Disruption of Thalamocortical Connectivity in Spastic Cerebral Palsy: A Probabilistic Tractography Study
Bibliographic record
Abstract
Objectives: This study aimed to investigate the probabilistic connectivity between the thalamus and motor areas of the cerebral cortex in spastic cerebral palsy (SCP). We explored the integrity of motor tracts between the thalamus and cerebral cortex by quantifying the thalamic probabilistic connectivity with motor cortices (namely primary motor cortex, supplementary motor area, and premotor cortex) in SCP using diffusion MRI. The current study also parcellated the thalamus according to its connectivity to the three motor cortices in healthy control and SCP. Methods: Probabilistic tractography was performed on secondary diffusion MRI data of eight SCP patients (mean age 11.9 years old) and ten healthy controls. The connection probability index, an indirect indicator of white matter integrity, was measured between the thalamus to three areas of the motor cortex; primary motor, premotor and supplementary motor. The thalamus was further parcellated according to its connection probability with the motor cortices. Results: The pattern of thalamocortical connectivity in cerebral palsy was found to be varied and mainly complied with the patient's clinical presentation. In comparison with controls, the SCP patients showed either lower or higher connection probabilities to the motor cortices. A striking feature of thalamic parcellation in SCP was the presence of a cluster with a positive connection to the supplementary motor area. Conclusion: Our findings suggest that the thalamocortical connectivity in SCP was different from healthy individuals and largely follows the clinical manifestation. There was also evidence of neuroplasticity serving as a compensatory mechanism for the motor deficit in patients with SCP.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".