2017/03/29 by Sofia Ira Ktena, Salim Arslan, Ktena, Sofia Ira +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Environmental Science · Medicine · Neuroscience · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #EEG and Brain-Computer Interfaces #FOS: Biological sciences #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Health, Environment, Cognitive Aging #Neural and Evolutionary Computing (cs.NE) #Neurons and Cognition (q-bio.NC) #cs.NE #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1703.10062
accepted at ISBI 2017: International Symposium on Biomedical Imaging, Apr 2017, Melbourne, Australia
arxiv created 2017/03/29 · openalex publication_date 2017/03/29 · arxiv updated 2017/03/30 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
Data-driven brain parcellations aim to provide a more accurate representation of an individual's functional connectivity, since they are able to capture individual variability that arises due to development or disease. This renders comparisons between the emerging brain connectivity networks more challenging, since correspondences between their elements are not preserved. Unveiling these correspondences is of major importance to keep track of local functional connectivity changes. We propose a novel method based on graph edit distance for the comparison of brain graphs directly in their domain, that can accurately reflect similarities between individual networks while providing the network element correspondences. This method is validated on a dataset of 116 twin subjects provided by the Human Connectome Project.