{"doi":"10.1101/2020.10.21.348367","title":"Explainable Machine Learning Approach to Predict and Explain the Relationship between Task-based fMRI and Individual Differences in Cognition","abstract":"Abstract Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based fMRI. Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n=3,989). We compared nine predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and OLS multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than non-linear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects and Friedman’s H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study (Sripada et al. 2020) and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":124136,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"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":0.9496,"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":568434,"name":"Yue Wang","orcid":"0000-0001-6170-6451","position":1,"is_corresponding":false},{"id":568435,"name":"Adam Bartonicek","orcid":"0000-0002-4528-5543","position":2,"is_corresponding":false},{"id":243006,"name":"Julián Candia","orcid":"0000-0001-5793-8989","position":3,"is_corresponding":false},{"id":289915,"name":"Argyris Stringaris","orcid":"0000-0002-6264-8377","position":4,"is_corresponding":false},{"id":567986,"name":"Narun Pat","orcid":null,"position":0,"is_corresponding":true}],"reference_count":91,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:15:07.789881Z","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":[]}