Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders

• Published in: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis Vol. 12443; pp. 131 - 141
• Main Authors: Shen, Rui Sherry, Alappatt, Jacob A., Parker, Drew, Kim, Junghoon, Verma, Ragini, Osmanlıoğlu, Yusuf
• Format: Book Chapter Journal Article
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Connectome; Graph edit distance; Graph matching; Imaging biomarker; Learning edit costs; MCMC; MCMC; Connectome; Imaging biomarker; Learning edit costs; Graph edit distance; Graph matching
• ISBN: 3030603644; 9783030603649
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-60365-6_13

Advances in neuroimaging techniques such as diffusion MRI and functional MRI enabled evaluation of the brain as an information processing network that is called connectome. Connectomic analysis has led to numerous findings on the organization of the brain its pathological changes with diseases, providing imaging-based biomarkers that help in diagnosis and prognosis. A large majority of connectomic biomarkers benefit either from graph-theoretical measures that evaluate brain’s network structure, or use standard metrics such as Euclidean distance or Pearson’s correlation to show between-connectomes relations. However, such methods are limited in diagnostic evaluation of diseases, because they do not simultaneously measure the difference between individual connectomes, incorporate disease-specific patterns, and utilize network structure information. To address these limitations, we propose a graph matching based method to quantify connectomic similarity, which can be trained for diseases at functional systems level to provide a subject-specific biomarker assessing the disease. We validate our measure on a dataset of patients with traumatic brain injury and demonstrate that our measure achieves better separation between patients and controls compared to commonly used connectomic similarity measures. We further evaluate the vulnerability of the functional systems to the disease by utilizing the parameter tuning aspect of our method. We finally show that our similarity score correlates with clinical scores, highlighting its potential as a subject-specific biomarker for the disease.