P4‐163: Effects of Scanner Manufacturer and Strength On Cortical Surfaces, Thicknesses and Volumes in the Aging Brain
Bibliographic record
Abstract
Previous studies have shown that scanner characteristics such as magnetic field strength (MFS) and original equipment manufacturer source (OEM) have an impact on brain segmentation results. Our objective was to verify this impact throughout aging in a large-scale database of cognitively healthy controls. T1-weighted MRI scans from 2194 healthy adults (1078 women; 49.1%) aged 18 to 94 years old (mean: 49.6; SD: 20.9) were gathered from fifteen datasets (Table 1). OEMs included Siemens Healthcare (54.3%), Philips Medical Systems (35%), or GE Healthcare (10.7) with magnet strengths of either 1.5 (44.4%) or 3 Tesla (55.6%). We used FreeSurfer (Version 5.3) to produce cortical surface, thickness, and volume estimates. Multiple regression models were produced for each measure of each hemisphere with age, sex, total intracranial volume (TIV), MFS, and OEMs as predictors. Quadratic and cubic terms for age and TIV were tested, as well as interactions OEMs-MFS, TIV-MFS, TIV-OEMs, and age-sex. Predictors’ selection was based on the predicted residual sum of squares statistic using a 10-fold cross-validation. Scanners' characteristics had small effects on cortical measures (R2 surface: .02, thickness: .04, volume: .03; proportion of explained variance surface: 3%, thickness: 5%, volume: 5%). Results were very similar between hemispheres. Figure 1 shows the effects of OEMs and MFS on the cortical measures according to participants' age. While the explained overall variance was small, striking in-between OEM and in-between MFS appeared, specifically for GE Healthcare, when compared to others, especially for surfaces and thicknesses. Siemens Healthcare MRIs had very similar fitted curves for surface, thickness, and volume, while Philips Medical Systems MRIs had thickness curves with a wider discrepancy, albeit surfaces and volumes were similar.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".