Altered Structural and Functional Abnormalities of Hippocampus in Classical Trigeminal Neuralgia: A Combination of DTI and fMRI Study
Bibliographic record
Abstract
Purpose. Diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) were applied to speculate the altered structural and functional abnormalities within the hippocampus in classical trigeminal neuralgia (CTN) patients by detecting the alteration of apparent diffusion coefficient (ADC), fractional anisotropy (FA), and regional homogeneity (ReHo). Patients and Methods. Multimodal MRI dataset (DTI and fMRI) and clinical indices (pain and neuropsychological scores) were collected in 27 CTN patients and 27 age- and gender-matched healthy controls (HC). Two independent-sample t-tests were performed to compare the ADC, FA, and ReHo values in hippocampus areas between CTN patients and HC. Correlation analyses were applied between all the DTI and fMRI parameters within the hippocampus and the VAS (visual analog scale), MoCA (Montreal cognitive assessment), and CDR (clinical dementia rating) scores. Results. CTN patients showed a significantly increased FA values in the right hippocampus (t = 2.387, P = 0.021 ) and increased ReHo values in the right hippocampus head (voxel P < 0.001 , cluster P < 0.05 , FDR correction) compared with HC. A positively significant correlation was observed between the ReHo values and MOCA scores in the right hippocampus head; FA values were also positively correlated with MOCA scores in the right hippocampus. Conclusion. CTN patients demonstrated an abnormality of structures and functions in the hippocampus, which may help to provide novel insights into the neuropathologic change related to the pain-related dysfunction of CTN.
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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".