Systematic Differences between Lean and Obese Adolescents in Brain Spin-Lattice Relaxation Time: A Quantitative Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Emerging evidence suggests that obese adolescents show changes in brain structure compared with lean adolescents. In addition, obesity impacts body development during adolescence. We tested a hypothesis that T1, a marker of brain maturation, can show brain differences associated with obesity. MATERIALS AND METHODS: Adolescents similar in sex, family income, and school grade were recruited by using strict entry criteria. We measured brain T1 in 48 obese and 31 lean adolescents by quantitative MR imaging at 1.5T. We combined MPRAGE and inversion-recovery sequences with normalization to standard space and automated skull stripping to obtain T1 maps with a symmetric voxel volume of 1 mm(3). RESULTS: Sex, income, triglycerides, total cholesterol, and fasting glucose did not differ between groups, but obese adolescents had significantly lower HDL, higher LDL, and higher fasting insulin levels than lean adolescents. Intracranial vault volume did not differ between groups, but obese adolescents had smaller intracranial vault-adjusted brain parenchymal volumes. Obese adolescents had 4 clusters (>100 contiguous voxels) of T1 relaxation that were significantly different (P < .005) from those in lean adolescents. Three of these clusters had longer T1s in obese adolescents (in the orbitofrontal and parietal regions), and 1 cluster had shorter T1s, compared with lean adolescents. CONCLUSIONS: Our results suggest that obesity may have a significant impact on brain development, especially in the frontal and parietal lobes. It is unclear if these changes persist into adulthood or whether they indicate that obese subjects follow a different developmental trajectory during adolescence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".