{"doi":"10.1101/2020.09.12.294322","title":"A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI","abstract":"Abstract We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent grey-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired at a resolution of 0.547mm × 0.547mm × 0.800mm five times and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing adjusted Rand indices between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject’s resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":120753,"datarank":0.5232234248326803,"base_score":2.302585092994046,"endowment":2.302585092994046,"self_citation_contribution":0.3453877639491069,"citation_network_contribution":0.1778356608835733,"self_endowment_contribution":0.3453877639491069,"citer_contribution":0.1778356608835733,"corpus_percentile":63.0076583894175,"corpus_rank":4783,"citation_count":9,"citer_count":7,"citers_with_citation_signal":5,"citers_with_endowment":5,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9218,"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":407812,"name":"Soyoung Choi","orcid":"0000-0001-7165-4691","position":1,"is_corresponding":false},{"id":558896,"name":"Yijun Liu","orcid":"0000-0003-1104-9056","position":2,"is_corresponding":false},{"id":559829,"name":"Minqi Chong","orcid":null,"position":3,"is_corresponding":false},{"id":559830,"name":"Gaurav Sonkar","orcid":null,"position":4,"is_corresponding":false},{"id":533602,"name":"Jorge González-Martínez","orcid":"0000-0002-9936-7034","position":5,"is_corresponding":false},{"id":227164,"name":"Dileep Nair","orcid":"0000-0001-9663-7222","position":6,"is_corresponding":false},{"id":364409,"name":"Jessica L. Wisnowski","orcid":"0000-0002-4039-8362","position":7,"is_corresponding":false},{"id":479662,"name":"Justin P. Haldar","orcid":"0000-0002-1838-0211","position":8,"is_corresponding":false},{"id":557929,"name":"David W. Shattuck","orcid":"0000-0002-8424-8905","position":9,"is_corresponding":false},{"id":559831,"name":"Hanna Damásio","orcid":null,"position":10,"is_corresponding":false},{"id":330856,"name":"Richard M. Leahy","orcid":"0000-0002-7278-5471","position":11,"is_corresponding":false},{"id":364410,"name":"Anand A. Joshi","orcid":"0000-0002-9582-3848","position":0,"is_corresponding":true}],"reference_count":75,"raw_metadata":null,"created_at":"2026-07-18T23:14:38.147936Z","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":[]}