GANORM: Lifespan Normative Modeling of EEG Network Topology based on Multinational Cross-Spectra

TL;DR

This study develops a normative model of EEG network topology across the human lifespan using a novel deep learning framework, enabling early detection of brain dysfunctions with high accuracy.

Contribution

It introduces GANORM, an interpretable encoder-decoder model for lifespan EEG network analysis, filling a gap in functional connectome mapping across ages.

Findings

GANORM accurately predicts age with R^2 of 0.796

Deviations from normative trajectories distinguish healthy from diseased groups

The model is validated across multiple international datasets

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.