Fundamental questions about network structure and dynamics arise in many fiends - including economics, chemistry and the neurosciences. We engage in graph theoretical analyses of domain datasets to gain insights into complex systems.  A particular focus over the last decade has been analyses of large brain imaging datasets, to accelerate understanding of the consequences of traumatic brain injury on brain functioning over time. Our role in this work has been organized around developing novel methods that can be used by investigators to examine the large-scale brain changes after neurological disruption, including indicators of recovery and patient outcome.​

  • M. Gomez, S. Garcia, S. Rajtmajer, C. Grady, A. Mejia. Fragility of a Multilayer Network of Intranational Supply Chains. Applied Network Science, September 2020.

  • S. Garcia, S. Rajtmajer, C. Grady, P. Mohammadpour, A. Mejia. Performance of a Multiplex Commodity Flow Network in the United States Under Disturbance. International Conference on Complex Networks and their Applications (Complex Networks), December 2019.

  • N. Gilbert, R. Bernier, V. Calhoun, E. Brenner, E. Grossner, S. Rajtmajer and F. Hillary. Diminished Neural Network Dynamics after Moderate and Severe Traumatic Brain Injury. PLoS One, June 2018. 

  • S. Rajtmajer, A. Roy, R. Albert, P. Molenaar and F. Hillary. A Voxelwise Approach to Determine Consensus Regions-of-Interest for the Study of Brain Network Plasticity. Frontiers in Neuroanatomy, July 2015. 

  • F. Hillary, N. Castellanos, R. Bajo and S. Rajtmajer. Hyperconnectivity as a Common Neural Response to Neurological Disorder. Neuropsychology, January 2015.

  • U. Venkatesan, S. Rajtmajer and F. Hillary. Connectivity modeling and neuroplasticity after trauma. In Plasticity of Cognition in Neurologic Disorders. Oxford University Press, January 2015.

  • F. Hillary, S. Rajtmajer, C. Roman, J. Medaglia, J. Slocomb, D. Good and G. Wylie. The Rich Get Richer: Brain Injury Elicits Hyperconnectivity in Core Subnetworks. PLoS One, August 2014. 

  • S. Rajtmajer, B. Smith and S. Phoha. Non-Negative Sparse Autoencoder Neural Networks for the Detection of Overlapping, Hierarchical Communities in Networked Datasets. Chaos, December 2012. 

  • S. Rajtmajer and D. Vukicevic. A Note on the Estrada Communicability Algorithm for Community Structure Detection in Complex Networks. Applied Mathematics and Computation, December 2010. 

  • D. Vukicevic, S. Rajtmajer and N. Trinajstic. Trees with Maximal Second Zagreb Index and Prescribed Number of Vertices of Given Degree. MATCH Communications in Mathematical and Computer Chemistry, January 2008. 

  • D. Vukicevic, J. Sedlar and S. Rajtmajer. A Graph-Theoretical Method for the Partial Ordering of Alkanes. Croatica Chemica Acta 80: 169-179, February 2007. 

  • S. Rajtmajer, A. Milicevic, N. Trinajstic, M. Randic and D. Vukicevic. On the Complexity of the Archimedian Solids. Journal of Mathematical Chemistry, January 2006. 

©2020 Sarah Rajtmajer, PhD