Exploring psychedelic-assisted therapy in the treatment of functional seizures: A review of underlying mechanisms and associated brain networks
Bibliographic record
Abstract
Functional seizures (FS), the most common subtype of functional neurological disorder (FND), cause serious neurological disability and significantly impact quality of life. Characterized by episodic disturbances of functioning that resemble epileptic seizures, FS coincide with multiple comorbidities and are treated poorly by existing approaches. Novel treatment approaches are sorely needed. Notably, mounting evidence supports the safety and efficacy of psychedelic-assisted therapy (PAT) for several psychiatric conditions, motivating investigations into whether this efficacy also extends to neurological disorders. Here, we synthesize past empirical findings and frameworks to construct a biopsychosocial mechanistic argument for the potential of PAT as a treatment for FS. In doing so, we highlight FS as a well-defined cohort to further understand the large-scale neural mechanisms underpinning PAT. Our synthesis is guided by a complexity science perspective which we contend can afford unique mechanistic insight into both FS and PAT, as well as help bridge these two domains. We also leverage this perspective to propose a novel analytic roadmap to identify markers of FS diagnostic specificity and treatment success. This endeavor continues the effort to bridge clinical neurology with psychedelic medicine and helps pave the way for a new field of psychedelic neurology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".