Adult Hereditary White Matter Diseases With Psychiatric Presentation: Clinical Pointers and MRI Algorithm to Guide the Diagnostic Process
Bibliographic record
Abstract
OBJECTIVE: The investigators aimed to provide clinical and MRI guidelines for determining when genetic workup should be considered in order to exclude hereditary leukoencephalopathies in affected patients with a psychiatric presentation. METHODS: A systematic literature review was conducted, and clinical cases are provided. Given the central role of MRI pattern recognition in the diagnosis of white matter disorders, the investigators adapted an MRI algorithm that guides the interpretation of MRI findings and thus directs further investigations, such as genetic testing. RESULTS: Twelve genetic leukoencephalopathies that can present with psychiatric symptoms were identified. As examples of presentations that can occur in clinical practice, five clinical vignettes from patients assessed at a referral center for adult genetic leukoencephalopathies are provided. CONCLUSIONS: Features such as drug-resistant symptoms, presence of long-standing somatic features, trigger events, consanguinity, and positive family history should orient the clinician toward diagnostic workup to exclude the presence of a genetic white matter disorder. The identification of MRI white matter abnormalities, especially when presenting a specific pattern of involvement, should prompt genetic testing for known forms of genetic leukoencephalopathies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".