Structural connectome fingerprinting and age prediction in pediatric development: assessing voxel- and surface-based white matter connectivity
Bibliographic record
Abstract
Abstract Mapping structural white matter connectivity is a challenge, with many barriers to accurate representation. Here, we assessed the replicability and reliability of two connectome-generating methods, voxel- or surface-based, using test-retest analyses, fingerprinting and age prediction. The two connectomic methods are initiated by the same state-of-the-art dMRI processing pipeline before diverging at the tractography and connectome-generating steps using either voxels or surfaces. While both methods performed very well across all analyses, voxel-based connectomes performed marginally better than surface-based connectomes. Notably, structural connectomes derived from either method demonstrate reliably accurate representations of both individuals and their chronological age, comparable to similar analyses employing multi-modal features. The difference in methodological performance could be attributed to a number of method-specific features but ultimately show that cutting-edge tractography with robust dMRI processing produces reliable white matter connectivity measures.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".