Welcome to OneOverF Discussions!

[MakotoMiyakoshi]

Invitation to collaborate on an open paper about EEG’s 1/f

This space continues the EEGLAB mailing-list thread "Invitation to collaborate on an open paper about EEG’s 1/f." We’re moving the discussion here to make collaboration easier — for sharing ideas, asking questions, and posting announcements.

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[AaronGibbings]

Hi Makoto, Thanks so much for suggesting and organizing this project! Looking forward to working with everyone!

[Marjanz69]

Hi all,
“I’ve been thinking about a multi-scale hypothesis for EEG 1/f dynamics, linking dendritic filtering, network E/I balance, and Griffiths-like effects. I’d love to hear your thoughts on whether this could fit within the cable theory subgroup, and whether there are particular analyses you’d suggest to test it.”
@MakotoMiyakoshi

[Marjanz69]

Introduction and Overall Hypothesis

Here is a proposed multi-scale hypothesis and analysis pipeline for EEG 1/f dynamics. It’s speculative and intended for discussion and collaborative exploration. Based on my reading of relevant papers, my guess is that the origin of 1/f in EEG may start at the neuronal level and seamlessly extend to network scales—this hypothesis requires further empirical testing.

Neuronal and Network Level Mechanisms

At the neuronal level, dendritic filtering based on cable theory (Pettersen et al., 2014) can produce 1/f-like patterns independently, without complex interactions. As neurons aggregate, volume conduction and the collective activity of neurons can amplify this pattern. Transitioning to the network level, this could be stabilized by excitation/inhibition (E/I) balance (Gao et al., 2017), which keeps the network in a controlled critical regime, preventing quiescence or runaway activity. This balance may facilitate Griffiths-like dynamics (Moretti & Muñoz, 2013; Ponce-Alvarez et al., 2018), where hierarchical and modular brain structures create rare regions that sustain stable, long-term activity, resulting in a broader power-frequency spectrum in EEG.

Spectral Components and Mechanisms

Essentially, network dynamics could enhance heterogeneity from variations in dendritic filtering across neurons. This neural power spectrum can be parameterized into periodic (e.g., rhythmic oscillations) and aperiodic (e.g., 1/f background slope) components, enabled by algorithms like FOOOF (Donoghue et al., 2020). Both aperiodic (1/f slope) and periodic (oscillations) components likely arise from similar mechanisms like cable theory, volume conduction, and Griffiths phases, but differ in their localization: the aperiodic 1/f occurs globally across the brain, while oscillations are local and cluster-based, perhaps in specific regions like occipital or frontal areas or within neuronal clusters. This idea could provide a multi-scale framework for understanding 1/f dynamics.

Analytical Pipeline and Methods

For analytical testing of this hypothesis, one could start with standard EEG preprocessing (e.g., filtering and ICA to extract independent sources), followed by wavelet transforms for time-frequency analysis across frequency bands. Then, apply FOOOF (Donoghue et al., 2020) to separate periodic and aperiodic components.

Network Graphs and Connectivity Analysis

To delve deeper, network graphs can be built on the time-frequency signals from wavelets: for the periodic part (oscillations), define local graphs to compute features like node degree or centrality, highlighting cluster-based dynamics. For the aperiodic part (1/f slope), construct global graphs to extract network-level properties like modularity or small-worldness, emphasizing whole-brain aspects. Effective connectivity methods such as Granger causality or transfer entropy can examine causal influences between neurons or regions.

Graph Neural Networks for Multi-Scale Analysis

After graph construction, Graph Neural Networks (GNN) can be utilized: apply GNN at the node and local level for local features (e.g., cluster oscillations), and at the overall network level for global features (e.g., 1/f slope). This approach can provide comprehensive predictions of multi-scale dynamics and effectively model functional connectivity across time-frequency scales.

[Siddhesh6344]

Hi @Marjanz69 ,

The idea looks very promising. I would be interested to contribute towards the modelling and GNN part. What do you say?
Those who all are interested in this, we can have a chat?

@MakotoMiyakoshi