Diffusion tensor tractography of the limbic system.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The limbic system, relevant to memory and emotion, is an interesting subject of study in healthy and diseased individuals. It consists of a network of gray matter structures interconnected by white matter fibers. Although gray matter components of this system have been studied by using MR imaging, the connecting fibers have not been analyzed to the same degree. Cerebrospinal fluid (CSF) signal intensity contamination of the fornix and cingulum, the 2 major white matter tracts of the limbic system, can alter diffusion-tensor imaging (DTI) measurements and affect tractography. We investigated the effect of CSF signal intensity suppression on fiber tracking of the limbic connections and characterized the diffusion properties of these structures in healthy volunteers. METHODS: Nine healthy individuals were scanned with standard and CSF-suppressed DTI. Tractography of the fornix and cingulum was performed for both acquisition methods. We report mean diffusivity and fractional anisotropy measurements of the crus, body, and columns of the fornix, and descending, superior, and anterior portions of the cingulum. RESULTS: Diffusion measurements were improved and tractography was facilitated by using CSF-suppressed DTI. In particular, tract volume increased, whereas decreases of the mean diffusivity and increases of diffusion anisotropy more accurately represented the underlying tissue by minimizing deleterious partial volume averaging from CSF. This was particularly true for the fornix because it is in closest contact to CSF. Diffusion measurements throughout the limbic connections were consistent in healthy volunteers. CONCLUSION: We recommend the use of CSF suppression when performing diffusion-tensor tractography of the limbic system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".