Version 1
: Received: 15 August 2024 / Approved: 15 August 2024 / Online: 16 August 2024 (08:10:37 CEST)
How to cite:
Kathuria, A.; Cheng, K.; Williams, A.; Kshirsagar, A.; Kulkarni, S.; Karmacharya, R.; Sarma, S. Machine Learning Classification of Schizophrenia and Bipolar Disorder Using Electrophysiology: Insights from Baseline and Post-Stimulus Conditions. Preprints2024, 2024081191. https://doi.org/10.20944/preprints202408.1191.v1
Kathuria, A.; Cheng, K.; Williams, A.; Kshirsagar, A.; Kulkarni, S.; Karmacharya, R.; Sarma, S. Machine Learning Classification of Schizophrenia and Bipolar Disorder Using Electrophysiology: Insights from Baseline and Post-Stimulus Conditions. Preprints 2024, 2024081191. https://doi.org/10.20944/preprints202408.1191.v1
Kathuria, A.; Cheng, K.; Williams, A.; Kshirsagar, A.; Kulkarni, S.; Karmacharya, R.; Sarma, S. Machine Learning Classification of Schizophrenia and Bipolar Disorder Using Electrophysiology: Insights from Baseline and Post-Stimulus Conditions. Preprints2024, 2024081191. https://doi.org/10.20944/preprints202408.1191.v1
APA Style
Kathuria, A., Cheng, K., Williams, A., Kshirsagar, A., Kulkarni, S., Karmacharya, R., & Sarma, S. (2024). Machine Learning Classification of Schizophrenia and Bipolar Disorder Using Electrophysiology: Insights from Baseline and Post-Stimulus Conditions. Preprints. https://doi.org/10.20944/preprints202408.1191.v1
Chicago/Turabian Style
Kathuria, A., Rakesh Karmacharya and Sridevi Sarma. 2024 "Machine Learning Classification of Schizophrenia and Bipolar Disorder Using Electrophysiology: Insights from Baseline and Post-Stimulus Conditions" Preprints. https://doi.org/10.20944/preprints202408.1191.v1
Abstract
Importance: Neuropsychiatric disorders like schizophrenia and bipolar disorder lack objective diagnostic markers, hindering accurate diagnosis and treatment. A novel approach using patient-derived cerebral organoids and digital analysis of electrophysiological recordings could provide much-needed objective biomarkers.Objective: To develop a digital analysis pipeline that can identify distinct electrophysiological signatures of schizophrenia and bipolar disorder using multi-electrode array recordings from patient-derived cerebral organoids.Design: This was an experimental study using previously published data. The study type can be classified as a case-control study, comparing cerebral organoids and 2D neuronal cultures derived from patients with schizophrenia, bipolar disorder, and healthy controls. The study utilized multi-electrode array recordings for analysis. Setting: The study was conducted in a laboratory setting using previously recorded data. Participants: The study included a total of 24 Caucasian subjects, comprising three groups: patients with schizophrenia, patients with bipolar disorder, and healthy controls. The specific number of participants in each group is not provided. The participant pool consisted of 17 males and 7 females, with an average age of 38.8 years.Main Outcomes and Measures: The primary outcome was the accuracy of a support vector machine classifier in distinguishing between healthy control, schizophrenia, and bipolar disorder samples based on electrophysiological features extracted from the recordings. Key cellular-digital biomarkers were identified using minimum redundancy maximum relevance feature selection.Results: This study included 24 Caucasian subjects (17 males, 7 females; mean age 38.8 years) with schizophrenia (SCZ), bipolar disorder (BPD), and healthy controls. Our Support Vector Machine classifier achieved 95.8% accuracy in distinguishing SCZ from control samples in 2D neuronal cultures under both baseline and post-electrical stimulation (PES) conditions. For cerebral organoids, classification accuracy was 83.3% under baseline conditions, improving to 91.6% under PES when distinguishing between control, SCZ, and BPD samples. Key features for classification included channel-specific measures such as median, covariance, autocorrelation, and kurtosis of neural activity. Confusion matrices visualize classification performance, with most misclassifications occurring in the BPD class under baseline conditions. These results demonstrate that our digital analysis pipeline can effectively distinguish between healthy control and patient-derived samples using electrophysiological features from multi-electrode array recordings, particularly under PES conditions.Conclusions and Relevance: This digital analysis pipeline demonstrates the potential to identify objective electrophysiological signatures of schizophrenia and bipolar disorder using patient-derived cerebral organoids. This approach could lead to improved diagnostic accuracy and personalized treatment strategies for neuropsychiatric disorders.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
