Combination of dopamine transporter and D2 receptor SPECT in the diagnostic evaluation of PD, MSA, and PSP
Bibliographic record
Abstract
It is often difficult to differentiate clinically between Parkinson's disease (PD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). The objective of this work was to investigate whether combined pre- and postsynaptic dopaminergic single photon emission computed tomography (SPECT) scanning can reliably demonstrate changes in the nigrostriatal dopaminergic system and help differentiate between normal controls, PD, MSA, and PSP patients. We performed SPECT evaluation of the dopamine transporter (DAT) and dopamine D2 receptors (D2). SPECT scans using [123I]beta-CIT (for DAT) and [123I]IBF (for D2) were performed in 18 patients with PD (12 dopa-naïve and 6 on levodopa and/or dopamine agonists), 7 with MSA of the striatonigral degeneration type, 6 with PSP, and 29 normal controls. Antiparkinsonian drugs were withheld for at least 12 hours before the scans. DAT and D2 binding potentials (Rv = V3/V2) were measured for caudate, anterior, and posterior putamen on the sides ipsilateral and contralateral to the worst motor symptoms. DAT binding in the posterior putamen was markedly reduced in all patients. However, D2 binding in posterior putamen was significantly increased in dopa-untreated PD, being greater than the normal range in 4 of 12 (33%), and it was significantly reduced in MSA, being below the normal range in 5 of 7 (71%). None of the patients with PD showed reduced D2 binding below the normal range in posterior putamen. The degree of DAT binding could not discriminate between the patient groups. The ratio of posterior putamen to caudate percentage D2 Rv compared with the controls showed an opposite pattern between PD or PSP and MSA; the caudate was greater in 16 of 18 with PD and 6 of 6 with PSP, whereas caudate was less in 5 of 7 with MSA. These findings suggest that DAT SPECT may be useful in differentiating parkinsonism from controls and D2 SPECT in further differentiating MSA from Parkinson's disease and possibly PSP.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".