Grey and white matter volumes in early childhood: A comparison of voxel-based morphometry pipelines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early childhood is an important period of sensory, motor, cognitive and socio-emotional maturation, yet relatively little is known about the brain changes specific to this period. Voxel-based morphometry (VBM) is a technique to estimate regional brain volumes from magnetic resonance (MR) images. The default VBM processing pipeline can be customized to increase accuracy of segmentation and normalization, yet the impact of customizations on analyses in young children are not clear. Here, we assessed the impact of different preprocessing steps on T1-weighted MR images from typically developing children in two separate cohorts. Data were processed with the Computational Anatomy Toolbox (CAT12), using seven different VBM pipelines with distinct combinations of tissue probability maps (TPMs) and DARTEL templates created using the Template-O-Matic, and CerebroMatic. The first cohort comprised female children aged 3.9-7.9 years (N = 62) and the second included boys and girls aged 2.7-8 years (N = 74). We found that pipelines differed significantly in their tendency to classify voxels as grey or white matter and the conclusions about some age effects were pipeline-dependent. Our study helps to both understand age-associations in grey and white matter volume across early childhood and elucidate the impact of VBM customization on brain volumes in this age range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it