Brain Fog and Cognitive Dysfunction in Posttraumatic Stress Disorder: An Evidence-Based Review
Bibliographic record
Abstract
The term "brain fog" has long been used both colloquially and in research literature in reference to various neurocognitive phenomenon that detract from cognitive efficiency. We define "brain fog" as the subjective experience of cognitive difficulties, in keeping with the most common colloquial and research use of the term. While a recent increase in use of this term has largely been in the context of the post-coronavirus-19 condition known as long COVID, "brain fog" has also been discussed in relation to several other conditions including mental health conditions such as post-traumatic stress disorder (PTSD). PTSD is associated with both subjective cognitive complaints and relative deficits on cognitive testing, but the phenomenology and mechanisms contributing to "brain fog" in this population are poorly understood. PTSD psychopathology across cognitive, affective and physiological symptom domains have been tied to "brain fog". Furthermore, dissociative symptoms common in PTSD also contribute to the experience of "brain fog". Comorbid physical and mental health conditions may also increase the risk of experiencing "brain fog" among individuals with PTSD. Considerations for the assessment of "brain fog" in PTSD as part of psychodiagnostic assessment are discussed. While standard psychological intervention for PTSD is associated with a reduction in subjective cognitive deficits, other cognitive interventions may be valuable when "brain fog" persists following PTSD remission or when "brain fog" interferes with treatment. Limitations of current research on "brain fog" in PTSD include a lack of consistent definition and operationalization of "brain fog" in the literature, as well as limited tools for measurement. Future research should address these limitations, as well as further evaluate the use of cognitive remediation as an intervention for "brain fog".
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".