Mineralization of the Deep Gray Matter with Age: A Retrospective Review with Susceptibility-Weighted MR Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Susceptibility-weighted imaging (SWI) is an advanced MR imaging sequence that can be implemented at high resolution. This sequence can be performed on conventional MR imaging scanners and is very sensitive to mineralization. The purpose of this study was to establish the course of mineralization in the deep gray matter with age by using SWI. MATERIALS AND METHODS: We retrospectively reviewed susceptibility-weighted images of 134 patients (age range, 1 to 88 years). Inclusion criteria comprised a normal conventional MR imaging (T1, T2, and fluid-attenuated inversion recovery sequences). We statistically analyzed the relative signal intensities of the globus pallidus, putamen, substantia nigra, caudate nucleus, red nucleus, and thalamus for correlation with age. The putamen was graded according to a modified scale, based on previous work that described a systematic pattern of mineralization with age. Bands of hypointensity in the globus pallidus, dubbed "waves," were also evaluated. RESULTS: We documented decreasing intensity (ie, increasing mineralization) with age in all deep gray matter areas analyzed. We confirmed the age-related posterolateral to anteromedial progression of mineralization in the putamen. Characteristic medial and lateral bands of mineralization were exhibited in the globus pallidus in all children and young adults older than 3 years. Finally, an increase in the number of "waves" present in the globus pallidus was associated with increased age by category. CONCLUSION: This study documents the course and pattern of mineralization in the deep gray matter with age, as determined by SWI. These findings may play a role in evaluating diseased brains in the future.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".