Free water diffusion MRI differentiates suicide ideators from attempters with treatment-resistant depression
Bibliographic record
Abstract
Abstract Suicide attempt is highly prevalent in treatment-resistant depression (TRD), however, the neurobiological profile of suicidal ideation versus suicide attempt is unclear. Neuroimaging methods including diffusion MRI (dMRI) based free water imaging may identify neural correlates underlying the progression from suicidal ideation to attempts in individuals with TRD. dMRI data were obtained from 64 participants, including 39 patients with TRD (n=21 suicide ideators and n=18 suicide attempters), and 25 age- and sex-matched healthy controls. Depression and suicidal ideation severity were examined using clinician-rated and self-report measures. Whole-brain analysis of neuroimaging data was conducted using tract-based spatial statistics via FMRIB Software Library to identify between-group differences in white matter microstructural integrity in suicide ideators versus attempters, and in patients with TRD versus controls. Free water imaging revealed elevated axial diffusivity (AD) and extracellular free water (FW) in fronto-thalamo-limbic white matter tracts of suicide attempters compared to ideators (thresholded p<0.05, FWE corrected). In a separate comparison, patients with TRD were found to have widespread reductions in fractional anisotropy (FA) and axial diffusivity (AD), as well as elevated radial diffusivity (RD) compared to healthy controls. A unique neural signature consisting of elevated AD and FW is identified for the first time in suicide attempters with TRD. Findings of reduced FA, AD, and elevated RD in patients versus controls are consistent with previously published studies. Multimodal and prospective investigations are recommended to better understand biological correlates of suicide attempt.
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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".