MRI Texture Analysis Reveals Brain Abnormalities in Medically Refractory Trigeminal Neuralgia
Bibliographic record
Abstract
Background: Several neuroimaging studies report structural alterations of the trigeminal nerve in trigeminal neuralgia (TN). Less attention has been paid to structural brain changes occurring in TN, even though such changes can influence the development and response to treatment of other headache and chronic pain conditions. The purpose of this study was to apply a novel neuroimaging technique—texture analysis—to identify structural brain differences between classical TN patients and healthy subjects. Methods: We prospectively recruited 14 medically refractory classical TN patients and 20 healthy subjects. 3-Tesla T1-weighted brain MRI scans were acquired in all participants. Three texture features (autocorrelation, contrast, energy) were calculated within four a priori brain regions of interest (anterior cingulate, insula, thalamus, brainstem). Voxel-wise analysis was used to identify clusters of texture difference between TN patients and healthy subjects within regions of interest ( p < 0.001, cluster size >20 voxels). Median raw texture values within clusters were also compared between groups, and further used to differentiate TN patients from healthy subjects (receiver-operator characteristic curve analysis). Median raw texture values were correlated with pain severity (visual analog scale, 1–100) and illness duration. Results: Several clusters of texture difference were observed between TN patients and healthy subjects: right-sided TN patients showed reduced autocorrelation in the left brainstem, increased contrast in the left brainstem and right anterior insula, and reduced energy in right and left anterior cingulate, right midbrain, and left brainstem. Within-cluster median raw texture values also differed between TN patients and healthy subjects: TN patients could be segregated from healthy subjects using brainstem autocorrelation ( p = 0.0040, AUC = 0.84, sensitivity = 89%, specificity = 70%), anterior insula contrast ( p = 0.0002, AUC = 0.92, sensitivity = 78%, specificity = 100%), and anterior cingulate energy ( p = 0.0004, AUC = 0.92, sensitivity = 78%, specificity = 100%). Additionally, anterior insula contrast and duration of TN were inversely correlated ( p = 0.030, Spearman r = −0.73). Conclusions: Texture analysis reveals distinct brain abnormalities in TN, which relate to clinical features such as duration of illness. These findings further implicate structural brain changes in the development and maintenance of TN.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".