2024
Model selection to achieve reproducible associations between resting state EEG features and autism
Carson W, Major S, Akkineni H, Fung H, Peters E, Carpenter K, Dawson G, Carlson D. Model selection to achieve reproducible associations between resting state EEG features and autism. Scientific Reports 2024, 14: 25301. PMID: 39455733, PMCID: PMC11511871, DOI: 10.1038/s41598-024-76659-5.Peer-Reviewed Original ResearchConceptsElectroencephalography spectral powerCustom machine learning modelsPredictive performanceGamma powerMachine learning modelsRegularized generalized linear modelModel selectionBiomarker discoverySpectral powerMidline regionMultiple featuresLearning modelsFunctional connectivity featuresPosterior midline regions
2023
Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders
Zhu T, Wang W, Chen Y, Kranzler H, Li C, Bi J. Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2023, 9: 326-336. PMID: 37696489, PMCID: PMC10976073, DOI: 10.1016/j.bpsc.2023.08.010.Peer-Reviewed Original ResearchNicotine use disorderHealthy controlsFunctional connectivity featuresUse disordersMagnetic resonance imagingNUD subjectsVisual cortexResonance imagingClinical metricsFunctional connectivityNoninvasive toolNeural phenotypesSample of individualsMulti-task learningTransdiagnostic approachUK BiobankReplication setGenetic profileMarkersReplication sampleHighest areaDisordersDepressionAUDBody of literatureA Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082903, DOI: 10.1109/embc40787.2023.10339949.Peer-Reviewed Original ResearchConceptsStructural MRI dataResting-state functional MRI dataFunctional MRI dataFunctional magnetic resonance imaging dataMRI dataMagnetic resonance imaging dataSchizophrenia patientsFunctional connectivity featuresBrain imaging modalitiesMental disordersNeuroimaging dataNeuroimaging techniquesBorderline subjectsHealthy control groupSchizophrenia datasetSchizophreniaConnectivity featuresBrainPsychosisMoodNosologyControl groupDisordersLabel noiseSubjects
2019
Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors
Yoo K, Rosenberg MD, Noble S, Scheinost D, Constable RT, Chun MM. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. NeuroImage 2019, 197: 212-223. PMID: 31039408, PMCID: PMC6591084, DOI: 10.1016/j.neuroimage.2019.04.060.Peer-Reviewed Original ResearchConceptsFunctional brain organizationFunctional connectivityFunctional connectivity featuresTest-retest sampleMultivariate functional connectivityCognitive skillsMental representationsIndividual differencesFMRI measuresBrain organizationBrain statesStrong predictionSpatial activity patternsFMRI datasetsConnectivity featuresIndividual behaviorProject samplesConnectivity estimatesTimecoursesActivity patternsCognitionPearson correlationIndividualsConnectivityUnivariate approach
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