Spatial Profiling of the Corticospinal Tract in Amyotrophic Lateral Sclerosis Using Diffusion Tensor Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) was used as a noninvasive method to evaluate the anatomy of the corticospinal tract (CST) and the pattern of its degeneration in amyotrophic lateral sclerosis (ALS). METHODS: Fourteen patients with ALS and 15 healthy controls underwent DTI. Parameters reflecting coherence of diffusion (fractional anisotropy, FA), bulk diffusion (apparent diffusion coefficient, ADC), and directionality of diffusion (eigenvalues) parallel to (lambda( parallel)) or perpendicular to (lambda( perpendicular)) fiber tracts were measured along the intracranial course of the CST. RESULTS: FA and lambda( parallel) increased, and ADC and lambda( perpendicular) decreased progressively from the corona radiata to the cerebral peduncle in all subjects. The most abnormal finding in patients with ALS was reduced FA in the cerebral peduncle contralateral to the side of the body with the most severe upper motor neuron signs. lambda( parallel) was increased in the corona radiata. Internal capsule FA correlated positively with symptom duration, and cerebral peduncle ADC positively with the Ashworth spasticity score. CONCLUSION: There is a spatial dependency of diffusion parameters along the CST in healthy individuals. Evidence of intracranial CST degeneration in ALS was found with distinct diffusion changes in the rostral and caudal regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".