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Review
. 2023 Dec;64 Suppl 3(Suppl 3):S25-S36.
doi: 10.1111/epi.17578. Epub 2023 Mar 22.

Passive and active markers of cortical excitability in epilepsy

Georgia Ramantani  1   2 , M Brandon Westover  3   4   5   6 , Stephen Gliske  7 , Johannes Sarnthein  8 , Sridevi Sarma  9 , Yujiang Wang  10 , Maxime O Baud  11 , William C Stacey  12   13   14 , Erin C Conrad  15   16
Affiliations
Review

Passive and active markers of cortical excitability in epilepsy

Georgia Ramantani et al. Epilepsia. 2023 Dec.

Abstract

Electroencephalography (EEG) has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the past decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high-frequency oscillations, novel markers involving advanced postprocessing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.

Keywords: EEG biomarkers; cortical excitability; epilepsy; high-frequency oscillations; interictal spikes.

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Conflict of interest statement

S.G. and W.S. have a licensing agreement with Natus Medical. W.S. has a consulting agreement with Neuronostics. B.W. is a cofounder of Beacon Biosignals and receives royalties for authoring Pocket Neurology from Wolters Kluwer and Atlas of Intensive Care Quantitative EEG from Demos Medical. M.O.B. is a shareholder of Epios, a medical device company based in Geneva, Switzerland. E.C.C. has a consulting agreement with Epiminder. The remaining authors report no conflicts of interest. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.

Figures

FIGURE 1
FIGURE 1
Quantitative analysis reveals the latency of spike detection across electrodes.
FIGURE 2
FIGURE 2
An interictal epileptic discharge (blue) recorded in human epileptic hippocampus and associated with a high-frequency oscillation (red, bandpass filtered 80–500 Hz and shifted by 200 μV) on its rising flank.
FIGURE 3
FIGURE 3
Normative band power maps and abnormality mapping in an example patient. (A) Intracranial electroencephalographic (iEEG) normative band power maps in five frequency bands derived from >200 patients and >21 000 electrode contacts. (B) iEEG band power abnormality for an example patient calculated as a function of brain region and time. Band power abnormality is persistently higher in the resected tissue; the patient was seizure-free after surgery. However, band power abnormality can also fluctuate, for example, in the midtemporal area in a circadian rhythm. l, left; r, right; ROI, region of interest.
FIGURE 4
FIGURE 4
Network analysis from intracranial EEG (iEEG) recordings, showing an example of data that could be shown to a clinician. Raw iEEG (left) is converted to network models (middle) with nodes quantified as “sinks” and “sources”. The pink nodes represent “sinks” as they are heavily influenced by other nodes (inward arrows) and are not influential themselves (no outgoing arrows). The blue nodes are “sources”. They heavily influence other nodes (only outgoing arrows). (Right) Source-sink index overlayed on implantation map of a patient. Letters represent the clinical labels of each electrode track. Colors represent values of the source-sink index, one of many possible iEEG markers. Here, red indicates the index is a stronger source; blue is a stronger sink.
See this image and copyright information in PMC

References

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