Prefrontal glutamate neurotransmission in PTSD: A novel approach to estimate synaptic strength in vivo in humans
Bibliographic record
Abstract
ABSTRACT Trauma and chronic stress are believed to induce and exacerbate psychopathology by disrupting glutamate synaptic strength. However, in vivo in human methods to estimate synaptic strength are limited. In this study, we established a novel putative biomarker of glutamatergic synaptic strength, termed energy-per-cycle (EPC). Then, we used EPC to investigate the role of prefrontal neurotransmission in trauma psychopathology. Healthy control (n=18) and patients with posttraumatic stress (PTSD; n=16) completed 13 C-acetate magnetic resonance spectroscopy scans to estimate prefrontal EPC, which is the ratio of neuronal energetic needs per glutamate neurotransmission cycle (V TCA /V Cycle ). Patients with PTSD were found to have 28% reduction in prefrontal EPC ( t =3.0; df =32, p =0.005). There was no effect of sex on EPC, but age was negatively associated with prefrontal EPC across groups ( r =–0.46, n=34, p =0.006). Controlling for age did not affect the study results. The feasibility and utility of EPC were established. Patients with PTSD were found to have reduced prefrontal glutamatergic synaptic strength. These findings suggest that reduced glutamatergic synaptic strength may contribute to the pathophysiology of PTSD and could be targeted by new treatments. Highlights Glutamatergic synaptic strength is critical for brain function in health and disease. In vivo in human methods to estimate glutamatergic synaptic strength are limited. We here propose a new approach to estimate glutamatergic synaptic strength. The new method employs carbon-13 magnetic resonance spectroscopy ( 13 C MRS). The utility of the new approach was demonstrated in posttraumatic stress disorder (PTSD).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".