Brain imaging and cognitive dysfunctions in Huntington’s disease
Bibliographic record
Abstract
Recent decades have seen tremendous growth in our understanding of the cognitive dysfunctions observed in Huntington's disease (HD). Advances in neuroimaging have contributed greatly to this growth. We reviewed the role that structural and functional neuroimaging techniques have played in elucidating the cerebral bases of the cognitive deficits associated with HD. We conducted a computer-based search using PubMed and PsycINFO databases to retrieve studies of patients with HD published between 1965 and December 2004 that reported measures on cognitive tasks and used neuroimaging techniques. Structural neuroimaging has provided important evidence of morphological brain changes in HD. Striatal and cortical atrophy are the most common findings, and they correlate with cognitive deficits in attention, working memory and executive functions. Functional studies have also demonstrated correlations between striatal dysfunction and cognitive performance. Striatal hypoperfusion and decreased glucose utilization correlate with executive dysfunction. Hypometabolism also occurs throughout the cerebral cortex and correlates with performance on recognition memory, language and perceptual tests. Measures of presynaptic and postsynaptic dopamine biochemistry have also correlated with measurements of episodic memory, speed of processing and executive functioning. Aided by the results of numerous neuroimaging studies, it is becoming increasingly clear that cognitive deficits in HD involve abnormal connectivity between the basal ganglia and cortical areas. In the future, neuroimaging techniques may shed the most light on the pathophysiology of HD by defining neurodegenerative disease phenotypes as a valuable tool for knowing when patients become "symptomatic," having been in a gene-positive presymptomatic state, and as a biomarker in following the disease, thereby providing a prospect for improved patient care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".