2024
Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning
Liang L, Wang Y, Ma H, Zhang R, Liu R, Zhu R, Zheng Z, Zhang X, Wang F. Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning. Frontiers In Psychiatry 2024, 15: 1422020. DOI: 10.3389/fpsyt.2024.1422020.Peer-Reviewed Original ResearchVocal acoustic featuresHealthy control groupSeverity of depressive symptomsTotal depression scoreAcoustic featuresClassification accuracyMDD groupDepressive disorderAnxiety comorbiditiesDepression prediction modelDeep learning methodsDepressive symptomsDepression scoresHC groupSpeech characteristicsMean Absolute Error(MAEDepressionNeural networkEnhanced classificationControl groupLearning methodsMachine learningClassification modelOpen-source algorithmAbsolute error(MAE
2009
Functional and Structural Connectivity Between the Perigenual Anterior Cingulate and Amygdala in Bipolar Disorder
Wang F, Kalmar JH, He Y, Jackowski M, Chepenik LG, Edmiston E, Tie K, Gong G, Shah MP, Jones M, Uderman J, Constable RT, Blumberg HP. Functional and Structural Connectivity Between the Perigenual Anterior Cingulate and Amygdala in Bipolar Disorder. Biological Psychiatry 2009, 66: 516-521. PMID: 19427632, PMCID: PMC2830492, DOI: 10.1016/j.biopsych.2009.03.023.Peer-Reviewed Original ResearchConceptsPerigenual anterior cingulate cortexHealthy comparison subjectsBipolar disorderFunctional magnetic resonance imagingDiffusion tensor imagingFunctional connectivityWhite matterFractional anisotropyHC groupPerigenual anterior cingulateAnterior cingulate cortexMagnetic resonance imagingGray matter structuresRegional fractional anisotropyWhite matter connectivityEmotional processingFunctional connectivity measuresSignificant positive associationBD groupComparison subjectsAnterior cingulateCingulate cortexResonance imagingSignificant associationBrain regions