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The 5% Pearson row count threshold rejected 31.54% of pair draws in 20 processed ds006192 movie runs under fixed spectra and independent relative phases

The usual 5% Pearson threshold rejected 31.54% of marginal draws under independent random relative phases. The audit used 20 processed movie fMRI runs from ten selected people and 100 cortical regions. A common run threshold averaged 4.92%, but 4,506 pair profiles exceeded 10% rejection. An exact variance and a proved probability bound support a reusable audit tool. A fresh source download and an independent checker supported the result. The result concerns fixed spectra and random phases. It does not measure false connections in observed brain data.

Pearson correlation · Fourier phase randomization · autocorrelation · functional MRI · conditional null model · calibration · Monte Carlo

Admitted 2 citing publications

References

6
  1. usesPeriodic Fourier interpolation gave a mean squared error ratio of 0.0066 for alternating samples in 100 cortical regions from 20 filtered ds006192 movie runs with odd input lengths sci:sha256:f9b8f153bc68998d84461095c422ed2710ebc81eae1f8534b36e748554270244

    Used its verified 215-file source subset, fixed participant and movie selection, source manifest, and adapted loader and retrieval code. Its interpolation result motivated a separate audit of correlation thresholds.

  2. usesFourier phase controls increased alternating correlation differences in 20 movie fMRI runs with fixed channel power spectra sci:sha256:c136d60b2672fc285ad424c209eb26d835b2fdcf2cf281f354d25cbad66a7585

    Read its description during analysis to define the scope relative to prior deterministic phase controls in the same movie runs. This work tests a marginal random phase null. No code or numerical results were reused from that study.

  3. usesdoi:10.18112/openneuro.ds006192.v1.0.0

    The audit used processed regional time series from OpenNeuro ds006192 version 1.0.0; the selected file subset, commit, retrieval URLs, sizes, and hashes are preserved.

  4. doi:10.52294/001c.151820
  5. doi:10.1016/j.neuroimage.2019.05.011
  6. doi:10.1016/0167-2789(92)90102-S

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