{"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/temporal-sequences-of-brain-activity-at-rest","title":"Temporal sequences of brain activity at rest are constrained by white matter structure and modulated by cognitive demands","arxiv_id":"1809.02849","date":"2019-09-30","proceeding":null,"authors":[],"abstract":"A diverse white matter network and finely tuned neuronal membrane properties\nallow the brain to transition seamlessly between cognitive states. However, it\nremains unclear how static structural connections guide the temporal\nprogression of large-scale brain activity patterns in different cognitive\nstates. Here, we analyze the brain's trajectories through a high-dimensional\nactivity space at the level of single time point activity patterns from\nfunctional magnetic resonance imaging data acquired during passive visual\nfixation (rest) and an n-back working memory task. We find that specific state\nspace trajectories, which represent temporal sequences of brain activity, are\nmodulated by cognitive load and related to task performance. Using\ndiffusion-weighted imaging acquired from the same subjects, we use tools from\nnetwork control theory to show that linear spread of activity along white\nmatter connections constrains the brain's state space trajectories at rest.\nAdditionally, accounting for stimulus-driven visual inputs explains the\ndifferent trajectories taken during the n-back task. We also used models of\nnetwork rewiring to show that these findings are the result of non-trivial\ngeometric and topological properties of white matter architecture. Finally, we\nexamine associations between age and time-resolved brain state dynamics,\nrevealing new insights into functional changes in the default mode and\nexecutive control networks. Overall, these results elucidate the structural\nunderpinnings of cognitively and developmentally relevant spatiotemporal brain\ndynamics.","url_abs":"http://arxiv.org/abs/1809.02849v2","url_pdf":"http://arxiv.org/pdf/1809.02849v2.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":"temporal-sequences-of-brain-activity-at-rest","repo_url":"https://github.com/ejcorn/brain_states","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"temporal-sequences","task_name":"Temporal Sequences"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}