Neurocognitive Endophenotypes for Eating Disorders: A Preliminary High-Risk Family Study
Bibliographic record
Abstract
Eating disorders (ED) are psychiatric disorders with a neurobiological, psychological and socio-environmental basis. Specific neuropsychological and brain structure and function characteristics have been identified in ED. However, evidence has often relied on patients in the acute phase or recovered, precluding an understanding of whether such differences are a direct effect of ED or precede the development of ED. In order to address this challenge, we carried out the first ‘children at risk’ study in this field, by investigating healthy offspring of women with ED (high-risk design). The aim of this preliminary study was to investigate neurocognitive and neural differences in children at high-risk for ED compared to control children. Sixteen (16) girls at high-risk for ED and 20 control girls (age range: 8-15), completed a battery of neuropsychological tests assessing executive functions. Children also underwent a resting-state fMRI scan in order to extract resting-state networks functional connectivity (FC), using an independent component analysis.Girls at high-risk for ED performed worse on a cognitive flexibility task compared to controls adjusting for age and IQ (F=5.53, p=0.02). Moreover, compared to controls, those at high-risk for ED showed reduced FC in the default-mode, sensorimotor and dorsal attentional networks, as well as increased FC in the medial visual network (all p<0.05 FDR corrected). This is the first study on girls at high-risk for ED. Differences identified in cognitive flexibility and in FC are in line with those identified in individuals with ED, strongly pointing to a role as potential endophenotypes of ED.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".