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Simon Kern
@skjerns.de

🧠 New preprint! How well can current algorithms *actually* detect neural "replay" in the human brain under absolutely optimal conditions? We built FASTIMAGES: A combined MEG + fMRI benchmark with KNOWN neural sequences, so replay-detection methods can finally be validated against a ground truth.

cimh-clinical-psychology.github.ioFASTIMAGES: Validating replay detection methods in human neuroimagingA combined MEG + fMRI benchmark dataset with known neural sequences to validate replay detection methods (TDLM and SODA).
Simon Kern
@skjerns.de

The problem: Methods like TDLM & SODA are widely used to find replay, but we rarely know *if* or *when* replay actually happened. Without invasive recordings -> no ground truth. Therefore, we created a dataset that contains clear neural sequences to benchmark current algorithms.

Simon Kern
@skjerns.de

FASTIMAGES = 70 participants (30 MEG + 40 fMRI) who saw 5 images flashing in a fast sequences (132–612 ms onset-to-onset), plus localizer trials to train decoders. This produces clearly-expressed neural sequences with a KNOWN order and timing. See our project page for some animations.

cimh-clinical-psychology.github.ioFASTIMAGES: Validating replay detection methods in human neuroimagingA combined MEG + fMRI benchmark dataset with known neural sequences to validate replay detection methods (TDLM and SODA).
Simon Kern
@skjerns.de

As a first step, we recorded a localizer, in which participants saw the five images slowly, in random order. Here, apply standard techniques to train a brain decoder that will be used later for sequence detection. Decoders work as expected ~55% in MEG (peak ~150 ms) and ~70% in fMRI (peak ~4 s).

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Simon Kern
@skjerns.de

You can literally see the sequence order in the averaged decoder output of the MEG and partially in the fMRI 👀 however, individual trials are much noisier. Perfect, so let's test TDLM and SODA!

Simon Kern
@skjerns.de

TDLM (built for MEG, Liu et al. 2021) successfully finds significant "sequenceness" at the expected time lag in ALL four speed conditions. But variance is high! Only ~half of participants are individually significant, and sequenceness is highly correlated to decoder quality, not behaviour.

Simon Kern
@skjerns.de

SODA (built for fMRI, Wittkuhn et al. 2021) also recovers the sequences, mainly driven by the "offset" period, which is significant in every speed condition. Similarly, individual participants are not always significant, and variance between participants is high. Dependency on the decoder is less!

Simon Kern
@skjerns.de

Effect sizes are comparable in the native modality (TDLM Cohen's d ≈ 0.77). Bootstrap power analysis shows: ~20 participants × 20 trials → 80% power. As this was an absolutely best case scenario and real effects are likely much smaller, you'll likely need to go beyond that for finding real replay.