Abstract #0950

Bayesian Exponential Random Graph Modeling of Whole-Brain Structural Networks across Lifespan

Michel R.T. Sinke¹, Willem M. Otte^1,2, Alberto Caimo³, Cornelis J. Stam⁴, and Rick M. Dijkhuizen¹

¹Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, ²Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands, ³Social Network Analysis Research Centre, Interdisciplinary Institute of Data Science, University of Lugano, Lugano, Switzerland, ⁴Department of Clinical Neurophysiology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands

Comparison of brain networks that differ in size or edge density may be inadequate with frequently applied descriptive graph analysis methods. To resolve this, we propose an alternative framework based on Bayesian generative modeling, allowing unbiased assessment of local substructures that shape the global network topology. Structural networks were derived from DTI-based whole-brain tractography of 382 healthy subjects (age: 20-86 years), and successfully simulated. Despite clear effects of age and hub damage on network topologies, relative contributions of local substructures did not change significantly. The use of generative models may shed new light on the complex (re)organization of the brain.

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