{"doi":"10.1101/2021.02.13.431091","title":"A brain-based universal measure of attention: predicting task-general and task-specific attention performance and their underlying neural mechanisms from task and resting state fMRI","abstract":"Abstract Attention is central for many aspects of cognitive performance, but there is no singular measure of a person’s overall attentional functioning across tasks. To develop a universal measure that integrates multiple components of attention, we collected data from more than 90 participants performing three different attention-demanding tasks during fMRI. We constructed a suite of whole-brain models that can predict a profile of multiple attentional components – sustained attention, divided attention and tracking, and working memory capacity – from a single fMRI scan type within novel individuals. Multiple brain regions across the frontoparietal, salience, and subcortical networks drive accurate predictions, supporting a universal (general) attention factor across tasks, which can be distinguished from task-specific attention factors and their neural mechanisms. Furthermore, connectome-to-connectome transformation modeling enhanced predictions of an individual’s attention-task connectomes and behavioral performance from their rest connectomes. These models were integrated to produce a new universal attention measure that generalizes best across multiple, independent datasets, and which should have broad utility for both research and clinical applications.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2021,"id":216397,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8709,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":264247,"name":"Monica D. Rosenberg","orcid":"0000-0001-6179-4025","position":1,"is_corresponding":false},{"id":563618,"name":"Young Hye Kwon","orcid":"0000-0001-7754-4223","position":2,"is_corresponding":false},{"id":773615,"name":"Emily W. Avery","orcid":"0000-0002-8481-3978","position":3,"is_corresponding":false},{"id":571236,"name":"Qi Lin","orcid":"0000-0001-9702-8584","position":4,"is_corresponding":false},{"id":282065,"name":"Dustin Scheinost","orcid":"0000-0002-6301-1167","position":5,"is_corresponding":false},{"id":282066,"name":"R. Todd Constable","orcid":"0000-0001-5661-9521","position":6,"is_corresponding":false},{"id":61620,"name":"Marvin M. Chun","orcid":"0000-0003-1070-7993","position":7,"is_corresponding":false},{"id":563617,"name":"Kwangsun Yoo","orcid":"0000-0002-5213-4575","position":0,"is_corresponding":true}],"reference_count":78,"raw_metadata":null,"created_at":"2026-07-18T23:53:11.245932Z","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":[]}