Riemmanian Intrinsic Lifespan Norms for The EEG Crosspectrum
Poster No:
1342
Submission Type:
Abstract Submission
Authors:
Yu Jin1, Ronaldo García Reyes2, Ying Wang1, Min Li3, Maria Bringas-Vega1, Pedro Valdes-Sosa1
Institutions:
1University of Electronic Science and Technology of China, Chengdu City, Sichuan, 2Cuban Center for Neurocience, La Habana, Cuba, 3Hangzhou Dianzi University, Hangzhou, Zhejiang
First Author:
Co-Author(s):
Introduction:
The accurate quantification of age-related changes in individual brain development is of paramount importance for the clinical diagnosis of neurodevelopmental disorders. By comparing individual electroencephalogram (EEG) patterns with established norms, abnormalities in brain function can be effectively identified. Consequently, standardized quantitative EEG (qEEG) analysis has become a key diagnostic tool for neurodevelopmental disorders. However, current qEEG normative research is constrained by three significant limitations: Firstly, there is a need for large multinational datasets to establish accurate neurodevelopmental norms. （The HarMNqEEG project is a step in the right direction, with 9 countries, 12 devices, and 14 studies, including 1564 subjects (Min et al., 2022). Secondly, traditional research relies too heavily on spectrum analysis rather than cross-spectrum analysis, which limits our ability to fully understand brain networks. Additionally, the cross-spectrum matrix is a non-linear object that exists on the Positive Definite Manifold (PD), where conventional Euclidean statistical analysis proves inadequate. To properly analyze these cross-spectrum tensors, Riemannian-based statistical tools are necessary to account for their non-linear nature. Extrinsic-based Riemannian regression methods are used in current literature, but pure Intrinsic Methods could yield better classification results.
Methods:
This study used the HarMNqEEG dataset, a standardized database of EEG cross-spectrum tensors. Preprocessing steps included average referencing, global scale factor adjustment, and regularization to ensure Symmetric Positive Definite (SPD) matrices. We employed Intrinsic Multivariate Local Polynomial Regression (ILPR) (Ronaldo et al., 2023) to model EEG cross-spectrum tensors as response variables on the SPD manifold, using the Log-Cholesky Riemannian metric. Frequency and age were predictors in the regression model. This approach preserved the intrinsic geometric structure of the data, enhancing the accuracy and reliability of the analysis. A Riemannian Z-score was introduced to quantify deviations from normative means(Fig. 1). This Z-score provides a standardized metric to quantify deviations of individual data points from the normative mean while preserving the intrinsic geometric structure of the data.
Results:
The first multivariate qEEG normative model based on intrinsic regression was constructed, showing developmental trajectories for Fp1 and O1 channel pairs across frequency and age dimensions (Fig. 2). The distribution of Riemannian Z-scores across research sites followed Gaussian distributions, validating the reliability of the approach. These results provide precise age-related brain developmental trajectories and reliable standards for clinical diagnosis.
Conclusions:
This study introduces a multivariate qEEG normative model based on intrinsic regression using the HarMNqEEG dataset. By employing ILPR with the Log-Cholesky Riemannian metric, the method preserves the intrinsic geometric structure of EEG cross-spectrum tensors, enabling accurate modeling of brain development. The novel Riemannian Z-score provides a robust tool for assessing individual deviations.
This framework addresses key limitations in qEEG normative research and offers a precise, standardized approach for early screening and diagnosis of neurodevelopmental disorders. It also establishes a new paradigm for exploring brain development in neuroscience research.
This framework addresses key limitations in qEEG normative research and offers a precise, standardized approach for early screening and diagnosis of neurodevelopmental disorders. It also establishes a new paradigm for exploring brain development in neuroscience research.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 2
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Multivariate Approaches
Neuroinformatics and Data Sharing:
Databasing and Data Sharing
Keywords:
Data analysis
Development
Electroencephaolography (EEG)
Other - Riemannian Geometry; Positive Definite Manifold; Normative EEG Models
1|2Indicates the priority used for review
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[2] Reyes, R. G., Wang, Y., Li, M., Ortega, M. O., Paz-Linares, D., García, L. G., & Sosa, P. A. V. (2023). Multivariate intrinsic local polynomial regression on isometric Riemannian manifolds: Applications to positive definite data. arXiv preprint arXiv:2305.01789.