Cerebral Degeneration in Amyotrophic Lateral Sclerosis Revealed by 3-Dimensional Texture Analysis
Bibliographic record
Abstract
INTRODUCTION: Routine MR images do not consistently reveal pathological changes in the brain in ALS. Texture analysis, a method to quantitate voxel intensities and their patterns and interrelationships, can detect changes in images not apparent to the naked eye. Our objective was to evaluate cerebral degeneration in ALS using 3-dimensional texture analysis of MR images of the brain. METHODS: In a case-control design, voxel-based texture analysis was performed on T1-weighted MR images of 20 healthy subjects and 19 patients with ALS. Four texture features, namely, autocorrelation, sum of squares variance, sum average, and sum variance were computed. Texture features were compared between the groups by statistical parametric mapping and correlated with clinical measures of disability and upper motor neuron dysfunction. RESULTS: Texture features were different in ALS in motor regions including the precentral gyrus and corticospinal tracts. To a lesser extent, changes were also found in the thalamus, cingulate gyrus, and temporal lobe. Texture features in the precentral gyrus correlated with disease duration, and in the corticospinal tract they correlated with finger tapping speed. CONCLUSIONS: Changes in MR image textures are present in motor and non-motor regions in ALS and correlate with clinical features. Whole brain texture analysis has potential in providing biomarkers of cerebral degeneration in ALS.
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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 | 0.000 | 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".