Similarities in brain functional connectivity under various imaging conditions have led to resting-state fMRI becoming the preferred alternative to task-based fMRI in many experimental and clinical contexts. However, the equal or superior quality of the data obtained from resting-state fMRI for inferring neural responses is yet to be demonstrated. Here, a systematic comparison of the quality of inferences derived from both imaging paradigms was conducted by means of Bayesian Data Comparison. In this framework, data quality is formally quantified in information theoretic terms as the precision and amount of information provided by the data on the parameters of interest. Parameters of effective connectivity models, estimated from the cross-spectral densities of resting-state- and task time series by means of Dynamic Causal Modelling, were subjected to the analysis. Data from 50 individuals undergoing a Theory-of-Mind task were compared to data from the same individuals undergoing resting-state, both datasets provided by the Human Connectome Project. The examined network was specified from an independent component extracted from the resting-state data and the specific component was selected based on its overlap with the Theory-of-Mind activation map. A threshold of very strong evidence was reached in favour of the Theory-of-Mind condition regarding information gain over connectivity parameters. No notable differences were observed in parameter precision. The active task elicited stronger effective connectivity in this specific network and should be preferred over resting-state in settings where the detectability of the network is crucial, such as in clinical applications.
","journal":null,"year":null,"id":654753,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"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":58480,"name":"Karsten Specht","orcid":"0000-0002-9946-3704","position":1,"is_corresponding":false},{"id":1708867,"name":"Liucija Vaisvilaite","orcid":null,"position":2,"is_corresponding":false},{"id":456207,"name":"Peter Zeidman","orcid":"0000-0003-3610-6619","position":3,"is_corresponding":false},{"id":1085099,"name":"Julia Tuominen","orcid":"0000-0002-0152-1700","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"An Information-Theoretic Analysis of Resting-State versus Task fMRI","abstract":"Similarities in brain functional connectivity under various imaging conditions have led to resting-state fMRI becoming the preferred alternative to task-based fMRI in many experimental and clinical contexts. However, the equal or superior quality of the data obtained from resting-state fMRI for inferring neural responses is yet to be demonstrated. Here, a systematic comparison of the quality of inferences derived from both imaging paradigms was conducted by means of Bayesian Data Comparison. In this framework, data quality is formally quantified in information theoretic terms as the precision and amount of information provided by the data on the parameters of interest. Parameters of effective connectivity models, estimated from the cross-spectral densities of resting-state- and task time series by means of Dynamic Causal Modelling, were subjected to the analysis. Data from 50 individuals undergoing a Theory-of-Mind task were compared to data from the same individuals undergoing resting-state, both datasets provided by the Human Connectome Project. The examined network was specified from an independent component extracted from the resting-state data and the specific component was selected based on its overlap with the Theory-of-Mind activation map. A threshold of very strong evidence was reached in favour of the Theory-of-Mind condition regarding information gain over connectivity parameters. No notable differences were observed in parameter precision. The active task elicited stronger effective connectivity in this specific network and should be preferred over resting-state in settings where the detectability of the network is crucial, such as in clinical applications.
","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19965766","pmcid":null,"openalex_id":"https://openalex.org/W4280499032","authors":[],"funders":[{"funder_name":"The Research Council of Norway","grant_id":"276044","title":"When default is not default: Solutions to the replication crisis and beyond"},{"funder_name":"National Institutes of Health","grant_id":"3U54MH091657-03S1","title":"Mapping the Human Connectome: Structure, Function, and Heritability"}],"total_grants":2,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2025,"count":1}],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://bora.uib.no/bora-xmlui/bitstream/11250/3071118/4/netn_a_00302.pdf","host_type":"repository"},{"url":"https://bora.uib.no/bora-xmlui/bitstream/11250/3071118/4/netn_a_00302.pdf","host_type":"repository"},{"url":"https://hdl.handle.net/11250/3071118","host_type":"repository"},{"url":"https://doi.org/10.31219/osf.io/8wngh","host_type":""},{"url":"https://discovery.ucl.ac.uk/id/eprint/10173254/","host_type":"repository"},{"url":"http://doi.org/10.31219/OSF.IO/8WNGH","host_type":"repository"},{"url":"https://discovery.ucl.ac.uk/10173254/1/netn_a_00302.pdf","host_type":"repository"},{"url":"https://doi.org/10.1162/netn_a_00302","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/37397893","host_type":""},{"url":"http://dx.doi.org/10.1162/netn_a_00302","host_type":""},{"url":"https://doaj.org/article/6984d8674ae041d6852e716e12279deb","host_type":""},{"url":"https://hdl.handle.net/10037/28971","host_type":""},{"url":"https://discovery-pp.ucl.ac.uk/id/eprint/10173254/","host_type":""},{"url":"https://doi.org/https://doi.org/10.1162/netn_a_00302","host_type":""}],"fields_of_study":["Functional Brain Connectivity Studies","Neural dynamics and brain function","Advanced MRI Techniques and Applications","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":[],"keywords":["Resting state fMRI","Human Connectome Project","Connectome","Task (project management)","Computer science","Independent component analysis","Bayesian probability","Artificial intelligence","Functional connectivity","Functional magnetic resonance imaging","Data quality","Pattern recognition (psychology)","Machine learning","Data mining","Psychology","Neuroscience","Resting-state","150","610","Neurosciences. Biological psychiatry. Neuropsychiatry","Effective connectivity","Bayesian data comparison","Task fMRI","RC321-571","Research Article"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-11T08:14:34.877026Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}