Visualizing functional network connectivity differences using an explainable machine-learning method
Sendi M, Itkyal V, Edwards-Swart S, Chun J, Mathalon D, Ford J, Preda A, van Erp T, Pearlson G, Turner J, Calhoun V. Visualizing functional network connectivity differences using an explainable machine-learning method. Physiological Measurement 2025, 46: 045009. PMID: 40245920, DOI: 10.1088/1361-6579/adce52.Peer-Reviewed Original ResearchConceptsCognitive control networkFunctional network connectivitySubcortical networksSHapley Additive exPlanationsExplainable machine learningMachine learning modelsStatistical learning approachResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingClassification accuracyLack interpretabilityMachine-learning methodsMachine learningSynthetic dataLearning approachLearning modelsNetwork connectivityAging AdultsRandom forestOlder aged adultsNetworkSomatomotor networkConnectivity differencesNeural mechanismsCatBoost model
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