Meynert’s Nucleus Complex White Matter Abnormalities in Autism Spectrum Disorders: An MRI Study
Bibliographic record
Abstract
Introduction: Cholinergic dysfunction has been proposed to play a role in autistic symtomatology. However, to date, its structural correlates are poorly understood. Methods: Twenty-five low-functioning, non-verbal males with Autism Spectrum Disorders (ASD) and 25 controls were enrolled in the study. All underwent MR T1-weighted 3D Structural Imaging and Diffusion Tensor Imaging. Grey and white matter components of the Meynert’s Nucleus Complex were then identified on MR images, and both grey matter density and white matter mean Fractional Anisotropy in the Meynert’s Nucleus region of interest were quantified for each subject. Non-verbal IQ was assessed in all subjects with ASD. Results: We showed reduced white matter Fractional Anisotropy in the bundles surrounding the Meynert’s Nucleus in ASD subjects compared to controls. Fractional Anisotropy in these bundles was positively associated with non-verbal IQ, independently from whole brain white matter mean Fractional Anisotropy. ASD subjects did not show significant abnormalities in Meynert’s Nucleus grey matter density. Conclusions: Our findings suggest that white matter abnormalities in the Meynert’s Nucleus might be involved in the cholinergic deficits of ASD.
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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) | 0.001 | 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".