Mouse behavioral mutants have neuroimaging abnormalities
Bibliographic record
Abstract
Impaired cognitive, memory, or motor performance is a distinguishing characteristic of neurological diseases. Although these symptoms are frequently the most evident in human patients, additional markers of disease are critical for proper diagnosis and staging. Noninvasive neuroimaging methods have become essential in this capacity and provide means of evaluating disease and tracking progression. These imaging methods are also becoming available to scientists in the research laboratory for assessment of animal models of neurological disease. Imaging in mouse models of neurological disease is of particular interest, owing to the availability of inbred strains and genetic manipulation tools that permit detailed investigation of the roles of various genes and gene products in disease pathogenesis. However, the relative prevalence of neuroimaging abnormalities in mice exhibiting neurological symptoms has not been reported. This prevalence has both theoretical and practical value because it is influenced by both the sensitivity of macroscopic anatomical measures to underlying genetic and disease processes and by the efficiency of neuroimaging in detecting and characterizing these effects. In this paper, we describe a meta-analysis of studies involving behavioral mouse mutants at our laboratory. In summary, we have evaluated 15 different mutant genotypes, of which 13 showed abnormal neuroimaging findings. This indicates a surprisingly high prevalence of neuroimaging abnormalities (87%) and suggests that disease processes affecting behavior generally alter neuroanatomy as well. As a consequence, neuroimaging provides a highly sensitive marker of neurological disease in mice exhibiting abnormal behavior.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".