Assessing intracortical myelin in the living human brain using myelinated cortical thickness
Bibliographic record
Abstract
Alterations in the myelination of the cerebral cortex may underlie abnormal cortical function in a variety of brain diseases. Here, we describe a technique for investigating changes in intracortical myelin in clinical populations on the basis of cortical thickness measurements with magnetic resonance imaging (MRI) at 3 Tesla. For this, we separately compute the thickness of the shallower, lightly myelinated portion of the cortex and its deeper, heavily myelinated portion (referred to herein as unmyelinated and myelinated cortex, respectively). Our expectation is that the thickness of the myelinated cortex will be a specific biomarker for disruptions in myeloarchitecture. We show representative atlases of total cortical thickness, T, unmyelinated cortical thickness, G, and myelinated cortical thickness, M, for a healthy group of 20 female subjects. We further demonstrate myelinated cortical thickness measurements in a preliminary clinical study of 10 bipolar disorder type-I subjects and 10 healthy controls, and report significant decreases in the middle frontal gyrus in T, G, and M in the disorder, with the largest percentage change occurring in M. This study highlights the potential of myelinated cortical thickness measurements for investigating intracortical myelin involvement in brain disease at clinically relevant field strengths and resolutions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".