Magnetization Transfer Imaging Demonstrates a Distributed Pattern of Microstructural Changes of the Cerebral Cortex in Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To date, damage of the cerebral cortex neurons in ALS was investigated by using conventional MR imaging and proton MR spectroscopy. We explored the capability of MTI to map the microstructural changes in cerebral motor and extramotor cortices of patients with ALS. MATERIALS AND METHODS: Twenty patients with ALS and 17 age-matched healthy controls were enrolled. A high-resolution 3D SPGR sequence with and without MT saturation pulses was obtained on a 1.5T scanner to compute MTR values. Using the FMRIB Software Library tools, we automatically computed the MTR of the cerebral cortex GM in 48 regions of the entire cerebral cortex derived from the standard Harvard-Oxford cortical atlas. RESULTS: The MTR values were significantly lower in patients with ALS than in healthy controls in the primary motor cortex (precentral gyrus), nonprimary motor areas (superior and middle frontal gyri and superior parietal lobe), and some extramotor areas (frontal pole, planum temporale, and planum polare). No correlation was found between regional MTR values and the severity of clinical deficits or disease duration. CONCLUSIONS: MTI analysis can detect the distributed pattern of microstructural changes of the GM in the cerebral cortex of patients with ALS with involvement of both the motor and extramotor areas.
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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) | 0.001 | 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".