Characterization of white matter alterations using diffusion kurtosis imaging in patients with amyotrophic lateral sclerosis
Bibliographic record
Abstract
BACKGROUND: To evaluate the degeneration of the corticospinal tract (CST) and corpus callosum (CC) in patients with motor neuron disease and upper motor neuron (UMN) dysfunction using diffusion kurtosis imaging (DKI). METHODS: Twenty-seven patients and 33 healthy controls underwent magnetic resonance imaging along with clinical and neuropsychological testing. Tractography of diffusion tensor images was performed to extract tracts of the bilateral CST and CC. Group mean differences both across the entire averaged tract and along each tract were assessed, including correlations between diffusion metrics and clinical measures. Tract-based spatial statistics (TBSS) was performed to evaluate the spatial distribution of whole-brain microstructural abnormalities in patients. RESULTS: In comparison to controls, patients had significantly higher mean and radial diffusivity and lower fractional anisotropy (FA), kurtosis anisotropy, mean kurtosis (MK), and radial kurtosis (RK) in the CST and CC (p < .017). Along-the-tract analysis revealed changes concentrated in the posterior limb of the internal capsule, corona radiata, and primary motor cortex (false-discovery rate p < .05). FA of the left CST correlated with disease progression rate, whereas MK of the bilateral CST correlated with UMN burden (p < .01). TBSS results corroborated along-tract analysis findings and additionally revealed reduced RK and MK in the fornix, where diffusion tensor imaging (DTI) changes were absent. CONCLUSION: DKI abnormalities in the CST and CC are present in patients with UMN dysfunction, potentially revealing complementary information to DTI regarding the pathology and microstructural alterations occurring in such patients. DKI shows promise as a potential in vivo biomarker for cerebral degeneration in amyotrophic lateral sclerosis.
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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) | 0.001 | 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".