The Plasticity of Brain Gray Matter and White Matter following Lower Limb Amputation
Bibliographic record
Abstract
Accumulating evidence has indicated that amputation induces functional reorganization in the sensory and motor cortices. However, the extent of structural changes after lower limb amputation in patients without phantom pain remains uncertain. We studied 17 adult patients with right lower limb amputation and 18 healthy control subjects using T1-weighted magnetic resonance imaging and diffusion tensor imaging. Cortical thickness and fractional anisotropy (FA) of white matter (WM) were investigated. In amputees, a thinning trend was seen in the left premotor cortex (PMC). Smaller clusters were also noted in the visual-to-motor regions. In addition, the amputees also exhibited a decreased FA in the right superior corona radiata and WM regions underlying the right temporal lobe and left PMC. Fiber tractography from these WM regions showed microstructural changes in the commissural fibers connecting the bilateral premotor cortices, compatible with the hypothesis that amputation can lead to a change in interhemispheric interactions. Finally, the lower limb amputees also displayed significant FA reduction in the right inferior frontooccipital fasciculus, which is negatively correlated with the time since amputation. In conclusion, our findings indicate that the amputation of lower limb could induce changes in the cortical representation of the missing limb and the underlying WM connections.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".