{"doi":"10.1101/2022.04.05.487222","title":"Neuroscout, a unified platform for generalizable and reproducible fMRI research","abstract":"Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of naturalistic fMRI studies, allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":299151,"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":0.8883,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":655283,"name":"Roberta Rocca","orcid":"0000-0001-9017-8088","position":1,"is_corresponding":false},{"id":804508,"name":"Ross Blair","orcid":"0000-0003-3007-1056","position":2,"is_corresponding":false},{"id":559393,"name":"Christopher J. Markiewicz","orcid":"0000-0002-6533-164X","position":3,"is_corresponding":false},{"id":988607,"name":"Jeff Mentch","orcid":"0000-0002-7762-8678","position":4,"is_corresponding":false},{"id":785659,"name":"James D. Kent","orcid":"0000-0002-4892-2659","position":5,"is_corresponding":false},{"id":106638,"name":"Peer Herholz","orcid":"0000-0002-9840-6257","position":6,"is_corresponding":false},{"id":52885,"name":"Satrajit S. Ghosh","orcid":"0000-0002-5312-6729","position":7,"is_corresponding":false},{"id":326859,"name":"Russell A. Poldrack","orcid":"0000-0001-6755-0259","position":8,"is_corresponding":false},{"id":68673,"name":"Tal Yarkoni","orcid":"0000-0002-6558-5113","position":9,"is_corresponding":false},{"id":660562,"name":"Alejandro de la Vega","orcid":"0000-0001-9062-3778","position":0,"is_corresponding":true}],"reference_count":65,"raw_metadata":null,"created_at":"2026-07-19T00:31:40.528568Z","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":[]}