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Abstract #0589

“Multi-Layer Connectome” for Robust Multi-Subject Brain Network Analysis  and its Application to Baby Connectome Development Study

Han Zhang1, Weili Lin1, and Dinggang Shen1,2

1University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea

We propose a multi-layer connectome analysis method that extends the existing majority of single-layer brain network studies. In this method, multiple subjects’ connectome constitutes a multi-layer hyper-network with hyper-edges across layers. Result from applying this method to delineating neonatal brain development indicates that our method can capture robust group-level modules while keeping meaningful individual variability. The “increasing functional segregation/integration” model is further refined by us with a “consistent large-scale functional segregation/integration” with “rewiring-induced module refinement”, as well as an invert-U-shaped subject variability in modular structure in the first two years of life.

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