Does neuroimaging research examining the pathophysiology of posttraumatic stress disorder require medication-free patients?
Bibliographic record
Abstract
Background: In an attempt to avoid unknown influence, most neuroimaging studies examining the pathophysiology of posttraumatic stress disorder (PTSD) exclude patients taking medications. Here we review the empirical evidence for relevant medications having a confounding effect on task performance or cerebral blood flow (CBF) in this population. The evidence for potentially confounding effects of psychotherapy in PTSD are also discussed. Methods: The literature that we reviewed was obtained through a PubMed search from 1980 to 2009 using the search terms posttraumatic stress disorder, PTSD, psychotropic medications, neuroimaging, functional magnetic resonance imaging, positron emission tomography, cerebral blood flow, CBF, serotonin-specific reuptake blocker, benzodiazepine, ketamine, methamphetamine, lamotrigine and atypical antipsychotic agents. Results: The empirical evidence for relevant medications having a confounding effect on task performance or CBF in relevant areas remains sparse for most psychotropic medications among patients with PTSD. However, considerable evidence is accumulating for 2 of the most commonly prescribed medication classes (serotonin-specific reuptake inhibitors and benzodiazepines) in healthy controls. Compelling data for the potentially confounding effects on brain areas relevant to PTSD for psychotherapeutic interventions are also accumulating. Conclusion: Neuroimaging studies examining the pathophysiology of PTSD should ideally recruit both medicated (assuming that the medication treatment has not resulted in the remission of symptoms) and unmedicated participants, to allow the findings to be generalized with greater confidence to the entire population of patients with PTSD. More research is needed into the independent effects of medications on task performance and CBF in regions of interest in PTSD. Neuro-imaging studies should also take into account whether patients are currently engaged in psychotherapeutic treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".