{"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/nonparametric-modeling-of-dynamic-functional","title":"Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data","arxiv_id":"1601.00496","date":"2016-01-04","proceeding":null,"authors":["Søren F. V. Nielsen","Kristoffer H. Madsen","Rasmus Røge","Mikkel N. Schmidt","Morten Mørup"],"abstract":"Dynamic functional connectivity (FC) has in recent years become a topic of\ninterest in the neuroimaging community. Several models and methods exist for\nboth functional magnetic resonance imaging (fMRI) and electroencephalography\n(EEG), and the results point towards the conclusion that FC exhibits dynamic\nchanges. The existing approaches modeling dynamic connectivity have primarily\nbeen based on time-windowing the data and k-means clustering. We propose a\nnon-parametric generative model for dynamic FC in fMRI that does not rely on\nspecifying window lengths and number of dynamic states. Rooted in Bayesian\nstatistical modeling we use the predictive likelihood to investigate if the\nmodel can discriminate between a motor task and rest both within and across\nsubjects. We further investigate what drives dynamic states using the model on\nthe entire data collated across subjects and task/rest. We find that the number\nof states extracted are driven by subject variability and preprocessing\ndifferences while the individual states are almost purely defined by either\ntask or rest. This questions how we in general interpret dynamic FC and points\nto the need for more research on what drives dynamic FC.","url_abs":"http://arxiv.org/abs/1601.00496v2","url_pdf":"http://arxiv.org/pdf/1601.00496v2.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":"nonparametric-modeling-of-dynamic-functional","repo_url":"https://github.com/sfvnielsen/ndfc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":null,"task_name":"Functional Connectivity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}