[123I]-IBVM SPECT imaging of cholinergic systems in multiple system atrophy: A specific alteration of the ponto-thalamic cholinergic pathways (Ch5–Ch6)
Bibliographic record
Abstract
UNLABELLED: We evaluated in vivo the integrity of brain cholinergic pathways in Multiple System Atrophy (MSA) and the relationship between cholinergic dysfunction and motor disturbances, by measuring the vesicular acetylcholine transporter (VAChT) expression using Single Photon Emission Computed Tomography (SPECT) and [(123)I]-iodobenzovesamicol ([(123)I]-IBVM). METHODS: Nine patients with probable MSA and 12 healthy volunteers underwent a dynamic [(123)I]-IBVM SPECT-CT scan and a magnetic resonance imaging (MRI) scan. All patients were examined with the Unified MSA Rating Scale (UMSARS; subscale I = activities of daily living (ADL), II = motor and IV = disability). CT and MRI images were used to register the dynamic SPECT image to the Montreal Neurological Institute brain template, which includes the regions of interest (ROI) of striatum and Ch1 (medial septum nucleus-hippocampus), Ch4 (nucleus basalis of Meynert-cortex) and Ch5-Ch6 (pedunculopontine and laterodorsal tegmental nuclei-thalamus) cholinergic pathways. For each ROI, pharmacokinetic modeling of regional time activity curves led to the calculation of [(123)I]-IBVM to VAChT binding potential (BPND) value, proportional to VAChT expression. RESULTS: When compared to controls, BPND values for MSA in Ch5-Ch6 were significantly decreased in both the pedunculopontine-laterodorsal nuclei and the thalamus (p = 0.004 and p = 0.006, respectively). Additionally, thalamus BPND values were correlated with UMSARS ADL (p = 0.006), motor (p = 0.002) and disability (p = 0.02) sub-scores. UMSARS motor subscale items 13 (postural instability) and 14 (gait) were also correlated with thalamus BPND values (p = 0.04). CONCLUSION: Ch5-Ch6 are the most affected cholinergic pathways in MSA at both cell bodies and thalamic cholinergic terminals. These results underscore the relevant role of [(123)I]-IBVM SPECT for improving our understanding of the pathophysiology in MSA.
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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 | 0.000 | 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 | 0.000 |
| 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".