{"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/resting-state-fmri-functional-connectivity","title":"Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture","arxiv_id":"1707.06682","date":"2017-07-20","proceeding":null,"authors":["Regina Meszlényi","Krisztian Buza","Zoltán Vidnyánszky"],"abstract":"Machine learning techniques have become increasingly popular in the field of\nresting state fMRI (functional magnetic resonance imaging) network based\nclassification. However, the application of convolutional networks has been\nproposed only very recently and has remained largely unexplored. In this paper\nwe describe a convolutional neural network architecture for functional\nconnectome classification called connectome-convolutional neural network\n(CCNN). Our results on simulated datasets and a publicly available dataset for\namnestic mild cognitive impairment classification demonstrate that our CCNN\nmodel can efficiently distinguish between subject groups. We also show that the\nconnectome-convolutional network is capable to combine information from diverse\nfunctional connectivity metrics and that models using a combination of\ndifferent connectivity descriptors are able to outperform classifiers using\nonly one metric. From this flexibility follows that our proposed CCNN model can\nbe easily adapted to a wide range of connectome based classification or\nregression tasks, by varying which connectivity descriptor combinations are\nused to train the network.","url_abs":"http://arxiv.org/abs/1707.06682v1","url_pdf":"http://arxiv.org/pdf/1707.06682v1.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":"resting-state-fmri-functional-connectivity","repo_url":"https://github.com/MRegina/connectome_conv_net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":null,"task_name":"Functional Connectivity"},{"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}