{"doi":"10.1016/j.dib.2020.105213","title":"MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data","abstract":"Resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been extensively used for diagnostic classification because it does not require task compliance and is easier to pool data from multiple imaging sites, thereby increasing the sample size. A MATLAB-based toolbox called Machine Learning in NeuroImaging (MALINI) for feature extraction and disease classification is presented. The MALINI toolbox extracts functional and effective connectivity features from preprocessed rs-fMRI data and performs classification between healthy and disease groups using any of 18 popular and widely used machine learning algorithms that are based on diverse principles. A consensus classifier combining the power of multiple classifiers is also presented. The utility of the toolbox is illustrated by accompanying data consisting of resting-state functional connectivity features from healthy controls and subjects with various brain-based disorders: autism spectrum disorder from autism brain imaging data exchange (ABIDE), Alzheimer's disease and mild cognitive impairment from Alzheimer's disease neuroimaging initiative (ADNI), attention deficit hyperactivity disorder from ADHD-200, and post-traumatic stress disorder and post-concussion syndrome acquired in-house. Results of classification performed on the above datasets can be obtained from the main article titled \"Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets\" [1]. The data was divided into homogeneous and heterogeneous splits, such that 80% could be used for training, model building and cross-validation, while the remaining 20% of the data could be used as a hold-out independent test data for replication of the classification performance, to ensure the robustness of the classifiers to population variance in image acquisition site and age of the sample.","journal":"Data in Brief","year":2020,"id":119526,"datarank":0.44166584687496613,"base_score":2.9444389791664403,"endowment":2.9444389791664403,"self_citation_contribution":0.44166584687496613,"citation_network_contribution":0.0,"self_endowment_contribution":0.44166584687496613,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8934,"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":554633,"name":"D. Rangaprakash","orcid":"0000-0001-9553-1354","position":1,"is_corresponding":false},{"id":555323,"name":"Sai Sheshan Roy Gotoor","orcid":null,"position":2,"is_corresponding":false},{"id":554634,"name":"Michael N. Dretsch","orcid":"0000-0001-8773-6376","position":3,"is_corresponding":false},{"id":554635,"name":"Jeffrey S. Katz","orcid":"0000-0001-9966-1033","position":4,"is_corresponding":false},{"id":418666,"name":"Thomas S. Denney","orcid":"0000-0002-6695-4777","position":5,"is_corresponding":false},{"id":197009,"name":"Gopikrishna Deshpande","orcid":"0000-0001-7471-5357","position":6,"is_corresponding":false},{"id":554632,"name":"Pradyumna Lanka","orcid":"0000-0002-5820-5928","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:14:08.313144Z","pmid":"32090157","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":[]}