Towards a Quantified Network Portrait of a Population

Area of Science:

• Neuroscience
• Computational Biology
• Network Science

Background:

• Brain connectome analysis reveals interactions between anatomical regions.
• Subnetworks are crucial for understanding functional segregation and integration.
• Subject-specific subnetwork expression in the human connectome is underexplored.

Purpose of the Study:

• To develop a framework for quantifying subject-specific subnetwork expression.
• To create a network portrait of a sample for group comparisons.
• To investigate differences in brain connectivity in autism spectrum disorder (ASD).

Main Methods:

• Multi-view spectral clustering for subnetwork detection.
• Nonnegative matrix decomposition for quantifying subject-specific subnetwork manifestations.
• Framework applied to a clinical sample of children with ASD and typically developing controls (TDCs).

Main Results:

• The proposed framework successfully quantifies subject-specific subnetwork expression.
• A network portrait was delineated for ASD and TDC groups.
• Decreased connectivity was observed in the ASD group between regions involved in social cognition, executive functions, and emotion processing.

Conclusions:

• The developed framework enables quantitative analysis of subject-specific brain subnetworks.
• Findings highlight altered connectivity patterns in children with ASD.
• This approach facilitates group comparisons and understanding of neurodevelopmental disorders.