Simon Kern @skjerns.de · 15/06/2026Effect 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. 110
Simon Kern @skjerns.de · 15/06/2026SODA (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! 110
Simon Kern @skjerns.de · 15/06/2026TDLM (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. 110
Simon Kern @skjerns.de · 15/06/2026You 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! 110
Simon Kern @skjerns.de · 15/06/2026As 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). 110
Simon Kern @skjerns.de · 10/04/20265/6 Previous papers simulated replay under completely unrealistic conditions, inserting more than 2000 events per minute. We show that a hybrid approach using real data approximates realistic conditions better. Generally, the defining factor is how well patterns (true reactivations) are separable! 110
Simon Kern @skjerns.de · 10/04/20263/6 We inserted replay in various densities in the control resting state - this way we could determine TDLM's sensitivity. To our surprise, more than 80 replay events per minute (A->B transitions), were necessary to find an effect! Far beyond biologically plausible rates (cf. ripple density). 121
Simon Kern @skjerns.de · 10/04/20262/6 However, we struggled to find any sequenceness and got mixed results, which made us curious. Our decoders worked well, so what was the problem? Was there really no replay? We set out to simulate realistic conditions using a novel hybrid simulation approach, combining pre-rest with localizer data 110
Simon Kern @skjerns.de · 02/11/2025Last week I was being trained as a #MNE-Python TrainEErs at #PracticalMEEG2025 - it was a lot of fun to look behind the scenes and learn how to run a good workshop :) thanks to @cuttingeeg.bsky.social for hosting and Marijn van Vliet and @nschawor.bsky.social for organizing this amazing workshop! 1213
Simon Kern @skjerns.de · 25/06/2025Not sure how exactly they measure multilingual capabilities, but 1b seems pretty bad - might explain the german results 210
Simon Kern @skjerns.de · 19/06/2025>30ms Interesting! That is also my experience. For some pilot data I've once created these heatmaps in which I plot sequenceness as a function of training time point. There's some interesting patterns in there but I'm not sure how they occur. Have not found a good expansion for what is happening 120
Simon Kern @skjerns.de · 16/06/20258/ Additionally, when re-using the sample simulation published in the TDLM methods paper, we found that replay was simulated with 2000 reactivations per minute! When simulating with more realistic 15 events per minute, even the sample code fails to reach significance. 141
Simon Kern @skjerns.de · 16/06/20257/ To our surprise, we needed >80 replay events per minute to reach the lowest bar of significance, with more than 120 events per minute for solid evidence. Cf. typical ripple rates in humans (as a proxy for estimating replay density) are ~15 per minute. 130
Simon Kern @skjerns.de · 16/06/20255/ We took the control resting state (from before the experiment) of each participant as a basis. We then inserted subtle patterns of each stimulus from the localizer with specific time intervals into the resting state. 120
Simon Kern @skjerns.de · 16/06/20254/ Our decoders work well (~35-40% accuracy/10% chance) so we decided to investigate. We came up with a hybrid simulation approach that simulates replay under more naturalistic conditions (i.e. endogenous oscillations). Previous studies only used completely synthetic data. 120
Simon Kern @skjerns.de · 16/06/20253/ We let 30 participants learn a hidden graph structure while in the MEG (criterion 80% memory performance). We expected to find task-related replay in a resting state session after learning. However no clear sequenceness was found, despite similar pipeline as previous papers. 120
Simon Kern @skjerns.de · 20/11/2023Lovely SQUID sensors #MEG ChatGPT understands what Dall-e doesn't 010
Simon Kern @skjerns.de · 20/11/2023Dall-e is really awesome for creating lecture illustrations without all the copyright-hassle🤩 120