{"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/a-constrained-weighted-l1-minimization","title":"A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs","arxiv_id":"1709.04090","date":"2017-09-13","proceeding":"arXiv 2017 9","authors":["Chandan Singh","Beilun Wang","Yanjun Qi"],"abstract":"Determining functional brain connectivity is crucial to understanding the\nbrain and neural differences underlying disorders such as autism. Recent\nstudies have used Gaussian graphical models to learn brain connectivity via\nstatistical dependencies across brain regions from neuroimaging. However,\nprevious studies often fail to properly incorporate priors tailored to\nneuroscience, such as preferring shorter connections. To remedy this problem,\nthe paper here introduces a novel, weighted-$\\ell_1$, multi-task graphical\nmodel (W-SIMULE). This model elegantly incorporates a flexible prior, along\nwith a parallelizable formulation. Additionally, W-SIMULE extends the\noften-used Gaussian assumption, leading to considerable performance increases.\nHere, applications to fMRI data show that W-SIMULE succeeds in determining\nfunctional connectivity in terms of (1) log-likelihood, (2) finding edges that\ndifferentiate groups, and (3) classifying different groups based on their\nconnectivity, achieving 58.6\\% accuracy on the ABIDE dataset. Having\nestablished W-SIMULE's effectiveness, it links four key areas to autism, all of\nwhich are consistent with the literature. Due to its elegant domain adaptivity,\nW-SIMULE can be readily applied to various data types to effectively estimate\nconnectivity.","url_abs":"http://arxiv.org/abs/1709.04090v2","url_pdf":"http://arxiv.org/pdf/1709.04090v2.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":"a-constrained-weighted-l1-minimization","repo_url":"https://github.com/QData/SIMULE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"a-constrained-weighted-l1-minimization","repo_url":"https://github.com/QData/JointNets","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"connectivity-estimation","task_name":"Connectivity Estimation"},{"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}