New perspectives on the neurobiology of PTSD: High-resolution imaging of neural circuit (dys)function with magnetoencephalography
Bibliographic record
Abstract
Introduction: Combat-related posttraumatic stress disorder (PTSD) is increasingly conceptualized in psychiatry as a disorder of dysfunctional neural circuits. Advances in neuroimaging have enabled the study of those networks non-invasively. PTSD is currently assessed using subjective self-reporting to inform crucial decisions, such as fitness to deploy, but objective markers would aid in diagnosis and return-to-deployment decisions. Methods: Magnetoencephalography (MEG) allows investigation of neural circuit function via imaging of brain waves (known as neural oscillations) that index information processing in the brain and would prove a reliable, objective, biomarker. These measures of brain function establish how regions communicate to form brain circuits that support thinking and behaviour. Results: Studies into intrinsic brain function, both during rest and when engaged in a task designed to tap into cognitive dysfunction, have found these neurobiological mechanisms are disrupted in PTSD and are a reliable objective marker of illness. We now know that these alterations in brain function are directly related to core symptoms of PTSD and comorbid cognitive-behavioural challenges. Discussion: Continued characterization of neural function using MEG and related methods will advance understanding of the neurobiology underlying PTSD; allow for the identification of biomarkers that, coupled with machine learning, will aid in diagnoses; provide individualized therapeutic targets for neurostimulation; predict treatment outcomes; and track disorder remission in military personnel and Veterans who are disproportionately affected by this devastating illness.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".