Neuroanatomical abnormalities as risk factors for bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: Neuroimaging studies show structural brain abnormalities in bipolar patients. Some of the abnormalities may represent biological risk factors conveying vulnerability for the disease. This paper aims to identify neuroanatomical risk factors for bipolar disorder (BD). METHODS: We reviewed magnetic resonance imaging (MRI) findings in populations in which the effects of the disease or treatment are minimal or where the chances of finding genetically coded risk factors shared within the families are increased. Such populations include unaffected relatives of bipolar patients, first-episode patients, children or adolescents with BD and patients with familial BD. RESULTS: MEDLINE search revealed 30 relevant scientific papers. Abnormalities in the volume of the striatum, left hemispheric white matter, thalamus and anterior cingulate as well as quantitative MRI signal hyperintensities were identified already in unaffected relatives of bipolar patients. Subjects in the early stages of the disease showed volume changes of the ventricles, white matter, caudate, putamen, amygdala, hippocampus and the subgenual prefrontal cortex. Reduction in the subgenual prefrontal cortex volume was replicated in three of four studies in patients with familial BD. CONCLUSIONS: Possible candidates for neuroanatomical risk factors for BD are volumetric abnormalities of the subgenual prefrontal cortex, striatum, white matter, and probably also the hippocampus and amygdala. Qualitative finding of white matter hyperintensities was already utilized as an endophenotype.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".