<scp>WONOEP</scp> appraisal: Optogenetic tools to suppress seizures and explore the mechanisms of epileptogenesis
Bibliographic record
Abstract
Optogenetics is a novel technology that combines optics and genetics by optical control of microbial opsins, targeted to living cell membranes. The versatility and the electrophysiologic characteristics of the light-sensitive ion-channels channelrhodopsin-2 (ChR2), halorhodopsin (NpHR), and the light-sensitive proton pump archaerhodopsin-3 (Arch) make these optogenetic tools potent candidates in controlling neuronal firing in models of epilepsy and in providing insights into the physiology and pathology of neuronal network organization and synchronization. Opsins allow selective activation of excitatory neurons and inhibitory interneurons, or subclasses of interneurons, to study their activity patterns in distinct brain-states in vivo and to dissect their role in generation of synchrony and seizures. The influence of gliotransmission on epileptic network function is another topic of great interest that can be further explored by using light-activated Gq protein-coupled opsins for selective activation of astrocytes. The ever-growing optogenetic toolbox can also be combined with emerging techniques that have greatly expanded our ability to record specific subtypes of cortical and hippocampal neurons in awake behaving animals such as juxtacellular recording and two-photon guided whole-cell recording, to identify the specific subtypes of neurons that are altered in epileptic networks. Finally, optogenetic tools allow rapid and reversible suppression of epileptic electroencephalography (EEG) activity upon photoactivation. This review outlines the most recent advances achieved with optogenetic techniques in the field of epilepsy by summarizing the presentations contributed to the 13th ILAE WONOEP meeting held in the Laurentian Mountains, Quebec, in June 2013.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.041 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".