{"doi":"10.1101/2020.11.04.368357","title":"Lowering the Thermal Noise Barrier in Functional Brain Mapping with Magnetic Resonance Imaging","abstract":"Abstract Functional magnetic resonance imaging (fMRI) has become one of the most powerful tools for investigating the human brain. However, virtually all fMRI studies have relatively poor signal-to-noise ratio (SNR). Here we introduce a novel fMRI denoising technique, which suppresses noise that is indistinguishable from zero-mean, Gaussian-distributed noise. Thermal noise, falling in this category, is a major source of noise in fMRI, particularly, but not exclusively, at high spatial and/or temporal resolutions. Using 7-Tesla high-resolution data, we demonstrate improvements in temporal-SNR, the detection of stimulus-induced signal changes, and functional maps, while leaving stimulus-induced signal change amplitudes, image spatial precision, and functional point-spread-function unaltered. We also show that the method is equally applicable when using supra-millimeter resolution 3- and 7-Tesla fMRI data, different cortical regions, stimulation/task paradigms, and acquisition strategies. This denoising approach improves key metrics of functional activation detection while preserving spatial precision.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":119676,"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":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9547,"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":256594,"name":"Steen Moeller","orcid":"0000-0003-1698-7260","position":1,"is_corresponding":false},{"id":256598,"name":"Logan T. Dowdle","orcid":"0000-0002-1879-705X","position":2,"is_corresponding":false},{"id":256600,"name":"Mehmet Akçakaya","orcid":"0000-0001-6400-7736","position":3,"is_corresponding":false},{"id":486434,"name":"Federico De Martino","orcid":"0000-0002-0352-0648","position":4,"is_corresponding":false},{"id":12945,"name":"Essa Yacoub","orcid":"0009-0000-1007-9056","position":5,"is_corresponding":false},{"id":256599,"name":"Kâmil Uǧurbil","orcid":"0000-0002-8475-9334","position":6,"is_corresponding":false},{"id":430772,"name":"Luca Vizioli","orcid":"0000-0001-9450-1647","position":0,"is_corresponding":true}],"reference_count":69,"raw_metadata":null,"created_at":"2026-07-18T23:14:13.002105Z","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":[]}