciousness, we applied spectral dynamic causal modeling to assess effective connectivity within the default mode network in patients with disorders of consciousness.We included 12 controls, 12 patients in minimally conscious state and 13 in vegetative state in this study. For each subject, we first defined the four key regions of the default mode network employing a subject-specific independent component analysis approach. The resulting regions were then included as nodes in a spectral dynamic causal modeling analysis in order to assess how the causal interactions across these regions as well as the characteristics of neuronal fluctuations change with the level of consciousness.The resulting pattern of interaction in controls identified the posterior cingulate cortex as the main driven hub with positive afferent but negative efferent connections. In patients, this pattern appears to be disrupted. Moreover, the vegetative state patients exhibit significantly reduced self-inhibition and increased oscillations in the posterior cingulate cortex compared to minimally conscious state and controls. Finally, the degree of self-inhibition and strength of oscillation in this region is correlated with the level of consciousness.These findings indicate that the equilibrium between excitatory connectivity towards posterior cingulate cortex and its feedback projections is a key aspect of the relationship between alterations in consciousness after severe brain injury and the intrinsic functional architecture of the default mode network. This impairment might be principally due to the disruption of the mechanisms underlying self-inhibition and neuronal oscillations in the posterior cingulate cortex.