Comparison of post-stroke white matter assessment using disconnectome-symptom mapping versus quantitative diffusion MRI
Bibliographic record
Abstract
Indirect structural disconnection-symptom mapping allows white matter impairment to be determined without the need for multi-directional diffusion (MDDW) imaging for each individual. Although widely used this method has not been validated. We analyzed a multicenter dataset obtained from 166 individuals in the chronic stage after stroke with upper limb impairment quantified with Fugl-Meyer upper extremity score (FMUE) comprising stroke lesion maps and MDDW imaging. White matter integrity was quantified (1) by diffusion-tensor-imaging-based fractional anisotropy in preselected tracts (fractional anisotropy method; FAM) and (2) by extracting a percentage of tract disconnection by masking each tract of a predefined tractography atlas using the individual map (disconnection-symptom mapping; DSM). We also calculated a lateralization index for the fractional anisotropy between both hemispheres. The following tracts were tested: corticospinal tract (CST), superior lateral fasciculus (SLF) and corpus callosum (CC) but also optic radiation (OR) as a control tract. Both methods (FAM, DSM) showed comparable results for the association of white matter integrity of the CST with FMUE. DSM showed a strong association with FMUE likely because of the number of participants who failed to show an overlap of the tracts and lesion masks (for CST: n = 57 out of 166; for CC: n = 103 out of 166) whereas with FAM these participants could be used for further analyses. On the first view, our data support the use of white matter integrity quantification based on DSM in individuals with chronic stroke. However, at least one-third-of cases (for CC even worse) showed no overlap of lesion and tract resulting in artificially high associations with clinical parameters.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".