2024
Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling
Liang Q, Adkinson B, Jiang R, Scheinost D. Overcoming Atlas Heterogeneity in Federated Learning for Cross-Site Connectome-Based Predictive Modeling. Lecture Notes In Computer Science 2024, 15010: 579-588. DOI: 10.1007/978-3-031-72117-5_54.Peer-Reviewed Original Research
2023
Connectome-based machine learning models are vulnerable to subtle data manipulations
Rosenblatt M, Rodriguez R, Westwater M, Dai W, Horien C, Greene A, Constable R, Noble S, Scheinost D. Connectome-based machine learning models are vulnerable to subtle data manipulations. Patterns 2023, 4: 100756. PMID: 37521052, PMCID: PMC10382940, DOI: 10.1016/j.patter.2023.100756.Peer-Reviewed Original ResearchData manipulationNoise attacksPrediction performanceMachine learning modelsManipulated dataLearning modelHigh trustworthinessConnectome dataTrustworthinessAttacksModel performancePredictive modelDownstream analysisPerformanceAcademic researchMachineRobustnessModelConnectomeConnectome-based modelsFunctional connectomeManipulation
2020
Connectome-based neurofeedback: A pilot study to improve sustained attention
Scheinost D, Hsu TW, Avery EW, Hampson M, Constable RT, Chun MM, Rosenberg MD. Connectome-based neurofeedback: A pilot study to improve sustained attention. NeuroImage 2020, 212: 116684. PMID: 32114151, PMCID: PMC7165055, DOI: 10.1016/j.neuroimage.2020.116684.Peer-Reviewed Original ResearchConceptsFunctional connectivityRt-fMRIReal-time functional magnetic resonance imaging (rt-fMRI) neurofeedbackWhole-brain functional connectivityClinical trial designFunctional magnetic resonance imaging (fMRI) neurofeedbackDistinct brain areasConnectome-based modelsClinical symptomsTrial designBrain areasBrain regionsSustained attentionTherapeutic toolPilot studyBrain activityFunctional connectionsSymptomsNeurofeedbackFunctional networksTraining durationAttention taskComplex functional networksPilot sample