{"doi":"10.3389/fnbeh.2023.1128610","title":"Influence of goals on modular brain network organization during working memory","abstract":"Introduction: Top-down control underlies our ability to attend relevant stimuli while ignoring irrelevant, distracting stimuli and is a critical process for prioritizing information in working memory (WM). Prior work has demonstrated that top-down biasing signals modulate sensory-selective cortical areas during WM, and that the large-scale organization of the brain reconfigures due to WM demands alone; however, it is not yet understood how brain networks reconfigure between the processing of relevant versus irrelevant information in the service of WM. Methods: Here, we investigated the effects of task goals on brain network organization while participants performed a WM task that required participants to detect repetitions (e.g., 0-back or 1-back) and had varying levels of visual interference (e.g., distracting, irrelevant stimuli). We quantified changes in network modularity-a measure of brain sub-network segregation-that occurred depending on overall WM task difficulty as well as trial-level task goals for each stimulus during the task conditions (e.g., relevant or irrelevant). Results: First, we replicated prior work and found that whole-brain modularity was lower during the more demanding WM task conditions compared to a baseline condition. Further, during the WM conditions with varying task goals, brain modularity was selectively lower during goal-directed processing of task-relevant stimuli to be remembered for WM performance compared to processing of distracting, irrelevant stimuli. Follow-up analyses indicated that this effect of task goals was most pronounced in default mode and visual sub-networks. Finally, we examined the behavioral relevance of these changes in modularity and found that individuals with lower modularity for relevant trials had faster WM task performance. Discussion: These results suggest that brain networks can dynamically reconfigure to adopt a more integrated organization with greater communication between sub-networks that supports the goal-directed processing of relevant information and guides WM.","journal":"Frontiers in Behavioral Neuroscience","year":2023,"id":372584,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9559,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":329188,"name":"Kai Hwang","orcid":"0000-0002-1064-7815","position":1,"is_corresponding":false},{"id":1133625,"name":"Anthony J.-W. Chen","orcid":null,"position":2,"is_corresponding":false},{"id":463342,"name":"Emily G. Jacobs","orcid":"0000-0003-0001-5096","position":3,"is_corresponding":false},{"id":810209,"name":"Taraz G. Lee","orcid":"0000-0002-3186-8625","position":4,"is_corresponding":false},{"id":236462,"name":"Mark D’Esposito","orcid":"0000-0002-3462-006X","position":5,"is_corresponding":false},{"id":867027,"name":"Courtney L. Gallen","orcid":"0000-0002-4828-268X","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T01:15:58.101083Z","pmid":"37138661","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":[]}