{"doi":"10.21037/qims-20-404","title":"Quantitative assessment of inter-individual variability in fMRI-based human brain atlas","abstract":"BACKGROUND: Inter-individual variability is an inherent and ineradicable feature of group-level brain atlases that undermines their reliability for clinical and other applications. To date, there have been no reports quantifying inter-individual variability in brain atlases. METHODS: In the present study, we compared inter-individual variability in nine brain atlases by task-based functional magnetic resonance imaging (MRI) mapping of motor and temporal lobe language regions in both cerebral hemispheres. We analyzed complete motor and language task-based fMRI and T1 data for 893 young, healthy subjects in the Human Connectome Project database. Euclidean distances (EDs) between hotspots in specific brain regions were calculated from task-based fMRI and brain atlas data. General linear model parameters were used to investigate the influence of different brain atlases on signal extraction. Finally, the inter-individual variability of ED and extracted signals and interdependence of relevant indicators were statistically evaluated. RESULTS: We found that inter-individual variability of ED varied across the nine brain atlases (P<0.0001 for motor regions and P<0.0001 for language regions). There was no correlation between parcel number and inter-individual variability in left to right (LtoR; P=0.7959 for motor regions and P=0.2002 for language regions) and right to left (RtoL; P=0.7654 for motor regions and P=0.3544 for language regions) ED; however, LtoR (P≤0.0001) and RtoL (P≤0.0001) inter-individual variability differed according to brain region: the LtoR (P=0.0008) and RtoL (P=0.0004) inter-individual variability was greater for the right hand than for the left hand, the LtoR (P=0.0019) and RtoL (P=0.0179) inter-individual variability was greater for the right language than for the left language, but there was no such difference between the right foot and left foot (LtoR, P=0.2469 and RtoL, P=0.6140). Inter-individual variability in one motor region was positively correlated with mean values in the other three motor regions (left hand, P=0.0145; left foot, P=0.0103; right hand, P=0.1318; right foot, P=0.3785). Inter-individual variability in language region was positively correlated with mean values in the four motor regions (left language, P=0.0422; right language, P=0.0514). Signal extraction for LtoR (P<0.0001) and RtoL (P<0.0001) varied across the nine brain atlases, which also showed differences in inter-individual variability. CONCLUSIONS: These results underscore the importance of quantitatively assessing the inter-individual variability of a brain atlas prior to use, and demonstrate that mapping motor regions by task-based fMRI is an effective method for quantitatively assessing the inter-individual variability in a brain atlas.","journal":"Quantitative Imaging in Medicine and Surgery","year":2020,"id":108348,"datarank":0.3470592052346052,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.07829528485039691,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.07829528485039691,"corpus_percentile":49.39274386942059,"corpus_rank":6543,"citation_count":5,"citer_count":5,"citers_with_citation_signal":3,"citers_with_endowment":3,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.67,"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":519243,"name":"Jinping Sun","orcid":"0000-0003-1124-3254","position":1,"is_corresponding":false},{"id":519244,"name":"Dong Cui","orcid":"0000-0002-5406-4435","position":2,"is_corresponding":false},{"id":519245,"name":"Xin Wang","orcid":"0000-0003-2602-3967","position":3,"is_corresponding":false},{"id":519246,"name":"Jingna Jin","orcid":"0009-0005-1714-0688","position":4,"is_corresponding":false},{"id":519247,"name":"Ying Li","orcid":"0000-0001-5319-3094","position":5,"is_corresponding":false},{"id":519248,"name":"Zhipeng Liu","orcid":"0000-0002-4032-8532","position":6,"is_corresponding":false},{"id":519249,"name":"Tao Yin","orcid":"0000-0002-1747-6027","position":7,"is_corresponding":false},{"id":519242,"name":"He Wang","orcid":"0009-0001-4617-8117","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-18T23:12:43.081337Z","pmid":"33532279","pmcid":"PMC7779929","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":[]}