Can we use intraoperative high-frequency oscillations to guide tumor-related epilepsy surgery?

Objective: In people with low-grade intrinsic brain tumors, an epileptic focus is often located close to the lesion. High-frequency oscillations (HFOs) in electrocorticography (ECoG) might help to delineate this focus. We investigated the relationship between HFOs and low-grade brain tumors and their potential value for tumor-related epilepsy surgery. Methods: We analyzed pre- and postresection intraoperative ECoG in 41 patients with refractory epilepsy and a low-grade lesion. Electrodes were designated as overlying the tumor, adjacent resected tissue (peritumoral), or outside the resection bed using magnetic resonance imaging (MRI) and intraoperative photographs. We then used a semiautomated approach to detect HFOs as either ripples (80–250 Hz) or fast ripples (250–500 Hz). Results: The rate of fast ripples was higher in electrodes covering tumor and peritumoral tissue than outside the resection (p =.04). Mesiotemporal tumors showed more ripples (p =.002), but not more fast ripples (p =.07), than superficial tumors. Rates of fast ripples were higher in glioma and extraventricular neurocytoma than in ganglioglioma or dysembryoplastic neuroepithelial tumor (DNET). The rate of ripples and fast ripples in postresection ECoG was not higher in patients with residual tumor tissue on MRI than those without. The rate of ripples in postresection ECoG was higher in patients with good than bad seizure outcome (p =.03). Fast ripples outside the resection and in post-ECoG seem related to seizure recurrence. Significance: Fast ripples in intraoperative ECoG can be used to help guide resection in tumor-related epilepsy surgery. Preresection fast ripples occur predominantly in epileptogenic tumor and peritumoral tissue. Fast ripple rates are higher in glioma and extraventricular neurocytoma than in ganglioglioma and DNET

Developmental trajectory of transmission speed in the human brain

The structure of the human connectome develops from childhood throughout adolescence to middle age, but how these structural changes affect the speed of neuronal signaling is not well described. In 74 subjects, we measured the latency of cortico-cortical evoked responses across association and U-fibers and calculated their corresponding transmission speeds. Decreases in conduction delays until at least 30 years show that the speed of neuronal communication develops well into adulthood

Flow chart.

<p>Flow chart of the literature search and identification of studies for inclusion in the meta-analysis.</p

Meta-analysis of the average clustering coefficient.

<p>The forest plot displays the standardized mean differences (SMD) of the average clustering coefficient between focal epilepsy patients and controls with the 95% confidence intervals (CI). No difference is specified with a vertical line at 0. The overall pooled SMD was 0.35 (CI: 0.05 to 0.65, p = 0.02), that is, a significant higher average clustering coefficient was observed in whole brain networks of focal epilepsy patients relative to controls.</p

Sample characteristics and network properties of the studies included in the systematic review.

<p>Sample characteristics and network properties of the studies included in the systematic review.</p

Leave-one-out analysis.

<p>Leave-one-out analysis.</p

Can we use intraoperative high-frequency oscillations to guide tumor-related epilepsy surgery?

Objective: In people with low-grade intrinsic brain tumors, an epileptic focus is often located close to the lesion. High-frequency oscillations (HFOs) in electrocorticography (ECoG) might help to delineate this focus. We investigated the relationship between HFOs and low-grade brain tumors and their potential value for tumor-related epilepsy surgery. Methods: We analyzed pre- and postresection intraoperative ECoG in 41 patients with refractory epilepsy and a low-grade lesion. Electrodes were designated as overlying the tumor, adjacent resected tissue (peritumoral), or outside the resection bed using magnetic resonance imaging (MRI) and intraoperative photographs. We then used a semiautomated approach to detect HFOs as either ripples (80–250 Hz) or fast ripples (250–500 Hz). Results: The rate of fast ripples was higher in electrodes covering tumor and peritumoral tissue than outside the resection (p =.04). Mesiotemporal tumors showed more ripples (p =.002), but not more fast ripples (p =.07), than superficial tumors. Rates of fast ripples were higher in glioma and extraventricular neurocytoma than in ganglioglioma or dysembryoplastic neuroepithelial tumor (DNET). The rate of ripples and fast ripples in postresection ECoG was not higher in patients with residual tumor tissue on MRI than those without. The rate of ripples in postresection ECoG was higher in patients with good than bad seizure outcome (p =.03). Fast ripples outside the resection and in post-ECoG seem related to seizure recurrence. Significance: Fast ripples in intraoperative ECoG can be used to help guide resection in tumor-related epilepsy surgery. Preresection fast ripples occur predominantly in epileptogenic tumor and peritumoral tissue. Fast ripple rates are higher in glioma and extraventricular neurocytoma than in ganglioglioma and DNET

Brain Network Organization in Focal Epilepsy: A Systematic Review and Meta-Analysis

<div><p>Normal brain functioning is presumed to depend upon interacting regions within large-scale neuronal networks. Increasing evidence exists that interictal network alterations in focal epilepsy are associated with cognitive and behavioral deficits. Nevertheless, the reported network alterations are inconclusive and prone to low statistical power due to small sample sizes as well as modest effect sizes. We therefore systematically reviewed the existing literature and conducted a meta-analysis to characterize the changes in whole-brain interictal focal epilepsy networks at sufficient power levels. We focused on the two most commonly used metrics in whole-brain networks: average path length and average clustering coefficient. Twelve studies were included that reported whole-brain network average path length and average clustering coefficient characteristics in patients and controls. The overall group difference, quantified as the standardized mean average path length difference between epilepsy and control groups, corresponded to a significantly increased average path length of 0.29 (95% confidence interval (CI): 0.12 to 0.45, p = 0.0007) in the epilepsy group. This suggests a less integrated interictal whole-brain network. Similarly, a significantly increased standardized mean average clustering coefficient of 0.35 (CI: 0.05 to 0.65, p = 0.02) was found in the epilepsy group in comparison with controls, pointing towards a more segregated interictal network. Sub-analyses revealed similar results for functional and structural networks in terms of effect size and directionality for both metrics. In addition, we found individual network studies to be prone to low power due to the relatively small group differences in average path length and average clustering coefficient in combination with small sample sizes. The pooled network characteristics support the hypothesis that focal epilepsy has widespread detrimental effects, that is, reduced integration and increased segregation, on whole brain interictal network organization, which may relate to the co-morbid cognitive and behavioral impairments often reported in patients with focal epilepsy.</p></div

Clustering coefficient and path length.

<p>Explanation of the clustering coefficient and path length using a schematic whole-brain binary network representation of nodes (circles) and binary edges (black lines). The clustering coefficient is based on triangles (one triangle shown in blue) and is equivalent to the fraction of the node’s neighbors that are also neighbors of each other. The average clustering coefficient is a global measure of network segregation and reflects the clustered connections around individual nodes. The path length in a binary network is the minimal number of edges that must be traversed to travel from one node to another. In red an example path is given which contains the minimal number of edges (i.e. three) between the two uniform red nodes. The average path length, a global measure of network integration, is this average minimal number of edges of all possible node connections.</p