{"doi":"10.1002/mrm.29824","title":"Spatial and Spectral Components of the <scp>BOLD</scp> Global Signal in Rat <scp>Resting‐State</scp> Functional <scp>MRI</scp>","abstract":"PURPOSE: In resting-state fMRI (rs-fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, as well as widespread neural activity; however, relative contributions of neural and non-neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the BOLD global signal to an adjacent non-brain tissue signal, where neural activity was absent, from the same rs-fMRI scan obtained from anesthetized rats. In this dataset, motion was minimal and ventilation was phase-locked to image acquisition to minimize respiratory fluctuations. Data were acquired using three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane. METHODS: A power spectral density estimate, a voxel-wise spatial correlation via Pearson's correlation, and a co-activation pattern analysis were performed using the global signal and the non-brain tissue signal. Functional connectivity was calculated using Pearson's linear correlation on default mode network (DMN) regions. RESULTS: We report differences in the spectral composition of the two signals and show spatial selectivity within DMN structures that show an increased correlation to the global signal and decreased intra-network connectivity after global signal regression. All of the observed differences between the global signal and the non-brain tissue signal were maintained across anesthetics. CONCLUSION: These results show that the global signal is distinct from the noise contained in the tissue signal, as support for a neural contribution. This study provides a unique perspective to the contents of the global signal and their origins.","journal":"Magnetic Resonance in Medicine","year":2023,"id":364368,"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":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.922,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":406981,"name":"Eric Maltbie","orcid":"0000-0003-2489-9674","position":1,"is_corresponding":false},{"id":365462,"name":"Wen‐Ju Pan","orcid":null,"position":2,"is_corresponding":false},{"id":406984,"name":"Theodore J. LaGrow","orcid":"0000-0002-2680-7385","position":3,"is_corresponding":false},{"id":406983,"name":"Nan Xu","orcid":"0000-0002-3900-3220","position":4,"is_corresponding":false},{"id":271354,"name":"Shella Keilholz","orcid":"0000-0001-5737-1660","position":5,"is_corresponding":false},{"id":1012504,"name":"Nmachi Anumba","orcid":"0000-0002-3967-4761","position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":null,"created_at":"2026-07-19T01:14:36.728255Z","pmid":"37582301","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":[]}