Relationship between<scp>BDNF</scp>expression in major striatal afferents, striatum morphology and motor behavior in the<scp>R6/2</scp>mouse model of Huntington's disease
Bibliographic record
Abstract
Patients with Huntington's disease (HD) and transgenic mouse models of HD show neuronal loss in the striatum as a major feature, which contributes to cognitive and motor manifestations. Reduced expression of the neurotrophin brain-derived neurotrophic factor (BDNF) in striatal afferents may play a role in neuronal loss. How progressive loss of BDNF expression in different cortical or subcortical afferents contributes to striatal atrophy and behavioral dysfunction in HD is not known, and may best be determined in animal models. We compared age-dependent alterations of BDNF mRNA expression in major striatal afferents from the cerebral cortex, thalamus and midbrain in the R6/2 transgenic mouse model of HD. Corresponding changes in striatal morphology were quantified using unbiased stereology. Changes in motor behavior were measured using an open field, grip strength monitor, limb clasping and a rotarod apparatus. BDNF expression in cortical limbic and midbrain striatal afferents is reduced by age 4 weeks, prior to onset of motor abnormalities. BDNF expression in motor cortex and thalamic afferents is reduced by 6 weeks, coinciding with early motor dysfunction and reduced striatum volume. BDNF loss in afferents progresses until death at 13-15 weeks, correlating with progressive striatal neuronal loss and motor abnormalities. Mutant huntingtin protein expression in R6/2 mice results in progressive loss of BDNF in both cortical and subcortical striatal afferents. BDNF loss in limbic and dopaminergic striatal inputs may contribute to cognitive/psychiatric dysfunction in HD. Subsequent BDNF loss in cortical motor and thalamic afferents may accelerate striatal degeneration, resulting in progressive involuntary movements.
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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) | 0.000 | 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 | 0.001 |
| 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".