{"doi":"10.1101/2020.08.12.248112","title":"A dynamic gradient architecture generates brain activity states","abstract":"Abstract A central goal of systems neuroscience is to determine the functional-anatomical basis of brain-wide activity dynamics. While brain activity patterns appear to be low-dimensional and guided by spatial gradients, the set of gradients remains provisional and their mode of interaction is unclear. Here we applied deep learning-based dimensionality reduction to task-free fMRI images to derive an intrinsic latent space of human brain activity. Each dimension represented a discrete, dynamically fluctuating spatial activity gradient. The principal dimension was a novel unipolar sensory-association gradient underlying the global signal. A small set of gradients appeared to underlie key functional connectomics phenomena. Different task activation patterns were generated by gradients adopting task-specific configurations. Dynamical systems modelling revealed that gradients interact via state-specific coupling parameters, allowing accurate forecasts and simulations of task-specific brain activity. Together, these findings indicate that a small set of dynamic, interacting gradients create the repertoire of possible brain activity states.","journal":null,"year":2020,"id":120805,"datarank":0.3453877639491069,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.0,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8799,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":559074,"name":"Alex J. Lee","orcid":"0000-0003-0001-2848","position":1,"is_corresponding":false},{"id":351744,"name":"Lorenzo Pasquini","orcid":"0000-0002-7899-7061","position":2,"is_corresponding":false},{"id":32591,"name":"William W. Seeley","orcid":"0000-0003-1410-2027","position":3,"is_corresponding":false},{"id":351747,"name":"Jesse A. Brown","orcid":"0000-0002-3139-9451","position":0,"is_corresponding":true}],"reference_count":76,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:14:38.147936Z","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":[]}