# Demir Ege Ortac

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Demir Ege Ortac](https://cerebralvalley.ai/u/demiregeortac)
- **GitHub:** https://github.com/demiregeortac666/fiberqc
- **Demo video:** https://youtu.be/NAhpafIWrg4
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/26

I ran my tool on published, peer-reviewed data from the Wilbrecht lab at Berkeley
(DANDI:001340, dopamine in the nucleus accumbens during reward learning). 8 of 15 reasonable
preprocessing pipelines REVERSE the sign of the result. All 16 are significant. The choice
that decides it is the bleaching correction, and a researcher would probably never register
it as a choice at all.

fiberqc is a quality-control tool for fiber photometry, the technique used across systems
neuroscience to record dopamine release in behaving animals.

THE PROBLEM
Before a raw recording becomes a result, it passes through a chain of preprocessing decisions:
filter cutoff, bleaching correction, motion regression, normalization. Each one is defensible.
Each one is somewhat arbitrary. The field's own reference primer (Simpson, Akam, Patriarchi
et al., Neuron 2024) states plainly that there is no systematic comparison of these choices
and no established best practice. So results get published without anyone checking whether
they depend on a filter setting that was picked without thinking about it.

WHAT IT DOES
fiberqc runs the same analysis through every reasonable pipeline at once. You declare the
pipeline you actually ran. It locates your result in that space, tells you how many defensible
alternatives disagree with it, and names the choice that decides the outcome.

THE FINDING
The eight pipelines using high-pass bleaching correction find a significant decrease
(d = -0.33). The eight using a double-exponential fit find a significant increase (d = +0.50).
Nothing else flips the sign: not the filter cutoff, not motion correction, not normalization.
A researcher would draw opposite biological conclusions depending on that one decision.

WHAT THE TOOL FOUND IN ITSELF
Running on independently published data exposed three serious bugs in fiberqc. Each is fixed,
and each fix ships with a regression test that fails without it.

1. The verdict counted p-values but ignored sign, so a result where half the pipelines found
   a significant increase and half a significant decrease was labelled ROBUST. A QC tool that
   marks a dangerous result as safe is the worst failure it can have.
2. Non-uniform sampling was accepted silently. A 140-second LED-off gap was being filtered as
   if it were continuous signal, and that step discontinuity inverts the measured response.
3. Only the t-statistic was reported. But t = d * sqrt(n), so a trivially small effect over
   hundreds of trials looked overwhelming (Chen et al., 2017). Magnitude now leads, and the
   t-statistic is shown as the evidence for the effect rather than as the effect.

Three independent labs (Akam/Blanco-Pozo, Lerner/GuPPy, Wilbrecht/DANDI). pyPhotometry, TDT
and NWB formats. 29 tests, green on Python 3.10 to 3.12. API docs, CI, MIT.

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