A multicentric multimodal in vivo microscopy MRI study of bipolar disorder reveals axonal loss and demyelination
Bibliographic record
Abstract
Introduction Bipolar disorder has been repeatedly associated with abnormalities of white matter. However, DTI is intrinsically limited and the precise cellular mechanisms that underlie these alterations remains unknown. Objectives Our aim was to investigate microscopical characteristics of white matter using MRI in patients with bipolar and healthy controls. Methods 77 patients and 71 controls from 3 sites had a T1 structural MRI, a multi-shell HARDI MRI and at one site with a T1-weighted VFA-SPGR acquisition, and a T2 MSME acquisition. The volume fraction and the orientation dispersion was extracted using NODDI from DW images in each site. Myelin Water Fraction was extracted in 33 patients and 36 controls to probe myelin characteristics. White matter bundles were reconstructed using deterministic tractography. Statistical analyses were performed after harmonization by the ComBat algorithm and controlled for age, gender and handedness. Results We found significant lower axonal density in patients along the short fibers of the left cingulum, the left anterior arcuate and the left inferior fronto-occipital fasciculus. We found lower mean MWF in patients along the short fibers of the right cingulum, the left inferior fronto-occipital fasciculus, the left anterior arcuate and the splenium of the corpus callosum. We found higher mean orientation dispersion in patients only along the left uncinate fasciculus. Conclusions We report alterations of limbic and inter-hemispheric white matter tracts in patients with bipolar disorder reflecting axonal loss, demyelination and architecture alterations. These results contribute to better capture the plurality of the mechanisms involved in bipolar disorder that cannot be deciphered with classical diffusion MRI. Disclosure No significant relationships.
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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 | 0.000 |
| 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".