{"doi":"10.1093/cercor/bhae016","title":"A radiomics-based brain network in T1 images: construction, attributes, and applications","abstract":"T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.","journal":"Cerebral Cortex","year":2024,"id":444117,"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":0.8548,"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":1259266,"name":"Zhe Ma","orcid":"0009-0003-0202-3863","position":1,"is_corresponding":false},{"id":947092,"name":"Lijiang Wei","orcid":"0000-0001-7635-3468","position":2,"is_corresponding":false},{"id":1259668,"name":"Zhenpeng Chen","orcid":null,"position":3,"is_corresponding":false},{"id":453438,"name":"Yun Peng","orcid":"0000-0001-8213-9716","position":4,"is_corresponding":false},{"id":57587,"name":"Zhicheng Jiao","orcid":"0000-0002-6968-0919","position":5,"is_corresponding":false},{"id":259901,"name":"Harrison X. Bai","orcid":"0000-0002-7460-8866","position":6,"is_corresponding":false},{"id":947093,"name":"Bin Jing","orcid":"0000-0002-4478-8683","position":7,"is_corresponding":false},{"id":1259265,"name":"Han Liu","orcid":"0009-0005-5883-6278","position":0,"is_corresponding":true}],"reference_count":67,"raw_metadata":null,"created_at":"2026-07-19T02:01:33.526738Z","pmid":"38300184","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":[]}