Behavior-conditioned Koopman dynamics with Riemannian alignment for early substance use initiation prediction from longitudinal functional connectome data.
Behavior-conditioned Koopman dynamics with Riemannian alignment for early substance use initiation prediction from longitudinal functional connectome data.
This skill is based on research from arXiv:2603.29960v1 published on 2026-03-31.
Title: NeuroBRIDGE: Behavior-Conditioned Koopman Dynamics with Riemannian Alignment for Early Substance Use Initiation Prediction from Longitudinal Functional Connectome
Authors: Badhan Mazumder, Sir-Lord Wiafe, Vince D. Calhoun, Dong Hye Ye
arXiv: 2603.29960v1
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2026-03-31
0
connectome
Early identification of adolescents at risk for substance use initiation (SUI) is vital yet difficult, as most predictors treat connectivity as static or cross-sectional and miss how brain networks change over time and with behavior. We proposed NeuroBRIDGE (Behavior conditioned RIemannian Koopman Dynamics on lonGitudinal connEctomes), a novel graph neural network-based framework that aligns longitudinal functional connectome in a Riemannian tangent space and couples dual-time attention with behavioral-conditioned Koopman dynamics to capture temporal change. Evaluated on ABCD, NeuroBRIDGE improved future SUI prediction over relevant baselines while offering interpretable insights into neural pathways, refining our understanding of neurodevelopmental risk and informing targeted prevention.
# Example implementation based on paper methodology
# Note: This is a conceptual implementation
# Refer to the original paper for complete details
def analyze_brain_data(data, method="neurobridge_koopman_dynamics"):
"""
Apply NeuroBRIDGE: Koopman Dynamics for Brain Connectome Prediction methodology
Args:
data: Neural recording data (EEG, fMRI, calcium imaging, etc.)
method: Analysis method to apply
Returns:
Analysis results
"""
# Implementation based on paper
pass