{"doi":"10.1002/hbm.24982","title":"Multi‐scale network regression for brain‐phenotype associations","abstract":"Brain networks are increasingly characterized at different scales, including summary statistics, community connectivity, and individual edges. While research relating brain networks to behavioral measurements has yielded many insights into brain-phenotype relationships, common analytical approaches only consider network information at a single scale. Here, we designed, implemented, and deployed Multi-Scale Network Regression (MSNR), a penalized multivariate approach for modeling brain networks that explicitly respects both edge- and community-level information by assuming a low rank and sparse structure, both encouraging less complex and more interpretable modeling. Capitalizing on a large neuroimaging cohort (n = 1, 051), we demonstrate that MSNR recapitulates interpretable and statistically significant connectivity patterns associated with brain development, sex differences, and motion-related artifacts. Compared to single-scale methods, MSNR achieves a balance between prediction performance and model complexity, with improved interpretability. Together, by jointly exploiting both edge- and community-level information, MSNR has the potential to yield novel insights into brain-behavior relationships.","journal":"Human Brain Mapping","year":2020,"id":100588,"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":28,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9402,"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":493708,"name":"Zongming Ma","orcid":"0000-0003-2401-0177","position":1,"is_corresponding":false},{"id":235284,"name":"Zaixu Cui","orcid":"0000-0003-4385-8106","position":2,"is_corresponding":false},{"id":18614,"name":"Danilo Bzdok","orcid":"0000-0003-3466-6620","position":3,"is_corresponding":false},{"id":309143,"name":"Bertrand Thirion","orcid":"0000-0001-5018-7895","position":4,"is_corresponding":false},{"id":103395,"name":"Danielle S. Bassett","orcid":"0000-0002-6183-4493","position":5,"is_corresponding":false},{"id":230040,"name":"Theodore D. Satterthwaite","orcid":"0000-0001-7072-9399","position":6,"is_corresponding":false},{"id":52271,"name":"Russell T. Shinohara","orcid":"0000-0001-8627-8203","position":7,"is_corresponding":false},{"id":414572,"name":"Daniela Witten","orcid":"0000-0002-1764-1184","position":8,"is_corresponding":false},{"id":235286,"name":"Cedric Huchuan Xia","orcid":"0000-0002-9703-333X","position":0,"is_corresponding":true}],"reference_count":91,"raw_metadata":null,"created_at":"2026-07-18T22:39:01.863288Z","pmid":"32216125","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":[]}