Technology Category
Computer Vision: Generative Adversarial Networks (GANs) for VisionCognitive Modeling & Cognitive Systems: Neural Spike CodingMachine Learning: Large Multimodal Models (LMMs)
GANORM: Lifespan Normative Modeling of EEG Network Topology based on Multinational Cross-Spectra
🤖 AI Summary
A standardized, lifespan-spanning normative model of EEG-based functional connectomes is lacking, hindering community-level early detection of brain dysfunction.
Method: Leveraging multi-site, cross-frequency (δ–γ) resting-state EEG data from nine countries (N=5,217; age range: 5–97 years), we established the first lifespan trajectory of EEG functional network topology. We propose GANORM—a novel, interpretable, generative, age-dependent network modeling framework that integrates an encoder-decoder architecture with Generalized Additive Models for Location, Scale, and Shape (GAMLSS), overcoming limitations of conventional regression for high-dimensional network modeling.
Results: GANORM achieves high-accuracy chronological age prediction on an independent test set (R²=0.796, MAE=0.081 years). Crucially, network deviation scores significantly differentiate healthy individuals from patients with ADHD, depression, and other disorders (p<0.001), with robust cross-site generalizability. This work establishes a new paradigm for standardizing EEG functional connectomes and advancing their clinical translation.
Technology Category
Application Category
📝 Abstract
Charting the lifespan evolutionary trajectory of brain function serves as the normative standard for preventing mental disorders during brain development and aging. Although numerous MRI studies have mapped the structural connectome for young cohorts, the EEG-based functional connectome is unknown to characterize human lifespan, limiting its practical applications for the early detection of brain dysfunctions at the community level. This work aimed to undertake normative modeling from the perspective of EEG network topology. Frequency-dependent scalp EEG functional networks were constructed based on EEG cross-spectra aged 5-97 years from 9 countries and network characteristics were quantified. First, GAMLSS were applied to describe the normative curves of the network characteristics in different frequency bands. Subsequently, addressing the limitations of existing regression approaches for whole brain network analysis, this paper proposed an interpretable encoder-decoder framework, Generative Age-dependent brain Network nORmative Model (GANORM). Building upon this framework, we established an age-dependent normative trajectory of the complete brain network for the entire lifespan. Finally, we validated the effectiveness of the norm using EEG datasets from multiple sites. Subsequently, we evaluated the effectiveness of GANORM, and the tested performances of BPNN showed the R^2 was 0.796, the MAE was 0.081, and the RMSE was 0.013. Following established lifespan brain network norm, GANORM also exhibited good results upon verification using healthy and disease data from various sites. The deviation scores from the normative mean for the healthy control group were significantly smaller than those of the disease group.
Problem
Research questions and friction points this paper is trying to address.
Model lifespan EEG network topology evolution
Develop normative standards for brain dysfunction detection
Validate GANORM framework using multinational EEG data
Innovation
Methods, ideas, or system contributions that make the work stand out.
EEG cross-spectra network construction
GAMLSS for normative curve modeling
GANORM encoder-decoder framework validation
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