A systematic review of abnormalities in intracortical myelin across psychiatric illnesses
Bibliographic record
Abstract
Brain imaging studies have thoroughly investigated brain gray matter abnormalities to assess pathophysiological mechanisms underlying psychiatric illnesses, however white matter has only recently been investigated. Abnormalities in myelination and white matter structures found in the cerebral cortex, known as intracortical myelin (ICM), have been linked with psychiatric illnesses including bipolar disorder (BD) and schizophrenia (SCZ). Here, we provide a comprehensive review of findings that investigate the nature of ICM abnormalities in psychiatric illnesses from neuroimaging studies. This systematic search collected studies that evaluated ICM abnormalities using gray/white matter contrast, cortical magnetization transfer ratios or thickness measurements in SCZ, BD, major depressive disorder (MDD) and obsessive compulsive disorder (OCD). 20 studies were included. Evidence suggests that ICM abnormalities in the frontal lobe are common to all studied psychiatric illnesses. Prominent deficits were also identified across the gyri and insular regions in SCZ; and temporal, parietal and occipital cortices in both BD and MDD. In contrast, increases in ICM were identified across the parietal and temporal cortices in SCZ, and parietal cortex in OCD. This review exclusively used published, peer-reviewed articles which may overlook other available literature. Few studies across each psychiatric illness with non-standardized protocols may explain discrepancies in findings and limit a meta-analysis from being performed Overall, studies report that selective ICM abnormalities with prominent changes in the frontal cortices are associated with the aforementioned psychiatric illnesses. Further studies are required to elucidate how ICM alterations may be underpinning symptomatology including cognitive difficulty, emotional dysregulation, and memory impairment.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".