[Eeglablist] Open collaboration project: Multi-model Anatomical Grouping of Independent Components (MMAGIC)

[Cedric Cannard]

Hi Makoto,

I have experienced the same thing recently. A ROI-based approach worked better for me than the more classic k-means clustering, where I would lose many subjects who did not meet the criteria for inclusion in the group analysis (in line with a recent question on this list). My context is heartbeat-evoked potentials (HEPs), where the components involved are not as obvious as in typical ERPs, since they are subtler responses to the heartbeat (and have to be separate from the cardiac field artifact component that has much greater amplitude and signal). The study aimed to track meditation (interoception) depth with them, which is not trivial and comes with its own phenomenological difficulties. Although better, the ROI approach still did not give successful results. Interoceptive regions can be deep and unreliable, so I also tried DMN regions to capture processes more related to mind wandering, which worked better but remained non-significant. I then tried a third approach that I call functional IC selection, inspired by this paper: https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/39159703/__;!!Mih3wA!C-b4A1m52zlfmgoSsSDmRbxcvQ9yFQy15BZxlffsKTjdYoQkaUrNfTEJ2pQLdE-oTrDml3CrEE_J-kOmnjrr0LEjtA$ 

In brief, for each component we take the spectral power in the time window and frequency range associated with interoception in the literature (e.g., 300 to 600 ms post R-peak, 4 to 20 Hz), then select the component (or the few top candidates) with the highest score, where the score combines that power with a surrogate control ensuring the activity is time-locked to the heartbeat. The group analysis is then run on the selected components. This is functionally individualized to each participant and is not circular, since the selection is independent of the condition of interest (meditation depth).

The same logic could apply to classic ERP or task designs: any case where the component of interest has a well-established latency and frequency signature, but its topography or source location varies enough across participants that clustering either fails to converge or drops subjects. And because the criterion is functional rather than spatial, it can be defined in whichever domain best captures the process at hand, whether time, frequency, time-frequency, phase, or nonlinear measures, as long as it stays independent of the contrast being tested to avoid circularity.

There are caveats, mainly around making the selection robust to non-brain variance and other biases, but I think it is an interesting approach. Feedback and criticism welcome.

All that to say, I would be happy to take part in this new project. I also have experience with MNE, LORETA, and LCMV beamforming (roiconnect EEGLAB plugin), in case that is useful.

Best,
Cedric

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On Tuesday, September 15th, 2026 at 3:12 PM, Makoto Miyakoshi via eeglablist <eeglablist at sccn.ucsd.edu> wrote:

> Hi Marjan and list,
> 
> I'd like to propose a small open collaboration project.
> 
> The idea is that we want to use anatomical ROI membership as a common
> coordinate across subjects, instead of relying on conventional IC
> clustering approach in EEGLAB STUDY. In the proposed approach, users
> specify an anatomical ROI, optionally with a distance tolerance, and ICs
> whose source models fall within that region are collected across subjects.
> In this implementation, anatomical ROI membership replaces IC clustering as
> the cross-subject correspondence rule.
> 
> Here is a prototype I originally developed for a project with Ilaria
> Berteletti, which uses standard current dipole models. Although we
> ultimately did not use it in that study, I can tell you it worked better
> than I thought.
> 
> https://urldefense.com/v3/__https://github.com/MakotoMiyakoshi/MMAGIC__;!!Mih3wA!AoKTTZuSu2CVqhzgmxuMpdjqVNFyIVR-LQZYBeLZwVRfqta0reT9bYiTM3uHevgACv2_Bf8HE91bDX8kg7QX9GoBaAI$
> <https://urldefense.com/v3/__https://github.com/MakotoMiyakoshi/AnatomicalROIGroupICA__;!!Mih3wA!AoKTTZuSu2CVqhzgmxuMpdjqVNFyIVR-LQZYBeLZwVRfqta0reT9bYiTM3uHevgACv2_Bf8HE91bDX8kg7QXcKIZKQg$ >
> 
> I cleaned the implementation to publish here and tested it on real
> ICA/DIPFIT datasets; details are available in the repository.
> 
> However, my recent work (under review) showed that more than 80% of the
> pre-qualified brain ICs by IC Label were localized deeper than
> physiologically plausible cortical source depths:
> 
> https://urldefense.com/v3/__https://www.medrxiv.org/content/10.64898/2026.01.23.26344529v2__;!!Mih3wA!AoKTTZuSu2CVqhzgmxuMpdjqVNFyIVR-LQZYBeLZwVRfqta0reT9bYiTM3uHevgACv2_Bf8HE91bDX8kg7QXDaIe1jw$
> 
> So it's time to try different cortex-constrained source estimates such as
> MNE, the LORETA family, LCMV, or other methods. The results can then be
> compared within a common anatomical ROI space. Hence MMAGIC: Multi-model
> Anatomical Grouping of Independent Components.
> 
> Marjan and I would like to develop this into a methodological/software
> project. If anyone is interested in contributing a source-localization
> method, atlas-mapping approach, validation with empirical data, or
> methodological idea, you are welcome to join us. Please let us know.
> 
> Makoto
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