{"doi":"10.1162/imag_a_00217","title":"Optimization and validation of multi-echo, multi-contrast SAGE acquisition in fMRI","abstract":"Abstract The purpose of this study was to optimize and validate a multi-contrast, multi-echo fMRI method using a combined spin- and gradient-echo (SAGE) acquisition. It was hypothesized that SAGE-based blood oxygen level-dependent (BOLD) functional MRI (fMRI) will improve sensitivity and spatial specificity while reducing signal dropout. SAGE-fMRI data were acquired with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, and 1 spin-echo) across 12 protocols with varying acceleration factors, and temporal SNR (tSNR) was assessed. The optimized protocol was then implemented in working memory and vision tasks in 15 healthy subjects. Task-based analysis was performed using individual echoes, quantitative dynamic relaxation times T2* and T2, and echo time-dependent weighted combinations of dynamic signals. These methods were compared to determine the optimal analysis method for SAGE-fMRI. Implementation of a multiband factor of 2 and sensitivity encoding (SENSE) factor of 2.5 yielded adequate spatiotemporal resolution while minimizing artifacts and loss in tSNR. Higher BOLD contrast-to-noise ratio (CNR) and tSNR were observed for SAGE-fMRI relative to single-echo fMRI, especially in regions with large susceptibility effects and for T2-dominant analyses. Using a working memory task, the extent of activation was highest with T2*-weighting, while smaller clusters were observed with quantitative T2* and T2. SAGE-fMRI couples the high BOLD sensitivity from multi-gradient-echo acquisitions with improved spatial localization from spin-echo acquisitions, providing two contrasts for analysis. SAGE-fMRI provides substantial advantages, including improving CNR and tSNR for more accurate analysis.","journal":"Imaging Neuroscience","year":2024,"id":464181,"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":0.9494,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":632192,"name":"Maurizio Bergamino","orcid":"0000-0001-6618-3784","position":1,"is_corresponding":false},{"id":760900,"name":"Sudarshan Ragunathan","orcid":"0000-0002-2871-1758","position":2,"is_corresponding":false},{"id":247455,"name":"C. Chad Quarles","orcid":"0000-0002-1731-0940","position":3,"is_corresponding":false},{"id":471584,"name":"Allen T. Newton","orcid":"0000-0001-6539-8783","position":4,"is_corresponding":false},{"id":477665,"name":"Ashley M. Stokes","orcid":"0000-0003-1137-5003","position":5,"is_corresponding":false},{"id":936098,"name":"Elizabeth G. Keeling","orcid":"0000-0002-6528-9920","position":0,"is_corresponding":true}],"reference_count":72,"raw_metadata":null,"created_at":"2026-07-19T02:04:37.468651Z","pmid":"39449748","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":[]}