Detailing neuroanatomical development in late childhood and early adolescence using NODDI
Bibliographic record
Abstract
Diffusion tensor imaging (DTI) studies have provided much evidence of white and subcortical gray matter changes during late childhood and early adolescence that suggest increasing myelination, axon density, and/or fiber coherence. Neurite orientation dispersion and density imaging (NODDI) can be used to further characterize development in white and subcortical grey matter regions in the brain by improving specificity of the MRI signal compared to conventional DTI. We used measures from NODDI and DTI to examine white and subcortical gray matter development in a group of 27 healthy participants aged 8-13 years. Neurite density index (NDI) was strongly correlated with age in nearly all regions, and was more strongly associated with age than fractional anisotropy (FA). No significant correlations were observed between orientation dispersion index (ODI) and age. This suggests that white matter and subcortical gray matter changes during late childhood and adolescence are dominated by changes in neurite density (i.e., axon density and myelination), rather than increasing coherence of axons. Within brain regions, FA was correlated with both ODI and NDI while mean diffusivity was only related to neurite density, providing further information about the structural variation across individuals. Data-driven clustering of the NODDI parameters showed that microstructural profiles varied along layers of white matter, but that that much of the white and subcortical gray matter matured in a similar manner. Clustering highlighted isolated brain regions with decreasing NDI values that were not apparent in region-of-interest analysis. Overall, these results help to more specifically understand patterns of white and gray matter development during late childhood and early 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".