2023
Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP)
Koen J, Lewis L, Rugg M, Clementz B, Keshavan M, Pearlson G, Sweeney J, Tamminga C, Ivleva E. Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP). Scientific Reports 2023, 13: 12980. PMID: 37563219, PMCID: PMC10415369, DOI: 10.1038/s41598-023-38101-0.Peer-Reviewed Original ResearchConceptsPsychosis biotypesPsychosis casesBrain-based biomarkersLogistic regression modelsT1-weighted imagesBipolar-Schizophrenia NetworkHealthy controlsDisease neurobiologyPsychotic disordersClinical diagnosisStructural MRIBrain structuresGrey matter density mapsDSM diagnosesEvidence of specificityAbove-chance classification accuracy
2018
9.3 PSYCHOSIS BIOTYPES VERSUS CLINICAL SYNDROMES THROUGH THE PRISM OF INTRINSIC NEURAL ACTIVITY
Clementz B, Pearlson G, Tamminga C, Sweeney J, Keshavan M. 9.3 PSYCHOSIS BIOTYPES VERSUS CLINICAL SYNDROMES THROUGH THE PRISM OF INTRINSIC NEURAL ACTIVITY. Schizophrenia Bulletin 2018, 44: s14-s14. PMCID: PMC5888365, DOI: 10.1093/schbul/sby014.031.Peer-Reviewed Original ResearchInter-trial intervalNeural activityIntrinsic neural activityPsychosis biotypesDSM diagnosesSingle-trial powerOngoing neural activityBipolar-Schizophrenia NetworkHealthy personsStimulus salienceNeural responsesNeural oscillationsB-SNIPNeurobiological similaritiesNeurophysiological modelFirst-degree relativesEEG measuresTreatment developmentSensory cortexTranslational research programClinical featuresDistinct physiological mechanismsHealthy groupPsychosis diagnosisPsychosis cases