A whole brain longitudinal study in the YAC128 mouse model of Huntington’s disease shows distinct trajectories of neurochemical, structural connectivity and volumetric changes
Bibliographic record
Abstract
Huntington's disease (HD) is a neurodegenerative disorder causing cognitive and motor impairments, evolving to death within 15-20 years after symptom onset. We previously established a mouse model with the entire human HD gene containing 128 CAG repeats (YAC128) which accurately recapitulates the natural history of the human disease. Defined time points in this natural history enable the understanding of longitudinal trajectories from the neurochemical and structural points of view using non-invasive high-resolution multi-modal imaging. Accordingly, we designed a longitudinal structural imaging (MRI and DTI) and spectroscopy (1H-MRS) study in YAC128, at 3, 6, 9 and 12 months of age, at 9.4 T. Structural analysis (MRI/DTI), confirmed that the striatum is the earliest affected brain region, but other regions were also identified through connectivity analysis (pre-frontal cortex, hippocampus, globus pallidus and thalamus), suggesting a striking homology with the human disease. Importantly, we found for the first time, a negative correlation between striatal and hippocampal changes only in YAC128. In fact, the striatum showed accelerated volumetric decay in HD, as opposed to the hippocampus. Neurochemical analysis of the HD striatum suggested early neurometabolic alterations in neurotransmission and metabolism, with a significant increase in striatal GABA levels, and specifically anticorrelated levels of N-acetyl aspartate and taurine, suggesting that the later is homeostatically adjusted for neuroprotection, as neural loss, indicated by the former, is progressing. These results provide novel insights into the natural history of HD and prove a valuable role for longitudinal multi-modal panels of structural and metabolite/neurotransmission in the YAC128 model.
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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) | 0.000 | 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 | 0.001 | 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".