02068nas a2200229   4500000000100000008004100001260001500042100001400057700002000071700002300091700001700114700002300131700001600154700002100170700001900191245013500210856003800345300001100383490000600394520142400400022001401824       2025                            d  c2025-09-221 aKai Cheng1 aAutumn Williams1 aAnannya Kshirsagar1 aSai Kulkarni1 aRakesh Karmacharya1 aDeok-Ho Kim1 aSridevi V. Sarma1 aAnnie Kathuria00aMachine learning-enabled detection of electrophysiological signatures in iPSC-derived models of schizophrenia and bipolar disorder  uhttps://doi.org/10.1063/5.0250559  a0361180 v93 aNeuropsychiatric disorders such as schizophrenia (SCZ) and bipolar disorder (BPD) remain challenging to diagnose due to the absence of objective biomarkers, with current assessments relying largely on subjective clinical evaluations. In this study, we present a computational analysis pipeline designed to identify disease-specific electrophysiological signatures from multi-electrode array (MEA) recordings of patient-derived cerebral organoids (COs) and two-dimensional cortical interneuron cultures (2DNs). Using a Support Vector Machine classifier optimized for high-dimensional data, we achieved 95.8% classification accuracy in distinguishing SCZ from control samples in 2DNs under both baseline and post-electrical-stimulation (PES) conditions with the extracted electrophysiological signatures. In COs, classification accuracy improved from 83.3% at baseline to 91.6% following PES, enabling robust separation of control, SCZ, and BPD cohorts. Key discriminative features included channel-specific measures of network activity, with PES significantly enhancing classification performance, particularly for BPD. These results underscore the potential of MEA-based functional phenotyping, coupled with machine learning, to uncover reliable, stimulation-sensitive electrophysiological biomarkers, offering a path toward more objective diagnosis and personalized treatment strategies for neuropsychiatric disorders.  a2473-2877