{"doi":"10.1101/2020.09.12.277699","title":"Revealing the relevant spatiotemporal scale underlying whole-brain dynamics","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>The brain rapidly processes and adapts to new information by dynamically switching between activity in whole-brain functional networks. In this whole-brain modelling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100-900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify a fingerprint of healthy dynamics quantifying the richness of the dynamical repertoire at each spatiotemporal scale by computing the entropy of switching activity between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 milliseconds. Overall, this study provides much needed evidence for the relevant spatiotemporal scales needed to make sense of neuroimaging data.</jats:p>","journal":null,"year":null,"id":651711,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1699796,"name":"Ane López-González","orcid":null,"position":1,"is_corresponding":false},{"id":553619,"name":"Morten L. Kringelbach","orcid":"0000-0002-3908-6898","position":2,"is_corresponding":false},{"id":557847,"name":"Gustavo Deco","orcid":"0000-0002-8995-7583","position":3,"is_corresponding":false},{"id":326369,"name":"Xenia Kobeleva","orcid":"0000-0001-7695-1716","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Revealing the relevant spatiotemporal scale underlying whole-brain dynamics","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>The brain rapidly processes and adapts to new information by dynamically switching between activity in whole-brain functional networks. In this whole-brain modelling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100-900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify a fingerprint of healthy dynamics quantifying the richness of the dynamical repertoire at each spatiotemporal scale by computing the entropy of switching activity between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 milliseconds. Overall, this study provides much needed evidence for the relevant spatiotemporal scales needed to make sense of neuroimaging data.</jats:p>","is_dataset_classified":null,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W3163738467","authors":[],"funders":[{"funder_name":"Swiss National Science Foundation","grant_id":"170873","title":"Exploring brain communication pathways  by  combining diffusion based  quantitative structural connectivity and EEG source imaging : application to physiological and epileptic networks"},{"funder_name":"European Commission","grant_id":"945539","title":"Human Brain Project Specific Grant Agreement 3"},{"funder_name":"European Commission","grant_id":"615539","title":"The plasticity of parental caregiving: characterizing the brain mechanisms underlying normal and disrupted development of parenting"}],"total_grants":3,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2022,"count":1},{"year":2023,"count":1}],"oa_status":"green","license":"CC BY","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2021/03/06/2020.09.12.277699.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2021/03/06/2020.09.12.277699.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2020.09.12.277699","host_type":"publisher"},{"url":"https://doi.org/10.1101/2020.09.12.277699","host_type":"repository"},{"url":"https://pub.dzne.de/search?p=id:%22DZNE-2022-00098%22","host_type":"repository"},{"url":"https://europepmc.org/articles/pmc8569182?pdf=render","host_type":""},{"url":"https://doi.org/10.3389/fnins.2021.715861","host_type":""},{"url":"https://www.frontiersin.org/articles/10.3389/fnins.2021.715861/pdf","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/34744605","host_type":""},{"url":"http://dx.doi.org/10.3389/fnins.2021.715861","host_type":""},{"url":"http://doi.org/10.3389/fnins.2021.715861","host_type":""},{"url":"http://hdl.handle.net/10230/53592","host_type":""},{"url":"https://doaj.org/article/89c47744d58c4370acbe152142f7f439","host_type":""},{"url":"https://dx.doi.org/10.1101/2020.09.12.277699","host_type":""},{"url":"https://dx.doi.org/10.3389/fnins.2021.715861","host_type":""},{"url":"https://pub.dzne.de/record/163318","host_type":""},{"url":"https://ora.ox.ac.uk/objects/uuid:1ce33f5f-4b48-4a1a-bce3-053b20a46490","host_type":""},{"url":"https://hdl.handle.net/21.11116/0000-0009-9843-0","host_type":""},{"url":"https://hdl.handle.net/21.11116/0000-000D-7F5B-0","host_type":""},{"url":"https://pure.au.dk/portal/en/publications/2ad2eac0-5353-4387-b3d4-dfaf5b4ebf3b","host_type":""},{"url":"https://www.scopus.com/pages/publications/85118757582","host_type":""}],"fields_of_study":["Neural dynamics and brain function","Functional Brain Connectivity Studies","stochastic dynamics and bifurcation","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":[],"keywords":["Dynamic functional connectivity","Brain activity and meditation","Computer science","Temporal scales","Scale (ratio)","Neuroimaging","Millisecond","Spatiotemporal pattern","Functional connectivity","Neuroscience","Artificial intelligence","Electroencephalography","Cartography","Psychology","Biology","Geography","Physics","brain dynamics","Neurosciences. 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