A simple but tough-to-beat baseline for fMRI time-series classification
Popov P, Mahmood U, Fu Z, Yang C, Calhoun V, Plis S. A simple but tough-to-beat baseline for fMRI time-series classification. NeuroImage 2024, 303: 120909. PMID: 39515403, DOI: 10.1016/j.neuroimage.2024.120909.Peer-Reviewed Original ResearchConceptsComplex machine learning modelsBlack-box natureMulti-layer perceptronMachine learning modelsPrediction accuracyBlack-box modelsFMRI classificationComplex classifiersClassification accuracySequential informationHuman fMRI dataLearning modelsBlack-boxRich modelsSuperior performanceComplex model developmentFMRI dataTime-series fMRI dataTime series dataClassifierStand-alone pieceClassificationAccuracyDesign modelSeries dataAdding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy
Kaestner E, Hassanzadeh R, Gleichgerrcht E, Hasenstab K, Roth R, Chang A, Rüber T, Davis K, Dugan P, Kuzniecky R, Fridriksson J, Parashos A, Bagić A, Drane D, Keller S, Calhoun V, Abrol A, Bonilha L, McDonald C. Adding the third dimension: 3D convolutional neural network diagnosis of temporal lobe epilepsy. Brain Communications 2024, 6: fcae346. PMID: 39474046, PMCID: PMC11520928, DOI: 10.1093/braincomms/fcae346.Peer-Reviewed Original ResearchConvolutional neural networkTwo-dimension convolutional neural networkThree-dimension convolutional neural networksNeural network diagnosisSaliency mapNetwork diagnosisImage harmonizationTraining 3DNeural networkModel trainingMedical imagesTemporal lobe epilepsyModel performanceSubcortical regionsMedian accuracySignificant outperformanceLobe epilepsyStructural abnormalitiesAccuracyClassificationDatapointsEpilepsy lesionsCNN diagnosisPerformanceIdentifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures
Ellis C, Sancho M, Miller R, Calhoun V. Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. Communications In Computer And Information Science 2024, 2156: 102-124. DOI: 10.1007/978-3-031-63803-9_6.Peer-Reviewed Original ResearchDeep learning modelsExplainability methodsExplainability analysisConvolutional neural network architectureLearning modelsRaw electroencephalogramNeural network architectureDeep learning architectureMajor depressive disorderLearning architectureNetwork architectureDeep learningModel architectureMultichannel electroencephalogramTraining approachArchitectureBiomarkers of depressionFrequency bandElectroencephalogramResearch contextDepressive disorderElectroencephalogram biomarkerAccuracyRight hemisphereExplainability