Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks

• URL: http://arxiv.org/abs/2410.11064v1
• Date: Mon, 14 Oct 2024 20:21:11 GMT
• Title: Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks
• Authors: Rui Sherry Shen, Yusuf Osmanlıoğlu, Drew Parker, Darien Aunapu, Benjamin E. Yerys, Birkan Tunç, Ragini Verma,
• Abstract summary: We present a framework that integrates deep generative models with graph-based normative modeling to characterize brain network development in the neurotypical population. Our deep generative model incorporates bio-inspired wiring constraints to effectively capture the developmental trajectories of neurotypical brain networks. We demonstrate the clinical utility of this framework by applying it to a large sample of children with autism spectrum disorders.
• Score: 1.2115617129203957
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• Abstract: Many neurodevelopmental disorders can be understood as divergent patterns of neural interactions during brain development. Advances in neuroimaging have illuminated these patterns by modeling the brain as a network structure using diffution MRI tractography. However, characterizing and quantifying individual heterogeneity in neurodevelopmental disorders within these highly complex brain networks remains a significant challenge. In this paper, we present for the first time, a framework that integrates deep generative models with graph-based normative modeling to characterize brain network development in the neurotypical population, which can then be used to quantify the individual-level neurodivergence associated with disorders. Our deep generative model incorporates bio-inspired wiring constraints to effectively capture the developmental trajectories of neurotypical brain networks. Neurodivergence is quantified by comparing individuals to this neurotypical trajectory, enabling the creation of region-wise divergence maps that reveal latent developmental differences at each brain regions, along with overall neurodivergence scores based on predicted brain age gaps. We demonstrate the clinical utility of this framework by applying it to a large sample of children with autism spectrum disorders, showing that the individualized region-wise maps help parse the heterogeneity in autism, and the neurodivergence scores correlate with clinical assessments. Together, we provide powerful tools for quantifying neurodevelopmental divergence in brain networks, paying the way for developing imaging markers that will support disorder stratification, monitor progression, and evaluate therapeutic effectiveness.

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