{"doi":"10.1109/tbme.2023.3336241","title":"Matrix-Variate Regression for Sparse, Low-Rank Estimation of Brain Connectivities Associated With a Clinical Outcome","abstract":"Objective: We address the problem of finding brain connectivities that are associated with a clinical outcome or phenotype. Methods: The proposed framework regresses a (scalar) clinical outcome on matrix-variate predictors which arise in the form of brain connectivity matrices. For example, in a large cohort of subjects we estimate those regions of functional connectivities that are associated with neurocognitive scores. We approach this high-dimensional yet highly structured estimation problem by formulating a regularized estimation process that results in a low-rank coefficient matrix having a sparse set of nonzero entries which represent regions of biologically relevant connectivities. In contrast to the recent literature on estimating a sparse, low-rank matrix from a single noisy observation, our scalar-on-matrix regression framework produces a data-driven extraction of structures that are associated with a clinical response. The method, called Sp arsity I nducing N uclear- N orm E stimato r (SpINNEr), simultaneously constrains the regression coefficient matrix in two ways: a nuclear norm penalty encourages low-rank structure while an $\\ell _{1}$ norm encourages entry-wise sparsity. Results: Our simulations show that SpINNEr outperforms other methods in estimation accuracy when the response-related entries (representing the brain's functional connectivity) are arranged in well-connected communities. SpINNEr is applied to investigate associations between HIV-related outcomes and functional connectivity in the human brain. Conclusion and Significance: Overall, this work demonstrates the potential of SpINNEr to recover sparse and low-rank estimates under scalar-on-matrix regression framework.","journal":"IEEE Transactions on Biomedical Engineering","year":2023,"id":378042,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9547,"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":1141981,"name":"Xixi Hu","orcid":"0000-0003-1530-3015","position":1,"is_corresponding":false},{"id":385160,"name":"Joaquín Goñi","orcid":"0000-0003-3705-1955","position":2,"is_corresponding":false},{"id":226074,"name":"Beau M. Ances","orcid":"0000-0003-3862-7397","position":3,"is_corresponding":false},{"id":252201,"name":"Timothy W. Randolph","orcid":"0000-0001-8465-1588","position":4,"is_corresponding":false},{"id":262626,"name":"Jaroslaw Harezlak","orcid":"0000-0002-3070-7686","position":5,"is_corresponding":false},{"id":729976,"name":"Damian Brzyski","orcid":"0000-0002-6867-1877","position":0,"is_corresponding":true}],"reference_count":47,"raw_metadata":null,"created_at":"2026-07-19T01:16:44.708019Z","pmid":"37995175","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":[]}