RERUN
Claim 21

Does the brain's wiring predict where Parkinson's spreads? A separate rebuild

Independent rebuild

The study said the brain's wiring predicts where Parkinson's-like damage spreads in mice. The Lab retraced it: ✓ same result.

Why not the authors' own code?

It wasn't missing. Henderson and colleagues published their code openly, in R and MATLAB, under an MIT licence. This claim runs a separate Python rebuild, written by other researchers and published in a peer-reviewed journal, because we chose it before checking the original closely. MATLAB turns out to be used only for a side analysis. We have since run the authors' R code too, with no AI: it gave 0.69605, the same as this rebuild. That's claim 24.

See the authors' code result

If the brain's wiring predicts where Parkinson's spreads, it gives researchers a map of what to protect.

Cited by 302 studies

What this result means

We test-ran this code once while building the Lab, so we've already seen one result. Its official run came after forecasting closed.

We test one thing: does this code and data give this number? That's not the same as the science being right. The forecast price never changes the verdict.

See the proof
0.696match score, where 1 is perfect, the paper says. Counts if between 0.691 and 0.701.
Where the number comes fromPearson's r 0.696 (Henderson et al.), 0.696 (Replication)Table 2 of the ReScience C article

The exact claim as posted

Independent rebuild: brain-wiring model predicts spread of Parkinson's-linked protein in mice, r = 0.696

Henderson et al. (Nature Neuroscience, 2019) modelled how alpha-synuclein, the protein linked to Parkinson's disease, spreads along the mouse brain's wiring. They report that the model's predictions match lab measurements with a correlation of 0.696 at 3 months. This re-runs an independent Python rebuild by Bourdenx and Paul (ReScience C) on the study's data. It is not the authors' own R and MATLAB code, which is also public.

RERUNCLAIM #0021

Does the brain's wiring predict where Parkinson's spreads? A separate rebuild

Measured in
match score, where 1 is perfect
Paper says
0.696
Counts if
0.691 to 0.701
Lab got
0.6960522094835953
VerdictNumbers check out
f63ef50e89f8221d51bdNov 09, 2026

This result is final

Paper said 0.696. We got 0.6960522094835953.

The code and data produced the same result. That doesn't prove the study's method is right.

This slip is the Lab's .

✓ Verified on NEAR. See the contract's history

See the full receipt

  1. People forecast
  2. Forecasting ends
  3. The Lab retraces it
  4. Verdict posted, open to challenge
  5. Winners collect

What we didn't check

  • Whether the sample was fair or large enough.
  • Whether test data leaked into training or tuning.
  • Whether the result holds on new data.
  • Whether the study's method was the right one for its question.
  • Whether results in mice carry over to people.

Reading how the odds moved…

The evidence

See the full receipt

How much evidence is behind this?

Each step is a tougher test. Only the first one decides who wins the market.

  1. 1. ✓

    The Lab's retrace of the code and data named in the claim

    The Lab retraced the code and data and got a number inside the claim's range. YES wins.

  2. 2. –

    Stress test: does the number depend on luck?

    Fetching the extra runs…

  3. 3. –

    Rebuilt from the description (experimental)

    Fetching the AI's rebuild…

Coming next: a data-leakage check, for the same rows in training and test data, and a maths check on the statistics a paper reports. Neither has run on this claim.

Even all three steps can't prove the science is true. They show how well this result stands up to checking.

Verified

Your browser just re-checked these itself, so you don't have to trust this site.

Checking…

What the Lab retraces

Source
https://doi.org/10.5281/zenodo.5379631
Replication: M. Bourdenx, T. Paul. Original: M. Henderson et al., MIT (replication code and data in the repository)
Code, at this exact version
https://github.com/MathieuBo/PathoSpreading
commit 3031496c581800ebec3fb892f6dd05936e341dcb
Commands
python3.11
$ pip install numpy==1.26.4 pandas==1.5.3 scipy==1.11.4 statsmodels==0.14.1 matplotlib==3.8.4 seaborn==0.12.2 tqdm==4.67.1
$ python -c "import pandas as pd, runpy, Robustness_Stability as R; pd.Index._index_data = property(lambda s: s.values); R.random_robustness = lambda *a, **k: None; runpy.run_path('Pipeline.py', run_name='__main__')" > rerun_output.txt
Where the result is read
rerun_output.txt using regex Month Post Injection 3\s[\s\S]*?Pearson correlation coefficient ([0-9.]+)
Data files
None
Limits
2 CPU, 4 GB, 15 minutes
f63ef50e89f8221d51bde13cebd576b5b89b56f82842ae5fb33321418db60774
Show the claim exactly as stored on the blockchain
{"artifact":{"commit":"3031496c581800ebec3fb892f6dd05936e341dcb","data":[],"entrypoint":"python -c \"import pandas as pd, runpy, Robustness_Stability as R; pd.Index._index_data = property(lambda s: s.values); R.random_robustness = lambda *a, **k: None; runpy.run_path('Pipeline.py', run_name='__main__')\" > rerun_output.txt","output":{"extractor":{"expr":"Month Post Injection 3\\s[\\s\\S]*?Pearson correlation coefficient ([0-9.]+)","type":"regex"},"path":"rerun_output.txt"},"repo_url":"https://github.com/MathieuBo/PathoSpreading","runtime":"python3.11","setup":["pip install numpy==1.26.4 pandas==1.5.3 scipy==1.11.4 statsmodels==0.14.1 matplotlib==3.8.4 seaborn==0.12.2 tqdm==4.67.1"]},"metric":{"comparison":"within_tolerance","name":"correlation between predicted and measured pathology at 3 months","reported_value":"0.696","tolerance":{"type":"abs","value":"0.005"}},"private_data":null,"resources":{"cpu":2,"max_minutes":15,"mem_gb":4},"source":{"authors":"Replication: M. Bourdenx, T. Paul. Original: M. Henderson et al.","doi":"10.5281/zenodo.5379631","license":"MIT (replication code and data in the repository)","type":"paper","url":"https://doi.org/10.5281/zenodo.5379631"},"statement":"Henderson et al. (Nature Neuroscience, 2019) modelled how alpha-synuclein, the protein linked to Parkinson's disease, spreads along the mouse brain's wiring. They report that the model's predictions match lab measurements with a correlation of 0.696 at 3 months. This re-runs an independent Python rebuild by Bourdenx and Paul (ReScience C) on the study's data. It is not the authors' own R and MATLAB code, which is also public.","title":"Independent rebuild: brain-wiring model predicts spread of Parkinson's-linked protein in mice, r = 0.696","version":1}

Community notes

Notes are people's own words, kept on the blockchain. They never change a verdict, and the Lab's AI never reads them. Posting costs a little NEAR for storage, and what isn't used comes back.