Human interictal epileptiform discharges are bidirectional traveling waves echoing ictal discharges
Abstract
Interictal epileptiform discharges (IEDs), also known as interictal spikes, are large intermittent electrophysiological events observed between seizures in patients with epilepsy. Though they occur far more often than seizures, IEDs are less studied, and their relationship to seizures remains unclear. To better understand this relationship, we examined multi-day recordings of microelectrode arrays implanted in human epilepsy patients, allowing us to precisely observe the spatiotemporal propagation of IEDs, spontaneous seizures, and how they relate. These recordings showed that the majority of IEDs are traveling waves, traversing the same path as ictal discharges during seizures, and with a fixed direction relative to seizure propagation. Moreover, the majority of IEDs, like ictal discharges, were bidirectional, with one predominant and a second, less frequent antipodal direction. These results reveal a fundamental spatiotemporal similarity between IEDs and ictal discharges. These results also imply that most IEDs arise in brain tissue outside the site of seizure onset and propagate toward it, indicating that the propagation of IEDs provides useful information for localizing the seizure focus.
Data availability
Raw data is available upon establishment of a data use agreement with Columbia University Medical Center as required by their Institutional Review Board (IRB). Data from human subjects was analyzed, from which the dates of implants can potentially be reconstructed. This is especially true for a study like this one, in which chronic recordings were carried out for the full duration of the patients' hospital stays. Sharing these data widely could therefore expose private health information of participants, which is why a data use agreement is required by the IRB. Interested Researchers should contact Dr. Schevon to get the data use agreement process started with the Columbia University Medical Center IRB.Analysis code is upload to GitHub: https://github.com/elliothsmith/IEDs. We have included preprocessed data files for all IEDs, hosted online at OSF: https://osf.io/zhk24/. Data files include LFP, MUA event times, and traveling wave model coefficients for all detected IEDs.
-
Human interictal epileptiform discharges are bidirectional traveling waves echoing ictal dischargesOSF, DOI: https://osf.io/zhk24/.
Article and author information
Author details
Funding
National Institutes of Health (NINDS R21 NS113031)
- Elliot H Smith
- Catherine Schevon
- John Rolston
National Institutes of Health (NINDS K23 NS114178)
- John Rolston
National Institutes of Health (S10 OD018211)
- Catherine Schevon
National Institutes of Health (R01 NS084142)
- Catherine Schevon
American Epilepsy Society (JIA)
- Elliot H Smith
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The Institutional Review Boards at the University of Utah (IRB_00114691) and Columbia University Medical Center (IRB- AAAB6324) approved these studies. All participants provided informed consent prior to surgery for implantation of the clinical and research electrodes.
Copyright
© 2022, Smith et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 6,109
- views
-
- 539
- downloads
-
- 49
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Computational and Systems Biology
- Neuroscience
Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce the detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.
-
- Computational and Systems Biology
The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.