Developmental Timing of Adversity and Neural Network organization: An fNIRS Study of the Impact of Refugee Displacement
Bibliographic record
Abstract
This study investigated the neurodevelopmental impacts of displacement on resettled Syrian refugee children in Canada, focusing on how the timing and duration of adversity experienced during displacement influence neural network organization. Using graph theoretical approaches within a network neuroscience framework, we examined how the developmental timing of displacement (age of displacement, duration of displacement) related to functional integration, segregation, and small-worldness. Syrian refugee children (n=61, MAge=14 Range = 8-18), completed a resting state scan using functional Near Infrared Spectroscopy (fNIRS) neuroimaging. Data were analyzed to assess the link between neural network properties and developmental timing of adversity. Results indicate that prolonged displacement experienced earlier in life was significantly linked with neural network organization, impacting the balance between the brain's functional integration and segregation as quantified by the overall reduced small worldness in comparison to experiencing displacement at an older age. This study leverages the experiences of refugee children to advance our understanding of how the timing of adversity affects development, providing valuable insights into the broader impacts of early adversity on neurodevelopment.
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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 | 0.001 |
| 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".