Maternal affect and quality of parenting experiences are related to amygdala response to infant faces
Bibliographic record
Abstract
We examined how individual differences in mood and anxiety in the early postpartum period are related to brain response to infant stimuli during fMRI, with particular focus on regions implicated in both maternal behavior and mood/anxiety, that is, the subgenual anterior cingulate cortex (sgACC) and the amygdala. At approximately 3 months postpartum, 22 mothers completed an affect-rating task (ART) during fMRI, where their affective response to infant stimuli was explicitly probed. Mothers viewed/rated four infant face conditions: own positive (OP), own negative (ON), unfamiliar positive (UP), and unfamiliar negative (UN). Mood and anxiety were measured by the Edinburgh Postnatal Depression Scale (EDPS) and the State-Trait Anxiety Inventory-Trait Version (STAI-T); maternal factors related to parental stress and attachment were also assessed. Brain-imaging data underwent a random-effects analysis, and cluster-based statistical thresholding was applied to the following contrasts: OP-UP, ON-UN, OP-ON, and UP-UN. Our main finding was that poorer quality of maternal experience was significantly related to reduced amygdala response to OP compared to UP infant faces. Our results suggest that, in human mothers, infant-related amygdala function may be an important factor in maternal anxiety/mood, in quality of mothering, and in individual differences in the motivation to mother. We are very grateful to the staff at the Imaging Research Center of the Brain-Body Institute for their contributions to this project. This work was supported by an Ontario Mental Health Foundation operating grant awarded to Alison Fleming and a postdoctoral fellowship awarded to Jennifer Barrett.
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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".