Ketamine-Assisted Psychotherapy for Generalized Anxiety Disorder: A Comprehensive Case Report with Integrated Neurophysiological Imaging Using Magnetoencephalography
Bibliographic record
Abstract
Abstract This detailed case report explores the application of ketamine-assisted psychotherapy (KAP) in the treatment of a male patient in their late 30’s with Generalized Anxiety Disorder (GAD) and depressive symptoms. The N-methyl-D-aspartate (NMDA) receptor antagonist ketamine represents a significant breakthrough in the treatment of mood and anxiety disorders due to its rapid and robust antidepressant effects. Preclinical studies demonstrate that ketamine promotes biological alterations in the brain, including enhancing neuroplasticity. However, no studies to date have examined the longitudinal effects of KAP using magnetoencephalography (MEG), a powerful functional neuroimaging modality. Resting state MEG (rsMEG) scanning allowed the exploration of the neural correlates of KAP-related changes in mood and anxiety symptoms, including the functional connectivity between brain networks involved in cognition and emotion regulation. In this case study, an adult male participant with moderate-severe GAD underwent two rsMEG scans and cognitive testing at baseline and after 4 of 6 sessions of a standard ketamine administration and 2 integration sessions, part of a protocol consisting of a total of six KAP sessions and four sessions of integration. We measured functional connectivity in 5 functional networks – default mode, attention, central executive, motor, and visual, as well as neural oscillatory activity. We saw functional connectivity increases in 4 of the 5 networks. This coincided with significant increases in cortical beta activity, a marker of inhibition, decrease in theta oscillations, reductions in GAD7 and PHQ9 scores, and improved attention. In summary, these findings emphasize the ability of rsMEG to detect KAP-induced brain network changes, offering a promising tool for identifying clinically relevant neural correlates that can both predict and monitor therapeutic outcomes via electrophysiological changes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".