Neurological disorders, such as schizophrenia and bipolar disorder, remain challenging to diagnose due to the absence of objective biomarkers. Current assessments largely rely on subjective clinical evaluations. 

In a new study published in APL Bioengineering titled, “Machine Learning-Enabled Detection of Electrophysiological Signatures in iPSC-Derived Models of Schizophrenia and Bipolar Disorder,” researchers from Johns Hopkins University (JHU) present a computational analysis pipeline designed to identify disease-specific electrophysiological signatures from patient-derived cerebral organoids and two-dimensional cortical interneuron cultures. The findings may help reduce human error when diagnosing mental health disorders that currently only rely on clinical judgement.

“Schizophrenia and bipolar disorder are very hard to diagnose because no particular part of the brain goes off. No specific enzymes are going off like in Parkinson’s, another neurological disease where doctors can diagnose and treat based on dopamine levels even though it still doesn’t have a proper cure,” said Annie Kathuria, PhD, assistant professor of biomedical engineering at JHU and corresponding author of the study. “Our hope is that in the future we can not only confirm a patient is schizophrenic or bipolar from brain organoids, but that we can also start testing drugs on the organoids to find out what drug concentrations might help them get to a healthy state.”   

Kathuria’s team engineered the organoids by converting blood and skin cells from schizophrenic, bipolar, and healthy patients into stem cells with the ability to produce various types of organ-like tissue. The study identified neural firing patterns associated with healthy and unhealthy conditions.  

Using a support vector machine (SVM) classifier, the authors achieved 95.8% classification accuracy in distinguishing schizophrenia from control samples in cortical interneuron cultures with the extracted electrophysiological signatures.  

In addition, distinct features of the organoids’ brain-like activity served as biomarkers of schizophrenia and bipolar disorder, leading to a disease classification accuracy of 83%. That accuracy improved to 92% after the brain-like tissue received subtle electric shocks meant to reveal more of the neuroelectric impulses normally needed for brain activity.   

The discovered patterns involved intricate electrophysiological behavior unique for schizophrenic and bipolar patients, neural firing spikes and alterations at different intervals happening simultaneously across different parameters that created a distinct signature for both mental health disorders.  

“At least molecularly, we can check what goes wrong when we are making these brains in a dish and distinguish between organoids from a healthy person, a schizophrenia patient, or a bipolar patient based on these electrophysiology signatures,” Kathuria said. “We track the electrical signals produced by neurons during development, comparing them to organoids from patients without these mental health disorders.”  

The organoids contain various neural cell types found in the brain’s prefrontal cortex, a region known for higher cognitive functions. To study how the organoid cells formed neural networks, the researchers placed them on a microchip fitted with multi-electrode arrays resembling an electrical grid. The set up allowed the researchers to streamline data similar to a tiny electroencephalogram, or EEG, which doctors use to measure patients’ brain activity.  

While the research only involved 12 patients, Kathuria states that the findings will likely have real-world, clinical applications as the beginning of an important testbed for psychiatric drug therapies.   

The team is currently working with neurosurgeons, psychiatrists, and other neuroscientists at the John Hopkins School of Medicine to recruit blood samples from psychiatric patients and test how various drug concentrations might influence their findings.  

“That’s how most doctors give patients these drugs, with a trial-and-error method that may take six or seven months to finds the right drug,” Kathuria said. “Clozapine is the most common drug prescribed for schizophrenia, but about 40% of patients are resistant to it. With our organoids, maybe we won’t have to do that trial-and-error period. Maybe we can give them the right drug sooner than that.”