Diffusion kurtosis imaging of white matter in bipolar disorder
Bibliographic record
Abstract
Abstract Objectives White matter pathology is thought to contribute to the pathogenesis of bipolar disorder (BD). However, most studies of white matter in BD have used the simple diffusion tensor imaging (DTI) model, which has several limitations. DTI studies have reported heterogenous results, leading to a lack of consensus about the extent and location of white matter alterations. Here, we applied two advanced diffusion magnetic resonance imaging (MRI) techniques to investigate white matter microstructure in BD. Methods Twenty-five patients with BD and 24 controls comparable for age and sex were included in the study. Whole-brain voxel-based analysis (VBA) and a network-based connectivity approach using constrained spherical deconvolution (CSD)-tractography were used to assess group differences in diffusion kurtosis imaging (DKI) and DTI metrics. Results VBA showed lower mean kurtosis in the corona radiata and posterior association fibers in BD following threshold-free cluster enhancement. Regional differences in connectivity were indicated by lower mean kurtosis and kurtosis anisotropy in streamlines traversing the temporal and occipital lobes, and lower mean axial kurtosis in the right cerebellar, thalamo-subcortical pathways in BD. Significant differences were not seen in the DTI metrics following FDR- correction. Conclusions Differences between BD and controls were observed in DKI metrics in multiple brain regions, indicating altered connectivity across cortical, subcortical and cerebellar areas. DKI was more sensitive than DTI at detecting these differences, suggesting that DKI is useful for investigating white matter in BD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".