Negative childhood experiences alter a prefrontal‐insular‐motor cortical network in healthy adults: A preliminary multimodal rsfMRI‐fMRI‐MRS‐dMRI study
Bibliographic record
Abstract
Research in humans and animals has shown that negative childhood experiences (NCE) can have long-term effects on the structure and function of the brain. Alterations have been noted in grey and white matter, in the brain's resting state, on the glutamatergic system, and on neural and behavioural responses to aversive stimuli. These effects can be linked to psychiatric disorder such as depression and anxiety disorders that are influenced by excessive exposure to early life stressors. The aim of the current study was to investigate the effect of NCEs on these systems. Resting state functional MRI (rsfMRI), aversion task fMRI, glutamate magnetic resonance spectroscopy (MRS), and diffusion magnetic resonance imaging (dMRI) were combined with the Childhood Trauma Questionnaire (CTQ) in healthy subjects to examine the impact of NCEs on the brain. Low CTQ scores, a measure of NCEs, were related to higher resting state glutamate levels and higher resting state entropy in the medial prefrontal cortex (mPFC). CTQ scores, mPFC glutamate and entropy, correlated with neural BOLD responses to the anticipation of aversive stimuli in regions throughout the aversion-related network, with strong correlations between all measures in the motor cortex and left insula. Structural connectivity strength, measured using mean fractional anisotropy, between the mPFC and left insula correlated to aversion-related signal changes in the motor cortex. These findings highlight the impact of NCEs on multiple inter-related brain systems. In particular, they highlight the role of a prefrontal-insular-motor cortical network in the processing and responsivity to aversive stimuli and its potential adaptability by NCEs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".