Lifespan mapping of the EEG source spectral dynamics with Xi-AlphaNET
Poster No:
1003
Submission Type:
Abstract Submission
Authors:
Ronald Garcia Reyes1, Ariosky Areaces Gonzales1, Ying Wang1, Ludovico Minati2, Pedro Valdes-Sosa3
Institutions:
1Joint China-Cuba Lab for Neuroinformatics and Neurosciences, School of Life Science and Technology, Chengdu, Sichuan, 2University of Electronic Science and Technology of China, Chengdu, Sichuan, 3McGill University, Montreal, QC
First Author:
Ronald Garcia Reyes
Joint China-Cuba Lab for Neuroinformatics and Neurosciences, School of Life Science and Technology
Chengdu, Sichuan
Joint China-Cuba Lab for Neuroinformatics and Neurosciences, School of Life Science and Technology
Chengdu, Sichuan
Co-Author(s):
Ariosky Areaces Gonzales
Joint China-Cuba Lab for Neuroinformatics and Neurosciences, School of Life Science and Technology
Chengdu, Sichuan
Joint China-Cuba Lab for Neuroinformatics and Neurosciences, School of Life Science and Technology
Chengdu, Sichuan
Ying Wang
Joint China-Cuba Lab for Neuroinformatics and Neurosciences, School of Life Science and Technology
Chengdu, Sichuan
Joint China-Cuba Lab for Neuroinformatics and Neurosciences, School of Life Science and Technology
Chengdu, Sichuan
Introduction:
Neural oscillations are fundamental to both brain function and dysfunction, and they are encoded in the spectral components (SC) of the EEG. The modeling and fitting of these SC-whether univariatel or multivariately (via spectra or cross-spectra)-have a long history, from the pioneering works of (Zetterberg, 1969) or (Pascual-Marqui, 1988; Valdes, 1992) with the Xi-Alpha model to more recent approaches like FOOOF (Donoghue, 2020). These methods have enabled the mapping of SC distributions across both frequency and spatial domains, though largely in relatively small datasets. Generally, the Xi process (an aperiodic component of the spectrum) spans the entire brain. In contrast, the Alpha process is predominantly occipital and exhibits frequency shifts in relation to aging.
Despite advancements in the field, several gaps persist in current SC modeling. Notably, existing models often overlook structural information in their generative frameworks. Anatomical connectivity is crucial for accurately modeling coherence, while conduction delays are essential for reconstructing phase dynamics in source cross-spectra (Nolte, 2008). Additionally, the significant computational demands associated with estimating high-resolution source-level SC can be prohibitive for large-scale studies. To date, no broad, normative datasets exist that clarify the distribution of SC parameters in high-resolution source space.
Despite advancements in the field, several gaps persist in current SC modeling. Notably, existing models often overlook structural information in their generative frameworks. Anatomical connectivity is crucial for accurately modeling coherence, while conduction delays are essential for reconstructing phase dynamics in source cross-spectra (Nolte, 2008). Additionally, the significant computational demands associated with estimating high-resolution source-level SC can be prohibitive for large-scale studies. To date, no broad, normative datasets exist that clarify the distribution of SC parameters in high-resolution source space.
Methods:
To address these challenges, we introduce the Xi-AlphaNET model (Fig. 1A). This framework integrates inverse problem-solving with structural information, SC, and normative models into a single generative model. In doing so, Xi-AlphaNET can map source-level effective connectivity from scalp EEG cross-spectra, providing both the topological distribution of SCs in source space and the conduction delays between neural tracts with high resolution and accuracy.
We estimate the Xi-AlphaNET parameters by fitting the model to the HarMNqEEG dataset-2,000 scalp EEG cross-spectra acquired across 9 countries and multiple devices (Li, 2022). The fitting procedure employs stochastic FISTA and Bayesian Optimization for parameter tuning. To initialize anatomical connectivity and tract lengths, we use the Human Connectome Project (HCP-MNP1) atlas, and we derive initial conduction delays from the FTRAC-Consortium atlas (Lemaréchal, 2022; Rosen & Halgren, 2022). By integrating these resources, Xi-AlphaNET yields high-resolution (8K voxels) estimates of SC and inter-tract conduction delays for each individual in the dataset.
We estimate the Xi-AlphaNET parameters by fitting the model to the HarMNqEEG dataset-2,000 scalp EEG cross-spectra acquired across 9 countries and multiple devices (Li, 2022). The fitting procedure employs stochastic FISTA and Bayesian Optimization for parameter tuning. To initialize anatomical connectivity and tract lengths, we use the Human Connectome Project (HCP-MNP1) atlas, and we derive initial conduction delays from the FTRAC-Consortium atlas (Lemaréchal, 2022; Rosen & Halgren, 2022). By integrating these resources, Xi-AlphaNET yields high-resolution (8K voxels) estimates of SC and inter-tract conduction delays for each individual in the dataset.
Results:
By applying robust quadratic regression with Xi-AlphaNET-derived conduction delays as responses and age as a predictor, we defined a lifespan trajectory for conduction delays across the HarMNqEEG cohort (Fig. 2B). Notably, they follow a U-shaped relationship with age, and when mapped onto myelin proportions, they benchmark well against established empirical findings (De Faria, 2021). Furthermore, their normal distribution suggests a reliable foundation for constructing source-level norms.
By leveraging Xi-AlphaNET to estimate source-level spectral parameters, we benchmark the lifespan trajectory of the Alpha Peak Frequency (APF) (Fig. 2A,B). Across both hemispheres, the APF exhibits an inverted U-shaped pattern estimated using a zero inflated model, that gradually flattens from occipital to frontal regions. In addition, using a large cohort (N=2000), we confirm the expected topological distributions: Alpha activity remains primarily occipital, while the Xi component spans the entire brain (Fig. 2C,D). These findings align closely with earlier, smaller-scale studies (Donoghue, 2020; Pascual-Marqui, 2022).
By leveraging Xi-AlphaNET to estimate source-level spectral parameters, we benchmark the lifespan trajectory of the Alpha Peak Frequency (APF) (Fig. 2A,B). Across both hemispheres, the APF exhibits an inverted U-shaped pattern estimated using a zero inflated model, that gradually flattens from occipital to frontal regions. In addition, using a large cohort (N=2000), we confirm the expected topological distributions: Alpha activity remains primarily occipital, while the Xi component spans the entire brain (Fig. 2C,D). These findings align closely with earlier, smaller-scale studies (Donoghue, 2020; Pascual-Marqui, 2022).
Conclusions:
Xi-AlphaNET provides a robust framework for mapping the lifespan of the EEG source spectral dynamics across large data sets at high spatial resolution.
Lifespan Development:
Lifespan Development Other 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Electroencephaolography (EEG)
1|2Indicates the priority used for review
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