High‐resolution diffusion tensor imaging and T2 mapping detect regional changes within the hippocampus in multiple sclerosis
Bibliographic record
Abstract
Hippocampus demyelinating lesions in multiple sclerosis (MS) have been frequently observed in ex vivo histopathological studies; however, they are difficult to image and quantify in vivo. Diffusion tensor imaging (DTI) and T2 mapping could potentially detect such regional in vivo changes if acquired with sufficient spatial resolution. The goal here was to evaluate whether there are focal hippocampal abnormalities in 43 MS patients (35 relapsing-remitting, eight secondary progressive) with and without cognitive impairment (CI) versus 43 controls using high-resolution 1 mm isotropic DTI, as well as complementary methods of T2-weighted and T2 mapping at 3 T. Abnormal hippocampus regions were identified voxel-by-voxel by using mean diffusivity (MD)/T2 thresholds and avoiding voxels attributed to cerebrospinal fluid. When compared with controls, averaged left/right whole hippocampus MD was higher in both MS groups, while lower fractional anisotropy (FA) and volume, and higher T2 relaxometry and T2-weighted signal values, were only significant in CI MS. The hippocampal MD and T2 images/maps were not uniformly affected and focal regions of elevated MD/T2 were evident in MS patients. Both CI and not CI MS groups showed greater proportional areas of the hippocampus with elevated MD, whereas only the CI group showed a greater proportional area of elevated T2 relaxation times or T2-weighted signal. Higher T2 relaxometry and T2-weighted signal values of elevated regions correlated with greater disability and whole hippocampus FA negatively correlated with physical fatigue. High-resolution hippocampus DTI and T2 mapping with less partial volume effects showed whole hippocampus abnormalities with regional elevations of MD/T2 in MS, which could be interpreted as potentially from demyelination, neuron loss, and/or inflammation, and which overall were more extensive in the hippocampus of patients with larger total brain lesion volumes and CI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".