Abnormal sensory perception masks behavioral performance of Grin1 knockdown mice
Bibliographic record
Abstract
The development and function of sensory systems require intact glutamatergic neurotransmission. Changes in touch sensation and vision are common symptoms in autism spectrum disorders, where altered glutamatergic neurotransmission is strongly implicated. Further, cortical visual impairment is a frequent symptom of GRIN disorder, a rare genetic neurodevelopmental disorder caused by pathogenic variants of GRIN genes that encode NMDA receptors. We asked if Grin1 knockdown mice (Grin1KD), as a model of GRIN disorder, had visual impairments resulting from NMDA receptor deficiency. We discovered that Grin1KD mice had deficient visual depth perception in the visual cliff test. Since Grin1KD mice are known to display robust changes in measures of learning, memory, and emotionality, we asked whether deficits in these higher-level processes could be partly explained by their visual impairment. By changing the experimental conditions to improve visual signals, we observed significant improvements in the performance of Grin1KD mice in tests that measure spatial memory, executive function, and anxiety. We went further and found destabilization of the outer segment of retina together with the deficient number and size of Meissner corpuscles (mechanical sensor) in the hind paw of Grin1KD mice. Overall, our findings suggest that abnormal sensory perception can mask the expression of emotional, motivational and cognitive behavior of Grin1KD mice. This study demonstrates new methods to adapt routine behavioral paradigms to reveal the contribution of vision and other sensory modalities in cognitive performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".