Cognitive Deficits and Striatal Dopaminergic Denervation in Parkinson's Disease: A Single Photon Emission Computed Tomography Study Using 123Iodine-β-CIT in Patients ON and OFF Levodopa
Bibliographic record
Abstract
Cognitive deficits affecting executive (frontal) functions have been widely described in Parkinson's disease (PD). However, dopa therapies are generally ineffective at reversing these deficits, except for tasks involving a sharing of attention such as working memory or simultaneous processing tasks. The aim of this study was to assess the relation between the nigrostriatal dopaminergic denervation in PD, as measured by SPECT with (123)Iodine-beta-CIT and the cognitive deficits, as measured by a simultaneous processing task, which had already been shown to be sensitive to dopa treatment. Ten patients with PD and ten control subjects were selected and matched for age, sex, and education. All subjects were assessed using computed visuo-auditory tasks which allow for the measurement of three cognitive processing conditions: 1) a Selective Processing Time; 2) a Competitive Processing Time; and 3) a Simultaneous Processing Time. Patients with PD were assessed both with (ON) and without (OFF) their usual dopaminergic treatment. The simultaneous processing condition but not the selective or the competitive conditions took significantly more time for patients with PD OFF than for either the control subjects or the patients with PD ON. In addition, when patients with PD were OFF, the simultaneous processing condition was correlated with the (123)Iodine-beta-CIT binding, but not when they were ON. These results suggest that nigrostriatal DA denervation may be involved in the specific impairment that patients with PD experience with simultaneous cognitive processing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".