Diffusion Tensor Imaging of the Basal Ganglia for Functional Neurosurgery Applications
Bibliographic record
Abstract
Since its introduction, diffusion tensor imaging (DTI) has become an important tool in neuroscience given its unprecedented ability to image brain white matter in vivo. The interest in understanding the mechanisms of action of Deep Brain Stimulation in different targets and indications, together with the constant drive towards the improvement in long-term clinical outcomes, has found a logical complement in the application of tractography in this field. Diffusion tensor imaging has been traditionally associated with an increased susceptibility to MRI artifacts, and expensive computational resources. Recent advances have however improved these restrictions, allowing for countless applications in Neurosurgery, as demonstrated by the large number of original research papers published in the last decade. In this chapter, we review the current status of the implementation of DTI during DBS of the basal ganglia, discussing the findings, potential challenges and the expected improvements in surgical outcomes deriving by the routine use of tractography in functional neurosurgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".