Analyzing the co-localization of substantia nigra hyper-echogenicities and iron accumulation in Parkinson's disease: A multi-modal atlas study with transcranial ultrasound and MRI
Bibliographic record
Abstract
BACKGROUND: Transcranial B-mode sonography (TCS) can detect hyperechogenic speckles in the area of the substantia nigra (SN) in Parkinson's disease (PD). These speckles correlate with iron accumulation in the SN tissue, but an exact volumetric localization in and around the SN is still unknown. Areas of increased iron content in brain tissue can be detected in vivo with magnetic resonance imaging, using quantitative susceptibility mapping (QSM). METHODS: In this work, we i) acquire, co-register and transform TCS and QSM imaging from a cohort of 23 PD patients and 27 healthy control subjects into a normalized atlas template space and ii) analyze and compare the 3D spatial distributions of iron accumulation in the midbrain, as detected by a signal increase (TCS+ and QSM+) in both modalities. RESULTS: We achieved sufficiently accurate intra-modal target registration errors (TRE<1 mm) for all MRI volumes and multi-modal TCS-MRI co-localization (TRE<4 mm) for 66.7% of TCS scans. In the caudal part of the midbrain, enlarged TCS+ and QSM+ areas were located within the SN pars compacta in PD patients in comparison to healthy controls. More cranially, overlapping TCS+ and QSM+ areas in PD subjects were found in the area of the ventral tegmental area (VTA). CONCLUSION: Our findings are concordant with several QSM-based studies on iron-related alterations in the area SN pars compacta. They substantiate that TCS+ is an indicator of iron accumulation in Parkinson's disease within and in the vicinity of the SN. Furthermore, they are in favor of an involvement of the VTA and thereby the mesolimbic system in Parkinson's disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".