The maternal brain in women with a history of early-life maltreatment: an imagination-based fMRI study of conflictual versus pleasant interactions with children
Bibliographic record
Abstract
BACKGROUND: Early-life maltreatment has severe consequences for the affected individual, and it has an impact on the next generation. To improve understanding of the intergenerational effects of abuse, we investigated the consequences of early-life maltreatment on maternal sensitivity and associated brain mechanisms during mother-child interactions. METHODS: In total, 47 mothers (22 with a history of physical and/or sexual childhood abuse and 25 without, all without current mental disorders) took part in a standardized real-life interaction with their 7- to 11-year-old child (not abused) and a subsequent functional imaging script-driven imagery task. RESULTS: Mothers with early-life maltreatment were less sensitive in real-life mother-child interactions, but while imagining conflictual interactions with their child, they showed increased activation in regions of the salience and emotion-processing network, such as the amygdala, insula and hippocampus. This activation pattern was in contrast to that of mothers without early-life maltreatment, who showed higher activations in those regions in response to pleasant mother-child interactions. Mothers with early-life maltreatment also showed reduced functional connectivity between regions of the salience and the mentalizing networks. LIMITATIONS: Region-of-interest analyses, which were performed in addition to whole-brain analyses, were exploratory in nature, because they were not further controlled for multiple comparisons. CONCLUSION: Results suggest that for mothers with early-life maltreatment, conflictual interactions with their child may be more salient and behaviourally relevant than pleasant interactions, and that their salience network is poorly modulated by the brain regions involved in mentalizing processes. This activation pattern offers new insights into the mechanisms behind the intergenerational effects of maltreatment and into options for reducing these effects.
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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 | 0.000 | 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) | 0.001 | 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".