{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/modeling-brain-networks-with-artificial","title":"Modeling Brain Networks with Artificial Neural Networks","arxiv_id":"1807.08368","date":"2018-07-22","proceeding":null,"authors":["Baran Baris Kivilcim","Itir Onal Ertugrul","Fatos T. Yarman Vural"],"abstract":"In this study, we propose a neural network approach to capture the functional\nconnectivities among anatomic brain regions. The suggested approach estimates a\nset of brain networks, each of which represents the connectivity patterns of a\ncognitive process. We employ two different architectures of neural networks to\nextract directed and undirected brain networks from functional Magnetic\nResonance Imaging (fMRI) data. Then, we use the edge weights of the estimated\nbrain networks to train a classifier, namely, Support Vector Machines(SVM) to\nlabel the underlying cognitive process. We compare our brain network models\nwith popular models, which generate similar functional brain networks. We\nobserve that both undirected and directed brain networks surpass the\nperformances of the network models used in the fMRI literature. We also observe\nthat directed brain networks offer more discriminative features compared to the\nundirected ones for recognizing the cognitive processes. The representation\npower of the suggested brain networks are tested in a task-fMRI dataset of\nHuman Connectome Project and a Complex Problem Solving dataset.","url_abs":"http://arxiv.org/abs/1807.08368v1","url_pdf":"http://arxiv.org/pdf/1807.08368v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"modeling-brain-networks-with-artificial","repo_url":"https://github.com/baranbaris/modeling_brain_networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}