Bibliographic record
Abstract
Introduction: The globus pallidus interna (GPi) is the most common target for neuromodulation with deep brain stimulation (DBS) in children and young people (CAYP) with dystonia.Post-mortem immunohistochemistry and in vivo neurophysiological data support the existence of different functional zones within the GPi, the boundaries of which cannot be visualised with existing structural MRI sequences.We aimed to examine the volume, distribution and tissue properties of functional zones within the GPi as delineated through structural connectivity based parcellation.Methods: 28 CAYP with dystonia were prospectively recruited.At the time of clinical MRI (1.5 T Achieva, Philips) 64-direction diffusion weighted imaging and high-resolution whole brain T1-weighted images where obtained.Basal ganglia structures were automatically segmented from T1-weighted images.Streamlines where initiated from voxels within the GPi and tracked to the cortex using probabilistic constrained spherical deconvolution tractography.Streamlines passing through the caudate, putamen or thalamus were retained, on the basis of known connections between GPi and cortex.Parcellation of the GPi was performed on the basis of density of streamlines to limbic, associative, motor and somatosensory cortical regions.Volumes (absolute and relative), fractional anisotropy (FA) and mean diffusivity (MD) values from these parcellations were measured and compared.Results: GPi volume was larger on the right (Wilcoxon signed rank test, p=0.016).Consistent with priori expectations, motor and somatosensory parcellation volumes were larger than those of limbic and associative regions (p<0.001).Relative volumes of these zones did not differ between right and left GPi.Significant variation in the relative volume of the functional regions was evident at an individual participant level.FA and MD values varied across parcellated regions, suggesting different tissue composition.Conclusion: Tractography-based parcellation provides an invivo method for investigating the internal architecture of deep brain structures, with the potential to improve targeting for DBS and other interventions at an individual patient basis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.873 | 0.707 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".