{"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/evaluating-35-methods-to-generate-structural","title":"Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification","arxiv_id":"1706.06031","date":"2017-06-19","proceeding":null,"authors":["Dmitry Petrov","Alexander Ivanov","Joshua Faskowitz","Boris Gutman","Daniel Moyer","Julio Villalon","Neda Jahanshad","Paul Thompson"],"abstract":"There is no consensus on how to construct structural brain networks from\ndiffusion MRI. How variations in pre-processing steps affect network\nreliability and its ability to distinguish subjects remains opaque. In this\nwork, we address this issue by comparing 35 structural connectome-building\npipelines. We vary diffusion reconstruction models, tractography algorithms and\nparcellations. Next, we classify structural connectome pairs as either\nbelonging to the same individual or not. Connectome weights and eight\ntopological derivative measures form our feature set. For experiments, we use\nthree test-retest datasets from the Consortium for Reliability and\nReproducibility (CoRR) comprised of a total of 105 individuals. We also compare\npairwise classification results to a commonly used parametric test-retest\nmeasure, Intraclass Correlation Coefficient (ICC).","url_abs":"http://arxiv.org/abs/1706.06031v1","url_pdf":"http://arxiv.org/pdf/1706.06031v1.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":"evaluating-35-methods-to-generate-structural","repo_url":"https://github.com/lodurality/35_methods_MICCAI_2017","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"diffusion-mri","task_name":"Diffusion  MRI"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}